Processing math: 100%
Research article Special Issues

The improved stratified transformer for organ segmentation of Arabidopsis


  • Segmenting plant organs is a crucial step in extracting plant phenotypes. Despite the advancements in point-based neural networks, the field of plant point cloud segmentation suffers from a lack of adequate datasets. In this study, we addressed this issue by generating Arabidopsis models using L-system and proposing the surface-weighted sampling method. This approach enables automated point sampling and annotation, resulting in fully annotated point clouds. To create the Arabidopsis dataset, we employed Voxel Centroid Sampling and Random Sampling as point cloud downsampling methods, effectively reducing the number of points. To enhance the efficiency of semantic segmentation in plant point clouds, we introduced the Plant Stratified Transformer. This network is an improved version of the Stratified Transformer, incorporating the Fast Downsample Layer. Our improved network underwent training and testing on our dataset, and we compared its performance with PointNet++, PAConv, and the original Stratified Transformer network. For semantic segmentation, our improved network achieved mean Precision, Recall, F1-score and IoU of 84.20, 83.03, 83.61 and 73.11%, respectively. It outperformed PointNet++ and PAConv and performed similarly to the original network. Regarding efficiency, the training time and inference time were 714.3 and 597.9 ms, respectively, which were reduced by 320.9 and 271.8 ms, respectively, compared to the original network. The improved network significantly accelerated the speed of feeding point clouds into the network while maintaining segmentation performance. We demonstrated the potential of virtual plants and deep learning methods in rapidly extracting plant phenotypes, contributing to the advancement of plant phenotype research.

    Citation: Yuhui Zheng, Dongwei Wang, Ning Jin, Xueguan Zhao, Fengmei Li, Fengbo Sun, Gang Dou, Haoran Bai. The improved stratified transformer for organ segmentation of Arabidopsis[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 4669-4697. doi: 10.3934/mbe.2024205

    Related Papers:

    [1] Buthinah A. Bin Dehaish, Rawan K. Alharbi . On fixed point results for some generalized nonexpansive mappings. AIMS Mathematics, 2023, 8(3): 5763-5778. doi: 10.3934/math.2023290
    [2] Dong Ji, Yao Yu, Chaobo Li . Fixed point and endpoint theorems of multivalued mappings in convex b-metric spaces with an application. AIMS Mathematics, 2024, 9(3): 7589-7609. doi: 10.3934/math.2024368
    [3] Maryam Iqbal, Afshan Batool, Aftab Hussain, Hamed Al-Sulami . A faster iterative scheme for common fixed points of G-nonexpansive mappings via directed graphs: application in split feasibility problems. AIMS Mathematics, 2024, 9(5): 11941-11957. doi: 10.3934/math.2024583
    [4] Asima Razzaque, Imo Kalu Agwu, Naeem Saleem, Donatus Ikechi Igbokwe, Maggie Aphane . Novel fixed point results for a class of enriched nonspreading mappings in real Banach spaces. AIMS Mathematics, 2025, 10(2): 3884-3909. doi: 10.3934/math.2025181
    [5] Gonca Durmaz Güngör, Ishak Altun . Fixed point results for almost (ζθρ)-contractions on quasi metric spaces and an application. AIMS Mathematics, 2024, 9(1): 763-774. doi: 10.3934/math.2024039
    [6] Noor Muhammad, Ali Asghar, Samina Irum, Ali Akgül, E. M. Khalil, Mustafa Inc . Approximation of fixed point of generalized non-expansive mapping via new faster iterative scheme in metric domain. AIMS Mathematics, 2023, 8(2): 2856-2870. doi: 10.3934/math.2023149
    [7] Afrah. A. N. Abdou . Fixed points of Kannan maps in modular metric spaces. AIMS Mathematics, 2020, 5(6): 6395-6403. doi: 10.3934/math.2020411
    [8] Pragati Gautam, Vishnu Narayan Mishra, Rifaqat Ali, Swapnil Verma . Interpolative Chatterjea and cyclic Chatterjea contraction on quasi-partial b-metric space. AIMS Mathematics, 2021, 6(2): 1727-1742. doi: 10.3934/math.2021103
    [9] Shaoyuan Xu, Yan Han, Suzana Aleksić, Stojan Radenović . Fixed point results for nonlinear contractions of Perov type in abstract metric spaces with applications. AIMS Mathematics, 2022, 7(8): 14895-14921. doi: 10.3934/math.2022817
    [10] Liliana Guran, Khushdil Ahmad, Khurram Shabbir, Monica-Felicia Bota . Computational comparative analysis of fixed point approximations of generalized α-nonexpansive mappings in hyperbolic spaces. AIMS Mathematics, 2023, 8(2): 2489-2507. doi: 10.3934/math.2023129
  • Segmenting plant organs is a crucial step in extracting plant phenotypes. Despite the advancements in point-based neural networks, the field of plant point cloud segmentation suffers from a lack of adequate datasets. In this study, we addressed this issue by generating Arabidopsis models using L-system and proposing the surface-weighted sampling method. This approach enables automated point sampling and annotation, resulting in fully annotated point clouds. To create the Arabidopsis dataset, we employed Voxel Centroid Sampling and Random Sampling as point cloud downsampling methods, effectively reducing the number of points. To enhance the efficiency of semantic segmentation in plant point clouds, we introduced the Plant Stratified Transformer. This network is an improved version of the Stratified Transformer, incorporating the Fast Downsample Layer. Our improved network underwent training and testing on our dataset, and we compared its performance with PointNet++, PAConv, and the original Stratified Transformer network. For semantic segmentation, our improved network achieved mean Precision, Recall, F1-score and IoU of 84.20, 83.03, 83.61 and 73.11%, respectively. It outperformed PointNet++ and PAConv and performed similarly to the original network. Regarding efficiency, the training time and inference time were 714.3 and 597.9 ms, respectively, which were reduced by 320.9 and 271.8 ms, respectively, compared to the original network. The improved network significantly accelerated the speed of feeding point clouds into the network while maintaining segmentation performance. We demonstrated the potential of virtual plants and deep learning methods in rapidly extracting plant phenotypes, contributing to the advancement of plant phenotype research.



    For a metric space (H,d) with a nonempty subset D and a mapping T:DH, a point pD is called a fixed point of T if Tp=p. Throughout this paper, the set of such fixed points is denoted as Fix(T). Fixed points have significant applications across various fields. While contraction self-mappings on complete metric spaces have unique fixed points and are approachable through the Picard scheme, this principle does not hold for more general mappings, like the class of nonexpansive type mappings. A notable instance of these general mappings is the class of demicontractive mappings extensively studied by researchers since its introduction by Mǎruşter [38,39] and Hicks and Kubicek [24]. The class of demicontractive mappings, which includes nonexpansive and quasi-nonexpansive mappings, appears to be the largest class of nonexpansive type mappings whose fixed points can be approximated by means of iterative schemes.

    Recent advancements in the approximation of fixed points for demicontractive mappings within linear spaces can be found in Berinde [8] and references therein.

    Moreover, the theoretical study of demicontractive mappings in Hilbert and Banach spaces have important applications in solving various practical problems: Split problem [28,49,50,52,55], split common fixed point problem [21,29,30,43,56], split feasibility problem with multiple output sets [53], split variational inequality problem and split common null point problem [20], equilibrium problem and split generalized equilibrium problem [22,23,42,57] and more. Furthermore, fixed points of certain demicontractive mappings address real-world problems, including intensity-modulated radiation therapy, dynamic emission tomographic and image reconstruction.

    Furthermore, investigations into demicontractive mappings have been very recently extended to nonlinear geodesic spaces, as detailed in references [3,36,47,48] and related literature.

    In [34], Krasnoselskij suggested a modification of Picard iteration by using the so-called average mapping 12I+12T as the operator for the iterations. This approach efficiently approximates fixed points of nonexpansive mappings and some of their generalizations. The average mapping is generally considered for σ(0,1) by the following definition:

    Tσ:u(1σ)u+σTu.

    Through this technique of average mapping, several modifications of the Picard iteration have been formulated for generalized mappings, including well-known algorithms like the Krasnoselskij-Mann iteration [35], Ishikawa iteration [26], Noor iteration [41] and Agarwal iteration [2]. The technique of average mapping provides deeper insights into the inherent properties of the underlying operator. One noteworthy property is that Tσ can exhibit asymptotic regularity even when T lacks this property. This geometric characteristic, along with further analysis in this direction, has been explored [12,13,27,34], leading to the concepts of enriched contractions and enriched nonexpansive mappings [6,7].

    It is important to emphasize that convexity and linearity structures significantly influence the average mapping techniques. Moreover, numerous results in fixed point theory, such as extensions from one linear mapping to another or approximation schemes, revolve around the concept of average mapping. CAT(0) spaces, with their flexible linearity and convexity structures, are particularly well-suited for the concept of averaged mappings.

    Starting from these facts, the aim of this paper is to introduce the fundamentals of the theory of demicontractive mappings in metric spaces and expose the key concepts and tools for building a constructive approach to approximating the fixed points of demicontractive mappings in this setting. By using an appropriate geodesic averaged perturbation technique, we establish strong convergence and Δ-convergence theorems for a Krasnoselskij-Mann type iterative algorithm to approximate the fixed points of quasi-nonexpansive mappings within the framework of CAT(0) spaces. Next, by using the fact similar to the case of Hilbert spaces, demicontractive mappings in CAT(0) spaces are enriched quasi-nonexpansive mappings, we derive strong and Δ-convergence theorems for approximating fixed points of demicontractive mappings in CAT(0) spaces. The main results obtained in this nonlinear setting are natural extensions of the classical results from linear settings (Hilbert and Banach spaces) for both quasi-nonexpansive mappings and demicontractive mappings. We illustrate the relevance and effectiveness of the main theoretical results by providing appropriate supportive examples.

    Let (H,d) be a metric space and D be a nonempty subset of H. A mapping T:DH is said to be

    (1) Lipschitz if there exists >0, such that

    d(Tu,Tw)d(u,w), for all u,wD. (2.1)

    (2) Contraction if (2.1) holds with <1 and nonexpansive if (2.1) holds with =1.

    (3) Quasi-nonexpansive if Fix(T) and

    d(Tu,p)d(u,p), for all uD and pFix(T). (2.2)

    (4) κ-demicontractive [24] if Fix(T) and there exists κ<1, such that

    d2(Tu,p)d2(u,p)+κd2(u,Tu), for all uD and pFix(T). (2.3)

    Remark 2.1. 1) The definition of a demicontractive mapping as given by inequality (2.3) is the metric space correspondent of the original definition introduced by Hicks and Kubicsek [24] in Hilbert spaces.

    2) It is well known that contraction mappings are nonexpansive. Also, every nonexpansive mapping with a fixed point is quasi-nonexpansive and every quasi-nonexpansive mapping is demicontractive. However, the converses are not true in general. For supportive examples, see Examples 1.1–1.3 in [8].

    A metric space (H,d) is called a geodesic space if, for every two points u,wH, there exists a mapping ϕwu:[0,1]RH satisfying the following:

    (a) ϕwu(0)=u,

    (b) ϕwu(1)=w,

    (c) d(ϕwu(t1),ϕwu(t2))=|t1t2|d(u,w) for every t1, t2 [0,1].

    The image of ϕwu is often called a geodesic segment connecting u and w. For u,wH having a unique geodesic segment and for any t[0,1], there exists a unique point x on the segment connecting u and w, denoted by (1t)utw, with the following properties:

    d(u,x)=td(u,w)andd(x,w)=(1t)d(u,w). (2.4)

    It is known that CAT(0) spaces are geodesic spaces with the property that every pair of points is connected by a unique geodesic segment. A set is convex if it contains the geodesic segment connecting any pair of its points. Moreover, we have the following inequalities [18] for u,v,wH and t[0,1]:

    d((1t)utv,w)(1t)d(u,w)+td(v,w), (2.5)
    d2((1t)utv,w)(1t)d2(u,w)+td2(v,w)t(1t)d2(u,v), (2.6)

    where d2(x,y)=[d(x,y)]2 for all x,yH. When t=12, inequality (2.6) reduces to the CN-inequality of Bruhat and Tits [14]. A complete CAT(0) space is called a Hadamard space. For further details on CAT(0) spaces, see [10,33].

    Let (H,d) be a CAT(0) space and {un} be a bounded sequence in H. The asymptotic center of {un} is defined by

    A({un}):={uH:lim supnd(u,un)=infvHlim supnd(v,un)}.

    Furthermore, the sequence {un} Δ-converges to a point w in H if {w} is the asymptotic center of every subsequence of {un} and it strongly converges to w if limnd(un,w)=0. We write Δ-limnun=u to mean {un} is Δ-convergent to u.

    In the sequel, we shall need the following concept.

    Definition 2.1. A map T:DH is said to have a demiclosedness-type property if for any sequence {un}H,

    Δ0limnun=ulimnd(un,Tun)=0}u=Tu. (2.7)

    Remark 2.2. The notion of an operator having the demiclosedness-type property introduced in Definition 2.1 corresponds to the notion of demiclosedness introduced by Browder [11] in the case of Banach spaces. This property ensures the weak convergence of Krasnoselskij iteration to the fixed point of a demicontractive operator [24,39].

    The following theorem is crucial for our approximation results.

    Theorem 2.1. [45] Let D be a nonempty closed convex subset of a Hadamard space (H,d). Let T:DH be a mapping with nonempty fixed point set, which satisfies the demiclosedness-type property (2.7). Suppose that {un} is a sequence in D such that

    (P1) d(un,Tun)0,

    (P2) {d(un,u)} converges in R for every uFix(T).

    Then {un} Δ-converges to a fixed point of T.

    In this section, we consider the general setting of metric spaces and discuss significant inequalities associated with the class of demicontractive mappings. We show that demicontractive mappings and enriched quasi-nonexpansive mappings define the same set of mappings.

    In the sequel, we take

    Q(x,y,u,w):=12[d2(x,w)+d2(y,u)d2(x,u)d2(y,w)], (3.1)

    which is a metric version of the following known identity from inner-product spaces:

    xy,uw=12[xw2+yu2xu2yw2],

    where x,y,u,w are arbitrary points in the space. The term in (3.1) is reported as quasilinearization in [5].

    Proposition 3.1. Let (H,d) be a metric space and D be a nonempty subset of H. A mapping T:DH is κ-demicontractive if and only if

    Q(u,p,u,Tu)1κ2d2(u,Tu),foralluDandpFix(T). (3.2)

    Proof. Let uD and pFix(T). Then,

    d2(Tu,p)d2(u,p)+κd2(u,Tu)d2(u,p)+d2(u,Tu)+(κ1)d2(u,Tu)d2(Tu,p)0.12[d2(u,p)+d2(u,Tu)d2(Tu,p)]+κ12d2(u,Tu)0Q(u,p,u,Tu)+κ12d2(u,Tu)0Q(u,p,u,Tu)1κ2d2(u,Tu)

    as desired.

    Remark 3.1. It follows from Proposition 3.1 that any mapping T:DH satisfying

    αd2(u,Tu)Q(u,p,u,Tu),foralluDandpFix(T),

    for some α>0 is κ-demicontractive where max{0,12α}<κ<1.

    Moreover, when we consider the inequality (3.2) in an inner-product setting, it reduces to the following inequality:

    αuTu2up,uTu,foralluDandpFix(T), (3.3)

    which corresponds to the original definition of a demicontractive mapping as given by Mǎruşter in the case of Hilbert spaces [38,39].

    The class of mappings satisfying (3.3) was introduced in 1973 [38] for the case of Rn and in 1977 [39] in the setting of Hilbert spaces, while the class of demicontractive mappings in the sense of (2.3) was introduced in [24]. Although the two classes were introduced independently, it was later discovered that they coincide in the setting of real Hilbert spaces (see [37, pp. 2]).

    Thus, Proposition 3.1 guarantees that this result is true in general metric spaces, too. Hence, there is a need to further investigate the relationship between this class of mappings and other known classes of nonexpansive type mappings.

    According to [46], a mapping T:DH is called α-enriched nonexpansive if there exists α[0,+) such that

    d2(Tu,Tw)+α2d2(u,w)+2αQ(u,w,Tu,Tw)(α+1)2d2(u,w),u,wH. (3.4)

    Remark 3.2. It should be noted that the definition of α-enriched nonexpansive by inequality (3.4) corresponds to the original definition of enriched nonexpansive mappings given in [6] in the case of a Hilbert space. Thus, it is natural to define an enriched quasi-nonexpansive mapping in metric spaces as follows.

    Definition 3.1. Let (H,d) be a metric space and let D be a nonempty subset of H. A mapping T:DH is called α-enriched quasi-nonexpansive if Fix(T) and there exists α[0,+), such that

    d2(Tu,p)+α2d2(u,p)+2αQ(u,p,Tu,p)(α+1)2d2(u,p),uD,pFix(T). (3.5)

    Example 3.1. Let H=D=R3 be endowed with the metric d defined by

    d(u,w)=2i=1(uiwi)2+(u22+w3u3w22)2,

    for all u=(u1,u2,u3)R3 and w=(w1,w2,w3)R3. Consider T:R3R3 given by

    Tu=T(u1,u2,u3)=4(u1,u2,4u22).

    Clearly Fix(T)={0}. Also, T is not a quasi-nonexpansive mapping with respect to the both usual metric in (R3,d), since for any u=(u1,0,0), u10, we have

    d(Tu,0)=4|u1|>|u1|=d(u,0).

    However, T is an α-enriched quasi-nonexpansive mapping with respect to (R3,d) for α32. Indeed for all uR3, we have

    d2(Tu,0)=162i=1u2i,2Q(u,0,Tu,0)=d2(u,0)+d2(0,Tu)d2(u,Tu)
    =[2i=1u2i+(u22u3)2]+2i=1(4ui)2[2i=1(ui+4ui)2+(u22u3)2]
    =2i=1[u2i+(4ui)2(ui+4ui)2]=82i=1u2i.

    Thus, we get

    d2(Tu,0)+α2d2(u,0)+2αQ(u,0,Tu,0)=162i=1u2i+α22i=1u2i+α2(u22u3)28α2i=1u2i=(4α)22i=1u2i+α2(u22u3)2.

    It follows that for any α0 such that |4α|α+1, we get that

    d2(Tu,0)+α2d2(u,0)+2αQ(u,0,Tu,0)(α+1)2d2(u,0).

    It suffices to take α32.

    We now state the main theorem of this section.

    Theorem 3.1. Let (H,d) be a metric space and let D be a nonempty subset of H. Suppose that T:DH is a mapping. Then, T is κ-demicontractive mapping if and only if T is an α-enriched quasi-nonexpansive mapping, where α=κ1κ.

    Proof. Let uD and pFix(T). By (3.1), we obtain

    2Q(u,p,Tu,p)=d2(u,p)+d2(Tu,p)d2(u,Tu).

    This is equivalent to

    d2(u,Tu)=d2(u,p)+d2(Tu,p)2Q(u,p,Tu,p).

    Thus, T is κ-demicontractive mapping if and only if

    d2(Tu,p)d2(u,p)+κd2(u,Tu)d2(Tu,p)d2(u,p)+κ[d2(u,p)+d2(Tu,p)2Q(u,p,Tu,p)]d2(Tu,p)1+κ1κd2(u,p)2κ1κQ(u,p,Tu,p)d2(Tu,p)+(κ1κ)2d2(u,p)+2κ1κQ(u,p,Tu,p)1+κ1κd2(u,p)+(κ1κ)2d2(u,p)d2(Tu,p)+(κ1κ)2d2(u,p)+2κ1κQ(u,p,Tu,p)(κ1κ+1)2d2(u,p)d2(Tu,p)+α2d2(u,p)+2αQ(u,p,Tu,p)(α+1)2d2(u,p),

    where α=κ1κ.

    Remark 3.3. Theorem 3.1 signifies that the class of demicontractive mappings coincides with the class of enriched quasi-nonexpansive mappings in the following sense:

    (1) every α-enriched quasi-nonexpansive mapping is α1+α-demicontractive;

    (2) every κ-demicontractive mapping is κ1κ-enriched quasi-nonexpansive.

    The above properties correspond to the similar ones existing in the case of Hilbert spaces (see Theorem 9 in [9]).

    Corollary 3.1. Let H be a real Hilbert space endowed with the usual metric d and D be a nonempty subset of H. Suppose that T:DH is a mapping. Then T is κ-demicontractive mapping if and only if

    αu+Tu(α+1)p(α+1)up,foralluDand;pFix(T), (3.6)

    where α=κ1κ.

    The immediate result holds because (3.5) reduces to (3.6) in real Hilbert spaces with the usual distance d.

    In this section, we consider a geodesic space (H,d) in which every pair of points is connected by a unique geodesic segment. Suppose that D is a nonempty subset of H and T:DH is a mapping. For σ(0,1], the average perturbation Tσ of T is defined by

    Tσu=(1σ)uσTu,uD. (4.1)

    This is a well-defined mapping by the properties of the space. Moreover, it follows from Lemma 3.5 of [46] that

    A1) Fix(T)=Fix(Tσ);

    A2) d2(Tσu,Tσw)(1σ)2d2(u,w)+σ2d2(Tu,Tw)+2σ(1σ)Q(u,w,Tu,Tw)

    for all u,wD. The last inequality guarantees existence of σ, upon which Tσ is nonexpansive mapping in CAT(0) spaces whenever T is enriched nonexpansive mapping. This is one of the impacts of the geodesic average perturbation technique on the class of enriched contractions. In a similar fashion, we investigate the effect of geodesic average perturbation techniques for the class of demicontractive mappings.

    Proposition 4.1. Let (H,d) be a CAT(0) space and D be a nonempty subset of H. If T:DH is κ-demicontractive mapping, then the average perturbation Tσ of T is κ-demicontractive where max{0,1+κ1σ}κ<1.

    Proof. Let uD and pFix(T). By (2.6) and the hypothesis that T is κ-demicontractive, we obtain

    d2(Tσu,p)=d2((1σ)uσTu,p)(1σ)d2(u,p)+σd2(Tu,p)σ(1σ)d2(u,Tu)(1σ)d2(u,p)+σ[d2(u,p)+κd2(u,Tu)]σ(1σ)d2(u,Tu)=d2(u,p)+σ(κ+σ1)d2(u,Tu).

    This and (2.4) yield

    d2(Tσu,p)|d2(u,p)+κ+σ1σσ2d2(u,Tu)=d2(u,p)+κ+σ1σd2(u,Tσu)=d2(u,p)+(1+κ1σ)d2(u,Tσu)d2(u,p)+max{0,1+κ1σ}d2(u,Tσu)d2(u,p)+κd2(u,Tσu) (4.2)

    as desired.

    Proposition 4.2. Let (H,d) be a CAT(0) space and D be a nonempty subset of H. If T:DH is κ-demicontractive mapping, then for any σ]0,1κ], the average perturbation Tσ of T is quasi-nonexpansive.

    Proof. Observe that

    σ(0,1κ]max{0,1+κ1σ}=0.

    Thus, the desired result is achieved using (4.2) of the proof of Proposition 4.1.

    Corollary 4.1. Let (H,d) be a CAT(0) space and D be a nonempty subset of H. If T:DD is an α-enriched quasi-nonexpansive mapping, then for any σ]0,11+α], the average perturbation Tσ of T is quasi-nonexpansive.

    Proof. SinceT is an α-enriched quasi-nonexpansive mapping, it follows from Theorem 3.1 that T is α1+α-demicontractive. Consequently, Proposition 4.2 yields the desired result.

    Next, we state some direct consequences of the results, which are linear versions of Propositions 4.1 and 4.2.

    Corollary 4.2. [8, Lemma 3.1] Let H be a real Hilbert space endowed with the usual metric d and D be a nonempty subset of H. If T:DH is κ-demicontractive mapping, then the average perturbation Tσ of T is κ-demicontractive, where max{0,1+κ1σ}κ<1.

    Corollary 4.3. [8, Lemma 3.2] Let H be a real Hilbert space endowed with the usual metric d and D be a nonempty subset of H. If T:DH is κ-demicontractive mapping, then for any σ]0,1κ], the average perturbation Tσ of T is quasi-nonexpansive.

    Given a mapping T, the set of fixed points of T is necessarily required to be nonempty for an iterative sequence to approach an element of the set. Under this assumption, various classes of mappings such as Banach contraction mappings, Kannan mappings, Bianchini mappings, nonexpansive mappings, Suzuki mappings and several others belong to the class of quasi-nonexpansive mappings. Furthermore, according to Proposition 4.2, the geodesic average perturbation of a demicontractive mapping is also a quasi-nonexpansive mapping. It is important to note that every strictly pseudocontractive mapping is demicontractive under this assumption.

    One of the well-known schemes for finding fixed points of quasi-nonexpansive mappings is the Krasnoselskij-Mann algorithm. This algorithm has been studied and modified in various aspects by different scholars. In the setting of CAT(0) spaces, the algorithm is updated as follows:

    un+1=(1αn)unαnTun,n1, (4.3)

    where {αn}[0,1]. Next, we utilize the scheme in (4.3) to approximate a fixed point of a demicontractive mapping through a quasi-nonexpansive mapping.

    Theorem 4.1. Let (H,d) be a CAT(0) space and D be a nonempty convex subset of H. Suppose that T:DD is a quasi-nonexpansive mapping and {un} is a sequence generated by (4.3) with 0<aαnb<1 for all nN. Then,

    (P1) d(un,Tun)0,

    (P2) {d(un,u)} converges in R for every uFix(T).

    Proof. Let uFix(T). Using (2.6), (4.3) and the hypothesis that T is a quasi-nonexpansive mapping, we have

    d2(un+1,u)=d2((1αn)unαnTun,u)(1αn)d2(un,u)+αnd2(Tun,u)αn(1αn)d2(un,Tun)d2(un,u)αn(1αn)d2(un,Tun).

    This implies that

    d(un+1,u)d(un,u) (4.4)

    and

    d2(un,Tun)1αn(1αn)[d2(un,u)d2(un+1,u)]1a(1b)[d2(un,u)d2(un+1,u)]. (4.5)

    Thus, (4.4) yields (P2) of Theorem 2.1. Consequently, (4.5) together with (P2) yield (P1).

    Based on the preceding facts, we obtain the following results as direct applications of Theorem 2.1 together with Theorem 4.1.

    Corollary 4.4. Let (H,d) be a complete CAT(0) space and D be a nonempty closed convex subset of H. Suppose that T:DD is a quasi-nonexpansive mapping that satisfies the demiclosedness-type property (2.7) and {un} is a sequence generated by (4.3) with 0<aαnb<1 for all nN. Then, {un} Δ-converges to a fixed point of T.

    Proof. By Theorem 4.1, conditions (P1) and (P2) of Theorem 2.1 hold. Consequently, Theorem 2.1 yields the desired result using the demiclosedness-type property of T.

    In real Hilbert spaces, the Δ-convergence coincides with weak convergence and, thus, we have the next result which corresponds to Theorem 8 in [19] (see also Theorem 4.3 in [8]).

    Corollary 4.5. Let H be a real Hilbert space endowed with the usual metric d and D be a nonempty closed convex subset of H. Suppose that T:DD is a quasi-nonexpansive mapping that satisfies the demiclosedness-type property (2.7) and {un} is a sequence generated by

    un+1=(1αn)un+αnTun,n1

    with 0<aαnb<1 for all nN. Then, {un} converges weakly to a fixed point of T.

    Remark 4.1. According to Remark 2.2, the fact that the mapping T satisfies the demiclosedness-type property (2.7) means that IT is demiclosed at zero, and so by Corollary 4.5 we recover Theorem 8 [19].

    Corollary 4.6. Let H, D, T and {un} be the same as in Corollary 4.4. Suppose D is compact, then {un} converges strongly to a common fixed point of T.

    Proof. Since D is compact, there exists a subsequence {unk} of {un} that strongly converges to uD. Also, by Theorem 4.1, conditions (P1) and (P2) of Theorem 2.1 hold. Following the lines of proof of Theorem 2.1 with {unk} in place of the subsequence of {un} that Δ-converges, (P2) yields that limnd(un,u)=0, where uFix(T).

    Theorem 4.2. Let (H,d) be a complete CAT(0) space and D be a nonempty closed convex subset of H. Suppose that T:DD is a κ-demicontractive mapping that satisfies the demiclosedness-type property (2.7) and {un} is a sequence generated by (4.3) with 0<aαn(1κ)bb<1 for all nN. Then, {un} Δ-converges to a fixed point of T.

    Proof. Let σ]0,1κ]. Since T is κ-demicontractive, it follows from Proposition 4.2 that Tσ is quasi-nonexpansive. By (2.4), the updated iterate un+1 in (4.3) is chosen in the geodesic segment connecting un and Tun in the sense that

    d(un+1,un)=αnd(un,Tun).

    This is equivalent to un+1 being chosen in the geodesic segment connecting un and Tun such that

    d(un+1,un)=αnd(un,Tun)=αnσd(un,Tσun).

    This implies that

    un+1=(1βn)unβnTσun,n1, (4.6)

    where βn=αnσ. Based on the hypotheses of the theorem, it follows from Corollary 4.4 that {un} generated by (4.6) Δ-converges to a fixed point of Tσ provided βn[a,b]]0,1[, for all nN. By A1), the convergence is to a fixed point of T. Observe that since 0<σ1κ, then βn[a,b]]0,1[ gives that aσαn(1κ)b, for all nN.

    The next result is the linear version of Theorem 4.2 that corresponds to the weak convergence result established in [39].

    Corollary 4.7. [39, Theorem 1] Let H be a real Hilbert space endowed with the usual metric d and D be a nonempty closed convex subset of H. Suppose that T:DD is a κ-demicontractive mapping that satisfies the demiclosedness-type property (2.7) and {un} is a sequence generated by

    un+1=(1αn)un+αnTun,n1,

    with 0<aαnb<1 for all nN. Then, {un} converges weakly to a fixed point of T.

    Remark 4.2. In view of Remark 2.2, the fact that the mapping T satisfies the demiclosedness-type property (2.7) means that IT is demiclosed at zero, and, hence, by Corollary 4.7 we recover Theorem 1 [39].

    The next result follows similar lines of proof as in Corollary 4.6.

    Corollary 4.8. Let H, D, T and {un} be the same as in Theorem 4.2. Suppose D is compact, then {un} converges strongly to a common fixed point of T.

    Let F:H×HR be a bifunction with F(u,u)=0 for all uH. Consider the problem of finding

    uHsuch thatF(u,w)0,wH. (5.1)

    This problem is known as the equilibrium problem and has been extensively analyzed by many scholars due to its applicability in various nonlinear problems. Detailed discussions on this problem in the context of Hadamard spaces can be found in [25,31]. As a special case, let H=R3 and define the bifunction F as follows:

    F(u,w):=2i=1(3uiwi5u2i)12(u22u3+w3w22)2,u,wH. (5.2)

    Suppose that T is given as in Example 3.1. Then, for all u,wR3, we have that

    d2(Tu,w)=(2i=1(4uiwi)2)andd2(u,Tu)=2i=1(ui(4ui))2.

    Consequently, we can express F(u,w) as

    F(u,w)=122i=1[16u2i+w2i+8uiwi(u2i+w2i2uiwi)25u2i]12(u22u3+w3w22)2=122i=1(4ui+wi)2122i=1(uiwi)212(u22u3+w3w22)22522i=1u2i=12[(2i=1(4uiwi)2)(2i=1(uiwi)2+(u22u3+w3w22)2)(2i=1(ui(4ui))2)]=12[d2(Tu,w)d2(u,w)d2(u,Tu)]=Q(u,Tu,w,u).

    We now have the following result.

    Proposition 5.1. For β>0, consider the bifunction Fβ:H×HR defined by

    Fβ(u,w)=βQ(u,Tu,w,u)u,wH,

    where T:HH is a mapping. Then, the set of the equilibrium points of Fβ coincides with the set of fixed points of T.

    Proof. For uFix(T), it is clear that

    Fβ(u,w)=βQ(u,u,w,u)=0,wH.

    For the converse, suppose that Fβ(u,w)0 for all wH. Then, we get

    0d2(Tu,w)d2(u,w)d2(u,Tu)d2(Tu,w)d2(u,Tu),

    for all wH. This implies that

    d(u,Tu)d(Tu,w)

    for all wH. Since w can be Tu, we conclude that uFix(T).

    Next, we show that (H,d) is a CAT(0) space and we can approximate the equilibrium of Fβ using our fixed-point results. Indeed, for u,wR3, let ϕ:[0,1]R3 be defined by

    ϕ(t)=(ϕ1(t),ϕ2(t),ϕ3(t)),

    where ϕi(t)=(1t)ui+twi for i=1,2, and

    ϕ3(t)=(Φ2(t))2(1t)(u22u3)t(w22w3).

    It can be easily shown that (H,d) is a CAT(0) space and ϕ is the geodesic connecting u and w. This follows from a straightforward computation using the identity

    (tx+(1t)y)2=tx2+(1t)y2t(1t)(xy)2,x,yR.

    Thus, the inequality (2.6) holds. It is also worth noting that (H,d) is not a Hilbert space.

    Based on Example 3.1, we observe that T is not a quasi-nonexpansive mapping with respect to (H,d), but it is a 32-enriched quasi-nonexpansive mapping. Therefore, according to Theorem 3.1, T is 35-demicontractive with respect to (H,d). Additionally, T satisfies a demiclosedness-type property. To demonstrate this, let {un} be a sequence such that Δ-limnun=u and limnd(un,Tun)=0. Then, for σ=25, we have

    Tσu=(1σ)uσTu=(u1,u2,25u22+35u3) for u=(u1,u2,u3),

    and, consequently,

    d(un,Tσu)d(un,Tσun)+d(Tσun,Tσu)=σd(un,Tun)+d(Tσun,Tσu)
    =σd(un,Tun)+2i=1(un,iui)2+(u2n,2+(25(u2)2+35(u3))(25u2n,2+35un,3)(u2)2)2
    =σd(un,Tun)+2i=1(un,iui)2+(35)2(u2n,2+u3un,3(u2)2)2
    d(un,Tun)+2i=1(un,iui)2+(u2n,2+u3un,3(u2)2)2=d(un,Tun)+d(un,u).

    Thus, lim supnd(un,Tσu)lim supnd(un,u). Utilizing the uniqueness of the asymptotic center (see [17, Proposition 7]), we conclude that x=Tσx. Hence, xFix(Tσ)=Fix(T), leading to the conclusion that x=Tx.

    To gain insight into the behavior of the Krasnoselskij-Mann algorithm (4.3) with respect to the fixed point (0,0,0), we set αn=n7n+1 and obtain the results displayed in Table 1.

    Table 1.  Few values of the sequence terms for three distinct initial points.
    n un un un
    1 (13, 11, 10) (18, -20, 5) (-20, 17, -8)
    2 (4.875, 4.125, -80.1094) (6.75, -7.5, -289.375) (-7.5, 6.375, -219.2344)
    3 (1.625, 1.375, -82.2844) (2.25, -2.5, -293.2917) (-2.5, 2.125, -220.7094)
    4 (0.51705, 0.4375, -72.5052) (0.71591, -0.79545, -258.0623) (-0.79545, 0.67614, -194.0553)
    5 (0.16046, 0.13578, -62.651) (0.22218, -0.24687, -222.9521) (-0.24687, 0.20984, -167.6392)
    6 (0.04903, 0.041487, -53.9637) (0.067888, -0.075431, -192.0333) (-0.075431, 0.064116, -144.3897)
    7 (0.014823, 0.012543, -46.4352) (0.020524, -0.022805, -165.2423) (-0.022805, 0.019384, -124.2455)
    8 (0.0044469, 0.0037628, -39.9344) (0.0061573, -0.0068414, -142.1088) (-0.0068414, 0.0058152, -106.8514)
    9 (0.0013263, 0.0011222, -34.3296) (0.0018364, -0.0020404, -122.1637) (-0.0020404, 0.0017344, -91.8548)
    10 (0.00039374, 0.00033316, -29.502) (0.00054518, -0.00060575, -104.9845) (-0.00060575, 0.00051489, -78.9377)
    90 (2.5882e-47, 2.19e-47, -0.00013713) (3.5836e-47, -3.9818e-47, -0.000488) (-3.9818e-47, 3.3845e-47, -0.00036693)
    91 (7.4241e-48, 6.2819e-48, -0.00011757) (1.028e-47, -1.1422e-47, -0.0004184) (-1.1422e-47, 9.7084e-48, -0.00031459)
    92 (2.1295e-48, 1.8019e-48, -0.0001008) (2.9485e-48, -3.2761e-48, -0.00035872) (-3.2761e-48, 2.7847e-48, -0.00026972)
    93 (6.1078e-49, 5.1681e-49, -8.6426e-05) (8.457e-49, -9.3966e-49, -0.00030755) (-9.3966e-49, 7.9871e-49, -0.00023125)
    94 (1.7518e-49, 1.4823e-49, -7.4099e-05) (2.4255e-49, -2.695e-49, -0.00026368) (-2.695e-49, 2.2908e-49, -0.00019826)
    95 (5.0241e-50, 4.2511e-50, -6.3529e-05) (6.9564e-50, -7.7293e-50, -0.00022607) (-7.7293e-50, 6.5699e-50, -0.00016998)
    96 (1.4408e-50, 1.2192e-50, -5.4467e-05) (1.995e-50, -2.2167e-50, -0.00019383) (-2.2167e-50, 1.8842e-50, -0.00014574)
    97 (4.132e-51, 3.4963e-51, -4.6698e-05) (5.7212e-51, -6.3569e-51, -0.00016618) (-6.3569e-51, 5.4033e-51, -0.00012495)
    98 (1.1849e-51, 1.0026e-51, -4.0037e-05) (1.6406e-51, -1.8229e-51, -0.00014247) (-1.8229e-51, 1.5495e-51, -0.00010712)
    99 (3.3978e-52, 2.875e-52, -3.4325e-05) (4.7046e-52, -5.2273e-52, -0.00012215) (-5.2273e-52, 4.4432e-52, -9.1843e-05)
    100 (9.7428e-53, 8.2439e-53, -2.9429e-05) (1.349e-52, -1.4989e-52, -0.00010472) (-1.4989e-52, 1.2741e-52, -7.8742e-05)

     | Show Table
    DownLoad: CSV

    In this article, we have extracted and analyzed the substantial properties of demicontractive mappings in the context of metric fixed point approximation. Based on Theorem 3.1, we have established that demicontractive mappings are enriched quasi-nonexpansive mappings in a general metric space. Furthermore, it is evident that geodesic average perturbation techniques play a significant role in approximating the fixed points of such mappings within the framework of CAT(0) spaces. Moreover, by geodesic average perturbation, we have proven that if T is κ-demicontractive, then Tσ is κ-demicontractive for 1+κ/σ1/σκ<1, and it is quasi-nonexpansive for σ1κ. These properties are established in Propositions 4.1 and 4.2.

    We have leveraged the Krasnoselskij-Mann iterative algorithm to approximate the fixed points of quasi-nonexpansive mappings, as shown in Theorem 4.1 and Corollary 4.4. Extending this algorithm through geodesic average perturbation techniques, we have successfully applied it to approximate the fixed points of demicontractive mappings, as expounded in Theorem 4.2. Furthermore, we presented an application of our results to solving an equilibrium problem in CAT(0) spaces and showed how we can approximate the equilibrium of Fβ using our fixed point results. Related to this problem, we also provided numerical examples in the case of a demicontractive mapping that is not a quasi-nonexpansive mapping and highlighted the convergence pattern of the algorithm in Table 1. It is important to note that the numerical example is set in non-Hilbert CAT(0) spaces.

    This work contributes to a unified understanding of demicontractive mappings and extends and generalizes many existing results found in the literature. Notably, it complements the work presented in [8] by extending the analysis from a linear framework to the broader scope of CAT(0) spaces. As such, the findings presented herein are applicable to all R-trees, Hadamard manifolds and all CAT(κ) spaces where κ0.

    Our results also open new avenues for applying demicontractive mappings to the solution of various problems in nonlinear analysis, like equilibrium problems, split problems, split feasibility problems, split common fixed point problems, split variational inequality problems, split common null point problems, split generalized equilibrium problems and more ([1,4,15,16,20,21,22,23,28,29,30,31,32,36,40,42,43,44,47,48,49,50,51,52,53,54,55,56,57] and references therein) by extending the existing results from linear settings to nonlinear settings.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    The authors acknowledge the financial support provided by the Center of Excellence in Theoretical and Computational Science (TaCS-CoE), KMUTT. Moreover, this research has received funding support from the NSRF through the Program Management Unit for Human Resources & Institutional Development, Research and Innovation [grant number B39G660025]. The first author was supported by the "Petchra Pra Jom Klao Ph.D. Research Scholarship" from "King Mongkut's University of Technology Thonburi" with the contract number 67/2563. The work was initiated while the first author visited North University Center at Baia Mare, Technical University of Cluj-Napoca, Romania during April-October, 2023.

    The authors declare they have no conflict of interest.



    [1] R. Pieruschka, U. Schurr, Plant phenotyping: past, present, and future, Plant Phenomics, 2019 (2019). https://doi.org/10.34133/2019/7507131 doi: 10.34133/2019/7507131
    [2] C. Costa, U. Schurr, F. Loreto, P. Menesatti, S. Carpentier, Plant phenotyping research trends, a science mapping approach, Front. Plant Sci., 9 (2019), 1933. https://doi.org/10.3389/fpls.2018.01933 doi: 10.3389/fpls.2018.01933
    [3] A. K. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh, Deep learning for plant stress phenotyping: trends and future perspectives, Trends Plant Sci., 23 (2018), 883–898. https://doi.org/10.1016/j.tplants.2018.07.004 doi: 10.1016/j.tplants.2018.07.004
    [4] S. Arya, K. S. Sandhu, J. Singh, S. Kumar, Deep learning: as the new frontier in high-throughput plant phenotyping, Euphytica, 218 (2022), 47. https://doi.org/10.1007/s10681-022-02992-3 doi: 10.1007/s10681-022-02992-3
    [5] S. Bhagat, M. Kokare, V. Haswani, P. Hambarde, R. Kamble, Eff-UNet++: A novel architecture for plant leaf segmentation and counting, Ecol. Inf., 68 (2022), 101583. https://doi.org/10.1016/j.ecoinf.2022.101583 doi: 10.1016/j.ecoinf.2022.101583
    [6] K. Khan, R. U. Khan, W. Albattah, A. M. Qamar, End-to-end semantic leaf segmentation framework for plants disease classification, Complexity, 2022 (2022). https://doi.org/10.1155/2022/1168700 doi: 10.1155/2022/1168700
    [7] D. Zendler, N. Malagol, A. Schwandner, R. Töpfer, L. Hausmann, E. Zyprian, High-throughput phenotyping of leaf discs infected with grapevine downy mildew using shallow convolutional neural networks, Agronomy, 11 (2021), 1768. https://doi.org/10.3390/agronomy11091768 doi: 10.3390/agronomy11091768
    [8] J. Wu, C. Wen, H. Chen, Z. Ma, T. Zhang, H. Su, et al., DS-DETR: A model for tomato leaf disease segmentation and damage evaluation, Agronomy, 12 (2022), 2023. https://doi.org/10.3390/agronomy12092023 doi: 10.3390/agronomy12092023
    [9] Y. Wu, L. Xu, Crop organ segmentation and disease identification based on weakly supervised deep neural network, Agronomy, 9 (2019), 737. https://doi.org/10.3390/agronomy9110737 doi: 10.3390/agronomy9110737
    [10] Z. Li, R. Guo, M. Li, Y. Chen, G. Li, A review of computer vision technologies for plant phenotyping, Comput. Electron. Agric., 176 (2020), 105672. https://doi.org/10.1016/j.compag.2020.105672 doi: 10.1016/j.compag.2020.105672
    [11] Y. Jiang, C. Li, Convolutional neural networks for image-based high-throughput plant phenotyping: a review, Plant Phenomics, 2020 (2020). https://doi.org/10.34133/2020/4152816 doi: 10.34133/2020/4152816
    [12] W. D. Kissling, Y. Shi, Z. Koma, C. Meijer, O. Ku, F. Nattino, et al., Laserfarm–A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds, Ecol. Inf., 72 (2022), 101836. https://doi.org/10.1016/j.ecoinf.2022.101836 doi: 10.1016/j.ecoinf.2022.101836
    [13] J. Zhou, X. Fu, S. Zhou, J. Zhou, H. Ye, H. T. Nguyen, Automated segmentation of soybean plants from 3D point cloud using machine learning, Comput. Electron. Agric., 162 (2019), 143–153. https://doi.org/10.1016/j.compag.2019.04.014 doi: 10.1016/j.compag.2019.04.014
    [14] X. Ma, K. Zhu, H. Guan, J. Feng, S. Yu, G. Liu, Calculation method for phenotypic traits based on the 3D reconstruction of maize canopies, Sensors, 19 (2019), 1201. https://doi.org/10.3390/s19051201 doi: 10.3390/s19051201
    [15] S. Wu, W. Wen, Y. Wang, J. Fan, C. Wang, W. Gou, et al., MVS-Pheno: a portable and low-cost phenotyping platform for maize shoots using multiview stereo 3D reconstruction, Plant Phenomics, 2020 (2020). https://doi.org/10.34133/2020/1848437 doi: 10.34133/2020/1848437
    [16] H. You, Y. Liu, P. Lei, Z. Qin, Q. You, Segmentation of individual mangrove trees using UAV-based LiDAR data, Ecol. Inf., (2023), 102200. https://doi.org/10.1016/j.ecoinf.2023.102200 doi: 10.1016/j.ecoinf.2023.102200
    [17] P. Li, X. Zhang, W. Wang, H. Zheng, X. Yao, Y. Tian, et al., Estimating aboveground and organ biomass of plant canopies across the entire season of rice growth with terrestrial laser scanning, Int. J. Appl. Earth Obs. Geoinf., 91 (2020), 102132. https://doi.org/10.1016/j.jag.2020.102132 doi: 10.1016/j.jag.2020.102132
    [18] Y. Sun, Y. Luo, Q. Zhang, L. Xu, L. Wang, P. Zhang, Estimation of crop height distribution for mature rice based on a moving surface and 3D point cloud elevation, Agronomy, 12 (2022), 836. https://doi.org/10.3390/agronomy12040836 doi: 10.3390/agronomy12040836
    [19] F. Tardieu, Virtual plants: modelling as a tool for the genomics of tolerance to water deficit, Trends Plant Sci., 8 (2003), 9–14. https://doi.org/10.1016/S1360-1385(02)00008-0 doi: 10.1016/S1360-1385(02)00008-0
    [20] P. Prusinkiewicz, Graphical applications of L-systems, in Proceedings of Graphics Interface, Canadian Information Processing Society, Vancouver, Canada, 86 (1986), 247–253.
    [21] R. Karwowski, P. Prusinkiewicz, Design and implementation of the L+ C modeling language, Electron. Notes Theor. Comput. Sci., 86 (2003), 134–152. https://doi.org/10.1016/S1571-0661(04)80680-7 doi: 10.1016/S1571-0661(04)80680-7
    [22] F. Boudon, C. Pradal, T. Cokelaer, P. Prusinkiewicz, C. Godin, L-Py: an L-system simulation framework for modeling plant architecture development based on a dynamic language, Front. Plant Sci., 3 (2012), 76. https://doi.org/10.3389/fpls.2012.00076 doi: 10.3389/fpls.2012.00076
    [23] R. Barth, J. IJsselmuiden, J. Hemming, E. J. V. Henten, Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation, Comput. Electron. Agric., 161 (2019), 291–304. https://doi.org/10.1016/j.compag.2017.11.040 doi: 10.1016/j.compag.2017.11.040
    [24] M. Cieslak, N. Khan, P. Ferraro, R. Soolanayakanahally, S. J. Robinson, I. Parkin, et al., L-system models for image-based phenomics: case studies of maize and canola, In Silico Plants, 4 (2021), diab039. https://doi.org/10.1093/insilicoplants/diab039 doi: 10.1093/insilicoplants/diab039
    [25] E. Fiestas, O. E. Ramos, S. Prado, RPA and L-system based synthetic data generator for cost-efficient deep learning model training, in 2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE), National Formosa University, Yunlin, Taiwan, (2021), 645–650. https://doi.org/10.1109/ECICE52819.2021.9645719
    [26] D. Ward, P. Moghadam, N. Hudson, Deep leaf segmentation using synthetic data, preprint, arXiv: 1807.10931. https://doi.org/10.48550/arXiv.1807.10931
    [27] R. Barth, J. IJsselmuiden, J. Hemming, E. J. V. Henten, Data synthesis methods for semantic segmentation in agriculture: A Capsicum annuum dataset, Comput. Electron. Agric., 144 (2018), 284–296. https://doi.org/10.1016/j.compag.2017.12.001 doi: 10.1016/j.compag.2017.12.001
    [28] J. Ubbens, M. Cieslak, P. Prusinkiewicz, I. Stavness, The use of plant models in deep learning: an application to leaf counting in rosette plants, Plant Methods, 14 (2018), 1–10. https://doi.org/10.1186/s13007-018-0273-z doi: 10.1186/s13007-018-0273-z
    [29] K. Turgut, H. Dutagaci, D. Rousseau, RoseSegNet: An attention-based deep learning architecture for organ segmentation of plants, Biosyst. Eng., 221 (2022), 138–153. https://doi.org/10.1016/j.biosystemseng.2022.06.016 doi: 10.1016/j.biosystemseng.2022.06.016
    [30] A. Chaudhury, P. Hanappe, R. Azaïs, C. Godin, D. Colliaux, Transferring PointNet++ segmentation from virtual to real plants, in ICCV 2021-International Conference on Computer Vision, IEEE computer society, Montreal, (2021), 13.
    [31] Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, M. Bennamoun, Deep learning for 3d point clouds: A survey, IEEE Trans. Pattern Anal. Mach. Intell., 43 (2020), 4338–4364. https://doi.org/10.1109/TPAMI.2020.3005434 doi: 10.1109/TPAMI.2020.3005434
    [32] H. Su, S. Maji, E. Kalogerakis, E. Learned-Miller, Multi-view convolutional neural networks for 3d shape recognition, in Proceedings of the IEEE International Conference on Computer Vision, IEEE computer society, Montreal, QC, Canada, (2015), 945–953. https://doi.org/10.1109/ICCV.2015.114
    [33] W. Shi, R. van de Zedde, H. Jiang, G. Kootstra, Plant-part segmentation using deep learning and multi-view vision, Biosyst. Eng., 187 (2019), 81–95. https://doi.org/10.1016/j.biosystemseng.2019.08.014 doi: 10.1016/j.biosystemseng.2019.08.014
    [34] X. Wang, C. Wang, B. Liu, X. Zhou, L. Zhang, J. Zheng, et al., Multi-view stereo in the deep learning era: A comprehensive review, Displays, 70 (2021), 102102. https://doi.org/10.1016/j.displa.2021.102102 doi: 10.1016/j.displa.2021.102102
    [35] D. Maturana, S. Scherer, Voxnet: A 3d convolutional neural network for real-time object recognition, in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, (2015), 922–928. https://doi.org/10.1109/IROS.2015.7353481
    [36] R. Du, Z. Ma, P. Xie, Y. He, H. Cen, PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage, ISPRS J. Photogramm. Remote Sens., 195 (2023), 380–392. https://doi.org/10.1016/j.isprsjprs.2022.11.022 doi: 10.1016/j.isprsjprs.2022.11.022
    [37] C. R. Qi, H. Su, K. Mo, L. J. Guibas, Pointnet: Deep learning on point sets for 3d classification and segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE computer society, Honolulu, HI, USA, (2017), 652–660. https://doi.org/10.48550/arXiv.1612.00593
    [38] C. R. Qi, L. Yi, H. Su, L. J. Guibas, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, Adv. Neural Inf. Process. Syst., 30 (2017). https://doi.org/10.48550/arXiv.1706.02413 doi: 10.48550/arXiv.1706.02413
    [39] H. Kang, H. Zhou, X. Wang, C. Chen, Real-time fruit recognition and grasping estimation for robotic apple harvesting, Sensors, 20 (2020), 5670. https://doi.org/10.3390/s20195670 doi: 10.3390/s20195670
    [40] T. Masuda, Leaf area estimation by semantic segmentation of point cloud of tomato plants, in Proceedings of the IEEE/CVF International Conference on Computer Vision, IEEE computer society, Montreal, QC, Canada, (2021), 1381–1389. https://doi.org/10.1109/ICCVW54120.2021.00159
    [41] D. Li, G. Shi, J. Li, Y. Chen, S. Zhang, S. Xiang, et al., PlantNet: A dual-function point cloud segmentation network for multiple plant species, ISPRS J. Photogramm. Remote Sens., 184 (2022), 243–263. https://doi.org/10.1016/j.isprsjprs.2022.01.007 doi: 10.1016/j.isprsjprs.2022.01.007
    [42] M. Ghahremani, B. Tiddeman, Y. Liu, A. Behera, Orderly disorder in point cloud domain, in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, (2020), 494–509. https://doi.org/10.1007/978-3-030-58604-1_30
    [43] M. Ghahremani, K. Williams, F. M. K. Corke, B. Tiddeman, Y. Liu, J. H. Doonan, Deep segmentation of point clouds of wheat, Front. Plant Sci., 12 (2021), 608732. https://doi.org/10.3389/fpls.2021.608732 doi: 10.3389/fpls.2021.608732
    [44] M. H. Guo, J. X. Cai, Z. N. Liu, T. J. Mu, R. R. Martin, S. M. Hu, Pct: Point cloud transformer, Comput. Visual Media, 7 (2021), 187–199. https://doi.org/10.1007/s41095-021-0229-5 doi: 10.1007/s41095-021-0229-5
    [45] H. Zhao, L. Jiang, J. Jia, P. H. Torr, V. Koltun, Point transformer, in Proceedings of the IEEE/CVF International Conference on Computer Vision, IEEE computer society, Montreal, QC, Canada, (2021), 16259–16268. https://doi.org/10.1109/ICCV48922.2021.01595
    [46] N. Engel, V. Belagiannis, K. Dietmayer, Point transformer, IEEE Access, 9 (2021), 134826–134840. https://doi.org/10.1109/ACCESS.2021.3116304 doi: 10.1109/ACCESS.2021.3116304
    [47] J. Lin, M. Rickert, A. Perzylo, A. Knoll, Pctma-net: Point cloud transformer with morphing atlas-based point generation network for dense point cloud completion, in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, (2021), 5657–5663. https://doi.org/10.1109/IROS51168.2021.9636483
    [48] L. Hui, H. Yang, M. Cheng, J. Xie, J. Yang, Pyramid point cloud transformer for large-scale place recognition, in Proceedings of the IEEE/CVF International Conference on Computer Vision, IEEE computer society, Montreal, QC, Canada, (2021), 6098–6107. https://doi.org/10.1109/ICCV48922.2021.00604
    [49] X. Yu, L. Tang, Y. Rao, T. Huang, J. Zhou, J. Lu, Point-bert: Pre-training 3d point cloud transformers with masked point modeling, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE computer society, New Orleans, LA, USA, (2022), 19313–19322. https://doi.org/10.48550/arXiv.2111.14819
    [50] D. Li, J. Li, S. Xiang, A. Pan, PSegNet: Simultaneous semantic and instance segmentation for point clouds of plants, Plant Phenomics, 2022 (2022). https://doi.org/10.34133/2022/9787643 doi: 10.34133/2022/9787643
    [51] E. Nezhadarya, E. Taghavi, R. Razani, B. Liu, J. Luo, Adaptive hierarchical down-sampling for point cloud classification, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE computer society, Seattle, WA, USA, (2020), 12956–12964. https://doi.org/10.1109/CVPR42600.2020.01297
    [52] X. Lai, J. Liu, L. Jiang, L. Wang, H. Zhao, S. Liu, et al., Stratified transformer for 3d point cloud segmentation, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE computer society, New Orleans, LA, USA, (2022), 8500–8509. https://doi.org/10.1109/CVPR52688.2022.00831
    [53] M. Tomkins, Towards modelling emergence in plant systems, Quant. Plant Biol., 4 (2023), e6. https://doi.org/10.1017/qpb.2023.6 doi: 10.1017/qpb.2023.6
    [54] A. Chaudhury, F. Boudon, C. Godin, 3D plant phenotyping: All you need is labelled point cloud data, in Computer Vision–ECCV 2020 Workshops, Glasgow, UK, 16 (2020), 244–260. https://doi.org/10.1007/978-3-030-65414-6_18
    [55] U. Krämer, Planting molecular functions in an ecological context with Arabidopsis thaliana, Elife, 4 (2015), e06100. https://doi.org/10.7554/eLife.06100 doi: 10.7554/eLife.06100
    [56] C. Wyman, The Alias Method for Sampling Discrete Distributions, Ray Tracing Gems Ⅱ: Next Generation Real-Time Rendering with DXR, Vulkan, and OptiX, (2021), 339–343. https://doi.org/10.1007/978-1-4842-7185-8_21 doi: 10.1007/978-1-4842-7185-8_21
    [57] S. Laine, T. Karras, Efficient sparse voxel octrees, in Proceedings of the 2010 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, Association for Computing Machinery, New York, NY, USA, (2010), 55–63. https://doi.org/10.1145/1730804.1730814
    [58] Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, et al., Learning semantic segmentation of large-scale point clouds with random sampling, IEEE Trans. Pattern Anal. Mach. Intell., 44 (2021), 8338–8354. https://doi.org/10.1109/TPAMI.2021.3083288 doi: 10.1109/TPAMI.2021.3083288
    [59] L. Li, L. He, J. Gao, X. Han, Psnet: Fast data structuring for hierarchical deep learning on point cloud, IEEE Trans. Circuits Syst. Video Technol., 32 (2022), 6835–6849. https://doi.org/10.1109/TCSVT.2022.3171968 doi: 10.1109/TCSVT.2022.3171968
    [60] R. Xiong, Y. Yang, D. He, K. Zheng, S. Zheng, C. Xing, et al., On layer normalization in the transformer architecture, in International Conference on Machine Learning, Association for Computing Machinery, New York, NY, USA, (2020), 10524–10533. https://doi.org/10.48550/arXiv.2002.04745
    [61] C. Moenning, N. A. Dodgson, A new point cloud simplification algorithm, in Proc. Int. Conf. Visualization Imaging Image Proc., (2003), 1027–1033.
    [62] M. Connor, P. Kumar, Fast construction of k-nearest neighbor graphs for point clouds, IEEE Trans. Visual Comput. Graphics, 16 (2010), 599–608. https://doi.org/10.1109/TVCG.2010.9 doi: 10.1109/TVCG.2010.9
    [63] J. L. Ba, J. R. Kiros, G. E. Hinton, Layer normalization, Preprint. arXiv: 160706450. https://doi.org/10.48550/arXiv.1607.06450
    [64] O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 18 (2015), 234–241. https://doi.org/10.1007/978-3-319-24574-4_28
    [65] I. Ziamtsov, K. Faizi, S. Navlakha, Branch-Pipe: Improving graph skeletonization around branch points in 3D point clouds, Remote Sens., 13 (2021), 3802. https://doi.org/10.3390/rs13193802 doi: 10.3390/rs13193802
    [66] M. Xu, R. Ding, H. Zhao, X. Qi, PAConv: Position adaptive convolution with dynamic kernel assembling on point clouds, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE computer society, Nashville, TN, USA, (2021), 3172–3181. https://doi.org/10.1109/CVPR46437.2021.00319
    [67] J. Morel, A. Bac, T. Kanai, Segmentation of unbalanced and in-homogeneous point clouds and its application to 3D scanned trees, Visual Comput., 36 (2020), 2419–2431. https://doi.org/10.1007/s00371-020-01966-7 doi: 10.1007/s00371-020-01966-7
    [68] J. Le Louëdec, G. Cielniak, 3D Shape sensing and deep learning-based segmentation of strawberries, Comput. Electron. Agric., 190 (2021), 106374. https://doi.org/10.1016/j.compag.2021.106374 doi: 10.1016/j.compag.2021.106374
    [69] H. Weiser, L. Winiwarter, J. Schäfer, F. E. Fassnacht, K. Anders, A. M. E. Pena, et al., Virtual laser scanning (VLS) in forestry-Investigating appropriate 3D forest representations for LiDAR simulations with HELIOS++, in EGU General Assembly Conference Abstracts, Vienna, Austria, (2021), EGU21-9178. https://doi.org/10.5194/egusphere-egu21-9178
  • This article has been cited by:

    1. Wojciech M. Kozlowski, On stability of iteration processes convergent to stationary points of semigroups of nonlinear operators in metric spaces, 2024, 0233-1934, 1, 10.1080/02331934.2024.2410259
    2. Vasile Berinde, Single-Valued Demicontractive Mappings: Half a Century of Developments and Future Prospects, 2023, 15, 2073-8994, 1866, 10.3390/sym15101866
    3. Teodor Turcanu, Mihai Postolache, On Enriched Suzuki Mappings in Hadamard Spaces, 2024, 12, 2227-7390, 157, 10.3390/math12010157
    4. Sani Salisu, Vasile Berinde, Songpon Sriwongsa, Poom Kumam, Fixed point properties of saturated and unsaturated contractive mappings in CAT(0) spaces, 2024, 0971-3611, 10.1007/s41478-024-00868-4
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1700) PDF downloads(76) Cited by(1)

Figures and Tables

Figures(12)  /  Tables(7)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog