An Improved Stereo Matching Algorithm Applied to 3D Visualization of Plant Leaf

Liu, Zhichao; Xu, Lihong; Lin, Chuen Horng

doi:10.1109/iscid.2015.66

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…15 Estimated disparity maps of Cones achieved by a Kuo et al [27], b Kuo [28], c Hsieh [29], d Sun [31], e ASW [21], f S-ASW [23], g LF-SM [36], and h the proposed method Fig. 16 Estimated disparity maps of Teddy achieved by a Kuo et al [27], b Kuo [28], c Hsieh [29], d Sun [31], e ASW [21], f S-ASW [23], g LF-SM [36], and h the proposed methods adaptive cost selection, the segment-based adaptive weights for cost aggregation, twolevel WTA strategy, and dual-path depth refinement. For small holes, the depth refinement uses maximum-weighted candidate for the best filling process.…”

Section: Discussionmentioning

confidence: 99%

“…For performance evaluations, we compare the proposed method to other stereo matching algorithms. The compared methods include adaptive support weight (ASW) [21], segmentation-based adaptive support weight (S-ASW) [23], plant leaf stereo matching (LP-SM) [36], edge-based stereo matching method (E-SM) [37], stereo matching implemented on GPU platform [31], AdaStereo [38], comparative evaluation of SGM variants for dense stereo matching (tMGM) [39], learning-based disparity estimation (iResNet) [40], and DeepPruner [41] methods. Tables 3 and 4 show that the performance of the proposed multi-scale ASW is superior to traditional ASW and other methods.…”

Section: Comparisons With Other Approachesmentioning

confidence: 99%

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Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

Huang

Chan

et al. 2020

J Image Video Proc.

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Stereo matching is one of the most important topics in computer vision and aims at generating precise depth maps for various applications. The major challenge of stereo matching is to suppress inevitable errors occurring in smooth, occluded, and discontinuous regions. In this paper, the proposed stereo matching system uses segment-based superpixels and matching cost. After determination of edge and smooth regions and selection of matching cost, we suggest the segment-based adaptive support weights in cost aggregation instead of color similarity and spatial proximity only. The proposed dual-path depth refinements use the cross-based support region by referring texture features to correct the inaccurate disparities with iterative procedures to improve the depth maps for shape reserving. Specially for leftmost and rightmost regions, the segment-based refinement can greatly improve the mismatched disparity holes. The experimental results show that the proposed system can achieve higher accurate depth maps than the conventional methods.

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Section: Discussionmentioning

confidence: 99%

Section: Comparisons With Other Approachesmentioning

confidence: 99%

Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

Huang

Chan

et al. 2020

J Image Video Proc.

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“…In a study of automatic measurements for plant leaves, the method of segmenting leaf based on two-dimensional images processing is widely used, for example, tobacco [19], leafy vegetables [20], pest-damaged leaf [21]. Further, the stereo vision method can also be used for leaf segmentation and measurement [18,22,23]. With the development of three-dimensional vision, more researchers segment the main organs of the plant after reconstruction the whole plant, extract the leaves, and study morphological features of leaves.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Measurement of Three-Dimensional Plants Based on Point Cloud

Wang

Chen

2020

Plants

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In agriculture, information about the spatial distribution of plant growth is valuable for applications. Quantitative study of the characteristics of plants plays an important role in the plants’ growth and development research, and non-destructive measurement of the height of plants based on machine vision technology is one of the difficulties. We propose a methodology for three-dimensional reconstruction under growing plants by Kinect v2.0 and explored the measure growth parameters based on three-dimensional (3D) point cloud in this paper. The strategy includes three steps—firstly, preprocessing 3D point cloud data, completing the 3D plant registration through point cloud outlier filtering and surface smooth method; secondly, using the locally convex connected patches method to segment the leaves and stem from the plant model; extracting the feature boundary points from the leaf point cloud, and using the contour extraction algorithm to get the feature boundary lines; finally, calculating the length, width of the leaf by Euclidean distance, and the area of the leaf by surface integral method, measuring the height of plant using the vertical distance technology. The results show that the automatic extraction scheme of plant information is effective and the measurement accuracy meets the need of measurement standard. The established 3D plant model is the key to study the whole plant information, which reduces the inaccuracy of occlusion to the description of leaf shape and conducive to the study of the real plant growth status.

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An Improved Stereo Matching Algorithm Applied to 3D Visualization of Plant Leaf

Cited by 2 publications

References 12 publications

Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

Non-Destructive Measurement of Three-Dimensional Plants Based on Point Cloud

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