Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves

Wang, Bin; Gao, Yongsheng; Sun, Changming; Blumenstein, Michael

doi:10.1109/tip.2019.2921526

Cited by 20 publications

(15 citation statements)

References 53 publications

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“…The improvement rate of the experimental group was 1.63% better than that of the control group, which was 0.08%; the experimental group (P=0.001<0.05), the control group (P=0.001<0.05), it shows that both feet in the experimental group and the control group have a significant improvement in the performance of running, jumping, and reaching heights after the test. 6 In the control group, when only common resistance training was performed, the test scores of various indicators were also improved, it can be seen that the common resistance training enhanced the explosive power level of the lower limbs of the experimental subjects. The test scores of the control group before and after the experiment are shown in Table 3.…”

Section: Resultsmentioning

confidence: 91%

Effects of Stretching on Lower Limb Strength in Basketball Athletes

Chen

2023

Rev Bras Med Esporte

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Introduction: Basketball players depend on excellent explosive power in the lower limbs for training and competition activities such as starting, braking, accelerating, decelerating, running, and jumping instantly and quickly. The level of Chinese athletes in this aspect is lower than world powers, and formulating training focused on explosive strength can enhance the training of these athletes. Objective: Study the effects of stretching on lower limb strength in basketball athletes. Methods: 20 young male basketball players selected as volunteers for the research were randomly divided into an experimental and control group, with 10 people in each group for retrospective analysis. The effect of rapid stretching compound training on the explosive power of lower limbs in basketball players was evaluated using the comparative method of literature data, expert interviews, experimental methods, and mathematical statistics. Results: After 8 weeks of training, the scores of each test index in the control group were significantly improved, including the standing jump and long-distance scores (P < 0.01). Unipodal takeoff and post-run long-range scores were significantly improved (P < 0.05). Conclusion: Both traditional resistance and compound lower limb stretching training can improve explosive power in young basketball players, but compound training showed more prominent results. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

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Section: Resultsmentioning

confidence: 91%

Effects of Stretching on Lower Limb Strength in Basketball Athletes

Chen

2023

Rev Bras Med Esporte

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“…It can be seen from the experimental results that the proposed ASC‐Net achieves the highest accuracy of 85.21%, which exceeds the previous methods, including FD [21] (30.06%), AP&BAP [18] (64.23%), SC+DP [7] (73.77%), HSC [19] (75.28%), CBW [9] (80.77%), and ShapeNet [11] (72.99%). The accuracies of global descriptors such as HSC and CBW increase significantly, even exceeding SC+DP.…”

Section: Methodsmentioning

confidence: 86%

“…The experimental results are shown in Table 1. The proposed ASC‐Net achieves the highest accuracy of 68.64%, which exceeds other methods, including FD [21] (32.98%), AP&BAP [18] (32.21%), SC [7] (61.36%), HSC [19] (58.87%), CBW [9] (58.77%), and ShapeNet [11] (45.27%).…”

Section: Methodsmentioning

confidence: 96%

“…Hierarchical string cuts (HSC) [19] partition a contour into multiple level curve segments of different lengths from a point moving around the contour to describe the shape gradually and completely from the global information to the finest details. Chord bunch walks (CBW) [9] is developed through the chord walking behaviour that effectively integrates the shape image function over the walked chord to reflect both the contour features and the inner properties of the shape. Multiscale Fourier descriptor on group feature (MSFDGF) [10] achieves multiscale description by generating coarser contours, then a group of complementary features are extracted on the generated coarser contours, finally Fourier transform is performed on these features.…”

Section: Related Workmentioning

confidence: 99%

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A novel shape classification method using 1‐D convolutional neural networks

Zhang

Liu

Zheng

et al. 2023

IET Image Processing

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Most of the shape classification methods are based on a single closed contour. However, practical shapes always have complex contours, for example, a combination of multiple open contours. How to accurately identify complex shapes is an unsolved problem. In this research, a novel method is proposed to classify complex shapes. The proposed method firstly encodes a complex shape to an angle code and a sparsity code, then input these codes to a 1-D CNN for extracting features and classification. Experiments on two datasets show this novel method is superior in terms of classification accuracy. These two datasets are practical shape dataset collected by this paper on internet and MPEG-7 CE-1 Part B. The proposed method achieves higher classification accuracy than compared methods. In order to show the performance of the proposed method on each class, the accuracy on each class is analyzed. Ablation experiment is conducted to show the contribution of each module in the network. The result shows that each module is meaningful in the network, because without any module the accuracy drops.

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“…Gray level cooccurrence matrix (GLCM) and local binary pattern (LBP) are popular texture descriptors which have been found in the application of plant species identification [9][10][11]. Other attempts on the characterization of leaf venation pattern [12] and margin pattern [13] have also been found in the research community of leaf image analysis.…”

Section: Introductionmentioning

confidence: 99%

Pairwise Rotational-Difference LBP for Fine-Grained Leaf Image Retrieval

Chen

Wang

Gao

2022

2022 IEEE International Conference on Image Processing (ICIP)

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In this study, we address the challenging issue of fine-grained leaf image retrieval which focuses on distinguishing different cultivars within the same species. We propose a novel local binary pattern, named pairwise rotation-difference LBP (PRDLBP), for the characterization of leaf image patterns. Different from the conventional LBP which measure the local grayscale contrast between the center pixel and its circular neighboring pixels, we consider the grayscale contrast between the circular neighboring pixels that are rotational symmetric about the center pixel. The proposed PRDLBP is a co-occurrence LBP feature representation which can not only encode spatially symmetric co-occurrence information, but also be inherently invariant to rotation. Its stronger discriminative power over the state-of-the-arts has been validated on two challenging fine-grained leaf image retrieval tasks, soybean cultivar identification and peanut cultivar identification. This work may attract considerable attention to fine-grained leaf image retrieval and advance the research of leaf image pattern identification from species to cultivars.

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Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves

Cited by 20 publications

References 53 publications

Effects of Stretching on Lower Limb Strength in Basketball Athletes

Effects of Stretching on Lower Limb Strength in Basketball Athletes

A novel shape classification method using 1‐D convolutional neural networks

Pairwise Rotational-Difference LBP for Fine-Grained Leaf Image Retrieval

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