CONIC: Contour Optimized Non-Iterative Clustering Superpixel Segmentation

Cheng, Li; Guo, Baolong; Liao, Nannan; Gong, Jianglei; Han, Xiaodong; Hou, Shuwei; Chen, Zhijie; He, Wangpeng

doi:10.3390/rs13061061

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…The main purpose of superpixel segmentation is to preserve the related structure in the image while removing irrelevant details and reducing the impact of uncorrelated content on the generation of significant images [28]. Segmented cells are grouped together by the same attributes (such as colour) to represent an area of the image, with each cell being generally uniform in size, and the colour boundaries can be maintained between cells.…”

Section: A Super Pixel Segmentationmentioning

confidence: 99%

An Automatic Cardiac Computed Tomography (Ct) Images Sequence segmentation Technique

Ahmed,

Hongsong,

Khan

et al. 2024

Preprint

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Segmentation of the Whole Cardiac CT Image Sequence is the Key to Computer-baseddiagnosis and the analysis of internal lesions in the Human Heart. According to the flow of the blood, contraction and Dilation, the cardiac CT image is prone to weak boundaries and artefacts. The technique of Segmentation for a fast, accurate, reproducible, and fully facilitate the diagnosis of Cardiovascular diseases, it is also the consuming of time, intensive of clinical staff to produce over-segmentation. Therefore, in this paper, we proposed the automatic cardiac CT images sequence segmentation technique to depend on the visual saliency method to understand the saliency of the foreground object relative to the background area used for ImageSegmentation. Using visual attention mechanism we will find the accurate location information, filtering ribs, and muscles of the heart area. The distinctness between the background and the target area is not obvious, and internal structure diversification. It can filter out most of the non-heart tissue part, which is more conducive to the doctor on the patient's heart health status observation.

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Section: A Super Pixel Segmentationmentioning

confidence: 99%

An Automatic Cardiac Computed Tomography (Ct) Images Sequence segmentation Technique

Ahmed,

Hongsong,

Khan

et al. 2024

Preprint

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“…The ADE20K_2017 is a scene parsing benchmark which contains 150 object and stuff classes. The SLIC algorithm [11], VASLIC algorithm [13], MMTDSLIC algorithm [14], CONIC algorithm [15], ML-LISLIC algorithm [16], and our algorithm (LoclaNet) are compared experimentally. The partial segmentation results of each algorithm are shown in Figures 3-5.…”

Section: Analysis Of Segmentation Accuracymentioning

confidence: 99%

“…Li et al [15] proposed a CONIC algorithm, which introduced contour rating into the non-iterative clustering framework to improve conventional simple non-iterative clustering, to balance segmentation accuracy and visual consistency. Di et al [16] proposed an ML-LISLIC algorithm, a hierarchical multi-level segmentation framework.…”

Section: Introductionmentioning

confidence: 99%

Superpixel segmentation algorithm based on local network modularity increment

Liu

Dai

Guo

et al. 2022

IET Image Processing

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Superpixel segmentation is a kind of image preprocessing technology and a popular research direction in image processing. The purpose of superpixel segmentation is to reduce the complexity of image processing. The most widely applied Simple Linear Iterative Clustering (SLIC) superpixel segmentation algorithm has high operating efficiency. However, under-segmentation is prone to occur when the number of given superpixel regions is too small. In order to improve the segmentation accuracy, the superpixel segmentation algorithm based on local network modularity increment (LocalNet) from the perspective of network community detection is proposed here. The adjacency network is constructed according to the colour similarity of image pixels, the local community centre is found by the degree of network nodes, and the local network structure of the community is constructed. The modularity increment is employed as the boundary constraint to improve the segmentation accuracy of superpixel segmentation. Through the experimental comparison with the SLIC algorithm, its improved algorithm, and the algorithm proposed in recent years, the results show that our LocalNet algorithm significantly improves in segmentation accuracy Furthermore, the segmentation effect has obvious advantages under the premise that the segmentation speed is not much different from that of the other five algorithms.

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“…erefore, the color intensity, texture, and other characteristics are similar, contained in the superpixels. After the superpixels clustering segmentation of the image, the local consistency of the players can be guaranteed, and the wrong segmentation of the ambiguous pixels on the edge of the players can be effectively avoided [23][24][25].…”

Section: Clustering Segmentation Of Superpixelsmentioning

confidence: 99%

Video Analysis and System Construction of Basketball Game by Lightweight Deep Learning under the Internet of Things

Yang

Jiang

Li³

2022

Computational Intelligence and Neuroscience

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With the explosive growth of sports video data on the internet platform, how to scientifically manage this information has become a major challenge in the current big data era. In this context, a new lightweight player segmentation algorithm is proposed to realize the automatic analysis of basketball game video. Firstly, semantic events are expressed by extracting group and global motion features. A complete basketball game video is divided into three stages, and a basketball event classification method integrating global group motion patterns and domain knowledge is proposed. Secondly, a player segmentation algorithm based on lightweight deep learning is proposed to detect basketball players, segment the players, and finally extract players’ spatial features based on deep learning to realize players’ pose estimation. As the experimental results indicate, when a proposed 2-stage classification algorithm is used to classify the videos, the accuracy of identifying layup, the shooting, and other 2-pointers are improved by 21.26% and 6.41%, respectively. And the accuracy of average events sees an improvement of 2.74%. The results imply that the 2-stage classification based on event-occ is effective. After comparing the four methods of classifying players, it is found that there is no significant difference among these four methods about the accuracy of segmenting. Nevertheless, when judged with the time that these methods take separately, FCN-CNN (Fully Convolutional Network-Convolutional Neural Network) based on superpixels has overwhelming advantages. The event analysis method of basketball game video proposed here can realize the automatic analysis of basketball video, which is beneficial to promoting the rapid development of basketball and even sports.

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CONIC: Contour Optimized Non-Iterative Clustering Superpixel Segmentation

Cited by 5 publications

References 40 publications

An Automatic Cardiac Computed Tomography (Ct) Images Sequence segmentation Technique

An Automatic Cardiac Computed Tomography (Ct) Images Sequence segmentation Technique

Superpixel segmentation algorithm based on local network modularity increment

Video Analysis and System Construction of Basketball Game by Lightweight Deep Learning under the Internet of Things

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