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Unsupervised Video object segmentation via Weak User Interaction and Temporal Modulation

Chinese Journal of Electronics 2023年第3期32卷 507-518页

作者： FAN Jiaqing ZHANG Kaihua ZHAO Yaqian LIU Qingshan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics College of Computer and Software Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Inspur Suzhou Intelligent Technology Corporation

In unsupervised video object segmentation(UVOS), the whole video might segment the wrong target due to the lack of initial prior information. Also, in semi-supervised video object segmentation(SVOS), the initial video frame with a fine-grained pixel-level mask is essential to good segmentation accuracy. It is expensive and laborious to provide the accurate pixel-level masks for each training sequence. To address this issue, We present a weak user interactive UVOS approach guided by a simple human-made rectangle annotation in the initial frame. We first interactively draw the region of interest by a rectangle, and then we leverage the mask RCNN(region-based convolutional neural networks) method to generate a set of coarse reference labels for subsequent mask propagations. To establish the temporal correspondence between the coherent frames, we further design two novel temporal modulation modules to enhance the target representations. We compute the earth mover’s distance(EMD)-based similarity between coherent frames to mine the co-occurrent objects in the two images, which is used to modulate the target representation to highlight the foreground target. We design a cross-squeeze temporal modulation module to emphasize the co-occurrent features across frames, which further helps to enhance the foreground target representation. We augment the temporally modulated representations with the original representation and obtain the compositive spatio-temporal information, producing a more accurate video object segmentation(VOS) model. The experimental results on both UVOS and SVOS datasets including Davis2016,FBMS, Youtube-VOS, and Davis2017, show that our method yields favorable accuracy and complexity. The related code is available.

关键词： Earth Training Codes Annotations Modulation object segmentation Complexity theory

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Real-time object segmentation based on convolutional neural network with saliency optimization for picking

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Journal of Systems Engineering and Electronics 2018年第6期29卷 1300-1307页

作者： CHEN Jinbo WANG Zhiheng LI Hengyu School of Mechatronic Engineering and Automation Shanghai University

This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal *** the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.

关键词： convolutional neural network object detection object segmentation superpixel saliency optimization

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Formal Photograph Compression Algorithm Based on object segmentation

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International Journal of Automation and computing 2008年第3期5卷 276-283页

作者： Li Zhu Guo-You Wang Chen Wang Laboratory. for Biomedical Image Processing Institute for Pattern Recognition ＆ Artificial Intelligence Huazhong University of Science ＆ Technology Wuhan 430074 PRC School of Mechanical and Aeropace Engineering Nanyang Technological Vnlversity Sigapore 639798 Sigapore

Small storage space for photographs in formal documents is increasingly necessary in today＇s needs for huge amounts of data communication and storage. Traditional compression algorithms do not sufficiently utilize the distinctness of formal photographs. That is, the object is an image of the human head, and the background is in unicolor. Therefore, the compression is of low efficiency and the image after compression is still space-consuming. This paper presents an image compression algorithm based on object segmentation for practical high-efficiency applications. To achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. The areas of the human head and its background are compressed separately to reduce the coding redundancy of the background. Two methods, lossless image contour coding based on differential chain, and modified set partitioning in hierarchical trees （SPIHT） algorithm of arbitrary shape, are discussed in detail. The results of experiments show that when bit per pixel （bpp）is equal to 0.078, peak signal-to-noise ratio （PSNR） of reconstructed photograph will exceed the standard of SPIHT by nearly 4dB.

关键词： Image compression object segmentation lossless image contour coding differential chain set partitioning in hierarchical trees (SPIHT) coding of arbitrarily shaped object.

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A Fast object segmentation Approach Based on Integral Image in ALV System

A Fast Object Segmentation Approach Based on Integral Image ...

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2010 International Conference on E-Health Networking, Digital Ecosystems and Technologies

作者： Shenyi Qian, Xiaolei Chen, Yongquan Xia dept. of computer science & engineer Zhengzhou University of Light Industry Zhengzhou, China

In ALV system, the object segmentation approach is one of the most important research fields for object detection. In this paper, a simple and fast object segmentation approach is proposed for ALV system. Firstly, a binarizaton method based on integral image is applied to binarize the input images;Secondly, in order to save the computation time of gray mean of each pixels in squared window, a method of reduce the redundant computation is applied in our algorithm;After that, the "holes" in object are filled by inner-filling algorithm;Lastly, the filled images are segmented by using easy edge detection approach. Several experiment images of ALV system is used to verify the proposed algorithm, the result indicate that the approach is valid and feasible.

关键词： object segmentation object detection binarization iIntegral image inner filling

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Supervoxel-based Graph Clustering for Accurate object segmentation of Indoor Point Clouds

Supervoxel-based Graph Clustering for Accurate Object Segmen...

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第40届中国控制会议

作者： Jingyuan Yu Xiaowei Tu Qinghua Yang Liyong Liu School of Mechanical Engineering and Automation Shanghai University

The correct segmentation of clustered objects in indoor scenes is a critical step of machine understanding in the field of robotics. However, traditional segmentation methods can hardly meet the requirements of accuracy and real time. To this end, an unsupervised object segmentation method based on supervoxel and graph clustering is proposed in this paper. First, the background is removed based on RANSAC, and the remaining point cloud is over-segmented into supervoxels, which is used to improve the segmentation efficiency and to obtain fully connected graph. Second, a graph clustering method based on pure geometric features is proposed to obtain the corresponding subgraphs of each object using the concavity between ***, a new refinement method for processing the boundaries at the intersection is adapted for obtaining the boundaries with point-level accuracy to improve the segmentation accuracy. The method is experimented on both researcher community public and self-collected datasets. Qualitative and quantitative results show the applicability of our method to robotic real-time object manipulation and its broad development prospect.

关键词： object segmentation Point Cloud Supervoxels Graph Clustering

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Improved organs at risk segmentation based on modified U‐Net with self‐attention and consistency regularisation

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 850-865页

作者： Maksym Manko Anton Popov Juan Manuel Gorriz Javier Ramirez Electronic Engineering Department Igor Sikorsky Kyiv Polytechnic InstituteKyivUkraine Department of Signal Theory Telematics and CommunicationsUniversity of GranadaGranadaSpain

Cancer is one of the leading causes of death in the world,with radiotherapy as one of the treatment *** planning starts with delineating the affected area from healthy organs,called organs at risk(OAR).A new approach to automatic OAR seg-mentation in the chest cavity in Computed Tomography(CT)images is *** proposed approach is based on the modified U‐Net architecture with the ResNet‐34 encoder,which is the baseline adopted in this *** new two‐branch CS‐SA U‐Net architecture is proposed,which consists of two parallel U‐Net models in which self‐attention blocks with cosine similarity as query‐key similarity function(CS‐SA)blocks are inserted between the encoder and decoder,which enabled the use of con-sistency *** proposed solution demonstrates state‐of‐the‐art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient(oesophagus-0.8714,heart-0.9516,trachea-0.9286,aorta-0.9510)and Hausdorff distance(oesophagus-0.2541,heart-0.1514,trachea-0.1722,aorta-0.1114)and significantly outperforms the *** current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.

关键词： 3‐D computer vision deep learning deep neural networks image segmentation medical image processing object segmentation

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Detection of the farmland plow areas using RGB-D images with an improved YOLOv5 model

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International Journal of Agricultural and Biological Engineering 2024年第3期17卷 156-165页

作者： Jiangtao Ji Zhihao Han Kaixuan Zhao Qianwen Li Shucan Du College of Agricultural Equipment Engineering Henan University of Science and TechnologyLuoyang 471003HenanChina Science&Technology Innovation Center for Completed Set Equipment Longmen LaboratoryLuoyang 471023HenanChina Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province Luoyang 471003HenanChina School of Art and Design Henan University of Science and TechnologyLuoyang 471003HenanChina

Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural *** precisely recognize these boundaries,a detection method for unmanned tractor plow areas based on RGB-Depth(RGB-D)cameras was proposed,and the feasibility of the detection method was *** method applied advanced computer vision technology to the field of agricultural *** and improving the YOLOv5-seg object segmentation algorithm,first,the Convolutional Block Attention Module(CBAM)was integrated into Concentrated-Comprehensive Convolution Block(C3)to form C3CBAM,thereby enhancing the ability of the network to extract features from plow *** GhostConv module was also utilized to reduce parameter and computational ***,using the depth image information provided by the RGB-D camera combined with the results recognized by the YOLOv5-seg model,the mask image was processed to extract contour boundaries,align the contours with the depth map,and obtain the boundary distance information of the plowed ***,based on farmland information,the calculated average boundary distance was corrected,further improving the accuracy of the distance *** experiment results showed that the YOLOv5-seg object segmentation algorithm achieved a recognition accuracy of 99%for plowed areas and that the ranging accuracy improved with decreasing detection *** ranging error at 5.5 m was approximately 0.056 m,and the average detection time per frame is 29 ms,which can meet the real-time operational *** results of this study can provide precise guarantees for the autonomous operation of unmanned plowing units.

关键词： plow areas RGB-D camera YOLO object segmentation contour boundary average distance

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Sequential interactive image segmentation

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Computational Visual Media 2023年第4期9卷 753-765页

作者： Zheng Lin Zhao Zhang Zi-Yue Zhu Deng-Ping Fan Xia-Lei Liu TKLNDST College of Computer ScienceNankai UniversityTianjinChina Computer Vision Lab ETH ZurichZurichSwitzerland

Interactive image segmentation(IIS)is an important technique for obtaining pixel-level *** many cases,target objects share similar ***,IIS methods neglect this connection and in particular the cues provided by representations of previously segmented objects,previous user interaction,and previous prediction masks,which can all provide suitable priors for the current *** this paper,we formulate a sequential interactive image segmentation(SIIS)task for minimizing user interaction when segmenting sequences of related images,and we provide a practical approach to this task using two pertinent *** first is a novel interaction *** annotating a new sample,our method can automatically propose an initial click proposal based on previous *** dramatically helps to reduce the interaction burden on the *** second is an online optimization strategy,with the goal of providing semantic information when annotating specific targets,optimizing the model with dense supervision from previously labeled *** demonstrate the effectiveness of regarding SIIS as a particular task,and our methods for addressing it.

关键词： interactive segmentation user interaction object segmentation

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A comprehensive review of significant researches on content based indexing and retrieval of visual information

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Frontiers of Computer Science 2013年第5期7卷 782-799页

作者： R. PRIYA T. N. SHANMUGAM Department of Mathematics Anna University-Chennai Chennai 600 025 India

Developments in multimedia technologies have paved way for the storage of huge collections of video doc- uments on computer systems. It is essential to design tools for content-based access to the documents, so as to allow an efficient exploitation of these collections. Content based anal- ysis provides a flexible and powerful way to access video data when compared with the other traditional video analysis tech- niques. The area of content based video indexing and retrieval （CBVIR）, focusing on automating the indexing, retrieval and management of video, has attracted extensive research in the last decade. CBVIR is a lively area of research with endur- ing acknowledgments from several domains. Herein a vital assessment of contemporary researches associated with the content-based indexing and retrieval of visual information. In this paper, we present an extensive review of significant researches on CBV1R. Concise description of content based video analysis along with the techniques associated with the content based video indexing and retrieval is presented.

关键词： nultimedia information content based video retrieval (CBVR) content based video indexing and retrieval (CBVIR) shot segmentation object segmentation feature extraction indexing motion estimation querying key frame retrieval and indexing.

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Who Blocks Who: Simultaneous segmentation of Occluded objects

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Journal of Computer Science & Technology 2013年第5期28卷 890-906页

作者：王楠艾海舟汤锋 Department of Computer Science and Technology Tsinghua University Multimedia Interaction and Understanding Lab HP Labs

In this paper we present a simultaneous segmentation algorithm for multiple highly-occluded objects, which combines high-level knowledge and low-level information in a unified framework. The high-level knowledge provides sophis- ticated shape priors with the consideration of blocking relationship between nearby objects. Different from conventional layered model which attempts to solve the full ordering problem, we decompose the problem into a series of pairwise ones and this makes our algorithm scalable to a large number of objects. objects are segmented in pixel level with higher-order soft constraints from superpixels, by a dual-level conditional random field. The model is optimized alternately by object layout and pixel-wise segmentation. V^e evaluate our system on different objects, i.e., clothing and pedestrian, and show impressive segmentation results and significant improvement over state-of-the-art segmentation algorithms.

关键词： object segmentation occlusion reasoning object graph conditional random field random forest,

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