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A Robust object segmentation System Using a Probability-Based Background Extraction Algorithm

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2010年第4期20卷 518-528页

作者： Chiu, Chung-Cheng Ku, Min-Yu Liang, Li-Wey Natl Def Univ Dept Elect & Elect Engn Chung Cheng Inst Technol Tao Yuan 335 Taiwan Natl Def Univ Dept Comp Sci Chung Cheng Inst Technol Tao Yuan 335 Taiwan

A video-based monitoring system must be capable of continuous operation under various weather and illumination conditions. Moreover, background subtraction is a very important part of surveillance applications for successful segmentation of objects from video sequences, and the accuracy, computational complexity, and memory requirements of the initial background extraction are crucial in any background subtraction method. This paper proposes an algorithm to extract initial color backgrounds from surveillance videos using a probability-based background extraction algorithm. With the proposed algorithm, the initial background can be extracted accurately and quickly, while using relatively little memory. The intrusive objects can then be segmented quickly and correctly by a robust object segmentation algorithm. The segmentation algorithm analyzes the threshold values of the background subtraction from the prior frame to obtain good quality while minimizing execution time and maximizing detection accuracy. The color background images can be extracted efficiently and quickly from color image sequences and updated in real time to overcome any variation in illumination conditions. Experimental results for various environmental sequences and a quantitative evaluation are provided to demonstrate the robustness, accuracy, effectiveness, and memory economy of the proposed algorithm.

关键词： Background subtraction initial background extraction object segmentation probability-based background extraction

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Saliency-guided level set model for automatic object segmentation

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PATTERN RECOGNITION 2019年 93卷 147-163页

作者： Cai, Qing Liu, Huiying Qian, Yiming Zhou, Sanping Duan, Xiaojun Yang, Yee-Hong Northwestern Polytech Univ Sch Automat Youyi West Rd 127 Xian 710072 Shanxi Peoples R China Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xianning West Rd 28 Xian 710049 Shanxi Peoples R China Northwestern Polytech Univ Natl Key Lab UVA Technol Youyi West Rd 127 Xian 710072 Shanxi Peoples R China

The level set model is a popular method for object segmentation. However, most existing level set models perform poorly in color images since they only use grayscale intensity information to defined their energy functions. To address this shortcoming, in this paper, we propose a new saliency-guided level set model (SLSM), which can automatically segment objects in color images guided by visual saliency. Specifically, we first define a global saliency-guided energy term to extract the color objects approximately. Then, by integrating information from different color channels, we define a novel local multichannel based energy term to extract the color objects in detail. In addition, unlike using a length regularization term in the conventional level set models, we achieve segmentation smoothness by incorporating our SLSM into a graph cuts formulation. More importantly, the proposed SLSM is automatically initialized by saliency detection. Finally, the evaluation on public benchmark databases and our collected database demonstrates that the new SLSM consistently outperforms many state-of-the-art level set models and saliency detecting methods in accuracy and robustness. (C) 2019 Published by Elsevier Ltd.

关键词： Level set model object segmentation Visual saliency Graph cuts Automatic initialization

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Local Intensity Order Transformation for Robust Curvilinear object segmentation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 2557-2569页

作者： Shi, Tianyi Boutry, Nicolas Xu, Yongchao Geraud, Thierry Huazhong Univ Sci & Technol HUST Sch Elect Informat & Commun Wuhan 430074 Peoples R China Lab Rech & Dev EPITA LRDE F-94276 Paris France Wuhan Univ Sch Comp Sci Wuhan 430070 Peoples R China

segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these tasks. Yet, most of them mainly focus on finding powerful deep architectures but ignore capturing the inherent curvilinear structure feature (e.g., the curvilinear structure is darker than the context) for a more robust representation. In consequence, the performance usually drops a lot on cross-datasets, which poses great challenges in practice. In this paper, we aim to improve the generalizability by introducing a novel local intensity order transformation (LIOT). Specifically, we transfer a gray-scale image into a contrast-invariant four-channel image based on the intensity order between each pixel and its nearby pixels along with the four (horizontal and vertical) directions. This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes. Cross-dataset evaluation on three retinal blood vessel segmentation datasets demonstrates that LIOT improves the generalizability of some state-of-the-art methods. Additionally, the cross-dataset evaluation between retinal blood vessel segmentation and pavement crack segmentation shows that LIOT is able to preserve the inherent characteristic of curvilinear structure with large appearance gaps. An implementation of the proposed method is available at https://***/TY-Shi/LIOT.

关键词： Image segmentation Transforms object segmentation Retina Biomedical imaging Learning systems Blood vessels Curvilinear structure segmentation local intensity order deep learning generalizability

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Online Meta Adaptation for Fast Video object segmentation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2020年第5期42卷 1205-1217页

作者： Xiao, Huaxin Kang, Bingyi Liu, Yu Zhang, Maojun Feng, Jiashi Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 119077 Singapore

Conventional deep neural networks based video object segmentation (VOS) methods are dominated by heavily fine-tuning a segmentation model on the first frame of a given video, which is time-consuming and inefficient. In this paper, we propose a novel method which rapidly adapts a base segmentation model to new video sequences with only a couple of model-update iterations, without sacrificing performance. Such attractive efficiency benefits from the meta-learning paradigm which leads to a meta-segmentation model and a novel continuous learning approach which enables online adaptation of the segmentation model. Concretely, we train a meta-learner on multiple VOS tasks such that the meta model can capture their common knowledge and gains the ability to fast adapt the segmentation model to new video sequences. Furthermore, to deal with unique challenges of VOS tasks from temporal variations in the video, e.g., object motion and appearance changes, we propose a principled online adaptation approach that continuously adapts the segmentation model across video frames by exploiting temporal context effectively, providing robustness to annoying temporal variations. Integrating the meta-learner with the online adaptation approach, the proposed VOS model achieves competitive performance against the state-of-the-arts and moreover provides faster per-frame processing speed.

关键词： Adaptation models Task analysis object segmentation Optical imaging Motion segmentation Image segmentation Runtime Meta learning video object segmentation convolutional neural networks

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Moving object segmentation based on statistical motion model

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ELECTRONICS LETTERS 1999年第20期35卷 1719-1720页

作者： Lee, KW Kim, J ETRI Yusung Gu Taejon 305350 South Korea

An efficient algorithm for rigid or non-rigid moving object segmentation in the static camera environment is proposed. For robust motion detection, the algorithm performs statistical modelling of the moving objects and background and detects moving pixels by the Bayes decision method with a Gaussian blurred background probability density function.

关键词： Image segmentation Algorithms Mathematical models Cameras Probability density function Decision theory Random processes Image enhancement Computer simulation object segmentation Statistical motion model Bayes decision method Gaussian blurred probability density function object segmentation statistical models (nuclear) Moving image segmentation algorithms Stochastic Processes image enhancement Probability density function Cameras Decision Theory

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Unveiling the Power of Visible-Thermal Video object segmentation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第7期34卷 5376-5388页

作者： Yang, Jinyu Gao, Mingqi Cong, Runmin Wang, Chengjie Zheng, Feng Leonardis, Ales Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT England Univ Warwick WMG Coventry CV4 7AL England Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Tencent YouTu Lab Shanghai 200001 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

Despite recent progress, Video object segmentation (VOS) remains challenging in complex situations such as low light and dark scenes. In this paper, we tackle the visibility limitations by introducing thermal information as auxillary for VOS. Specifically, we generate a hybrid benchmark dataset for Visible-Thermal VOS, named VisT300, which contains 300 challenging videos with visible light and thermal frames and corresponding object mask annotations. Besides, a Visible-Thermal integration Network, named as VTiNet, is proposed to use both cross-modal and cross-frame propagation for accurate video object segmentation. It is advantageous in two aspects: 1) effective cross-modal feature fusion and propagation for strong expressions on visible, thermal, and fused modalities;2) effective modality-sensitive memory bank enables preserving the most valuable historical contexts in each modality. Extensive experiments demonstrate our VTiNet outperforms the state-of-the-art VOS works by a large margin (over 5% than RGB SotAs in Mean J&F). Our preliminary research clearly recovers that importing complementary modalities can effectively increase the strength of models to achieve robust segmentation in challenging scenarios. Data and code are released at https://***/yjybuaa/vtinet, and we hope this work will promote the progress of visible-thermal VOS.

关键词： object segmentation Task analysis Target tracking Annotations Hybrid power systems Benchmark testing Cameras Multi-modal learning video object segmentation visible-thermal fusion

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Video object segmentation: A compressed domain approach

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2004年第4期14卷 462-474页

作者： Babu, RV Ramakrishnan, KR Srinivasan, SH NTNU Ctr Quantifiable Qual Serv Commun Syst Trondheim Norway Indian Inst Sci Dept Elect Engn Bangalore 560012 Karnataka India Satyam Comp Serv Ltd Bangalore 560025 Karnataka India

This paper addresses the problem of extracting video objects from MPEG compressed video. The,only cues used for object segmentation are the motion vectors which are sparse in MPEG. A method for automatically estimating the number of objects and extracting independently moving video objects using Motion vectors is presented here. First, the motion vectors are accumulated over a few frames to enhance the motion information, which are further spatially interpolated to get dense motion vectors. The final segmentation, using the dense motion vectors, is obtained by applying the expectation maximization (EM) algorithm. A block-based affine clustering method is proposed for determining the number of appropriate motion models to be used for the EM step and the segmented objects are temporally tracked to obtain the video objects. Finally, a strategy for edge refinement is proposed to, extract the,precise object boundaries. Illustrative examples are provided to demonstrate the efficacy of the approach. A prominent application of the proposed method is that of object-based coding, which is part of the MPEG-4 standard.

关键词： compressed domain expectation maximization (EM) algorithm motion segmentation MPEG-4 object segmentation tracking video object planes

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Flow-Edge Guided Unsupervised Video object segmentation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第12期32卷 8116-8127页

作者： Zhou, Yifeng Xu, Xing Shen, Fumin Zhu, Xiaofeng Shen, Heng Tao Univ Elect Sci & Technol China Ctr Future Multimedia Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

Recently, deep learning techniques have achieved significant improvements in unsupervised video object segmentation (UVOS). However, many of existing approach cannot accurately identify the foreground objects and the background as they commonly use the coarse temporal features (e.g., optical flow and multi-frames attention). In this paper, we present a novel model termed Flow Edge-based Motion-Attentive Network (FEM-Net), to address the unsupervised video object segmentation problem. Firstly, a motion-attentive encoder is used to jointly learn the spatial and temporal features. Then, a Flow Edge Connect (FEC) module is designed to hallucinate edges of the ambiguous or missing region in the optical flow. During the segmentation stage, the complementary temporal feature composed by the motion-attentive feature and flow edge is fed into a decoder to infer the salient foreground objects. Experimental results on two challenging public benchmarks (i.e. DAVIS-16 and FBMS) demonstrate that the proposed FEM-Net compares favorably against the state-of-the-art methods.

关键词： Motion segmentation Feature extraction object segmentation Image edge detection Task analysis Image segmentation Computer vision Video segmentation optical flow attention mechanisms Index Terms deep learning

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Unsupervised 3D object segmentation of Point Clouds by Geometry Consistency

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 8459-8473页

作者： Song, Ziyang Yang, Bo Hong Kong Polytech Univ Shenzhen Res Inst VLAR Grp Hung Hom Hong Kong Peoples R China

In this paper, we study the problem of 3D object segmentation from raw point clouds. Unlike existing methods which usually require a large amount of human annotations for full supervision, we propose the first unsupervised method, called OGC, to simultaneously identify multiple 3D objects in a single forward pass, without needing any type of human annotations. The key to our approach is to fully leverage the dynamic motion patterns over sequential point clouds as supervision signals to automatically discover rigid objects. Our method consists of three major components, 1) the object segmentation network to directly estimate multi-object masks from a single point cloud frame, 2) the auxiliary self-supervised scene flow estimator, and 3) our core object geometry consistency component. By carefully designing a series of loss functions, we effectively take into account the multi-object rigid consistency and the object shape invariance in both temporal and spatial scales. This allows our method to truly discover the object geometry even in the absence of annotations. We extensively evaluate our method on five datasets, demonstrating the superior performance for object part instance segmentation and general object segmentation in both indoor and the challenging outdoor scenarios.

关键词： Point cloud compression Three-dimensional displays object segmentation Motion segmentation Annotations Geometry Vectors 3D object segmentation point cloud analysis scene flow unsupervised learning

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Learning of perceptual grouping for object segmentation on RGB-D data

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2014年第1期25卷 64-73页

作者： Richtsfeld, Andreas Moerwald, Thomas Prankl, Johann Zillich, Michael Vincze, Markus Vienna Univ Technol Automat & Control Inst ACIN A-1040 Vienna Austria

object segmentation of unknown objects with arbitrary shape in cluttered scenes is an ambitious goal in computer vision and became a great impulse with the introduction of cheap and powerful RGB-D sensors. We introduce a framework for segmenting RGB-D images where data is processed in a hierarchical fashion. After pre-clustering on pixel level parametric surface patches are estimated. Different relations between patch-pairs are calculated, which we derive from perceptual grouping principles, and support vector machine classification is employed to learn Perceptual Grouping. Finally, we show that object hypotheses generation with Graph-Cut finds a globally optimal solution and prevents wrong grouping. Our framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. We also tackle the problem of segmenting objects when they are partially occluded. The work is evaluated on publicly available object segmentation databases and also compared with state-of-the-art work of object segmentation. (C) 2013 The Authors. Published by Elsevier Inc. All rights reserved.

关键词： Computer vision object segmentation Perceptual organization RGB-D images B-spline fitting object reconstruction SVM learning Graph-based segmentation

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