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DOST: a distributed object segmentation tool

MULTIMEDIA TOOLS AND APPLICATIONS 2018年第16期77卷 20839-20862页

作者： Farid, Muhammad Shahid Lucenteforte, Maurizio Grangetto, Marco Univ Punjab Punjab Univ Coll Informat Technol Lahore Pakistan Univ Turin Dipartimento Informat Turin Italy

This paper presents a novel distributed object segmentation framework that allows one to extract potentially large coherent objects from digital images. The proposed approach requires minimum user supervision and permits to segment the objects accurately. It works in three steps starting with the user input in form of few mouse clicks on the target object. First, based on user input, the statistical characteristics of the target distributed object are modeled with Gaussian mixture model. This model serves as the primary segmentation of the object. In the second step, the segmentation result is refined by performing connected component analysis to reduce false positives. In the final step the resulting segmentation map is dilated to select the neighboring pixels that are potentially incorrectly classified;this allows us to recast the segmentation as a graph partitioning problem that can be solved using the well-known graph cut technique. Extensive experiments have been carried out on heterogeneous images to test the accuracy of the proposed method for the segmentation of various types of distributed objects. Examples of application of proposed technique in remote sensing to segment roads and rivers from aerial images are also presented. The visual and objective evaluation and comparison with the existing techniques show that the proposed tool can deliver optimal performance when applied to tough object segmentation tasks.

关键词： object segmentation Graph cuts Gaussian mixture model Connected component analysis

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Clickstream Analysis for Crowd-Based object segmentation with Confidence

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2018年第12期40卷 2814-2826页

作者： Heim, Eric Seitel, Alexander Andrulis, Jonas Isensee, Fabian Stock, Christian Ross, Tobias Maier-Hein, Lena German Canc Res Ctr Div Comp Assisted Med Intervent D-69120 Heidelberg Germany German Canc Res Ctr Div Med Image Comp D-69120 Heidelberg Germany Pallas Ludens GmbH D-69126 Heidelberg Germany German Canc Res Ctr Div Clin Epidemiol & Aging Res D-69120 Heidelberg Germany Univ Hosp Heidelberg Inst Med Biometry & Informat D-69120 Heidelberg Germany

With the rapidly increasing interest in machine learning based solutions for automatic image annotation, the availability of reference annotations for algorithm training is one of the major bottlenecks in the field. Crowdsourcing has evolved as a valuable option for low-cost and large-scale data annotation;however, quality control remains a major issue which needs to be addressed. To our knowledge, we are the first to analyze the annotation process to improve crowd-sourced image segmentation. Our method involves training a regressor to estimate the quality of a segmentation from the annotator's clickstream data. The quality estimation can be used to identify spam and weight individual annotations by their (estimated) quality when merging multiple segmentations of one image. Using a total of 29,000 crowd annotations performed on publicly available data of different object classes, we show that (1) our method is highly accurate in estimating the segmentation quality based on clickstream data, (2) outperforms state-of-the-art methods for merging multiple annotations. As the regressor does not need to be trained on the object class that it is applied to it can be regarded as a low-cost option for quality control and confidence analysis in the context of crowd-based image annotation.

关键词： Crowdsourcing quality control object segmentation confidence estimation clickstream analysis

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Saliency motivated improved simplified PCNN model for object segmentation

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NEUROCOMPUTING 2018年 275卷 2179-2190页

作者： Guo, Ya'nan Yang, Zhen Ma, Yide Lian, Jing Zhu, Lili Lanzhou Univ Sch Informat Sci Engn 222 Tianshui Rd South Lanzhou 730000 Gansu Peoples R China

Based on the fact that PCNN and saliency detection method all can achieve the better simulation of HVS to locate the objects that have the most interests in an image, a novel approach for object segmentation, termed as saliency motivated improved simplified pulse coupled neural network (SM-ISPCNN) algorithm, is proposed in this paper. Instead of adopting pure gray-scale to activate the ISPCNN neurons, it is better to introduce the saliency feature value to motivate this model. The introduced saliency stimulus applies a sliding window to precisely exploit the distributions of the objects and surroundings, weakens the influence of background while retaining the region of interest;the highlight of this ASLFC saliency feature lies on: (1) the saliency estimation is based on semi-local areas instead of pixel level;(2) the estimations of the conditional distributions is manipulated via integral histogram approach;(3) the adaptive prior probability setting method is employed to achieve more promising saliency map. For improvement of convergence speed of SM-ISPCNN model for object segmentation, at each iteration, we regard top 5 regions as feedback input for next iteration, which can raise the robustness of SM-ISPCNN model against noise and other interferences. We demonstrate the proposed model based on the mammograms from open and common database of MIAS, gray images from Weizmann segmentation evaluation database and color images from public database with ground truth annotations. Compared with five competitive methods, our model has the obvious superiority for segmentation capacity and algorithm robustness, furthermore, it does not requires any training and can be used in various occasions of object segmentation. In addition, this method is verified on the mammograms from Gansu Provincial Cancer Hospital, the detection results reveal that this model has great potential in clinical application. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Adaptive-semi-local feature contrast (ASLFC) Saliency motivated improved simplified pulse coupled neural network (SM-ISPCNN) object segmentation

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Efficient LiDAR-Based object segmentation and Mapping for Maritime Environments

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IEEE JOURNAL OF OCEANIC ENGINEERING 2019年第2期44卷 352-362页

作者： Thompson, David Coyle, Eric Brown, Jeremy Embry Riddle Aeronaut Univ Dept Mech Engn Daytona Beach FL 32114 USA

This paper proposes a method that utilizes a 3-D occupancy grid to efficiently map a large area while retaining simple representations of objects for path planning and provide spatial characteristics of objects, which may be used for object classification. To enable large-scale mapping of objects, a region around the unmanned surface vehicle (USV) is defined where a high density of LiDAR returns is expected, termed the visibility horizon. The polygon intersection between the visibility horizon and the newly detected objects is computed, as well as the polygon subtraction of the visibility horizon from the mapped list of polygons. The two polygon lists are then combined using a polygon union operation, with the objects retaining class designations. The result is a 2-D map that contains polygon representations of objects, where the object is described with a tunable number of vertices and may have an associated object class. Thus, providing necessary information for path planning and tasking. The resultant polygons are shown here to be accurate to 20 cm using a 10-cm occupancy grid and 16-ft-long unmanned surface vehicles with four multibeam LiDAR sensors.

关键词： LiDAR mapping object segmentation occupancy grid

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From Pixels to Semantics: Self-Supervised Video object segmentation With Multiperspective Feature Mining

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 5801-5812页

作者： Li, Ruoqi Wang, Yifan Wang, Lijun Lu, Huchuan Wei, Xiaopeng Zhang, Qiang Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Innovat & Entrepreneurship Dalian 116024 Peoples R China Dalian Univ Technol Ningbo Inst Ningbo 315012 Peoples R China Dalian Univ Technol Sch Artificial Intelligence Dalian 116024 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

Existing self-supervised methods pose one-shot video object segmentation (O-VOS) as pixel-level matching to enable segmentation mask propagation across frames. However, the two tasks are not fully equivalent since O-VOS is more reliant on semantic correspondence rather than accurate pixel matching. To remedy this issue, we explore a new self-supervised framework that integrates pixel-level correspondence learning with semantic-level adaptation. The pixel-level correspondence learning is performed through photometric reconstruction of adjacent RGB frames during offline training, while semantic-level adaption operates at test-time by enforcing a bi-directional agreement of the predicted segmentation masks. In addition, we further propose a new network architecture with multi-perspective feature mining mechanism which can not only enhance reliable features but also suppress noisy ones to facilitate more robust image matching. By training the network using the proposed self-supervised framework, we achieve state-of-the-art performance on widely adopted datasets, further closing up the gap between self-supervised learning methods and their fully supervised counterparts.

关键词： Semantics Training Feature extraction Image reconstruction Task analysis object segmentation Image segmentation Video object segmentation self-supervised learning pixel-level correspondence semantic-level adaption feature mining

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IDPro: Flexible Interactive Video object segmentation by ID-Queried Concurrent Propagation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第12期34卷 12171-12183页

作者： Li, Kexin Jiang, Tao Yang, Zongxin Yang, Yi Zhuang, Yueting Xiao, Jun Zhejiang Univ Hangzhou 310027 Peoples R China

Interactive Video object segmentation (iVOS) is inherently demanding, requiring real-time interaction between humans and computers. Enhancing user experience involves considerations such as user input habits, segmentation quality, running time, and memory consumption. However, existing methods compromise user experience by employing a single input mode and exhibiting slow running speeds. Specifically, these approaches restrict user interaction to a single frame, limiting the expression of user intent. To overcome these limitations and better align with user habits, we introduce a framework that facilitates flexible input modes by ID-queried concurrent propagation (IDPro). In particular, we have devised the Across-Frame Interaction Module (AFI), allowing users to freely annotate various objects across multiple frames. The AFI module transfers scribble information across interactive frames, generating multi-frame masks. Additionally, we leverage an id-queried mechanism to process multiple objects. To achieve more efficient propagation and a lightweight model, we propose a truncated re-propagation strategy, replacing the previous multi-round fusion module, which employs an across-round memory that stores crucial interaction information. Our SwinB-IDPro attains a new state-of-the-art performance on DAVIS 2017 (89.6%, J&F@60). Furthermore, our R50-IDPro exhibits over 3x faster performance than the leading competitor in challenging multi-object scenarios.

关键词： object segmentation Circuits and systems Streaming media Decoding Annotations Real-time systems object recognition Interactive video object segmentation video segmentation metric learning identification mechanism

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Adaptive Template and Transition Map for Real-Time Video object segmentation

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IEEE ACCESS 2021年 9卷 116914-116926页

作者： Park, Hyojin Yoo, Jayeon Venkatesh, Ganesh Kwak, Nojun Seoul Natl Univ Grad Sch Convergence Sci & Technol Seoul 08826 South Korea Facebook Inc Menlo Pk CA 94025 USA

Semi-supervised video object segmentation (semi-VOS) is required for many visual applications. This task is tracking class-agnostic objects from a given segmentation mask. Various approaches have been developed and achieved high accuracy in this field, but these previous models are hard to be utilized in real-world applications due to slow inference time and tremendous complexity. To significantly speed up inference while reducing performance gaps from those previous models, we introduce a fast segmentation model based on a template matching method and auxiliary loss with a transition map. Our template matching method consists of short-term and long-term matching. The short-term matching enhances target object localization by focusing on neighboring frames, while long-term matching improves fine details and handles object shape-changing by considering long-range frames. However, since both matching processes generate each template based on the previously estimated masks, this incurs error propagation for tracking objects in the next frames. To mitigate this problem, we add auxiliary loss with a newly proposed transition map for encouraging correction power to create accurate masks of the target object. Our model obtains 81.1% J&F score at the speed of 78.3 FPS on the DAVIS16 benchmark and achieves 1.4x faster speed and 11.3% higher accuracy than SiamMask, one of the fast semi-VOS models.

关键词： Optical imaging Task analysis object segmentation Computational modeling Adaptive optics Training Shape Semi-supervised video object segmentation video object segmentation video object tracking deep learning

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Real-Time Moving object segmentation and Classification From HEVC Compressed Surveillance Video

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2018年第6期28卷 1346-1357页

作者： Zhao, Liang He, Zhihai Cao, Wenming Zhao, Debin Harbin Inst Technol Dept Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China Hulu Inc Beijing 100084 Peoples R China Univ Missouri Dept Elect Engn Columbia MO 65211 USA Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China

Moving object segmentation and classification from compressed video plays an important role in intelligent video surveillance. Compared with H.264/AVC, High Efficiency Video Coding (HEVC) introduces a host of new coding features that can be further exploited for moving object segmentation and classification. In this paper, we present a real-time approach to segment and classify moving objects using unique features directly extracted from the HEVC compressed domain for video surveillance. In the proposed method, first, motion vector (MV) interpolation for intra-coded prediction unit (PU) and MV outlier removal are employed for preprocessing. Second, blocks with nonzero MVs are clustered into the connected foreground regions using the four-connectivity component labeling algorithm. Third, object region tracking based on temporal consistency is applied to the connected foreground regions to remove the noise regions. The boundary of moving object region is further refined by the coding unit size and PU size. Finally, a person-vehicle classification model using bag of spatial-temporal HEVC syntax words is trained to classify the moving objects, either persons or vehicles. The experimental results demonstrate that the proposed method provides solid performance and can classify moving persons and vehicles accurately.

关键词： Compression domain High Efficiency Video Coding (HEVC) object classification object segmentation video surveillance

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TRAVEL: Traversable Ground and Above-Ground object segmentation Using Graph Representation of 3D LiDAR Scans

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第3期7卷 7255-7262页

作者： Oh, Minho Jung, Euigon Lim, Hyungtae Song, Wonho Hu, Sumin Lee, Eungchang Mason Park, Junghee Kim, Jaekyung Lee, Jangwoo Myung, Hyun Korea Adv Inst Sci & Technol KAIST Sch Elect Engn Daejeon 34141 South Korea Korea Adv Inst Sci & Technol KAIST KI AI Daejeon 34141 South Korea LIG Nex1 Co Ltd Seongnam 13488 South Korea

Perception of traversable regions and objects of interest from a 3D point cloud is one of the critical tasks in autonomous navigation. A ground vehicle needs to look for traversable terrains that are explorable by wheels. Then, to make safe navigation decisions, the segmentation of objects positioned on those terrains has to be followed up. However, over-segmentation and under-segmentation can negatively influence such navigation decisions. To that end, we propose TRAVEL, which performs traversable ground detection and object clustering simultaneously using the graph representation of a 3D point cloud. To segment the traversable ground, a point cloud is encoded into a graph structure, tri-grid field, which treats each tri-grid as a node. Then, the traversable regions are searched and redefined by examining local convexity and concavity of edges that connect nodes. On the other hand, our above-ground object segmentation employs a graph structure by representing a group of horizontally neighboring 3D points in a spherical-projection space as a node and vertical/horizontal relationship between nodes as an edge. Fully leveraging the node-edge structure, the above-ground segmentation ensures real-time operation and mitigates over-segmentation. Through experiments using simulations, urban scenes, and our own datasets, we have demonstrated that our proposed traversable ground segmentation algorithm outperforms other state-of-the-art methods in terms of the conventional metrics and that our newly proposed evaluation metrics are meaningful for assessing the above-ground segmentation.

关键词： Autonomous navigation graph search LiDAR object segmentation traversable ground segmentation

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Efficient Semisupervised object segmentation for Long-Term Videos Using Adaptive Memory Network

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2024年第5期16卷 1789-1802页

作者： Zhong, Shan Li, Guoqiang Ying, Wenhao Zhao, Fuzhou Xie, Gengsheng Gong, Shengrong Changshu Inst Technol Sch Comp Sci & Engn Changshu 215500 Jiangsu Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215000 Jiangsu Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130000 Peoples R China Changshu Inst Technol Sch Automot Engn Suzhou 215000 Jiangsu Peoples R China Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China

Video object segmentation (VOS) uses the first annotated video mask to achieve consistent and precise segmentation in subsequent frames. Recently, memory-based methods have received significant attention owing to their substantial performance enhancements. However, these approaches rely on a fixed global memory strategy, which poses a challenge to segmentation accuracy and speed in the context of longer videos. To alleviate this limitation, we propose a novel semisupervised VOS model, founded on the principles of the adaptive memory network. Our proposed model adaptively extracts object features by focusing on the object area while effectively filtering out extraneous background noise. An identification mechanism is also thoughtfully applied to discern each object in multiobject scenarios. To further reduce storage consumption without compromising the saliency of object information, the outdated features residing in the memory pool are compressed into salient features through the employment of a self-attention mechanism. Furthermore, we introduce a local matching module, specifically devised to refine object features by fusing the contextual information from historical frames. We demonstrate the efficiency of our approach through experiments, substantially augmenting both the speed and precision of segmentation for long-term videos, while maintaining comparable performance for short videos.

关键词： Feature extraction Videos object recognition Data mining Adaptation models Adaptive systems Video sequences Long-term videos memory network object segmentation semisupervised learning

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