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2015 International Conference on Science in Information Technology (ICSITech)

作者： Vasthi, Priska Irenda Kusumaningrum, Retno Univ Diponegoro Dept Informat Semarang Indonesia

ISBN: (纸本)9781479983865

object segmentation is a key step in image analysis and the most difficult low-level image analysis tasks, specifically in the semantic objects approach. This approach is widely implemented in various domains including fruit images. object segmentation using OHTA colour space is one of successful methods to separate fruit object and background. However, the method is prone to remove shadows or other noises. Therefore, this study proposed an extended method of OHTA-based object segmentation to overcome those problems by applying cascade threshold. The selected threshold value is 50. It is based on the optimal threshold value to reduce both of over-segmented and under-segmented images. The proposed method increases the overall accuracy of about 41.25%, 52.5%, and 20% for tomato images, apple images, and banana images respectively.

关键词： object segmentation OHTA colour space cascade threshold fruit

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object segmentation in image sequences by watershed from markers: A generic approach 16

Object segmentation in image sequences by watershed from mar...

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16th Brazilian Symposium on Computer Graphics and Image Processing

作者： Flores, FU Lotufo, RD Departamento de Informática UEM Brazil Faculdade de Engenharia Elétrica e de Computação UNICAMP Brazil

ISBN: (纸本)0769520324

In this paper we propose a generic method to interactive object segmentation in image sequences. This method consists in the extension of efficient techniques of static image segmentation to image sequences by combining them to fast motion estimation techniques. The proposed method has the following characteristics: interactivity, generality, rapid response and progressive manual edition.

关键词： Biomedical imaging Image segmentation Image sequences Motion estimation MPEG 4 Standard object detection object segmentation Tracking Video coding Video surveillance

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object segmentation WITH DEEP REGRESSION

OBJECT SEGMENTATION WITH DEEP REGRESSION

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IEEE International Conference on Image Processing (ICIP)

作者： Li, Jianchao Wang, Dan Yan, Canxiang Shan, Shiguang Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Baidu Inc Inst Deep Learning Beijing 100085 Peoples R China

ISBN: (纸本)9781479983391

object segmentation has constantly received much attention due to its fundamental role in scene understanding. Traditional methods formulate it as a structured prediction problem, represented by graphical models (GMs). However, most GMs have difficulties in balancing the effectiveness of context modeling and efficiency of model inference. In this paper, we model the contexts implicitly using the deep convolutional neural network (DCNN). Specifically, we reformulate object segmentation as a regression problem and train a deep network end-to-end to learn the nonlinear mapping from the image to the object mask. The large receptive field of the network incorporates wide contexts to update the network parameters, giving an implicit context model. Moreover, the deep architecture is favorable for modeling nonlinearity. The inference of our method is quite efficient, involving only a simple feed-forward pass. Extensive experiments on public datasets demonstrate the advantages of our method.

关键词： object segmentation context modeling deep convolutional neural networks regression

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object segmentation using stereo images

Object segmentation using stereo images

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International Conference on Communications, Circuits and Systems

作者： An, P Lü, CH Zhang, ZY Shanghai Univ Sch Commun & Informat Engn Shanghai 200072 Peoples R China

ISBN: (纸本)0780386477

object segmentation is one of the vital tasks in various three-dimensional applications. This paper presents a hybrid object segmentation algorithm to combine intensity segmentation and disparity segmentation in stereoscopic vision system. The disparity maps of the stereo images are firstly estimated by using a foreground-based disparity estimation method. Then the intensity stereo images and their corresponding disparity maps are separately segmented by using region-growing technique. The real segmentation mask can be obtained and the semantic object be extracted by a fusion processing on the intensity and disparity segments. Computer simulations indicate the reliable performance of the proposed algorithm for stereoscopic segmentation.

关键词： stereo image object segmentation region growing disparity estimation

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object segmentation by fusion of multi-cues in stereoscopic sequences

Object segmentation by fusion of multi-cues in stereoscopic ...

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Conference on Mobile Multimedia/Image Processing for Military and Security Applications

作者： An, Ping Zhang, Zhaoyang Ma, Ran Shanghai Univ Sch Commun & Informat Engn 149 Yanchang Rd Shanghai 200072 Peoples R China Key Lab Adv Displays & Syst Appl Minist Educ Shanghai 200072 Peoples R China

ISBN: (纸本)081946306X

An algorithm for object segmentation from stereo sequences based on fusion of multi-cues of edge, disparity, motion and color is presented in this paper. Firstly, the accurate disparity field is obtained using a two-level disparity matching method based on image edge information. The morphological operators are then performed on the given disparity field to obtain coarse objects segments. "Split and merge" process is applied to extract the objects regions, and "erosion and dilation" process is used to fill some small inner holes in the target regions or smooth the discontinuous regions. On the other hand, spatial-temporal segments are obtained with image edge structure and motion change detection. Different object boundaries can be articulated according to disparity and spatial-temporal segments. At last, the multi-objects are extracted by further fusion of the color information. Experiments indicate this algorithm is an effective method for segmenting multi-objects overlapped each other from stereoscopic video that usually is difficult to be done in the case of monocular video.

关键词： object segmentation stereo sequences morphological processing stereo matching information fusion

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A Fully Dynamic Multi-Mode CMOS Vision Sensor With Mixed-Signal Cooperative Motion Sensing and object segmentation for Adaptive Edge Computing

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 2020年第6期55卷 1684-1697页

作者： Zhong, Xiaopeng Law, Man-Kay Tsui, Chi-Ying Bermak, Amine Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Univ Macau State Key Lab Analog & Mixed Signal VLSI Inst Microelect Macau Peoples R China Hamad Bin Khalifa Univ Informat & Comp Technol Div Coll Sci & Engn Doha Qatar

This article presents a low-power multi-mode CMOS vision sensor with mixed-signal in-sensor computation capabilities targeting the next-generation wireless sensing applications. To support the always-on and scene-adaptive edge computing scenarios with low power and low bandwidth, the sensor is reconfigurable for three operation modes, namely: 1) motion sensing (MS);2) object segmentation (OS);and 3) full imaging (FIM). A mixed-signal cooperative scheme of frame differencing (FD) and background subtraction (BS) is proposed to achieve high-accuracy MS with varying object sizes and speeds. The mixed-signal BS-based OS can minimize both object localizing and imaging efforts for object analysis upon motion triggering, while FIM enables complete scene recording for the identified object of interest. The complete CMOS vision sensor is implemented through reconfigurable and fully dynamic mixed-signal processing at both pixel and column levels cooperatively to achieve low power and compact area. Fabricated in a 0.18-mu m CMOS, the 256 x 216 chip prototype achieves the cooperative MS with only 2.36 mu W at 15 frames/s, when composed of 14 FD frames (147 nJ/frame) and 1 BS frame (302 nJ/frame). The OS mode consumes 1.44 similar to 2.04 mu J/frame at 0%similar to 100% object occupancy, linearly corresponding to 41%similar to 16% power saving when compared with the conventional digital OS. The FIM mode operates with only 1.41 mu J/frame for complete scene recording. The achieved energy efficiencies for all operation modes compare favorably with the state of the art.

关键词： Vision sensors Bandwidth Edge computing Wireless sensor networks Program processors object segmentation Background subtraction (BS) CMOS vision sensor cooperative motion sensing (MS) edge computing frame differencing (FD) fully dynamic in-sensor computation mixed-signal processing object segmentation (OS) wireless sensor networks (WSNs)

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3D-SeqMOS: A Novel Sequential 3D Moving object segmentation in Autonomous Driving

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 8782-8795页

作者： Zhuang, Yuan Li, Qipeng Chen, Yiwen Huai, Jianzhu Li, Miao Ma, Tianbing Tang, Yufei Liang, Xinlian Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430206 Peoples R China Wuhan Univ Inst Technol Sci Wuhan 430206 Peoples R China Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Anhui Peoples R China Florida Atlantic Univ Dept Comp Elect Engn & Comp Sci Boca Raton FL 33431 USA

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-object-segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the existing state-of-the-art methods. We extend the proposed method to the SemanticKITTI: Moving object segmentation competition and achieve the 3rd in the leaderboard, showing its effectiveness.

关键词： Point cloud compression Three-dimensional displays object segmentation Simultaneous localization and mapping Odometry Autonomous vehicles Automobiles Moving object segmentation robotics vision neural network sequential point cloud LiDAR-SLAM

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object segmentation through Optimized Algorithm

Object Segmentation through Optimized Algorithm

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International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME)

作者： Thilagamani, S. Shanthi, N. M Kumarasamy Coll Engn Dept IT Karur India KSR Coll Technol Dept IT Tiruchengode India

This paper gives a clear picture about segmenting the object from an image through the process of pattern matching. It is challenge to retrieve desired object from an image. The various steps involved are preprocessin... 详细信息

ISBN: (纸本)9781467358453;9781467358439

关键词： preprocessing superpixel object segmentation

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object segmentation using Parametric Representation

Object Segmentation using Parametric Representation

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14th Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Rhee, Hochang Koo, Hyung Il Cho, Nam Ik Seoul Natl Univ Dept ECE INMC Seoul South Korea Ajou Univ Dept ECE Suwon South Korea

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Most deep learning-based segmentation methods focus on accurate pixel-wise classification, disregarding higher order properties such as region connectivity, boundary smoothness, or the number of non-differentiable points on the object contour. To address these issues, we propose a new parameter-based object segmentation framework. Specifically, we represent the target object's boundary with parametric curves to handle the higher-order properties and find the parameters through a convolutional neural network. We also introduce a novel silhouette loss to train the network, enabling efficient parameter-based contour fitting. The proposed silhouette loss is based on a differentiable renderer and is suitable for the segmentation task because it has the same property as IoU (intersection over union). Experimental results show that the proposed method yields object masks with desirable properties and achieves comparable performance to the state-of-the-art on various tasks.

关键词： Fitting object segmentation Information processing Boosting Convolutional neural networks Task analysis Splines (mathematics)

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object segmentation using Independent Motion Detection 15

Object segmentation using Independent Motion Detection

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15th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

作者： Kumar, Sriram Odone, Francesca Noceti, Nicoletta Natale, Lorenzo Ist Italiano Tecnol iCub Facil Genoa Italy Univ Genoa Dipartimento Informat Bioingn Robot & Ingn Sistem Genoa Italy

ISBN: (纸本)9781479968855

Independent motion detection aims at identifying elements in the scene whose apparent motion is not due to the robot egomotion. In this work, we propose a method that learns the input-output relationship between the robot motion - described by the position and orientation sensors embedded on the robot - and the sparse visual motion detected by the cameras. We detect independent motion by observing discrepancies (anomalies) between the perceived motion and the motion that is expected given the position and orientation sensors on the robot. We then perform a higher level analysis based on the available disparity map, where we obtain dense profile of the objects moving independently from the robot. We implemented the proposed pipeline on the iCub humanoid robot. In this work, we report a thorough experimental analysis that covers typical laboratory settings, where the effectiveness of the method is demonstrated. The analysis shows in particular the robustness of the method to scene and object variations and to different kinds of robot's movements.

关键词： Motion detection Robot object segmentation Humanoid robots Robot kinematics Cameras INPUT-OUTPUT

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