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2nd International Conference on Advances in Computing, Communication, and Automation (ICACCA) (Fall)

作者： Kumar, Vinod Lal, Tauj Dhuliya, Piyush Pant, Diwaker Tulas Inst Dept Elect & Commun Dehra Dun Uttarakhand India

ISBN: (纸本)9781509034802

image segmentation is a process, which divide an image into different regions, which are homogeneous in some characteristics. image segmentation is the initial step in many image processing applications like Pattern recognition and image analysis. image analysis includes object characterization and representation and feature measurement. Higher order task follows the classification of object. Hence, characterization, visualization of region of interest in any image, delineation plays a significant role in image segmentation. There are many segmentation algorithms available in the literature, which divide an image into number of regions based on some image features like pixel intensity value, color, texture, etc. These all algorithms are categorized based on the segmentation method used. They are segmentation based on single or multiple thresh holding, segmentation based on edge detection, segmentation based on similar region and segmentation based on clustering, segmentation based on ANN and fuzzy logic technique etc. In this work, we have chose one algorithm from one segmentation category and implement the algorithm in MATLAB. The chosen algorithms are Otsu's algorithm, K-means, quad tree, Delta E, Region growing and fth algorithms. To check the performance of the algorithms, we have used 6 simple and complex images available in the literature. The obtained results shows the efficacy of the segmentation algorithms.

关键词： image segmentation algorithms Otsu's algorithm K-mean algorithm fth algorithm region growing algorithm Delta E algorithm

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Evaluation of image segmentation approaches for non-destructive detection and quantification of corrosion damage on stonework

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CORROSION SCIENCE 2007年第12期49卷 4415-4442页

作者： Kapsalas, P. Zervakis, M. Maravelaki-Kalaitzaki, P. Tech Univ Crete Dept Elect & Comp Engn Khania 73100 Greece Minist Culture Ephorate Prehistor & Classical Antiqu 25 Khania 73100 Greece

This paper approaches the non-destructive analysis of corrosion damage by testing and evaluating several image segmentation schemes for the detection of decay areas. The application test bed for algorithmic evaluation considers stonework surfaces for corrosion damage. Each of the detection approaches handles in a different way the background inhomogeneities. A semi-automated framework for validating the algorithms' performance is introduced. This framework guarantees reliable and objective estimation of algorithms' response, while also enabling informed experimental feedback for the design of improved segmentation algorithms. Further to elaborating on the robust points of each segmentation approach, this work also studies the corrosion mechanisms. The latter process involves investigation of the degradation state as reflected by the size of the decay areas and their darkness. The derived assessments closely converge to assessments based on chemical analyses, performed on the same stone surfaces. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： image segmentation algorithms statistical tests performance evaluation stone decay black crust

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Archimedes optimizer-based fast and robust fuzzy clustering for noisy image segmentation

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JOURNAL OF SUPERCOMPUTING 2023年第4期79卷 3691-3730页

作者： Dhal, Krishna Gopal Das, Arunita Ray, Swarnajit Rai, Rebika Ghosh, Tarun Kumar Midnapore Coll Autonomous Dept Comp Sci & Applicat Paschimmedinipur W Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata W Bengal India Sikkim Univ Dept Comp Applicat Sikkim India Haldia Inst Technol Dept Comp Sci & Engn Kolkata W Bengal India

Fuzzy C-means (FCM) is one of the prominent and effective cluster-based image segmentation techniques exceedingly susceptible to noise and initial cluster centers, thereby effortlessly converging toward local optima. However, FCM consumes exceptionally high computation time due to the repetitive computation of the distance amid cluster centers and pixels. To resolve this apprehension, this paper aims to consider a histogram-based fast fuzzy image clustering (HBFFIC) procedure that primarily tends to carry out morphological reconstruction (MR) operation over the image to assure noise immunity and safeguard details of the imagery. Further, as a replacement for pixels of a summed image, clustering is carried out based on gray-level histogram. This with no qualm radically trims down the computational time as the number of gray levels in an image is normally to a great extent lesser than that of the number of its pixels. Though HBFFIC is a proficient local optimizer however, owing to arbitrary initialization that is carried out in FCM, HBFFIC has the utmost possibility to get effortlessly wedge into local optima. Consequently, this is where the role of nature-inspired optimization algorithms (NIOA) comes into picture. For that reason, this paper successfully makes use of NIOA to prevail over the dilemma using Archimedes optimizer (AO) to discover the most favorable cluster centers. The real-world images particularly synthetic, grayscale, and color pathology images are exercised to perform the experimental study. The experimental study clearly highlights that the proposed hybrid algorithm (HBFFIC-AO) for noisy image segmentation outperforms the other state-of-art algorithms in terms of segmentation accuracy (SA), comparison score (CS), MSE, and PSNR. The visual along with numerical outcomes projected in the experimental study point toward the pre-eminence of the proposed algorithm as compared with the prevailing leading-edge image segmentation algorithms.

关键词： Fuzzy C-means Archimedes optimizer Noisy image segmentation Nature-inspired optimization algorithms Swarm intelligence Histogram-based fast fuzzy image clustering Fuzzy membership Membership filtering Gray-level histogram Morphological reconstruction image segmentation algorithms

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image segmentation through graph-based clustering from surface normals estimated by photometric stereo

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ELECTRONICS LETTERS 2010年第2期46卷 134-U51页

作者： Julia, C. Moreno, R. Puig, D. Garcia, M. A. Univ Rovira & Virgili Intelligent Robot & Comp Vis Grp Dept Math & Comp Sci Tarragona Spain Univ Autonoma Madrid Dept Informat Engn Madrid Spain

A method for segmenting 2D images based on 3D shape information is proposed. First, a robust photometric stereo technique estimates the 3D normals of the objects present in the scene for every image pixel. Then, the image is segmented by grouping its pixels according to their estimated normals through graph-based clustering. Differently from other image segmentation algorithms based on intensity, colour or texture, the regions of which are determined by the visual appearance of the depicted objects, the regions obtained with the proposed technique only depend on the 3D shapes of those objects. This can be advantageous for higher level scene understanding algorithms. This technique is especially suited to poorly illuminated scenarios and utilises a conventional camera and six inexpensive strobe lights.

关键词： 3D shape information graph theory graph-based clustering image colour analysis image pixel image resolution image segmentation image segmentation algorithms image sensors pattern clustering robust photometric stereo technique

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Capturing the spatio-temporal continuity for video semantic segmentation

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IET image PROCESSING 2019年第14期13卷 2813-2820页

作者： Chen, Xin Wu, Aming Han, Yahong Tianjin Univ Coll Intelligence & Comp Yaguan Rd Tianjin Peoples R China

In recent years, image semantic segmentation based on a convolutional neural network has achieved many advances. However, the development of video semantic segmentation is relatively slow. Directly applying the image segmentation algorithms to each video frame separately may ignore the temporal region continuity inherent in videos. In this study, the authors propose a novel deep neural network architecture with a newly devised spatio-temporal continuity (STC) module for video semantic segmentation. Particularly, the architecture includes an encoding network, an STC module, and a decoding network. The encoding network is used to extract a high-level feature map. The STC module then uses the high-level feature map as input to extract the STC feature map. For decoding, they use four dilated convolutional layers to obtain more abstract representation and a deconvolutional layer to increase the size of the representation. Finally, they fuse the current feature representation and the previous feature representation and get the class probabilities. Thus, this architecture receives a sequence of consecutive video frames and outputs the segmentation result of the current frame. They extensively evaluate the proposed approach on the CamVid and KITTI datasets. Compared with other methods, the authors' approach not only achieves competitive performance but also has lower complexity.

关键词： feature extraction image segmentation image representation video signal processing neural nets probability video semantic segmentation image semantic segmentation convolutional neural network image segmentation algorithms video frame temporal region continuity inherent videos deep neural network architecture newly devised spatio-temporal continuity encoding network STC module decoding network high-level feature map STC feature map current feature representation consecutive video frames segmentation result

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Analysis on the Basketball Match Video based on the image segmentation Algorithm

Analysis on the Basketball Match Video based on the Image Se...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Jiewei Yang Department of Physical Education Huanghuai University

ISBN: (纸本)9781510803084

With the development of the camera, video camera, infrared and various sensors such as visual information capture device with the technology, it has become a simple question that the visual information is obtained, but now in the era of information explosion, how to capture the interest of visual information in part become an important research direction, which is one of the most fundamental problems in the digital image processing. The image processing and segmentation is the bottom and the most basic work, only the good image segmentation result can perform image analysis work. This paper proposes the extracting method of the basketball match video games based on image segmentation algorithm.

关键词： image segmentation algorithms Basketball match Video

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Automated stable region generation, detection, and representation for applications to mission design

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CELESTIAL MECHANICS & DYNAMICAL ASTRONOMY 2015年第1期123卷 63-83页

作者： Nakhjiri, Navid Villac, Benjamin Calif State Polytech Univ Pomona Pomona CA 91768 USA Univ Calif Irvine Irvine CA 92697 USA

This paper presents an automated algorithm to extract dynamical features, such as stability constraints, from phase space maps. The functional representation of these constraints allows their inclusion in optimization problems and thus expands the use of dynamical tools in space mission design. The challenge to autonomously detect the regions of interest in stability maps is discussed through utilizing image processing algorithms to cluster map data. Additionally, to use the detected regions, both discrete and smooth functional representations are studied. Based on similar clustering techniques that have been considered in extracting and representing features of phase space maps, we proposed an adaptively map generation algorithm. It creates a nonuniform grid of points on a map which is denser near the boundaries of the regions of interest. Both representation and map generation algorithms provide significant performance enhancements in phase space analysis. All these techniques are illustrated on examples of stability maps in small body dynamics.

关键词： Phase space Stability maps image processing Data clustering Dynamical map generation Quadtree Stable region image segmentation algorithms

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