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color segmentation of multicolor porous printed fabrics by conjugating SOM and EDSC clustering algorithms

TEXTILE RESEARCH JOURNAL 2022年第19-20期92卷 3488-3499页

作者： Qian, Miao Wang, Zhenfei Huang, Xuantao Xiang, Zhong Wei, Pengli Hu, Xudong Zhejiang Sci Tech Univ Zhejiang Prov Key Lab Modern Text Machinery Hangzhou 310018 Zhejiang Peoples R China

In order to achieve accurate color segmentation of printed fabrics, the color segmentation algorithm combining the self-organizing maps neural network and the efficient dense subspace clustering was proposed in this paper. After pre-processing of the fabric image, the primary clustering was implemented by the self-organizing maps algorithm, then the secondary clustering was done by the efficient dense subspace clustering algorithm. The optimal silhouette coefficient is introduced into the clustering process of the efficient dense subspace clustering algorithm to determine the number of clustering centers automatically. Finally, by the post-processing including gray-scale transformation, binarization and open operation, the mis-segmentation of edge color was eliminated, making the algorithm more suitable for industrial application. Experiments were carried out and results show that the algorithm proposed in this paper can recognize the color of small areas accurately and segment the color of complex printed fabric images. The color segmented results of 20 printed fabrics show that the accuracy of the algorithm proposed in this paper reaches 88.3%.

关键词： Clustering color segmentation SOM algorithm EDSC algorithm multicolor printed fabric

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Reinforcement Learning-driven automatic calibration for color segmentation-based robot detection

Reinforcement Learning-driven automatic calibration for colo...

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2024 Latin American Robotics Symposium

作者： Acruchi, Clara Nunes, Geovany Icaro L. Souza, Thiago Jose A. Cavalcanti, Breno Filho, Adiel Teixeira de A. Machado, Mateus G. Banos, Edna N. Univ Fed Pernambuco Ctr Informat Recife PE Brazil

ISBN: (纸本)9798331508814;9798331508807

The systems designed to accomplish the color segmentation task are based on machine-learning-based color classifiers or deterministic algorithms, which usually rely on manual parameter calibration to perform well. Robot soccer competitions such as IEEE VSSS and RoboCup SSL rely on the latter type of vision software and thus suffer from manual calibration, which can be time-consuming and highly depend on operator proficiency and lighting conditions. As a challenge of IEEE VSSS, the teams must develop their robot detection software. Therefore, the present work proposes automatic calibration for color segmentation and robot detection systems as a black-box optimization problem. Then, applying the formulation of the IEEE VSSS robot detection software used in LARC 2023, it compares different methods to solve the problem with multi-armed bandit reinforcement learning algorithms. The analysis includes a detailed evaluation of the performance and efficiency of these methods in enhancing the precision and reliability of robot detection.

关键词： color segmentation object detection black box optimization reinforcement learning automatic calibration multi-armed bandit

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color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system

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TEXTILE RESEARCH JOURNAL 2021年第7-8期91卷 729-742页

作者： Zhang, Jianxin Zhang, Kangping Wu, Junkai Hu, Xudong Zhejiang Sci Tech Univ Fac Mech & Automat 928 2nd Ave Hangzhou Zhejiang Peoples R China

For multi-color yarn-dyed fabrics which are cross-woven by yarns with different colors, the different colors cannot be directly measured by a traditional spectrophotometer because it can only obtain the average color of solid-color sample in the limited aperture. In this paper, a novel method for color segmentation and extraction for multi-color yarn-woven fabrics based on a Hyperspectral Imaging System (HIS) was proposed. First, the multi-color yarn-woven fabric images were acquired with the HIS. Then a space transformation based on Frechet distance was used to transform the pre-processed hyperspectral fabric images into gray images, and then an improved watershed algorithm was used to segment the transformed gray images into different color regions. Finally, to solve the problems of over-segmentation with the improved watershed algorithm, an improvedk-means clustering algorithm was adopted to merge the over-segmented color regions. The experimental results on four multi-color yarn-woven fabrics showed that the color segmentation accuracy of the proposed method outperformed the ordinaryk-means, Fuzzy C-means (FCM), and Density peak cluster (DPC) algorithms on evaluation indexes of compactness (CP) and separation (SP), and the execution efficiency was improved by at least 55%. Furthermore, the color difference between the proposed method and the spectrophotometric measurements ranged from 0.60 to 0.88 CMC (2:1) (color Measurement Committee) units, which almost satisfied the accuracy of color measurement.

关键词： hyperspectral imaging system yarn-dyed fabric color segmentation color measurement

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Rapid recognition method of red brick content in recycled brick-concrete aggregates and powder based on color segmentation

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JOURNAL OF BUILDING ENGINEERING 2024年 84卷

作者： Xia, Peng Wang, Shiqi Gong, Fuyuan Cao, Wanlin Zhao, Yuxi Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Peoples R China Beijing Univ Technol Coll Architecture & Civil Engn Beijing Peoples R China

This paper investigated a color segmentation-based rapid recognition method for determining the red brick content in recycled brick-concrete aggregates and powder. The study explored various influencing factors, such as surface states, placement forms, image acquisition directions, and scale, on the recognition results. Additionally, the research examined the effect of microscope magnification on the accurate recognition of brick content in recycled powder. To reduce the time cost associated with manual processing of a large number of images, reference thresholds for different color channels of coarse aggregates, fine aggregates, and powder were derived through data statistics. It was demonstrated that the recognition results exhibited fluctuations under different factors. However, the prediction error can be effectively reduced by taking multiple predictions and calculating the average value, thus keeping it within a reasonable range. Particularly in engineering applications, a method that combines comprehensive image acquisition from multiple directions and adjustments based on reference thresholds can be employed to reduce time costs and achieve more desirable recognition results.

关键词： color segmentation Recycled brick-concrete aggregates Brick-concrete powder Red brick content Rapid recognition method

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color segmentation in multicolor images using node-growing self-organizing map

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color RESEARCH AND APPLICATION 2019年第2期44卷 184-193页

作者： Ouyang, Wenbin Xu, Bugao Yuan, Xiaohui Univ North Texas Dept Comp Sci & Engn Denton TX 76207 USA

This article presents a clustering algorithm based on node-growing self-organizing map (NGSOM) to classify colors on color images automatically and accurately partition the regions of different colors for color measurement. Based on the CIEDE2000 criterion, pixels in a multicolor image are grouped into a number of visually distinguishable color regions in which pixel distribution information is provided as the input of the NGSOM network for further segmentation. As an unsupervised clustering algorithm, the NGSOM randomly selects two initial nodes from the input source without a predefined network structure and grows nodes according to color differences between the first best matching unit (FBMU) and the second best matching unit (SBMU) of the current input data. Unlike a traditional SOM, the NGSOM utilizes learning rates varying with the pixel distributions of major color regions. The node-growing procedure is terminated when all the input data are examined. Compared with some commonly used color clustering algorithms, the proposed algorithm possesses a better peak signal-to-noise ratio (PSNR) and higher time efficiency. The NGSOM can be used for a wide range of applications, including fabric colorfastness assessment, painting conservation, and scenic identification in aerial images.

关键词： clustering color segmentation self-organizing map

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Real Time Fire Detection using color Probability segmentation and DenseNet Model for Classifier

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第9期13卷 300-305页

作者： Adhinata, Faisal Dharma Ramadhan, Nur Ghaniaviyanto Inst Teknol Telkom Purwokerto Fac Informat Purwokerto Indonesia

The forest is an outdoor environment not touched by the surrounding community, so it is not immediately handled when a fire occurs. Therefore, surveillance using cameras is needed to see the presence of fire hotspots in the forest. This study aims to detect hotspots through video data. As is known, fire has a variety of colors, ranging from yellow to reddish. The segmentation process requires a method that can recognize various fire colors to get a candidate fire object area in the video frame. The methods used for the color segmentation process are Gaussian Mixture Model (GMM) and Expectation-maximization (EM). The segmentation results are candidates for fire areas, which in the experiment used the value of K=4. This fire object candidate needs to be ascertained whether the segmented object is a fire object or another object. In the feature extraction stage, this research uses the DenseNet-169 or DenseNet-201 models. In this study, various color tests were carried out, namely RGB, HSV, and YCbCr. The test results show that RGB color produces the most optimal training accuracy. This RGB color configuration is used to test using video data. The test results show that the true positive and false negative values are quite good, 98.69% and 1.305%. This video data processing produces fps with an average of 14.43. So, it can be said that this combination of methods can be used to process real time data in case studies of fire detection.

关键词： Fire detection color segmentation GMM-EM DenseNet real time

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Emergency Vehicle Recognition and Classification Method Using HSV color segmentation 16

Emergency Vehicle Recognition and Classification Method Usin...

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16th IEEE International Colloquium on Signal Processing and its Applications (CSPA)

作者： Razalli, Husniza Ramli, Rusyaizila Alkawaz, Mohammed Hazim Management & Sci Univ Fac Informat Sci & Engn Shah Alam Selangor Malaysia

ISBN: (纸本)9781728153100

Classification and recognition of emergency vehicles in traffic surveillance cameras offer an early warning to ensure the prompt reaction to the traffic about emergency stating. Much existing traffic surveillance camera detection monitoring based on computer vision technology has achieved high detection rates. Some of them are very innovative and have a higher novelty index. One of the innovative ideas is detecting an emergency vehicle in the moving traffic routes from the input of a video surveillance camera. The Classification and recognition of emergency vehicles in traffic surveillance cameras offer an early warning to ensure the prompt reaction to the traffic about emergency stating. This paper proposes an algorithm which addresses a novel visual analysis technique for Detecting a moving emergency vehicle in traffic surveillance camera using a combination of Hue Saturation and Value (HSV) color segmentation and support vector machine (SVM). Experiments conducted on various benchmarking databases show that the proposed algorithm successfully distinguishes the emergency vehicles from the traffic in a surveillance camera. The results show that the proposed algorithm has an innovative classification method, a higher accuracy rate, as well as comparable recall and specificity, compared with other methods.

关键词： emergency vehicle detection traffic survillance camera emergency vehicle color classification color segmentation support vector mechine

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color segmentation Based on Human Perception Using Fuzzy Logic 1st

Color Segmentation Based on Human Perception Using Fuzzy Log...

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1st International Conference on Big Data Analysis and Deep Learning (ICBDL)

作者： Kyi, Tin Mar Zin, Khin Chan Myae Myanmar Inst Informat Technol Mandalay Myanmar

ISBN: (纸本)9789811308697;9789811308680

color segmentation is important in the field of remote sensing and Geographic Information System (GIS). Most of the color vision systems need to classify pixel color in a given image. Human perception-based approach to pixel color segmentation is done by fuzzy logic. Fuzzy sets are defined on the H, S and V components of the HSV color space. Three values (H, S and V), the fuzzy logic model has three antecedent variables (Hue, Saturation and Value) and one consequent variable, which is a color class ID are fuzzified with Triangular Fuzzy Numbering Method. Fuzzy Rules are constructed according to the linguistic fuzzy sets. One of Discrete Defuzzification method based on zero-order takagi-Sugeno model is used for color segmentation. To define the output color value, Fuzzy reasoning with zero order Takagi-Sugeno model is used for assigning the color of the given. There are sixteen output colors: Black, White, Red, Orange, Yellow, Dark Gray, Light Gray, Pink, Light Brown, Dark Brown, Aqua, Blue, Olive, Light Green, Dark Green and Purple.

关键词： color segmentation Fuzzy logic Takagi-Sugeno model

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Clustering color segmentation in multi-color space 18

Clustering color segmentation in multi-color space

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2nd International Conference on Video and Image Processing (ICVIP)

作者： Tu, Ting Zhou, Zhifeng Xiao, Peng Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

ISBN: (纸本)9781450366137

The quality of image segmentation seriously affects the results of digital images analysis and calculation. Aiming at the problem that the parameters of classification and initial center need to be entered for color image segmentation by using k-means clustering algorithm, a k-means clustering algorithm based on multiple color space is proposed for color image segmentation. Firstly, the study of color space and its mutual transformation showed that HIS color space and CIELAB color space were more consistent with human visual characteristics. Then, the gaussian mixture model was used to solve the H component of HIS color space. The gaussian mixture model parameters were obtained by the maximum expectation algorithm, and the clustering number and the initial center were obtained by optimizing the model with the Akaike information criterion. The simulated annealing algorithm is used to improve the traditional k-means algorithm, which avoids the problem that the traditional k-means algorithm is easy to fall into the local optimum. Then, k-means clustering analysis was conducted on (a, b) two-dimensional components under CIELAB color space, and finally color segmentation was conducted by the clustering results. The experiment on color segmentation of flower cluster images in MATLAB. Compared with the traditional k-means clustering algorithm, the proposed algorithm has good effect of color segmentation without human intervention and has good robustness.

关键词： color segmentation color space k-means clustering gaussian mixture model

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color segmentation of multi-colored fabrics using self-organizing-map based clustering algorithm

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TEXTILE RESEARCH JOURNAL 2017年第3期87卷 369-380页

作者： Mo, Haifang Xu, Bugao Ouyang, Wenbin Wang, Jiangqing South Cent Univ Nationalities Sch Comp Sci Wuhan Peoples R China Univ Texas Austin Sch Human Ecol 200 W 24th St Austin TX 78712 USA

Fabric prints may contain intricate and nesting color patterns. To evaluate colors on such a fabric, regions of different colors must be measured individually. Therefore, precise separation of colored patterns is paramount in analyzing fabric colors for digital printing, and in assessing the colorfastness of a printed fabric after a laundering or abrasion process. This paper presents a self-organizing-map (SOM) based clustering algorithm used to automatically classify colors on printed fabrics and to accurately partition the regions of different colors for color measurement. The main color categories of an image are firstly identified and flagged using the SOM's density map and U-matrix. Then, the region of each color category is located by divining the U-matrix map with an adaptive threshold, which is determined by recursively decreasing it from a high threshold until all the flagged neurons are assigned to different regions in the divided map. Finally, the regions with high color similarity are merged to avoid possible over-segmentation. Unlike many other clustering algorithms, this algorithm does not need to pre-define the number of clusters (e.g. main colors) and can automatically select a distance threshold to partition the U-matrix map. The experimental results show that the intricate color patterns can be precisely separated into individual regions representing different colors.

关键词： multi-colored fabric self-organizing map clustering color segmentation

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