Journal articles: 'HSV color model'

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Rasid Mamat, Abd, Fatma Susilawati Mohamed, Mohamad Afendee Mohamed, Norkhairani Mohd Rawi, and Mohd Isa Awang. "Silhouette index for determining optimal k-means clustering on images in different color models." International Journal of Engineering & Technology 7, no. 2.14 (April 6, 2018): 105. http://dx.doi.org/10.14419/ijet.v7i2.14.11464.

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Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.

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Cai, Zhao Quan, Wei Luo, Zhong Nan Ren, and Han Huang. "Color Recognition of Video Object Based on HSV Model." Applied Mechanics and Materials 143-144 (December 2011): 721–25. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.721.

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In the presented paper, we proposed a common color model and designed the color judgment method, which is based on the HSV model. This method will translate the RGB values of the points in video images to HSV values, and use HSV values to recognize the color. After that, software of real-time video object recognition was developed based on color features, which is also based on their search of target color identification. Besides, the system is developed by VC based on OpenCV, which has achieved the goal of real-time video motion detection and object color recognition. Finally, the experimental results indicate that the algorithm is accurate and similar to human recognition of the moving objects in videos view, which demonstrates the good performance of the target identification and color judgment.

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Hema, D., and Dr S. Kannan. "Interactive Color Image Segmentation using HSV Color Space." Science & Technology Journal 7, no. 1 (January 1, 2019): 37–41. http://dx.doi.org/10.22232/stj.2019.07.01.05.

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The primary goal of this research work is to extract only the essential foreground fragments of a color image through segmentation. This technique serves as the foundation for implementing object detection algorithms. The color image can be segmented better in HSV color space model than other color models. An interactive GUI tool is developed in Python and implemented to extract only the foreground from an image by adjusting the values for H (Hue), S (Saturation) and V (Value). The input is an RGB image and the output will be a segmented color image.

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Astrianda, Nica. "Klasifikasi Kematangan Buah Tomat Dengan Variasi Model Warna Menggunakan Support Vector Machine." VOCATECH: Vocational Education and Technology Journal 1, no. 2 (April 13, 2020): 45–52. http://dx.doi.org/10.38038/vocatech.v1i2.27.

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Abstract Tomato ripeness classification has been done manually through direct visual observation. However, manual classification is highly influenced by operator subjectivity so that on certain conditions, the classification process is not consistent. The development of information technology allows the identification of the ripeness level of tomatoes based on the characteristics of color with the help of computers. In this study Tomato fruit is classified by histogram color image input obtained from the capture result. This is done by changing all the colors in the image of the RGB color model (Red, Green, Blue) into several different color models ie HSV color model (Hue, Saturation, Value), CIElab color model and YCBCR color model. The obtained color model will be used as training data using SVM (Support Vector Machine) so that the system is able to classify the ripeness of tomato fruit later. The image processing process of this research is done using matlab. After being analyzed manually using 20 data as training, 54 data as data testing got success rate classification of tomato fruit ripeness using Support Vector Machine is 100% by using CIElab color model. Keywords: Support Vector Machine; CIElab; HSV; YCbCr; Ripeness of Tomato ____________________________ Abstrak Klasifikasi kematangan tomat telah dilakukan secara manual melalui pengamatan visual langsung. Namun, klasifikasi manual sangat dipengaruhi oleh subjektivitas operator sehingga pada kondisi tertentu, proses klasifikasi tidak konsisten. Perkembangan teknologi informasi memungkinkan identifikasi tingkat kematangan tomat berdasarkan karakteristik warna dengan bantuan komputer. Dalam penelitian ini buah tomat diklasifikasikan berdasarkan input gambar berwarna histogram yang diperoleh dari hasil tangkapan. Hal ini dilakukan dengan mengubah semua warna pada gambar model warna RGB (Red, Green, Blue) menjadi beberapa model warna yang berbeda yaitu model warna HSV (Hue, Saturation, Value), model warna CIElab dan model warna YCBCR. Model warna yang diperoleh akan digunakan sebagai data pelatihan menggunakan SVM (Support Vector Machine) sehingga sistem mampu mengklasifikasikan kematangan buah tomat. Proses pengolahan citra pada penelitian ini dilakukan dengan menggunakan matlab. Setelah dianalisis secara manual menggunakan 20 data sebagai data pelatihan, 54 data sebagai data pengujian mendapatkan klasifikasi tingkat keberhasilan kematangan buah tomat menggunakan Support Vector Machine adalah 100% dengan menggunakan model warna CIElab. Kata Kunci: Support Vector Machine; CIElab; HSV; YCbCr; Kematangan Tomat. __________________________

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Rahaman, G. M. Atiqur, and Md Zahidul Islam. "Color transform analysis for microscale image segmentation to study halftone model parameters." Open Computer Science 6, no. 1 (November 2, 2016): 148–67. http://dx.doi.org/10.1515/comp-2016-0013.

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AbstractThis article presents a comprehensive study of 30 color transforms to accurately segment images of halftone prints and thus calculating the parameters of a color prediction model. The transforms are evaluated combining three metrics: the model accuracy,Otsu’s discriminant, and correlation coefficients of histograms. Hierarchical cluster analysis is applied to determine the thresholds to segment the image histogram into paper, ink and mixed area. Among the 30 different transforms discussed in this article, 21 channels are of 7 color space models (RGB, CMYK, CIELAB, HSV, YIQ, YCbCr, and XYZ) and the other 9 channels are specially designed. Notable increase in model accuracy validates the segmentation accuracy and the necessity of choosing the appropriate transform. A set of 180 halftone images of different print properties (such as paper, halftone, ink and printing technology) has been used for the evaluation. It is found that, the most appropriate transform depends on the type of primary ink, but the corresponding transforms in CMYK color space model have shown consistent performance. CMYK-C, XYZ-Y and LAB-B are the best transforms for Cyan, Magenta and Yellow ink color respectively. YIQ-I and HSV-S are good candidates if a single transform is to be chosen for all primary ink colors.

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Chae, Soohwan, and Kyungkoo Jun. "HSV Color Model based Hand Contour Detector Robust to Noise." Journal of Korea Multimedia Society 18, no. 10 (October 30, 2015): 1149–56. http://dx.doi.org/10.9717/kmms.2015.18.10.1149.

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Lv, Jingqin, and Jiangxiong Fang. "A Color Distance Model Based on Visual Recognition." Mathematical Problems in Engineering 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/4652526.

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In computer vision, Euclidean Distance is generally used to measure the color distance between two colors. And how to deal with illumination change is still an important research topic. However, our evaluation results demonstrate that Euclidean Distance does not perform well under illumination change. Since human eyes can recognize similar or irrelevant colors under illumination change, a novel color distance model based on visual recognition is proposed. First, we find that various colors are distributed complexly in color spaces. We propose to divide the HSV space into three less complex subspaces, and study their specific distance models. Then a novel hue distance is modeled based on visual recognition, and the chromatic distance model is proposed in line with our visual color distance principles. Finally, the gray distance model and the dark distance model are studied according to the natures of their subspaces, respectively. Experimental results show that the proposed model outperforms Euclidean Distance and the related methods and achieves a good distance measure against illumination change. In addition, the proposed model obtains good performance for matching patches of pedestrian images. The proposed model can be applied to image segmentation, pedestrian reidentification, visual tracking, and patch or superpixel-based tasks.

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Li, Zhiyong, Pengfei Li, Xiaoping Yu, and Mervat Hashem. "Real-Time Tracking by Double Templates Matching Based on Timed Motion History Image with HSV Feature." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/793769.

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It is a challenge to represent the target appearance model for moving object tracking under complex environment. This study presents a novel method with appearance model described by double templates based on timed motion history image with HSV color histogram feature (tMHI-HSV). The main components include offline template and online template initialization, tMHI-HSV-based candidate patches feature histograms calculation, double templates matching (DTM) for object location, and templates updating. Firstly, we initialize the target object region and calculate its HSV color histogram feature as offline template and online template. Secondly, the tMHI-HSV is used to segment the motion region and calculate these candidate object patches’ color histograms to represent their appearance models. Finally, we utilize the DTM method to trace the target and update the offline template and online template real-timely. The experimental results show that the proposed method can efficiently handle the scale variation and pose change of the rigid and nonrigid objects, even in illumination change and occlusion visual environment.

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Chen, Ching Yi, and Chi Chiang Ko. "Designing FIRA Medium-Sized Soccer Robot Vision System Using Particle Swarm Optimization." Applied Mechanics and Materials 764-765 (May 2015): 675–79. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.675.

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Enabling FIRA medium-sized soccer robots to recognize target objects according to color information requires that competing teams manually set the range of colors according to ambient lighting conditions prior to games. This color information is used to differentiate features of target objects, such as the ball, the goals, and the field. Constructing a color-feature model such as this is extremely time-consuming and the resulting model is unable to adapt dynamically to changes in lighting conditions. This study applied a look-up table method to execute RGB-HSV color space conversion to accelerate video processing. A particle swarm optimization (PSO) scheme was developed to detect the color-feature parameters of the target objects in the HSV color space. This enables the automatic completion of color-feature modeling and the construction of the knowledge model required by the robot for object recognition. Experiment results demonstrate that the proposed method is capable of enhancing the robustness of the robot vision system in determining changes in lighting conditions. In addition, the manpower and time required to set robot parameters prior to games were reduced significantly.

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Su, Ching Hung, Huang Sen Chiu, Jui Hung Hung, and Tsai Ming Hsieh. "Color Space Comparison between RGB and HSV Based Images Retrieval." Advanced Materials Research 989-994 (July 2014): 4123–26. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4123.

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The visual attributes of color are suitable for human perception and computer vision. A Color space is defined as a model for representing the intensity value of color. We propose a color space comparison and analysis between RGB and HSV based images retrieval. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison between the color featurs of RGB and HSV to compare and analyze the images of database.

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Vaghela, Himali, Hardik Modi, Manoj Pandya, and M. B. "Comparative Study of HSV Color Model and Ycbcr Color Model to Detect Nucleus of White Cells." International Journal of Computer Applications 150, no. 8 (September 15, 2016): 38–42. http://dx.doi.org/10.5120/ijca2016911614.

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Choi, Na-Rae, and Sang-Il Choi. "Preprocessing Technique for Lane Detection Using Image Clustering and HSV Color Model." Journal of Korea Multimedia Society 20, no. 2 (February 28, 2017): 144–52. http://dx.doi.org/10.9717/kmms.2017.20.2.144.

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Lin, Ching I., Ching Hung Su, and Shih Hung Tai. "Color and Texture Features Based Image Retrieval." Applied Mechanics and Materials 441 (December 2013): 707–10. http://dx.doi.org/10.4028/www.scientific.net/amm.441.707.

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We propose a practical image retrieval scheme to retrieve images efficiently. We propose a scheme using color and texture features and address the unique algorithm to extract the color pixel features by the HSV color space and Tamura features of the texture features. The proposed scheme transfers each image to a quantized color code using the regulations of the properties in compliance with HSV color space model and then employing the quantized color code along with Tamura features of texture features to compare the images of database. Experimental of the proposed scheme on demonstrate more efficient and effective than the conventional schemes.

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Wen, Long, Cheng Xu, Tao Li, and Zheng Tian. "Implementation of RGB to HSV Color Space Conversion with Xilinx System Generator." Advanced Materials Research 816-817 (September 2013): 527–34. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.527.

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The HSV (Hue, Saturation, and Value) color model is more intuitive than the RGB color model and widely used in color recognition and color space segmentation. Currently as the requirements of high processing speed and special applications need to realize RGB to HSV color space conversion, in this paper a new Field Programmable Gate Array (FPGA) architecture named RGB2HSV module was developed via an accurate and visible FPGA implementation method in use of Xilinx System Generator (XSG). XSG is a design tool in Simulink of MATLAB which accelerates design by providing access to highly parameterized intellectual blockset for Xilinx FPGA. In this paper simulation test images were used to measure the deviation and the time consume by the RGB2HSV module and relevant C program. Experiment shows that the maximum frequency can reach 121.433MHz and lower deviation was achieved in Xilinx Zynq xc7z020 device. The full-pipelined and parallel RGB2HSV module had been adapted in order to speed up the RGB to HSV color space conversion and took as much as 87% less than that of C program in our experiment.

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Ryu, Jin-Kyu, and Dong-Kurl Kwak. "Flame Detection Based on Deep Learning Using HSV Color Model and Corner Detection Algorithm." Fire Science and Engineering 35, no. 2 (April 30, 2021): 108–14. http://dx.doi.org/10.7731/kifse.30befadd.

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Recently, many image classification or object detection models that use deep learning techniques have been studied; however, in an actual performance evaluation, flame detection using these models may achieve low accuracy. Therefore, the flame detection method proposed in this study is image pre-processing with HSV color model conversion and the Harris corner detection algorithm. The application of the Harris corner detection method, which filters the output from the HSV color model, allows the corners to be detected around the flame owing to the rough texture characteristics of the flame image. These characteristics allow for the detection of a region of interest where multiple corners occur, and finally classify the flame status using deep learning-based convolutional neural network models. The flame detection of the proposed model in this study showed an accuracy of 97.5% and a precision of 97%.

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Huang, Zhiwu, Jiqing Wu, and Luc Van Gool. "Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3886–93. http://dx.doi.org/10.1609/aaai.v33i01.33013886.

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Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generative modeling problem, we then introduce a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds, enabling us to achieve a tractable objective under the WGAN framework. In addition, we recommend three benchmark datasets that are CIFAR-10 HSV/CB color images, ImageNet HSV/CB color images, UCL DT image datasets. On the three datasets, we experimentally demonstrate the proposed manifold-aware WGAN model can generate more plausible manifold-valued images than its competitors.

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Tang, San. "Human Face Detection Method Based on Skin Color Model." Advanced Materials Research 706-708 (June 2013): 1877–81. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1877.

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Face detection is the first step of face recognition, and is a very active research topic in the filed of computer vision and pattern recognition. A skin color model based face detection method for chromatic images is proposed in this paper. The H-CgCr skin color model is established by taking advantage of the color pixels clustering distribution in the HSV and YCgCr color space. The noises are eliminated based on skin color segmentation, and the face candidate region is judged according to knowledge-based, finally, the position of the face area is marked by the box. The experimental results demonstrate that the proposed method is feasible and effective.

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McMahan, Brian, and Matthew Stone. "A Bayesian Model of Grounded Color Semantics." Transactions of the Association for Computational Linguistics 3 (December 2015): 103–15. http://dx.doi.org/10.1162/tacl_a_00126.

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Natural language meanings allow speakers to encode important real-world distinctions, but corpora of grounded language use also reveal that speakers categorize the world in different ways and describe situations with different terminology. To learn meanings from data, we therefore need to link underlying representations of meaning to models of speaker judgment and speaker choice. This paper describes a new approach to this problem: we model variability through uncertainty in categorization boundaries and distributions over preferred vocabulary. We apply the approach to a large data set of color descriptions, where statistical evaluation documents its accuracy. The results are available as a Lexicon of Uncertain Color Standards (LUX), which supports future efforts in grounded language understanding and generation by probabilistically mapping 829 English color descriptions to potentially context-sensitive regions in HSV color space.

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Xing, Deng, Yu Zhongming, Wang Lin, and Li Jinlan. "Smoke Image Segmentation Based on Color Model." Journal on Innovation and Sustainability. RISUS ISSN 2179-3565 6, no. 2 (August 11, 2015): 130. http://dx.doi.org/10.24212/2179-3565.2015v6i2p130-138.

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Smoke is the most significant feature in the process of fire, so it’s possible to rely on smoke detection to detect fire. While the smoke image segmentation is the most difficult and also indispensable step in the analysis of smoke image detection. In order to improve its accuracy and effectively exclude the disturbances of non-smoke image, and lower the false alarm rate, it puts forward a kind of smoke image segmentation based on color model. It uses K-means clustering in Lab color space and threshold segmentation in HSV color space, then merges the two results. Finally, it uses the method of shen filter and regional mark to denoise, Experimental results on segmentation of smoke image show that the proposed method is able to segment smoke from the background.

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Su, Ching Hung, Huang Sen Chiu, Mohd Helmy Abd Wahab, and Tsai Ming Hsieh. "An Efficient Image Retrieval Based on Combined Features." Advanced Materials Research 787 (September 2013): 1025–29. http://dx.doi.org/10.4028/www.scientific.net/amr.787.1025.

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An efficient image retrieval scheme to retrieve images is proposed based on the issue of texture and color space features extractions. The algorithm for an effective image retrieval scheme to retrieve images is presented. We propose a scheme using color and texture features and address the unique algorithm to extract the color pixel features by the HSV color space and the texture features of Homogeneous Texture Descriptor (HTD). The proposed scheme transfers each image to a quantized color code using the regulations of the properties in compliance with HSV color space model and then employing the quantized color code along with the texture feature of Homogeneous Texture Descriptor (HTD) to compare the images of database. Experimental of the proposed scheme performed on SIMPLIcity image database to demonstrate more efficient and effective than the conventional schemes.

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Kisan, Sumitra, Sarojananda Mishra, Ajay Chawda, and Sanjay Nayak. "Estimation of Fractal Dimension in Different Color Model." International Journal of Knowledge Discovery in Bioinformatics 8, no. 1 (January 2018): 75–93. http://dx.doi.org/10.4018/ijkdb.2018010106.

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This article describes how the term fractal dimension (FD) plays a vital role in fractal geometry. It is a degree that distinguishes the complexity and the irregularity of fractals, denoting the amount of space filled up. There are many procedures to evaluate the dimension for fractal surfaces, like box count, differential box count, and the improved differential box count method. These methods are basically used for grey scale images. The authors' objective in this article is to estimate the fractal dimension of color images using different color models. The authors have proposed a novel method for the estimation in CMY and HSV color spaces. In order to achieve the result, they performed test operation by taking number of color images in RGB color space. The authors have presented their experimental results and discussed the issues that characterize the approach. At the end, the authors have concluded the article with the analysis of calculated FDs for images with different color space.

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Kim, Gi-Woo, and Dae-Seong Kang. "Modified CAMshift Algorithm Based on HSV Color Model for Tracking Objects." International Journal of Software Engineering and Its Applications 9, no. 7 (July 31, 2015): 193–200. http://dx.doi.org/10.14257/ijseia.2015.9.7.20.

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Almazaydeh, L., M. Salah, M. Misto, E. Nairok, S. Alsayed, and K. Elleithy. "Speed-limit Signs Detection and Recognition Based on HSV Color Model." Asian Journal of Scientific Research 9, no. 4 (June 15, 2016): 219–22. http://dx.doi.org/10.3923/ajsr.2016.219.222.

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Xiong, Jie, and Lei Lu. "True Color Image Enhanced by Wavelet Illumination-Reflection Model." Applied Mechanics and Materials 397-400 (September 2013): 1500–1505. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1500.

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When a true color image is enhanced, the same hue should be kept and the useful details should be strengthened in the image enhanced. According to the imaging principle, wavelet illumination-reflection method is proposed. Firstly, true color image is transformed from RGB space to HSV space. Secondly, the saturation is attenuated by CLAHE. Thirdly, the value is decomposed into illumination and reflection by wavelet illumination-reflection model. The details of the reflection are strengthened. The dynamic range of the illumination is reduced in order to enhance the image. Experiments and analysis show that the enhancement algorithm based on wavelet illumination-reflection model is obviously better than multi-scales Retinexs with color restoration (MSRCR).

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Zhang, Hong Ying, Hong Li, and Yi Gang Sun. "Cast Shadow Removal in a Real-Time Environment." Applied Mechanics and Materials 275-277 (January 2013): 2548–54. http://dx.doi.org/10.4028/www.scientific.net/amm.275-277.2548.

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The cast shadows on the background of the object will distinctly affect the recognition of the foreground objects. Due to the limitation of shadow removal methods utilizing texture, a novel algorithm based on Gaussian Mixture Model (GMM) and HSV color space is proposed. Firstly, moving regions are detected using GMM. Secondly, we make two pre-classifiers accurate and adaptive to the change of shadow by using the features of shadow in RGB and HSV color space. Experimental results show that the proposed method is efficient and robust.

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Liang, Yun Juan, Xiao Ying Wu, Li Juan Ma, and Li Jun Zhang. "Face Localization in Color Images Based on Skin Color and Eye Gradient." Advanced Materials Research 268-270 (July 2011): 1382–85. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1382.

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In color images, skin color is the important information on human face. This paper proposes a method to detect and locate human face rapidly based on skin color information and eye gradient. First, normalized RGB space is converted to HSV space; Secondly, the images are pretreated by smoothing and light compensation to overcome the uneven illumination changes, and then the defined skin color model is used to determine candidate regions of the human face, finally the human face is located accurately through eye localization based on gradient template. Experiments show that the method is fast and effective.

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Wen, Chang Bao, Wen Zheng Sun, and Hong Liang Cheng. "Comprehensive Face Location Algorithm Based on Composite Skin Color." Advanced Materials Research 798-799 (September 2013): 781–84. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.781.

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In order to solve the slowly processing speed, low precision problems in the Face location and detection, the comprehensive face location algorithm based on composite skin color was proposed in this paper. The YCrCb model algorithm and HSV model algorithm are skillfully applied to the comprehensive face location algorithm. The human body region and background region can be detected and judged by the color value of every pixel for image in the YCrCb and HSV space. Then, the connecting region can be screened and decided by the geometric features of human face, and the face can be accurately located and detected. The experimental results confirm that the algorithm can achieve the accuracy ratio of face location in three cases, such as simple, medium and complexity, are 99%, 92% and 85%, respectively. Furthermore, the accuracy ratio of face location and detection speed was improved in this algorithm.

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Chu, Kai, and Guang-Hai Liu. "Image Retrieval Based on a Multi-Integration Features Model." Mathematical Problems in Engineering 2020 (March 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/1461459.

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Feature integration theory can be regarded as a perception theory, but the extraction of visual features using such a theory within the CBIR framework is a challenging problem. To address this problem, we extract the color and edge features based on a multi-integration features model and use these for image retrieval. A novel and highly simple but efficient visual feature descriptor, namely, a multi-integration features histogram, is proposed for image representation and content-based image retrieval. First, a color image is converted from the RGB to the HSV color space, and the color features and color differences are extracted. Then, the color differences are calculated to extract the edge features using a set of simple integration processes. Finally, combining the color, edge, and spatial layout features allows representing the image content. Experiments show that our method produces results comparable to existing and well-known methods on three datasets that contain 25,000 natural images. The performances are significantly better than that of the BOW histogram, local binary pattern histogram, histogram of oriented gradient, and multi-texton histogram, with performances similar to the color volume histogram.

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HIREMATH, P. S., and AJIT DANTI. "DETECTION OF MULTIPLE FACES IN AN IMAGE USING SKIN COLOR INFORMATION AND LINES-OF-SEPARABILITY FACE MODEL." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 01 (February 2006): 39–61. http://dx.doi.org/10.1142/s021800140600451x.

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In this paper, human faces are detected using the skin color information and the Lines-of-Separability (LS) face model. The various skin color spaces based on widely used color models such as RGB, HSV, YCbCr, YUV and YIQ are compared and an appropriate color model is selected for the purpose of skin color segmentation. The proposed approach of skin color segmentation is based on YCbCr color model and sigma control limits for variations in its color components. The segmentation by the proposed method is found to be more efficient in terms of speed and accuracy. Each of the skin segmented regions is then searched for the facial features using the LS face model to detect the face present in it. The LS face model is a geometric approach in which the spatial relationships among the facial features are determined for the purpose of face detection. Hence, the proposed approach based on the combination of skin color segmentation and LS face model is able to detect single as well as multiple faces present in a given image. The experimental results and comparative analysis demonstrate the effectiveness of this approach.

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Li, Si, and Hong E. Ren. "The Study of Forest Fire Color Image Segmentation." Key Engineering Materials 474-476 (April 2011): 2140–45. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.2140.

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Combined with the composition characteristics of forest fire image background when the forest fire occurred during different time periods of night and day, different image segmentation methods were applied to the forest fire color images of different time periods respectively, which could improve the efficiency of image processing. Meanwhile, application of H and S components from HSV color space, the strategy on color image segmentation which processed the segmentation processing to forest fire color images with complicated background was proposed combined with Otsu algorithm. The results of simulation experiment showed that the above-mentioned segmentation methods were obtained satisfactory segmentation effects when the segmentation on forest fire color images during different time periods of night and day were processed respectively. And also application of Otsu algorithm based on HSV color model, the forest fire image segmentation occurred in the daytime was processed, which overcame the interference factors of light and smoke, as well as the shortage of noise sensibility due to Otsu algorithm.

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Kwon, Young-Wook, Se-Hoon Jung, Dong-Gook Park, and Chun-Bo Sim. "A Key-Frame Extraction Method based on HSV Color Model for Smart Vehicle Management System." Journal of the Korea institute of electronic communication sciences 8, no. 4 (April 30, 2013): 595–604. http://dx.doi.org/10.13067/jkiecs.2013.8.4.595.

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Firmansyah, Satrio, Danang Lelono, and Rade Sumiharto. "Implementasi Pengolahan Citra Digital Sebagai Pengukur Nilai Resistor Pada Sistem Pemindai Resistor Berbasis Android." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 5, no. 1 (May 1, 2015): 1. http://dx.doi.org/10.22146/ijeis.7148.

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AbstrakSalah satu gadget yang sering digunakan adalah telepon pintar berbasis Android. Android bersifat Open Source sehingga memungkinkan pengguna dan pengembang dalam mengoperasikan maupun membuat aplikasi berbasis Android. Ada berbagai macam permasalahan yang membutuhkan citra sebagai masukan atau input sistem dikarenakan keterbatasan manusia dalam hal kecepatan memproses suatu fungsi matematis maupun algoritma pendukung didalamnya, selain itu juga masalah waktu dan lain sebagainya. Salah satu sistem yang membutuhkan citra sebagai masukannya adalah penentuan nilai resistor berdasarkan gelang warna. Untuk melakukan seleksi warna digunakan metode segmentasi warna pemodelan warna HSV. Dengan menggunakan model warna HSV dapat menjadi model warna yg dapat digunakan sebuah sistem untuk menentukan nilai warna resistor, karena komponen nilai hue adalah representasi dari nilai warna yang sebenarnya. Hal ini didukung dengan saturation yang berfungsi sebagai tingkat kejenuhan suatu warna dan nilai value sebagai nilai kecerahan warna. Uji coba sistem dilakukan dengan pengujian pengaruh intensitas cahaya dan jarak pendeteksian antara kamera dan resistor.Hasil dari penelitian ini berupa sebuah implementasi pengolahan citra digital sebagai pengukur nilai resistor. Hasil terbaik dicapai dengan kondisi ruangan pada intensitas cahaya lampu antara 400 lux hingga 1200 lux dengan jarak pendeteksian antar kamera dan resistor yaitu maksimal 20 cm. Kata kunci— pengolahan citra digital, Android, resistor, HSV, intensitas cahaya, java AbstractOne of the gadget that is often used is Android smart phones. Android is an OpenSource, it could help user and developer to operate and develop Android Application. There are several problems that need image as an input system, it is caused by the humas’s ability in doing some mathematic functions or supported algorythm. To make the selection color used HSV color space. By using HSV color space allows a system to determine the color value resistor, because the hue value of the component is a representation of the actual color value. This is supported by the saturation level that serves as a color saturation and value as a brightness of color.The results of this research is an implementation of digital image processing as a measure of the value of the resistor. The system is tested by the influence of light intensity and the distance between the camera and resistor. The best results were achieved with the conditions of the room in light intensity between 400 lux to 1200 lux the detection distance between the camera and resistor is 20 cm of maximum value. Keywords—digital image processing, Android, resitor, HSV, light intensity, java

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Tuama, Saba A., and Jamila H. Saud. "An Efficient Segmentation Method for Automated Tongue Extraction using HSV Color Model." International Journal of Advanced Science and Technology 133 (December 31, 2019): 1–10. http://dx.doi.org/10.33832/ijast.2019.133.01.

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Sabri, Nurbaity, Zaidah Ibrahim, and Dino Isa. "Evaluation of Color Models for Palm Oil Fresh Fruit Bunch Ripeness Classification." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 2 (August 1, 2018): 549. http://dx.doi.org/10.11591/ijeecs.v11.i2.pp549-557.

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This paper investigates the application of eight color models for automatic palm oil Fresh Fruit Bunch (FFB) ripeness classification with multi-class Support Vector Machine (SVM). Ripeness classification is important during harvesting to ensure that they are harvested during the correct ripe stage for optimum oil production. Since color is a significant indicator for agriculturists to determine the ripeness of FFB, it is critical to determine the right color model. Eight color models have been investigated namely, HSV, I1I2I3, LAB, XYZ, YCbCr, YIQ, YUV and RGB. Color moments were extracted from each of these color models for the classification of four stages of FFB ripeness that are unripe, under-ripe, ripe and over-ripe. A database of five hundred images of palm oil FFB has been constructed and experiments showed that YCbCr and YUV outperform the other color models.

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Shen, Zhen, Shan Zheng, Rui Dong, and Gong Chen. "Saturation of stool color in HSV color model is a promising objective parameter for screening biliary atresia." Journal of Pediatric Surgery 51, no. 12 (December 2016): 2091–94. http://dx.doi.org/10.1016/j.jpedsurg.2016.09.044.

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WACHS, JUAN, HELMAN STERN, and MARK LAST. "COLOR FACE SEGMENTATION USING A FUZZY MIN-MAX NEURAL NETWORK." International Journal of Image and Graphics 02, no. 04 (October 2002): 587–601. http://dx.doi.org/10.1142/s021946780200086x.

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This work presents an automated method of segmentation of faces in color images with complex backgrounds. Segmentation of the face from the background in an image is performed by using face color feature information. Skin regions are determined by sampling the skin colors of the face in a Hue Saturation Value (HSV) color model, and then training a fuzzy min-max neural network (FMMNN) to automatically segment these skin colors. This work appears to be the first application of Simpson's FMMNN algorithm to the problem of face segmentation. Results on several test cases showed recognition rates of both face and background pixels to be above 93%, except for the case of a small face embedded in a large background. Suggestions for dealing with this difficult case are proffered. The image pixel classifier is linear of order O(Nh) where N is the number of pixels in the image and h is the number of fuzzy hyperbox sets determined by training the FMMNN.

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Ling, Ling, and Wei Xin Ling. "Retrieval Algorithm of Images and its Applications in Recognition of Metallographic Pictures." Advanced Materials Research 291-294 (July 2011): 2356–59. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.2356.

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In order to improve the retrieval speed and precision of images, the improved algorithm of extraction of image color features based on the both RGB and HSV color models was proposed in this paper. The algorithm can remove the repetitious vectors of compost in quantization process. While evenly quantizing model space, we can bring the compression of dimensions of image color features into full play and guarantee not to lose the main components of color features for color image. Then using RBF neural network and incorporating the values of color features, the image retrieval can be performed. The experimental results show that the data model of image color features in precise and high effective terms can be established and described by this algorithm, and the satisfactory results are obtained by applying the algorithm to the recognition of metallographic pictures for metallic materials. In addition, the algorithm can also be used in the evaluation of the discoloration of metallic casting alloys.

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Chavolla, Edgar, Arturo Valdivia, Primitivo Diaz, Daniel Zaldivar, Erik Cuevas, and Marco A. Perez. "Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm." Mathematical Problems in Engineering 2018 (2018): 1–23. http://dx.doi.org/10.1155/2018/2786952.

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Accurate color image segmentation has stayed as a relevant topic between the researches/scientific community due to the wide range of application areas such as medicine and agriculture. A major issue is the presence of illumination variations that obstruct precise segmentation. On the other hand, the machine learning unsupervised techniques have become attractive principally for the easy implementations. However, there is not an easy way to verify or ensure the accuracy of the unsupervised techniques; so these techniques could lead to an unknown result. This paper proposes an algorithm and a modification to the HSV color model in order to improve the accuracy of the results obtained from the color segmentation using the K-means++ algorithm. The proposal gives better segmentation and less erroneous color detections due to illumination conditions. This is achieved shifting the hue and rearranging the H equation in order to avoid undefined conditions and increase robustness in the color model.

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Zhu, Hongjin, Honghui Fan, Feiyue Ye, and Xiaorong Zhao. "Improving Vehicle Detection Accuracy Based on Vehicle Shadow and Superposition Elimination." Open Mechanical Engineering Journal 9, no. 1 (October 7, 2015): 1039–44. http://dx.doi.org/10.2174/1874155x01509011039.

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Vehicle shadow and superposition have a great influence on the accuracy of vehicles detection in traffic video. Many background models have been proposed and improved to deal with detection moving object. This paper presented a method which improves Gaussian mixture model to get adaptive background. The HSV color space was used to eliminate vehicle shadow, it was based on a computational colour space that makes use of our shadow model. Vehicle superposition elimination was discussed based on vehicle contour dilation and erosion method. Experiments were performed to verify that the proposed technique is effective for vehicle detection based traffic surveillance systems. Detection results showed that our approach was robust to widely different background and illuminations.

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Ding, Xiao Ling, Qiang Zhao, Yi Bin Li, and Xin Ma. "A Real-Time and Effective Object Recognition and Localization Method." Applied Mechanics and Materials 615 (August 2014): 107–12. http://dx.doi.org/10.4028/www.scientific.net/amm.615.107.

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In this paper, we realize object recognition and localization in a real time based on appearance features of object. For object recognition, we proposed to use global feauture (color) of images, and with an improved color image segmentation algorithm to realize threshold segmentation based on pixels in the image’s HSV color model by using the tool OpenCV, so we can realize the special color object recognition. Further the object can be localized with the ground constrained method by using the camera perspective geometry model. In the lab conditions, we realized single color object recognition and localization by transplanting the algorithm into Amigobots mobile robot and proved this method is simple, effective and real-time.

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Liu, Shouxin, Wei Long, Lei He, Yanyan Li, and Wei Ding. "Retinex-Based Fast Algorithm for Low-Light Image Enhancement." Entropy 23, no. 6 (June 13, 2021): 746. http://dx.doi.org/10.3390/e23060746.

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We proposed the Retinex-based fast algorithm (RBFA) to achieve low-light image enhancement in this paper, which can restore information that is covered by low illuminance. The proposed algorithm consists of the following parts. Firstly, we convert the low-light image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and use the linear function to stretch the original gray level dynamic range of the V component. Then, we estimate the illumination image via adaptive gamma correction and use the Retinex model to achieve the brightness enhancement. After that, we further stretch the gray level dynamic range to avoid low image contrast. Finally, we design another mapping function to achieve color saturation correction and convert the enhanced image from the HSV color space to the RGB color space after which we can obtain the clear image. The experimental results show that the enhanced images with the proposed method have better qualitative and quantitative evaluations and lower computational complexity than other state-of-the-art methods.

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Kim, Hyun-Koo, Ju H. Park, and Ho-Youl Jung. "An Efficient Color Space for Deep-Learning Based Traffic Light Recognition." Journal of Advanced Transportation 2018 (December 6, 2018): 1–12. http://dx.doi.org/10.1155/2018/2365414.

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Traffic light recognition is an essential task for an advanced driving assistance system (ADAS) as well as for autonomous vehicles. Recently, deep-learning has become increasingly popular in vision-based object recognition owing to its high performance of classification. In this study, we investigate how to design a deep-learning based high-performance traffic light detection system. Two main components of the recognition system are investigated: the color space of the input video and the network model of deep learning. We apply six color spaces (RGB, normalized RGB, Ruta’s RYG, YCbCr, HSV, and CIE Lab) and three types of network models (based on the Faster R-CNN and R-FCN models). All combinations of color spaces and network models are implemented and tested on a traffic light dataset with 1280×720 resolution. Our simulations show that the best performance is achieved with the combination of RGB color space and Faster R-CNN model. These results can provide a comprehensive guideline for designing a traffic light detection system.

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Sayed, Usama, Mahmoud A. Mofaddel, Samy Bakheet, and Zenab El-Zohry. "An Elliptical Boundary Skin Model For Hand Detection Based on HSV Color Space." Information Sciences Letters 7, no. 1 (January 1, 2018): 13–17. http://dx.doi.org/10.18576/isl/070103.

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Dai, Hongqin. "Study on Human Body Contour Extraction from Images Based on HSV Color Model." Journal of Fiber Bioengineering and Informatics 8, no. 3 (June 2015): 501–11. http://dx.doi.org/10.3993/jfbim00148.

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Liu, Guang-Hai, and Zhao Wei. "Image Retrieval Using the Fused Perceptual Color Histogram." Computational Intelligence and Neuroscience 2020 (November 24, 2020): 1–10. http://dx.doi.org/10.1155/2020/8876480.

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Extracting visual features for image retrieval by mimicking human cognition remains a challenge. Opponent color and HSV color spaces can mimic human visual perception well. In this paper, we improve and extend the CDH method using a multi-stage model to extract and represent an image in a way that mimics human perception. Our main contributions are as follows: (1) a visual feature descriptor is proposed to represent an image. It has the advantages of a histogram-based method and is consistent with visual perception factors such as spatial layout, intensity, edge orientation, and the opponent colors. (2) We improve the distance formula of CDHs; it can effectively adjust the similarity between images according to two parameters. The proposed method provides efficient performance in similar image retrieval rather than instance retrieval. Experiments with four benchmark datasets demonstrate that the proposed method can describe color, texture, and spatial features and performs significantly better than the color volume histogram, color difference histogram, local binary pattern histogram, and multi-texton histogram, and some SURF-based approaches.

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Santos, João F. C. dos, Heider R. F. Silva, Francisco A. C. Pinto, and Igor R. de Assis. "Use of digital images to estimate soil moisture." Revista Brasileira de Engenharia Agrícola e Ambiental 20, no. 12 (December 2016): 1051–56. http://dx.doi.org/10.1590/1807-1929/agriambi.v20n12p1051-1056.

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ABSTRACT The objective of this study was to analyze the relation between the moisture and the spectral response of the soil to generate prediction models. Samples with different moisture contents were prepared and photographed. The photographs were taken under homogeneous light condition and with previous correction for the white balance of the digital photograph camera. The images were processed for extraction of the median values in the Red, Green and Blue bands of the RGB color space; Hue, Saturation and Value of the HSV color space; and values of the digital numbers of a panchromatic image obtained from the RGB bands. The moisture of the samples was determined with the thermogravimetric method. Regression models were evaluated for each image type: RGB, HSV and panchromatic. It was observed the darkening of the soil with the increase of moisture. For each type of soil, a model with best fit was observed and to use these models for prediction purposes, it is necessary to choose the model with best fit in advance, according to the soil characteristics. Soil moisture estimation as a function of its spectral response by digital image processing proves promising.

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Rogge, Christian, Steffen Zinn, Paolo Prosposito, Roberto Francini, and Andreas H. Foitzik. "Transmitted light pH optode for small sample volumes." Journal of Sensors and Sensor Systems 6, no. 2 (October 16, 2017): 351–59. http://dx.doi.org/10.5194/jsss-6-351-2017.

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Abstract. An innovative concept of a low-cost pH optode with working volumes of less than 150 µL is presented. The pH monitoring is based on the color changing effect of pH indicators. The optode includes an RGB color sensor patch TCS34725 from Adafruit, a controllable LED and reactor slides and is addressed by a self-written LabVIEW© software. Utilizing the hue value of the HSV color model, it is possible to analyze the color change of the indicator and estimate the pH value of the analyzed samples by exploiting sigmoidal fit models. Measurements carried out with phenol red and DMEM (Dulbecco's Modified Eagle's Medium) reported a standard error of calibration in the physiologic pH range (6.5–7.5) of ±0.04 pH units.

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Du, Jing, Yun Yang Yan, Xi Yin Wu, and Yian Liu. "Analysis on the Static Features of Flame Images." Advanced Materials Research 765-767 (September 2013): 2403–6. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2403.

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Fire detection based on images is an effective method for fire prevention, especially in big room or badly environment. It is important to extract the features of a flame image. According to the idea of visual saliency in computer vision, saliency model of brightness, color and flame texture are proposed here. The saliency of flame brightness is indicated by the V component in HSV color space. The saliency of flame color is expressed by the relation of R and B in the RGB color space. The saliency of flame texture is described by the distance between the feature vectors which are the combination of features with Local Binary Pattern. Experimental results show the saliency model is effective for flame feature extraction.

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Lee, Donggil, Seonghun Kim, Pyungkwan Kim, and Yongsu Yang. "Automatic sea squirt sorting algorithm based on the HSV color model and weight estimation." Journal of Intelligent & Fuzzy Systems 35, no. 2 (August 26, 2018): 1511–18. http://dx.doi.org/10.3233/jifs-169691.

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Qiao, Honghai, Zhenghong Deng, Jing Xue, and Qun Song. "Research of Image Retrieval Method Based on Improved Feature." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 4 (August 2018): 742–47. http://dx.doi.org/10.1051/jnwpu/20183640742.

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In the process of image retrieval, the traditional single feature can't reflect the distribution and details of image color and content, which have some adverse influence on the performance of image retrieval. This paper presents an image retrieval method based on improved color and texture feature. According to the mean of the HSV color model region, algorithm obtains the mean eigenvectors of the color feature by using the improved correlation weight model. The image decomposition transformation is obtained through the Haar wavelet. In the low-frequency component of the image decomposition, the low-frequency texture feature vector is obtained according to the low-frequency feature structure model. The similarity of image is calculated by the Canberra distance. Experimental results show that:the methods of retrieval are tested in Corel-1000 and Corel-5000 standard gallery, which accuracy rate and retrieval rate have been improved accordingly.

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Journal articles on the topic 'HSV color model'

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