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Application of color-based segmentation Approach to Detect Berlin's Edema

Procedia Technology 2012年 4卷 600-603页

作者： Sarbani Datta Monisha Chakraborty Student School of Bio-Science and Engineering Jadavpur University 188 Raja S. C. Mallik Road Kolkata India Assistant Professor School of Bio-Science and Engineering Jadavpur University 188 Raja S. C. Mallik Road Kolkata India

Berlin's edema is an initial condition to macular hole. This may progress to blindness as well, especially during acute traumatic maculopathy. In this paper an attempt has been made to locate Berlin's edema in a patient with acute traumatic maculopathy. Initially the fundus image has been pre-processed to sharpen the image. The processed image is then been used to locate the central macula for the diagnosis using color-based segmentation approach. This method is to help the ophthalmologists to detect any anatomical changes to the macula in patients with commotion ratinae or Berlin's edema directly from fundus camera images, rather than going for fluorescein angiography. The processing algorithms are developed on MATLAB 7.6.0 (R2008a) programming platform.

关键词： Berlin's edema fundus image pre-process color-based segmentation

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TEXTURE based color segmentation FOR INFRARED RIVER ICE IMAGES USING K-MEANS CLUSTERING

TEXTURE BASED COLOR SEGMENTATION FOR INFRARED RIVER ICE IMAG...

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International Conference on Signal Processing, Image Processing and Pattern Recognition (ICSIPR)

作者： Bharathi, P. T. Subashini, P. Avinashilingam Inst Home Sci & Higher Educ Women Dept Comp Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9781467348621;9781467348614

The problem of texture segmentation involves subdividing an image into differently textured regions. Gabor filters produce outputs which are notably distinct for the different textured regions. Detecting the discontinuity in the filters output and their statistical properties help in segmenting and classifying a given image with different texture regions. Feature calculation for every pixel in the image reduces the computational cost for color based segmentation. In this proposed method, firstly image texture segmentation is performed by using Gabor filter and the result obtained from Gabor filter differentiates the textures with a variety of colors as different textures, and these similar colors are extracted as different images by color-based segmentation with K-means clustering and finally the features are extracted by using first and second order statistical methods. The features extracted from first and second order statistical methods are given to PNN classifier. Using this methodology, it is found that texture and color segmentation followed by gray level co-occurrence matrix feature extraction method gives higher accuracy rate of 95.5% when compared with other feature extraction methods.

关键词： Texture segmentation color-based segmentation Gabor filter Feature extraction methods PNN classifier

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color subspaces as photometric invariants

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2008年第1期79卷 13-30页

作者： Zickler, Todd Mallick, Satya P. Kriegman, David J. Belhumeur, Peter N. Harvard Univ Cambridge MA 02138 USA Univ Calif San Diego San Diego CA 92103 USA Columbia Univ New York NY 10027 USA

Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image 'features' that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe a class of such invariants that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from illuminant-dependent 'subspaces' of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, photometric stereo, material-based segmentation, and motion estimation.

关键词： photometric invariants shape invariants color spaces dichromatic reflection multispectral imaging surface reconstruction photometric stereo shape from shading stereo color-based segmentation color-based optical flow

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Detection of urban features by multilevel classification of multispectral airborne LiDAR data fused with very high-resolution images

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JOURNAL OF APPLIED REMOTE SENSING 2021年第4期15卷

作者： Megahed, Yasmine Yan, Wai Yeung Shaker, Ahmed Ryerson Univ Fac Engn & Architectural Sci Dept Civil Engn Toronto ON Canada Hong Kong Polytech Univ Fac Construct & Environm Dept Land Surveying & Geoinformat Hung HomKowloon Hong Kong Peoples R China

A complex pattern of urban demographic transition has been taking shape since the onset of the COVID-19 pandemic. The long-standing rural-to-urban route of population migration that has propelled waves of massive urbanization over the decades is increasingly being juxtaposed with a reverse movement, as the pandemic drives urban dwellers to suburban communities. The changing dynamics of the flow of residents to and from urban areas underscore the necessity of comprehensive urban land-use mapping for urban planning/management/ assessment. These maps are essential for anticipating the rapidly evolving demands of the urban populace and mitigating the environmental and social consequences of uncontrolled urban expansion. The integration of light detection and ranging (LiDAR) and imagery data provides an opportunity for urban planning projects to take advantage of its complementary geometric and radiometric characteristics, respectively, with a potential increase in urban mapping accuracies. We enhance the color-based segmentation algorithm for object-based classification of multispectral LiDAR point clouds fused with very high-resolution imagery data acquired for a residential urban study area. We propose a multilevel classification using multilayer perceptron neural networks through vectors of geometric and spectral features structured in different classification scenarios. After an investigation of all classification scenarios, the proposed method achieves an overall mapping accuracy exceeding 98%, combining the original and calculated feature vectors and their output space projected by principal components analysis. This combination also eliminates some misclassifications among classes. We used splits of training, validation, and testing subsets and the k-fold cross-validation to quantitatively assess the classification scenarios. The proposed work improves the color-based segmentation algorithm to fit object-based classification applications and examines multiple cla

关键词： aerial images artificial neural networks color-based segmentation data integration multilevel classification multispectral airborne LiDAR principal component analysis spectral-geometric features urban mapping

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color-based road detection in urban traffic scenes

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2004年第4期5卷 309-318页

作者： He, YH Wang, H Zhang, B Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

Road detection is a key issue for autonomous driving in urban traffic. In this paper, after a brief overview of existing methods, we present a road-area detection algorithm based on color images. This algorithm is composed of two modules: boundaries are first estimated based on the intensity image and road areas are subsequently detected based on the full color image. In the first module, an edge image of the scene is analyzed to obtain the candidates for the left and right road borders and to delimit the area that will subsequently be used to compute the mean and variance of the Gaussian distribution, assumed to be obeyed by the color components of road surfaces. The second module effectively extracts the road area and reinforces boundaries that most appropriately fit the road-extraction result. The combination of these modules can overcome basic problems due to inaccuracies in edge detection based on the intensity image alone and due to the computational complexity of segmentation algorithms based on color images. Experimental results on real road scenes have substantiated the effectiveness of the proposed method.

关键词： color-based segmentation intelligent vehicle road detection

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A Novel Technique for Segmenting Platelets by k-Means Clustering 1st

A Novel Technique for Segmenting Platelets by k-Means Cluste...

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1st International Conference on Advances in Computing and Data Sciences (ICACDS)

作者： Roy, Kaushiki Dey, Ratnadeep Bhattacharjee, Debotosh Nasipuri, Mita Ghosh, Pramit Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India RCC Inst Informat Technol Dept Comp Sci & Engn Kolkata 700015 India

ISBN: (纸本)9789811054273;9789811054266

Platelet is a major component of various blood cells present in blood that helps in clotting of blood. Platelet count often becomes a crucial diagnostic parameter to identify several diseases like dengue, yellow fever, etc. The traditional process of counting platelets by examining blood slides under a conventional optical microscope is subjected to human errors due to manual inspection. In addition, the overhead on pathologist increases manifold when huge numbers of blood samples are to be tested. In this work, we have developed an Android-based mobile app, which takes as input the microscopic image of blood smear and gives as output the total platelet count present in the image. This system reduces the dependency on expert pathologists and avoids manual errors. A comparative study between platelet counts obtained from expert lab technicians and the one given by our developed app have shown it to be robust and efficient for automated platelet counting.

关键词： Platelet count color-based segmentation K-means clustering Android app L*a*b color space Telemedicine compatible Dengue detection

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Text Locating in Scene Images for Reading and Navigation Aids for Visually Impaired Persons 10

Text Locating in Scene Images for Reading and Navigation Aid...

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12th International ACM SIGACCESS Conference on Computers and Accessibility

作者： Yi, Chucai CUNY Grad Ctr Dept Comp Sci New York NY 10021 USA

ISBN: (纸本)9781605588810

Many reading assistants and navigation systems have been designed specifically for people who are blind or visually impaired, but text locating in scene image with complex background has not yet been successfully addressed. In this paper, we propose a novel method to locate scene text by combining color uniformity and high edge density together. We perform structural analysis of text strings which contain several characters in alignment. First, we calculate the edge image and then repaint the corresponding edge pixels in the original image by using a non-dominant color. Second, color reduction is performed by color histogram and K-means algorithms to segment the repainted image into color layers. Third, we perform edge detection and label the boundaries of both text characters and unexpected noises in each color layer. Each centroid is assigned a degree which is the number of overlap in the same position among color layers. Fourth, text line fitting among centroids with high degree is performed to cascade the character boundaries which belong to the same text string. The detected text string is presented by a rectangle region covering all character boundaries in its text line. Experimental results demonstrate that our algorithm is able to locate text strings with arbitrary orientations. The performance of our algorithm is comparable with the state-of-art algorithms.

关键词： Visual impairment Navigation Reading assistant Text Locating color-based segmentation Boundary Centroid Text Line Fitting

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Evaluation of color Spaces for Robust Image segmentation

Evaluation of Color Spaces for Robust Image Segmentation

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9th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Jungmann, Alexander Jatzkowski, Jan Kleinjohann, Bernd Univ Paderborn Cooperat Comp & Commun Lab Fuerstenallee 11 Paderbom Germany

ISBN: (纸本)9789897581335

In this paper, we evaluate the robustness of our color-based segmentation approach in combination with different color spaces, namely ROB, L*a*b*, HSV, and log-chromaticity (LCCS). For this purpose, we describe our deterministic segmentation algorithm including its gradually transformation of pixel-precise image data into a less error-prone and therefore more robust statistical representation in terms of moments. To investigate the robustness of a specific segmentation setting, we introduce our evaluation framework that directly works on the statistical representation. It is based on two ditlerent types of robustness measures, namely relative and absolute robustness. While relative robustness measures stability of segmentation results over time, absolute robustness measures stability regarding varying illumination by comparing results with ground truth data. The significance of these robustness measures is shown by evaluating our segmentation approach with different color spaces. For the evaluation process, an artificial scene was chosen as representative for application scenarios based on artificial landmarks.

关键词： Image Processing color-based segmentation color Spaces Evaluation of segmentation Results

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color-based road detection in urban traffic scenes

Color-based road detection in urban traffic scenes

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7th IEEE International Conference on Intelligent Transportation Systems

作者： He, YH Wang, H Zhang, B Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

关键词： color-based segmentation intelligent vehicle road detection

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A New color-based Lane Detection via Gaussian Radial Basis Function Networks

A New Color-Based Lane Detection via Gaussian Radial Basis F...

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1st International Conference on Connected Vehicles and Expo

作者： Chanawangsa, Panya Chen, Chang Wen SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

ISBN: (纸本)9780769549002;9781467347051

Lane detection plays a central role in intelligent transportation systems. While edge detection on intensity images has gained much popularity in the past, it usually results in noisy binary images. Most noticeably, the color information of the scene that may provide an important cue for lane detection has not been genuinely considered. In this paper, we propose a novel color-based lane detection system. Although color-based schemes have their fair share of issues, including varying illumination conditions, by relying on a lane mark color predictor obtained from an offline supervised training of Gaussian radial basis function (GRBF) networks, such issues can be appropriately overcome. Experimental results have demonstrated that the proposed approach, in contrast to predominantly edge-based approaches, can effectively eliminate erroneous edges that do not belong to the lane marks in well-structured scenes.

关键词： lane detection intelligent transportation system color-based segmentation radial basis function networks

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