Representations and transformations of color spaces via quaternions

Tato práce se zabývá využitím kvaternionů pro detekci hran obrazu. Zaměřuje se především na práci s obrazem reprezentovaným v různých barevných prostorech. Nejprve jsou uvedeny základní pojmy a vlastnosti algebry kvaternionů. Dále jsou popsány některé využívané barevné prostory a poté jsou uvedeny základní filtry sloužící pro detekci hran obrazu v odstínech šedi. Následně jsou představeny barevné filtry využívající kvaterniony pro detekci hran obrazu v barevném prostoru RGB. Na závěr se práce zabývá použitím těchto filtrů pro detekci hran obrazu v prostoru HSV.This thesis deals with quaternions for edge detection, particularly on image processing represented in various color spaces. First, the key concepts and the quaternion algebra properties are mentioned, then some of the commonly used color spaces are described and afterwards the thesis dedicates to the basic filters for edge detection in grayscale image. After that there are presented the color filters that use quaternions for color edge detection in RGB color space. Towards the end, these filters are used for color edge detection in HSV color space.

Determining ‘Carabao’ Mango Ripeness Stages Using Three Image Processing Algorithms

Harvested mangoes are commonly classified or sorted manually. This method is tedious, time consuming, inaccurate and prone to errors. Human inspection is also subjective and factors like visual stress and tiredness may arise that can result in the inconsistencies in judgment. The use of a chroma meter is reliable but the equipment is expensive. This study explored the use of three digital image processing algorithms to determine harvested ‘Carabao’ mango ripeness stages. Canny edge detection, Sobel edge detection, and Laplacian of Gaussian detection algorithms were used to extract a mango image from its original image. The mean red, green, and blue (RGB) values of the detected images were converted to L*a*b* color values which were used to identify the ripeness level of the mango image based on the standard generated from gathered data of ‘Carabao’ mangoes. The standard generated was also based on the mango peel color index scale from University of the Philippines Los Baños Postharvest Horticulture Training and Research Center (PHTRC). The algorithms’ performance had an overall accuracy of 80.5% for canny edge detection algorithm and L*a*b* color extraction using neural networks; 63.88% for Sobel edge detection algorithm and L*a*b* color extraction using rgb2lab function in MATLAB software; and 17.33% Laplacian of Gaussian detection and L*a*b* color extraction using OpenCV. Overall, the implementation of Canny edge detection algorithm for image processing and L*a*b* color extraction using neural networks performed best among the algorithms used in classifying ‘Carabao’ mango ripeness stages. To improve the performances of the algorithms, it is recommended to improve the quality of the sample images by controlling the light, exposure, and camera to be used, matching it with more chroma meter sample points on the ‘Carabao’ mango to attain a better color average of the sample mango. Keywords: ‘Carabao’ mango · image processing · L*a*b* color values · neural network

Color and Shape Recognition

The object "car" and "cat" can be easily distinguished by humans, but how these labels are assigned? Grouping these images is easy for a person into different categories, but its very tedious for a computer. Hence, an object recognition system finds objects in the real world from an image. Object recognition algorithms rely on matching, learning or pattern recognition algorithms using appearance-based or feature-based techniques. In this thesis, the use of color and shape attributes as an explicit color and shape representation respectively for object detection is proposed. Color attributes are dense, computationally effective, and when joined with old-fashioned shape features provide pleasing results for object detection. The procedure of shape detection is actually a natural extension of the job of edge detection at the pixel level to the difficulty of global contour detection. A tool for a systematic analysis of edge based shape detection is provided by this filtering scheme. This enables us to find distinctions between objects based on color and shape

Composite Feature-Based Face Detection Using Skin Color Modeling and SVM Classification

This report proposes a face detection algorithm based on skin color modeling and support vector machine (SVM) classification. Said classification is based on various face features used to detect specific faces in an input color image. A YCbCr color space is used to filter the skin color pixels from the input color image. Template matching is used on the result with various window sizes of the template created from an ORL face database. The candidates obtained above, are then classified by SVM classifiers using the histogram of oriented gradients, eigen features, edge ratio, and edge statistics features

Color Recognition in Challenging Lighting Environments: CNN Approach

Light plays a vital role in vision either human or machine vision, the perceived color is always based on the lighting conditions of the surroundings. Researchers are working to enhance the color detection techniques for the application of computer vision. They have implemented proposed several methods using different color detection approaches but still, there is a gap that can be filled. To address this issue, a color detection method, which is based on a Convolutional Neural Network (CNN), is proposed. Firstly, image segmentation is performed using the edge detection segmentation technique to specify the object and then the segmented object is fed to the Convolutional Neural Network trained to detect the color of an object in different lighting conditions. It is experimentally verified that our method can substantially enhance the robustness of color detection in different lighting conditions, and our method performed better results than existing methods

Tensor voting for robust color edge detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-94-007-7584-8_9This chapter proposes two robust color edge detection methods based on tensor voting. The first method is a direct adaptation of the classical tensor voting to color images where tensors are initialized with either the gradient or the local color structure tensor. The second method is based on an extension of tensor voting in which the encoding and voting processes are specifically tailored to robust edge detection in color images. In this case, three tensors are used to encode local CIELAB color channels and edginess, while the voting process propagates both color and edginess by applying perception-based rules. Unlike the classical tensor voting, the second method considers the context in the voting process. Recall, discriminability, precision, false alarm rejection and robustness measurements with respect to three different ground-truths have been used to compare the proposed methods with the state-of-the-art. Experimental results show that the proposed methods are competitive, especially in robustness. Moreover, these experiments evidence the difficulty of proposing an edge detector with a perfect performance with respect to all features and fields of application.This research has been supported by the Swedish Research Council under the project VR 2012-3512