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This paper compares the performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven an effective technique for restoration, and has recently been extended to classification. The main issue with histology sections classification is inherent heterogeneity, which is a result of technical and biological...
Analysis of fertile material such as flowers and fruit is a key factor in the proper identification of plant species. Despite object recognition being a mature research area, the use of it in automated plant identification is still relatively new. This paper describes a novel method of detecting fertile material in plant images using rectangular features. Rectangular features are obtained for the...
Traffic Signs provide drivers with very valuable information about the road, in order to make driving safer and easier. They are designed to be easily recognized by human drivers mainly because their color and shape are very different from natural environments. Automatic traffic sign detection and recognition is important in the development of unmanned vehicles, and is expected to provide information...
A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is...
Segmenting images into distinct material types is a very useful capability. Most work in image segmentation addresses the case where only a single image is available. Some methods improve on this by collecting HDR or multispectral images. However, it is also possible to use the reflectance properties of the materials to obtain better results. By acquiring many images of an object under different lighting...
In the robot vision system of the apple harvesting robot, the key is to recognize and locate the apple. To solve recognition questions such as high error rate, too much calculation and time consuming, a new recognizing method, support vector machine (SVM) is applied to improve recognition accuracy and efficiency. At first, vector median filter is used to remove the color images noise of apple fruit...
In this paper, we propose a novel static hand gesture recognition method, which is based on a new support vector machine (abbreviated as SVM) classifier. SVM is a classification method based on statistics theory. Typical SVMs can be sufficient to deal with small scale data, but these methods cause a lot of computation in quadratic programming while dealing with non-linear problems. SVM combined with...
MPEG-7 standard describes visual descriptors and performance metrics for image classification. Amongst all image features, color and texture are more visually expressive and hence are attractive for visual descriptors. Further, combination of features makes image classification more relevant and robust. This paper proposes efficient methods for image classification using 3 MPEG-7 descriptors to represent...
We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower dataset. We compute four different features for the flowers, each describing different aspects, namely the local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour. We combine...
A computer aided diagnosis (CADx) system for oral mucosal lesions has been developed using clinical cases from India as training examples. The investigated classifiers were support vector machine (SVM) and Bayes point machine (BPM), and the task was to discriminate potentially precancerous lesions from non-precancerous lesions. The discriminating features consisted of color differences and lesionspsila...
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