This paper presents a novel approach for bird species classification based on color features extracted from unconstrained images. This means that the birds may appear in different scenarios as well may present different poses, sizes and angles of view. Besides, the images present strong variations in illuminations and parts of the birds may be occluded by other elements of the scenario. The proposed approach first applies a color segmentation algorithm in an attempt to eliminate background elements and to delimit candidate regions where the bird may be present within the image. Next, the image is split into component planes and from each plane, normalized color histograms are computed from these candidate regions. After aggregation processing is employed to reduce the number of the intervals of the histograms to a fixed number of bins. The histogram bins are used as feature vectors to by a learning algorithm to try to distinguish between the different numbers of bird species. Experimental results on the CUB-200 dataset show that the segmentation algorithm achieves 75% of correct segmentation rate. Furthermore, the bird species classification rate varies between 90% and 8%, depending on the number of classes taken into account.
-In this paper, we introduce a new hue-preserving histogram equalization method based on the RGB color space for image enhancement. We use R-red, G-green, and B-blue 1D histograms to estimate the histogram to be equalized using a Naive Bayes rule. The histogram equalization is performed by shift hue-preserving transformations. Our method has linear time and space complexities, which complies with realtime applications requirements. A subjective assessment comparing our method and other three is performed. Experiments showed that our method is more robust than the others in the sense that neither unrealistic colors nor over-enhancement are produced.
Audio-visual Speech Recognition has been an active area of research lately. A bit, and yet unsolved, part of this problem is the visual only recognition, or lip reading. Considering an image sequence of a person pronouncing a word, a full image analysis solution would have to segment the mouth area, extract relevant features, and use them to be able to classify the word from those visual features. In this paper we approach this problem by proposing a segmentation technique for the lips contours together with a set of features based on the extracted contours which is able to perform lip reading with promising results. We have collected visual speech sequences in our lab and show the results here for a set of ten words in Brazilian Portuguese, spoken by different speakers in more than 150 samples. The approach can be extended and applied to other spoken languages as well.
A new approach to extract the lesion region of melanoma is presented. This approach is based on color morphological operators which are defined from a lexicographic order on the HSI color space. The morphological filtering allows highlighting the region of melanoma that is then segmented by binarization. No a priori knowledge about the process of image acquisition and the type of melanoma is employed and a few heuristics are used. Tests were performed for two sets of benign and malignant melanoma images and compared with the ground-truth lesion segmentation by applying twelve metrics. The results prove the efficiency of this approach with regard to the automatic segmentation of both benign and malignant melanoma.
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