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Image segmentation seeks to partition the pixels in images into distinct regions to assist other image processing functions such as object recognition. Over the last few years dictionary learning methods have become very popular for image processing tasks such as denoising, and recently structured low rank dictionary learning has been shown to be capable of promising results for recognition tasks...
The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, an occlusion-robust traffic sign recognition method is proposed. To achieve occlusion-robust detection, we design a cascaded tree detector based on the MN-LBP features and a cascaded tree. For occlusion-robust traffic...
Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the...
Automated classification of histopathological images is an important research problem in medical imaging. Digital histopathology exhibits two principally distinct characteristics: 1) invariably histopathological images are multi-channel (color) with key geometric information spread across the color channels instead of being captured by luminance alone, and 2) the richness of geometric structures in...
The Kinect provides an opportunity to collect large quantities of training data for visual learning algorithms relatively effortlessly. To this end we investigate learning to automatically segment humans from cluttered images (without depth information) given a bounding box. For this algorithm, obtaining a large dataset of images with segmented humans is crucial as it enables the possible variations...
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