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In this paper we present a novel class of so-called Radon-Like features, which allow for aggregation of spatially distributed image statistics into compact feature descriptors. Radon-Like features, which can be efficiently computed, lend themselves for use with both supervised and unsupervised learning methods. Here we describe various instantiations of these features and demonstrate there usefulness...
Video artificial text detection is a challenging problem of pattern recognition. Current methods which are usually based on edge, texture, connected domain, feature or learning are always limited by size, location, language of artificial text in video. To solve the problems mentioned above, this paper applied SOM (Self-Organizing Map) based on supervised learning to video artificial text detection...
Unconstrained face recognition is the problem of deciding if an image pair is showing the same individual or not, without having class specific training material or knowing anything about the image conditions. In this paper, an approach of learning suited similarity measurements is introduced. For this the image is partitioned into several parts, to extract image region based histograms of gradients,...
To better understand the central nervous system, neurobiologists need to reconstruct the underlying neural circuitry from electron microscopy images. One of the necessary tasks is to segment the individual neurons. For this purpose, we propose a supervised learning approach to detect the cell membranes. The classifier was trained using AdaBoost, on local and context features. The features were selected...
This paper proposes a computational scheme for fuzzy similarity analysis and classification of images that uses first an information granulation procedure followed by a subsequent fuzzy decision procedure. A special new version of the growing unsupervised learning algorithm is introduced in the paper for information granulation. It reduces the original ldquoraw datardquo (the RGB pixels) of the image...
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