Andrew Gleibman

Andrew Gleibman

In this paper, a concept of multipurpose object detection system, recently introduced in our previous work, is clarified. The business aspect of this method is transformation of a classifier into an object detector/locator via an image grid. This is a universal framework for locating objects of interest through classification. The framework standardizes and simplifies implementation of custom systems by doing only a custom analysis of the classification results on the image grid.

Andrew Gleibman

The first contribution of this paper is architecture of a multipurpose system, which delegates a range of object detection tasks to a classifier, applied in special grid positions of the tested image. The second contribution is Gray Level-Radius Co-occurrence Matrix, which describes local image texture and topology and, unlike common second order statistics methods, is robust to image resolution. The third contribution is a parametrically controlled automatic synthesis of unlimited number of numerical features for classification. The fourth contribution is a method of optimizing parameters C and gamma in LibSVM-based classifier, which is 20-100 times faster than the commonly applied method. The work is essentially experimental, with demonstration of various methods for definition of objects of interest in images and video sequences.