Novel visual object descriptor using SURF and clustering algorithms

In this paper we propose a method for object description based on two wellknown clustering algorithms (k-means and mean shift) and the SURF method for keypoints detection. We also perform a comparison of these clustering methods in object description area. Both of these algorithms require one input parameter; k-means (k, number of objects) and mean shift (h, window). Our approach is suitable for images with a non-homogeneous background thus, the algorithm can be used not only on trivial images. In the future we will try to remove non-important keypoints detected by the SURF algorithm. Our method is a part of a larger CBIR system and it is used as a preprocessing stage

Active Control of Camera Parameters and Algorithm Selection for Object Detection

In this thesis, we quantitatively investigate the effect of camera parameters, shutter speed and voltage gain, on the performance of several popular object detection algorithms, under various illumination conditions. Our experimental results indicate a significant difference in sensitivity of the evaluated algorithms to these camera parameters. Based on the experimental benchmark results, a novel active control of camera parameters method and an algorithm selection extension are proposed. In empirical evaluation, our active control approach outperforms the conventional auto-exposure method for most algorithms. Also, the proposed algorithm selection extension has demonstrated the capability of selecting a proper tuple, in order to deal with varying light conditions