The performance of the tracking algorithm relies heavily on the target structural information accuracy. In this paper, we propose a robust object tracking method based on the log-Gabor texture and color histogram. Our hypothesis is that by adding log-Gabor filter to color features, and then embedded it in the mean shift framework, tracking performances will notably enhance. Compared with several methods of state-of-the-art mean shift trackers, our approach extracts the target information efficiently. Experimental results on various challenging videos show that the proposed method improves the tracking with fewer mean shift iterations. 2 CONVENTIONAL MEAN SHIFT ALGORITHM 2.1 Target Representation In the mean shift algorithm, the target model is

In this article, the handwriting signals are represented based on geometric and spatio-temporal characteristics to increase the feature vectors relevance of each object. The main goal was to extract features in the form of a numeric vector based on the extraction of frequent patterns. We used two types of frequent motifs (closed frequent patterns and maximal frequent patterns) that can represent handwritten characters pertinently. These common features patterns are generated from a raw data transformation method to achieve high relevance. A database of words consisting of two different letters was created. The proposed application gives promising results and highlights the advantages that frequent pattern extraction algorithms can achieve, as well as the central role played by the “minimum threshold” parameter in the overall description of the characters.

No abstract