An iterative algorithm for color space optimization on image segmentation

This paper proposes, a novel hybrid color component (HCC) issued from amounts number of color space with iterative manner, in fact traditional images obtained by RGB sensor weren’t the effective way in image processing applications, for this purpose we have propose a supervised algorithm to substitute RGB level by hybrid and suitable color space at the aim to make well representation of the handled amounts of data, this step is extremely important because the obtained results it will be injected in many future studies like tracking, classification, steganography and cryptography. The second part of this paper consists to segment image coded in hybrid color space already selected, the used algorithm is inspired from kernel function where statistical distribution was used to model background and Bayes rule to make decision of the membership of each pixel, in this research topics we have extended this algorithm in the aim to improve compactness of these distribution. Cauchy background modeling and subtraction is used, and shows the high accuracy of automatic player detection

Automatic segmentation of white spot lesions on smooth tooth surfaces

Computer technologies are ubiquitous and in the recent times, these technologies are penetrating more into the field of medicine, where they play a vital role in modern healthcare. In this master’s thesis, we are solving a problem, which dentists typically encounter during teeth alignment treatment. At the end of the treatment, when permanent orthodontic braces are removed, the initial phase of tooth demineralization often appears as white spot lesions on the smooth surfaces of a tooth. We developed a prototype for automatic segmentation of teeth and white spot lesions, which may contribute to a more accurate and objective way of treatment monitoring. In the process of development, we used various image processing techniques and image segmentation algorithms. The developed prototype was evaluated against a database, which we built from the selected images of clinical examinations. The prototype showed promising results with a lot of potential for improvements and future wor