Computer-aided Diagnosis of Skin Cancer: A Review.

Cancer is currently one of the main health issues in the world. Among different varieties of cancers, skin cancer is the most common cancer in the world and accounts for 75% of the world's cancer. Indeed, skin cancer involves abnormal changes in the outer layer of the skin. Although most people with skin cancer recover, it is one of the major concerns of people due to its high prevalence. Most types of skin cancers grow only locally and invade adjacent tissues, but some of them, especially melanoma (cancer of the pigment cells), which is the rarest type of skin cancer, may spread through the circulatory system or lymphatic system and reach the farthest points of the body. Many papers have been reviewed about the application of image processing in cancer detection. In this paper, the automatic skin cancer detection and also different steps of such a process have been discussed based on the implantation capabilities.

A Study on the Effect of Regularization Matrices in Motion Estimation.

Inverse problems are very frequent in computer vision and machine learning applications. Since noteworthy hints can be obtained from motion data, it is important to seek more robust models. The advantages of using a more general regularization matrix such as Λ=diag{λ1,…,λ K } to robustify motion estimation instead of a single parameter λ (Λ=λ I ) are investigated and formally stated in this paper, for the optical flow problem. Intuitively, this regularization scheme makes sense, but it is not common to encounter high-quality explanations from the engineering point of view. The study is further confirmed by experimental results and compared to the nonregularized Wiener filter approach.