Field programmable Gate Array based Real Time Object Tracking using Partial Least Square Analysis

In this paper, we proposed an object tracking algorithm in real time implementation of moving object tracking system using Field programmable gate array (FPGA). Object tracking is considered as a binary classification problem and one of the approaches to this problem is that to extract appropriate features from the appearance of the object based on partial least square (PLS) analysis method, which is a low dimension reduction technique in the subspace. In this method, the adaptive appearance model integrated with PLS analysis is used for continuous update of the appearance change of the target over time. For robust and efficient tracking, particle filtering is used in between every two consecutive frames of the video. This has implemented using Cadence and Virtuoso software integrated environment with MATLAB. The experimental results are performed on challenging video sequences to show the performance of the proposed tracking algorithm using FPGA in real time

Image Enhancement and Segmentation Techniques for Detection of Knee Joint Diseases: A Survey

Knee bone diseases are rare but might be highly destructive. Magnetic resonance imaging (MRI) is the main approach to identify knee cancer and its treatment. Normally, the knee cancers are pointed out with the help of different MRI analysis techniques and latter image analysis strategies understand these images. Computer-based medical image analysis is getting researcher's interest due to its advantages of speed and accuracy as compared to traditional techniques. The focus of current research is MRI-based medical image analysis for knee bone disease detection. Accordingly, several approaches for features extraction and segmentation for knee bone cancer are analyzed and compared on benchmark database. Finally, the current state of the art is investigated and future directions are proposed