Showing papers by "Mandeep Kaur published in 2011"

Comparative Study of Facial Expression Recognition Techniques

Abstract: This paper explores and compares techniques for automatically recognizing facial actions in sequences of images.The comparative study of Facial Expression Recognition techniques namely Principal Component analysis (PCA), PCA with SVD (Singular Value Decomposition) is done .The objective of this research is to show that PCA with SVD is superior to former technique in terms of recognition rate .To test and evaluate their performance, experiments are performed using JAFEE and real database using both techniques. The universally accepted five principal emotions to be recognized are: Angry, Happy, Sad, Disgust and Surprise along with neutral. The recognition rate is obtained on all the facial expressions.

Performance Evaluation of ROI-based Image Compression Techniques

Abstract: Medical diagnostic data produced by hospitals has increased exponentially. The coming era of digitized medical information and film-less imaging, has made it a challenge to deal with the storage and transmission requirement of enormous data. With this, selective medical image compression, a technique where explicitly defined regions of interest are compressed in a lossless way whereas image regions containing unimportant information are compressed in a lossy manner are in demand, day by day. Such techniques are of great interest in telemedicine which is a rapidly developing application of clinical medicine, where medical information is transferred through interactive audiovisual media. Archiving and retaining these data for at least more than two years is expensive, difficult and requires sophisticated data compression techniques. In the current research work, the focus has been solely on the performance evaluation on the ROI-based compression of medical images, but in a different prospective. The Mammogram images are used for the study. The image is divided into regions; ROI and the background. Then the arbitrary shape ROI breast region is compressed losslessly using losses image compression algorithms like SPIHT, JPEG2000 and Adaptive SPIHT. The background can be discarded or compressed as user's will. The work also introduces an ROI medical image compression technique that is able to assign priorities in case of multiple ROIs. Experimental results show that the proposed method offer potential advantages like extraction and integration of arbitrary shaped ROI, energy efficiency, ROI priority etc. in medical applications of digital mammography applications.