Image registration is a classic problem of computer vision with several applications across areas like defence, remote sensing, medicine etc. Feature based image registration methods traditionally used hand-crafted feature extraction algorithms, which detect key points in an image and describe them using a region around the point. Such features are matched using a threshold either on distances or ratio of distances computed between the feature descriptors. Evolution of deep learning, in particular convolution neural networks, has enabled researchers to address several problems of vision such as recognition, tracking, localization etc. Outputs of convolution layers or fully connected layers of CNN which has been trained for applications like visual recognition are proved to be effective when used as features in other applications such as retrieval. In this work, a deep CNN, AlexNet, is used in the place of handcrafted features for feature extraction in the first stage of image registration. However, there is a need to identify a suitable distance measure and a matching method for effective results. Several distance metrics have been evaluated in the framework of nearest neighbour and nearest neighbour ratio matching methods using benchmark dataset. Evaluation is done by comparing matching and registration performance using metrics computed from ground truth.

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Advances in imaging and computing hardware have led to an explosion in the use of color images in image processing, graphics and computer vision applications across various domains such as medical imaging, satellite imagery, document analysis and biometrics to name a few. However, these images are subjected to a wide variety of distortions during its acquisition, subsequent compression, transmission, processing and then reproduction, which degrade their visual quality. Hence objective quality assessment of color images has emerged as one of the essential operations in image processing. During the last two decades, efforts have been put to design such an image quality metric which can be calculated simply but can accurately reflect subjective quality of human perception. In this paper, the authors evaluated the quality assessment of color images using SSIM (structural similarity index) metric across various color spaces. They experimented to study the effect of color spaces in metric based and distance based quality assessment. The authors proposed a metric using CIE Lab color space and SSIM, which has better correlation to the subjective assessment in a benchmark dataset.

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Motor execution induces variation in the amplitude of the ongoing electrical activity of the brain or the electroencephalogram(EEG) of the brain.The relative power decrease in sensorimotor rhythms,mu rhythm(8-13 Hz) and beta rhythm(14-30 Hz),of EEG is referred to as Event related desychronization(ERD)and increase in their relative power is referred to as Event related synchronization(ERS). In this study, we examine mu and beta ERD/ERS during attempted foot dorsiflexion movement of stroke affected foot drop subjects and normal subjects. Single channel EEG at Cz location according to 10-20 electrode system was recorded for brief period of 6 minutes for ten stroke and ten healthy subjects. Maximum amplitude and latency of the ERD/ERS was computed for each of the group. This work addresses the question whether stroke subjects are able to produce similar ERD and ERS electrophysiological signal components as compared to the healthy subjects, which can be used as a reliable signal component for EEG based brain computer (BCI) system design for neurorehabilitation of the subjects.