Advanced driver assistance systems (ADAS) have been developed to automate and modify vehicles for safety and better driving experience. Among all computer vision modules in ADAS, 360-degree surround view generation of immediate surroundings of the vehicle is very important, due to application in on-road traffic assistance, parking assistance etc. This paper presents a novel algorithm for fast and computationally efficient transformation of input fisheye images into required top down view. This paper also presents a generalized framework for generating top down view of images captured by cameras with fish-eye lenses mounted on vehicles, irrespective of pitch or tilt angle. The proposed approach comprises of two major steps, viz. correcting the fish-eye lens images to rectilinear images, and generating top-view perspective of the corrected images. The images captured by the fish-eye lens possess barrel distortion, for which a nonlinear and non-iterative method is used. Thereafter, homography is used to obtain top-down view of corrected images. This paper also targets to develop surroundings of the vehicle for wider distortion less field of view and camera perspective independent top down view, with minimum computation cost which is essential due to limited computation power on vehicles.

Medical image compression applications are quality-driven applications which demand high quality for certain regions that have diagnostic importance in an image, where even small quality reduction introduced by lossy coding might alter subsequent diagnosis, which might cause severe legal consequences. Due to this, lossless techniques have been extensively used. As an alternative, owing to the observation that only some part of the image actually is of interest to the practitioners, ROI-based techniques are becoming popular. This paper proposes four techniques for this purpose. The four techniques are based on Mixed Raster Content layering, block-based thresholding, region growing and active contour algorithms. All the four techniques are enhanced and have the common objective of determining a ROI that can improve the compression process. Experiments results prove that all the four algorithms are efficient in determining the ROI and are efficient in terms of segmentation, compression and speed.

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