Video Deinterlacing using Control Grid Interpolation Frameworks

abstract: Video deinterlacing is a key technique in digital video processing, particularly with the widespread usage of LCD and plasma TVs. This thesis proposes a novel spatio-temporal, non-linear video deinterlacing technique that adaptively chooses between the results from one dimensional control grid interpolation (1DCGI), vertical temporal filter (VTF) and temporal line averaging (LA). The proposed method performs better than several popular benchmarking methods in terms of both visual quality and peak signal to noise ratio (PSNR). The algorithm performs better than existing approaches like edge-based line averaging (ELA) and spatio-temporal edge-based median filtering (STELA) on fine moving edges and semi-static regions of videos, which are recognized as particularly challenging deinterlacing cases. The proposed approach also performs better than the state-of-the-art content adaptive vertical temporal filtering (CAVTF) approach. Along with the main approach several spin-off approaches are also proposed each with its own characteristics.Dissertation/ThesisM.S. Electrical Engineering 201

Guidelines for the study of the epibenthos of subtidal environments

These Guidelines for the Study of the Epibenthos of Subtidal Environments document a range of sampling gears and procedures for epibenthos studies that meet a variety of needs. The importance of adopting consistent sampling and analytical practices is highlighted. Emphasis is placed on ship‐based techniques for surveys of coastal and offshore shelf environments, but diver‐assisted surveys are also considered

Application of deinterlacing for the enhancement of surveillance video

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 91-93).As the cost of video technology has fallen, surveillance cameras have become an integral part of a vast number of security systems. However, even with the introduction of progressive video displays, the majority of these systems still use interlaced scanning so that they may be connected to standard television monitors. When law enforcement officials analyze surveillance video, they are often interested in carefully examining a few frames of interest. However, it is impossible to perform frame-by-frame analysis of interlaced surveillance video without performing interlaced to progressive conversion, also known as deinterlacing. In most surveillance systems, very basic techniques are used for deinterlacing, resulting in a number of severe visual artifacts and greatly limiting the intelligibility of surveillance video. This thesis investigates fourteen deinterlacing algorithms to determine methods that will improve the quality and intelligibility of video sequences acquired by surveillance systems. The advantages and disadvantages of each algorithm are discussed followed by both qualitative and quantitative comparisons. Motion adaptive deinterlacing methods are shown to have the most potential for surveillance video, demonstrating the highest performance both visually and in terms of peak signal-to-noise ratio.by Brian A. HEng.S.M

Spherical mosaic construction using physical analogy for consistent image alignment

The research contained in this thesis is an investigation into mosaic construction. Mosaic techniques are used to obtain images with a large field of view by assembling a sequence of smaller individual overlapping images. In existing methods of mosaic construction only successive images are aligned. Accumulation of small alignment errors occur, and in the case of the image path returning to a previous position in the mosaic, a significant mismatch between nonconsecutive images will result (looping path problem). A new method for consistently aligning all the images in a mosaic is proposed in this thesis. This is achieved by distribution of the small alignment errors. Each image is allowed to modify its position relative to its neighbour images in the mosaic by a small amount with respect to the computed registration. Two images recorded by a rotating ideal camera are related by the same transformation that relates the camera's sensor plane at the time the images were captured. When two images overlap, the intensity values in both images coincide through the intersection line of the sensor planes. This intersection line has the property that the images can be seamlessly joined through that line. An analogy between the images and the physical world is proposed to solve the looping path problem. The images correspond to rigid objects, and these are linked with forces which pull them towards the right positions with respect to their neighbours. That is, every pair of overlapping images are "hinged" through their corresponding intersection line. Aided by another constraint named the spherical constraint, this network of selforganising images has the ability of distributing itself on the surface of a sphere. As a direct result of the new concepts developed in this research work, spherical mosaics (i.e. mosaics with unlimited horizontal and vertical field of view) can be created

Direct occlusion handling for high level image processing algorithms

Many high-level computer vision algorithms suffer in the presence of occlusions caused by multiple objects overlapping in a view. Occlusions remove the direct correspondence between visible areas of objects and the objects themselves by introducing ambiguity in the interpretation of the shape of the occluded object. Ignoring this ambiguity allows the perceived geometry of overlapping objects to be deformed or even fractured. Supplementing the raw image data with a vectorized structural representation which predicts object completions could stabilize high-level algorithms which currently disregard occlusions. Studies in the neuroscience community indicate that the feature points located at the intersection of junctions may be used by the human visual system to produce these completions. Geiger, Pao, and Rubin have successfully used these features in a purely rasterized setting to complete objects in a fashion similar to what is demonstrated by human perception. This work proposes using these features in a vectorized approach to solving the mid-level computer vision problem of object stitching. A system has been implemented which is able extract L and T-junctions directly from the edges of an image using scale-space and robust statistical techniques. The system is sensitive enough to be able to isolate the corners on polygons with 24 sides or more, provided sufficient image resolution is available. Areas of promising development have been identified and several directions for further research are proposed