Th. doct-ing.--Grenoble, I.N.P.G., 1979. N°: DI 79.

Th doct.-ing.--Grenoble, I.N.P.G., 1979. N°: DI 136. / Extr. en partie des Annales des télécommunications, 35, 1979, 3-4.

Th. doct.-ing.--Grenoble, I.N.P.G., 1979. N°: DI 121.

Contrast enhancement techniques are used widely to improve the visual quality of images. The difference in luminance reflected from two adjacent surfaces creates contrast between the surfaces in the image. The greater the contrast, the easier it is to recognize and differentiate objects in an image. Thus object contrast is an important factor in the perception of the visual quality of an image and in its usefulness for object recognition and image analysis applications. The focus of this thesis is on studying how well different contrast enhancement techniques developed for visible spectrum photographic images work on infrared images; to determine which techniques might be best suited for incorporation into commercial infrared imaging applications. The database for this thesis consists of night vision infrared images taken using a Photon 320 camera by FLIR Systems, Inc. Numerous contrast enhancement techniques found in literature were reviewed in this project, out of which four (4) representative techniques have been selected and presented in detail. Homomorphic filtering, fuzzy-logic enhancement, and single-scale retinex techniques have been implemented based on published papers. These were each compared to the classical technique of histogram equalization using metrics of computational time, histogram standard deviation, sharpness and user observations. These metrics provide both quantitative and qualitative analyses of the implemented techniques which are relevant to the end user applications of infrared imaging. Based on the metric calculations and results, homomorphic filtering and histogram equalization proved to have better results compared to the other techniques. All the implemented techniques are global contrast enhancement methods and for future work local contrast enhancement techniques may be applied to the results obtained in this investigation as a post-processing technique.

Includes bibliographies. / Sonar beamforming is usually accomplished using a multi-element transducer array. To obtain high resolution, such a system is costly and complex. In contrast, many mammals are capable of good angular resolution using only a single active element surrounded by an irregular reflector – the ear. A study of monaural localisation was therefore undertaken, with a view to the development of a novel beamforming system which uses only a single active element. Computer simulations have shown that the direction of a source can be determined by cross –correlating the output signal spectrum with the known spectral responses of the receiving system for all angles.

Forest fires constantly threaten ecological systems, infrastructure and human lives. The purpose behind this study is minimizing the devastating damage caused by forest fires. Since it is impossible to completely avoid their occurrences, it is essential to accomplish a fast and appropriate intervention to minimize their destructive consequences. The most traditional method for detecting forest fires is human based surveillance through lookout towers. However, this study presents a more modern technique. It utilizes land-based real-time multispectral video processing to identify and determine the possibility of fire occurring within the camera’s field of view. The temporal, spectral, and spatial signatures of the fire are exploited. The methods discussed include: (1) Range filtering followed by entropy filtering of the infrared (IR) video data, and (2) Principal Component Analysis of visible spectrum video data followed by motion analysis and adaptive intensity threshold. The two schemes presented are tailored to detect the fire core, and the smoke plume, respectively. Cooled Midwave Infrared (IR) camera is used to capture the heat distribution within the field of view. The fire core is then isolated using texture analysis techniques: first, range filtering applied on two consecutive IR frames, and then followed by entropy filtering of their absolute difference. Since smoke represents the earliest sign of fire, this study also explores multiple techniques for detecting smoke plumes in a given scene. The spatial and temporal variance of smoke plume is captured using temporal Principal Component Analysis, PCA. The results show that a smoke plume is readily segmented via PCA applied on the visible Blue band over 2 seconds sampled every 0.2 seconds. The smoke plume exists in the 2nd principal component, and is finally identified, segmented, and isolated, using either motion analysis or adaptive intensity threshold. Experimental results, obtained in this study, show that the proposed system can detect smoke effectively at a distance of approximately 832 meters with a low false-alarm rate and short reaction time. Applied, such system would achieve early forest fire detection minimizing fire damage. Keywords: Image Processing, Principal Component Analysis, PCA, Principal Component, PC, Texture Analysis, Motion Analysis, Multispectral, Visible, Cooled Midwave Infrared, Smoke Signature, Gaussian Mixture Model.

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Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xv, 162 p.: ill. (some col.). Includes abstract and vita. Advisor: Randolph L. Moses, Dept. of Electrical Engineering. Includes bibliographical references (p. 156-162).

Thesis (Ph.D.)-- University of Adelaide, Dept. of Electrical and Electronic Engineering, 2000. / Bibliography: p. 235-243.

Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 122-126). Available also in a digital version from Dissertation Abstracts.