Browsing by Subject "Edge detection"

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Open Access
Lossless image compression using an edge adapted lifting predictor
(IEEE, 2005-09) Gerek, Ö. N.; Çetin, A .Enis
We present a novel and computationally simple prediction stage in a Daubechies 5/3 - like lifting structure for lossless image compression. In the 5/3 wavelet, the prediction filter predicts the value of an odd-indexed polyphase component as the mean of its immediate neighbors belonging to the even-indexed polyphase components. The new edge adaptive predictor, however, predicts according to a local gradient direction estimator of the image. As a result, the prediction domain is allowed to flip + or - 45 degrees with respect to the horizontal or vertical axes in regions with diagonal gradient. We have obtained good compression results with conventional lossless wavelet coders. © 2005 IEEE.
Open Access
Simultaneous extraction of geometry and surface properties of targets using simple infrared sensors
(SPIE, 2004) Aytaç, T.; Barshan, B.
We investigate the use of low-cost infrared (IR) sensors for the simultaneous extraction of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and recognition process. We propose the use of angular intensity scans and present an algorithm to process them to determine the geometry and the surface type of the target and estimate its position. The method is verified experimentally with planes, 90-deg corners, and 90-deg edges covered with aluminum, white cloth, and Styrofoam packaging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1 deg, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99 and 81%, respectively, which shows that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple IR sensors, when coupled with appropriate processing, can be used to extract substantially more information than that for which such devices are commonly employed. © 2004 Society of Photo-Optical Instrumentation Engineers.