Pattern Recognition
Volume 27, Issue 8, August 1994, Pages 1079-1092
Subpixel pattern recognition by image histograms
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Tamás Szirányia
aAnalogical and Neural Computing Systems Research Laboratory, Computer and Automation Institute, Hungarian Academy of Sciences (MTA SZTAKI), P.O. Box 63, H-1518 Budapest, Hungary
Received 6 January 1993;
revised 1 February 1994;
accepted 24 February 1994.
Available online 19 May 2003.
Abstract
Recognition of small patterns covering only a few pixels in an image cannot be done by conventional recognition methods. A theoretically new pattern recognition method has been developed for undersampled objects which are (much) smaller than the window-size of a picture element (pixel), i.e. these objects are of subpixel size. The proposed statistical technique compares the gray-level histogram of the patterns of a set of scanned objects to be examined with the (calculated) gray-level densities of different (in shape or size) possible objects, and the recognition is based on this comparison. This method does not need high-precision movement of scanning sensors or any additional hardware. Moreover, the examined patterns should be randomly distributed on the screen, or a random movement of camera is (or target or both are) needed. Effects of noise are analysed, and filtering processes are suggested in the histogram domain. Several examples of different object shapes (triangle, rectangle, square, circle, curving lines, etc.) are presented through simulations and experiments. A number of possible application areas are suggested, including astronomy, line-drawing analysis and industrial laser measurements.
Stochastic View Registration of Overlapping Cameras Based on Arbitrary Motion
This paper appears in: Image Processing, IEEE Transactions on
Issue Date: March 2007
Volume: 16 Issue:3
On page(s): 710 - 720
Szlavik, Z.; Sziranyi, T.; Havasi, L.;
Comput. & Autom. Res. Inst., Hungarian Acad. of Sci., Budapest
Abstract
A new motion-based method is presented for automatic registration of images in multicamera systems, to permit synthesis of wide-baseline composite views. Unlike existing static-image and motion-based methods, our approach does not need any a priori information about the scene, the appearance of objects in the scene, or their motion. We introduce an entropy-based preselection of motion histories and an iterative Bayesian assignment of corresponding image areas. Finally, correlated point-histories and data-set optimization lead to the matching of the different views. The method is validated by demonstrating its successful use on several real-life indoor and outdoor stereo video image-sequence pairs