Applications of Digital Image Processing XIX: 7-9 August 1996, Denver, ColoradoAndrew G. Tescher |
From inside the book
Results 1-3 of 87
Page 65
... objects as described in [ 10 ] . In this approach , using the coordinates of a point inside an object , the algorithm will find the boundaries ( edges ) of the object an it would recognize it as well . The method uses the concept of ...
... objects as described in [ 10 ] . In this approach , using the coordinates of a point inside an object , the algorithm will find the boundaries ( edges ) of the object an it would recognize it as well . The method uses the concept of ...
Page 261
... object recognition in images regardless of their scale and orientation is considered in this paper . A framework is used to train and to recognize or classify a transformed object . A set of features obtained from the short - time ...
... object recognition in images regardless of their scale and orientation is considered in this paper . A framework is used to train and to recognize or classify a transformed object . A set of features obtained from the short - time ...
Page 267
... object . The object is assigned to the class of the object with which it has the minimum distance . Several other image sets with original and transformed objects were used . The framework correctly classified all the training objects ...
... object . The object is assigned to the class of the object with which it has the minimum distance . Several other image sets with original and transformed objects were used . The framework correctly classified all the training objects ...
Contents
Comparison of methods for superresolving passive millimeter wave images 284735 | 35 |
Parallel processing system based on LAN for image processing applications 284705 | 58 |
Analysis of skin oil by FTIR spectroscopy 284706 | 67 |
Copyright | |
51 other sections not shown
Common terms and phrases
algorithm analysis angle applications approach axons blur calculated camera cell channel coding classified coder coding coefficients color components Computer Computer Vision correlation corresponding d)-components dynamic range compression edge detection encoder equation error extract feature filter focal spot Fourier transform frames function gray values histogram IEEE IEEE Trans image compression image processing implementation input intensity invariant iterative JPEG kernels layer linear matched matched filter measure method motion multiset neurons noise object obtained operations optical original image output parameters pattern recognition phase pixels plane prediction problem processor Prolog PSNR quantization radar reconstructed region resolution retinex Rotational Information sample segmentation shown in Figure shows simulation spatial frequency spectra SPIE Vol sub-images subband target techniques threshold tracking performance vector vector quantization velocity vision visual VMEbus voxels waveform wavelet packet wavenumbers WP's X-ray