Modelling the Stereovision-Correspondence-Analysis task by Lateral Inhibition in Accumulative Computation problem-solving method

Recently, the Algorithmic Lateral Inhibition (ALI) method and the Accumulative Computation (AC) method have proven to be efficient in modelling at the knowledge level for general-motion-detection tasks in video sequences. More precisely, the task of persistent motion detection has been widely expressed by means of the AC method, whereas the ALI method has been used with the objective of moving objects detection, labelling and further tracking. This paper exploits the current knowledge of our research team on the mentioned problem-solving methods to model the Stereovision-Correspondence-Analysis (SCA) task. For this purpose, ALI and AC methods are combined into the Lateral Inhibition in Accumulative Computation (LIAC) method. The four basic subtasks, namely ''LIAC 2D Charge-Memory Calculation'', ''LIAC 2D Charge-Disparity Analysis'' and ''LIAC 3D Charge-Memory Calculation'' in our proposal of SCA are described in detail by inferential CommonKADS schemes. It is shown that the LIAC method may perfectly be used to solve a complex task based on motion information inherent to binocular video sequences.

[1]  W. van de Velde,et al.  CommonKADS Library for Expertise Modelling: reusable problem solving components , 1994 .

[2]  H. Knutsson,et al.  A multiresolution stereopsis algorithm based on the Gabor representation , 1989 .

[3]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[4]  Antonio Fernández-Caballero,et al.  Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation , 2003, Pattern Recognit..

[5]  Sung-Il Chien,et al.  Stereo System for Tracking Moving Object Using Log-Polar Transformation and Zero Disparity Filtering , 2003, CAIP.

[6]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[7]  Antonio Fernández-Caballero,et al.  Algorithmic lateral inhibition method in dynamic and selective visual attention task: Application to moving objects detection and labelling , 2006, Expert Syst. Appl..

[8]  W. Eric L. Grimson,et al.  Computational Experiments with a Feature Based Stereo Algorithm , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[10]  Rama Chellappa,et al.  Hierarchical stereo and motion correspondence using feature groupings , 1995, International Journal of Computer Vision.

[11]  Yeong-Ho Ha,et al.  Stereo matching using genetic algorithm with adaptive chromosomes , 2001, Pattern Recognit..

[12]  Luigi di Stefano,et al.  A fast area-based stereo matching algorithm , 2004, Image Vis. Comput..

[13]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Gonzalo Pajares Martinsanz,et al.  Local stereovision matching through the ADALINE neural network , 2001 .

[15]  Antonio Fernández-Caballero,et al.  Knowledge modelling for the motion detection task: the algorithmic lateral inhibition method , 2004, Expert Syst. Appl..

[16]  Giuseppe Marino,et al.  Neural adaptive stereo matching , 2004, Pattern Recognit. Lett..

[17]  J. Koenderink,et al.  Geometry of binocular vision and a model for stereopsis , 2004, Biological Cybernetics.

[18]  Gonzalo Pajares Martinsanz,et al.  Stereovision matching through support vector machines , 2003 .

[19]  Richard P. Wildes,et al.  Direct Recovery of Three-Dimensional Scene Geometry From Binocular Stereo Disparity , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Antonio Fernández-Caballero,et al.  A Model of Neural Inspiration for Local Accumulative Computation , 2003, EUROCAST.

[21]  Panos Liatsis,et al.  Hybrid symbiotic genetic optimisation for robust edge-based stereo correspondence , 2001, Pattern Recognit..

[22]  Reinhard Männer,et al.  Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation , 2004, International Journal of Computer Vision.

[23]  Guus Schreiber,et al.  Knowledge Engineering and Management: The CommonKADS Methodology , 1999 .

[24]  Yoshihiro Kawai,et al.  3D Object Recognition in Cluttered Environments by Segment-Based Stereo Vision , 2004, International Journal of Computer Vision.

[25]  Antonio Fernández-Caballero,et al.  Stereovision Disparity Analysis by Two-Dimensional Motion Charge Map Inspired in Neurobiology , 2005, BVAI.

[26]  José L. Marroquín,et al.  Robust approach for disparity estimation in stereo vision , 2004, Image Vis. Comput..