Cellular neural network: Difference between revisions

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=== Image processing ===

CNN processors were designed to perform image processing; specifically, real-time ultra-high frame-rate (>10,000 frame/s) processing for applications like particle detection in jet engine fluids and spark-plug detection. Currently, CNN processors can achieve up to 50,000 frames per second, and for certain applications such as missile tracking, flash detection, and spark-plug diagnostics these microprocessors have outperformed a conventional [[supercomputer]]. CNN processors lend themselves to local, low-level, processor intensive operations and have been used in feature extraction,<ref>O. Lahdenoja, M. Laiho and A. Paasio, "Local Binary Pattern Feature Vector Extraction with CNN", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> level and gain adjustments, color constancy detection,<ref>L. Torok and A. Zarandy, "CNN Based Color Constancy Algorithm", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> contrast enhancement, [[deconvolution]],<ref>L. Orzo, "Optimal CNN Templates for Deconvolution", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006</ref> [[image compression]],<ref>P. Venetianer and T. Roska, "Image Compression by Cellular Neural Networks," IEEE Trans. Circuits Syst., 45(3): 205-215, 1998.</ref><ref>R. Dogarut, R. Tetzlaffl and M. Glesner, "Semi-Totalistic CNN Genes for Compact Image Compression", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> [[motion estimation]],<ref name="Y. Cheng, J. Chung 2005">Y. Cheng, J. Chung, C. Lin and S. Hsu, "Local Motion Estimation Based On Cellular Neural Network Technology for Image Stabilization Processing", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>A. Gacsadi, C. Grava, V. Tiponut, and P. Szolgay, "A CNN Implementation of the Horn & Schunck Motion Estimation Method", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> image encoding, image decoding, [[image segmentation]],<ref>S. Chen, M. Kuo and J. Wang, "Image Segmentation Based on Consensus Voting", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>G. Grassi, E. Sciascio, A. Grieco and P. Vecchio, "A New Object-oriented Segmentation Algorithm based on CNNs - Part II: Performance Evaluation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> orientation preference maps,<ref>J. Wu, Z. Lin and C. Liou, "Formation and Variability of Orientation Preference Maps in Visual Cortex: an Approach Based on Normalized Gaussian Arrays", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> pattern learning/recognition,<ref name=":2" /><ref>C. Wu and S. Tsai, "Autonomous Ratio-Memory Cellular Nonlinear Network (ARMCNN) for Pattern Learning and Recognition", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> multi-target tracking,<ref>G. Timar and C. Rekeczky, "Multitarget Tracking Applications of the Bi-I Platform: Attention-selection, Tracking and Navigation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> [[image stabilization]],<ref name="Y. Cheng, J. Chung 2005"/> resolution enhancement,<ref>T. Otake, T. Konishi, H. Aomorit, N. Takahashit and M. Tanakat, "Image Resolution Upscaling Via Two-Layered Discrete Cellular Neural Network", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> image deformations and mapping, image inpainting,<ref>A. Gacsadi and P. Szolgay, "Image Inpainting Methods by Using Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> optical flow,<ref>B. Shi, T. Roska and L. Chua, "Estimating Optical Flow with Cellular Neural Networks," Int’l Journal of Circuit Theory and Applications, 26: 344-364, 1998.</ref> contouring,<ref>Szalka, G. Soos, D. Hillier, L. Kek, G. Andrassy and C. Rekeczky, "Space-time Signature Analysis of 2D Echocardiograms Based on Topographic Cellular Active Contour Techniques", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>T. Szabot and P. Szolgay, "CNN-UM-Based Methods Using Deformable Contours on Smooth Boundaries", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> [[moving object detection]],<ref>G. Costantini, D. Casali, and R. Perfetti, "Detection of Moving Objects in a Binocular Video Sequence", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> axis of symmetry detection,<ref>G Costantini, D. Casafi., and R. Perfetti, "A New CNN-based Method for Detection of the Axis of Symmetry.", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> and [[image fusion]].<ref>I. Szatmari, P. Foldesy, C. Rekeczky and A. Zarandy, "Image Processing Library for the Aladdin Computer", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>I. Szatmari, P. Foldesy, C. Rekeczky and A. Zarandy, "Image processing library for the Aladdin Visual Computer", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref><ref>K. Wiehler, M. Perezowsky, R. Grigat, "A Detailed Analysis of Different CNN Implementations for a Real-Time Image Processing System", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.</ref>

Due to their processing capabilities and flexibility, CNN processors have been used and prototyped for novel field applications such as flame analysis for monitoring combustion at a waste [[Incineration|incinerator]],<ref>L. Bertucco, A. Fichaa, G. Nmari and A. Pagano, "A Cellular Neural Networks Approach to Flame Image Analysis for Combustion Monitoring", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.</ref> mine-detection using [[infrared]] imagery, [[calorimeter]] cluster peak for [[high energy physics]],<ref>C. Baldanza, F. Bisi, M. Bruschi, I. D’Antone, S. Meneghini, M. Riui, M. Zufa, "A Cellular Neural Network For Peak Finding In High-Energy Physics", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.</ref> anomaly detection in potential field maps for geophysics,<ref>E. Bilgili, O. Ucan, A. Albora and I. Goknar, "Potential Anomaly Separation Using Genetically Trained Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> laser dot detection,<ref>C. Rekeczky and G. Timar "Multiple Laser Dot Detection and Localization within an Attention Driven Sensor Fusion Framework", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> metal inspection for detecting manufacturing defects,<ref>Z. Szlavikt, R. Tetzlaff, A. Blug and H. Hoefler, "Visual Inspection of Metal Objects Using Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> and [[Seismology|seismic]] horizon picking. They have also been used to perform [[Biometrics|biometric]] functions<ref>R. Dogaru and I. Dogaru, "Biometric Authentication Based on Perceptual Resonance Between CNN Emergent Patterns and Humans", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> such as [[fingerprint recognition]],<ref>T. Su, Y. Du, Y. Cheng, and Y. Su, "A Fingerprint Recognition System Using Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> vein feature extraction, face tracking,<ref>S. Xavier-de-Souza, M. Van Dyck, J. Suykens and J. Vandewalle, "Fast and Robust Face Tracking for CNN Chips: Application to Wheelchair Driving", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> and generating visual stimuli via emergent patterns to gauge perceptual [[resonance]]s.