A SIFT-Based Fingerprint Verification System Using Cellular Neural Networks

Recently, with the increasing demand of high security, person identification has become more and more important in our everyday life. The purpose of establishing the identity is to ensure that only a legitimate user, and not anyone else, accesses the rendered services. The traditional identification methods are based on “something that you possess ” and “somethin

The CNNUC3: an analog I/O 64x64 CNN universal machine chip prototype with 7-bit analog accuracy

This paper describes a full-custom mixed-signal chip which embeds distributed optical signal acquisition, digitally-programmable analog parallel processing, and distributed image memory (cache) on a common silicon substrate. This chip, designed in a 0.5 /spl mu/m CMOS standard technology contains around 1000000 transistors, 80% of which operate in analog mode. Chip functional features are in accordance to the CNN Universal Machine paradigm. The chip is capable to complete complex spatio-temporal image processing tasks within short computation time and using a low power budget. The internal circuitry of the chip has been designed to operate in robust manner with >7-bit equivalent accuracy in the internal analog operations, which has been confirmed by experimental measurements. Hence, to all practical purposes, processing tasks completed by the chip have the same accuracy than those completed by digital processors preceded by 7-bit digital-to-analog converters for image digitalization.Office of Naval Research (USA) 68171-98-C-9004DJunta de Andalucía TIC 99082

Discriminatively Trained Latent Ordinal Model for Video Classification

We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF -- it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text overlap with arXiv:1604.0150

Split and Shift Methodology: Overcoming Hardware Limitations on Cellular Processor Arrays for Image Processing

Na era multimedia, o procesado de imaxe converteuse nun elemento de singular importancia nos dispositivos electrónicos. Dende as comunicacións (p.e. telemedicina), a seguranza (p.e. recoñecemento retiniano) ou control de calidade e de procesos industriais (p.e. orientación de brazos articulados, detección de defectos do produto), pasando pola investigación (p.e. seguimento de partículas elementais) e diagnose médica (p.e. detección de células estrañas, identificaciónn de veas retinianas), hai un sinfín de aplicacións onde o tratamento e interpretación automáticas de imaxe e fundamental. O obxectivo último será o deseño de sistemas de visión con capacidade de decisión. As tendencias actuais requiren, ademais, a combinación destas capacidades en dispositivos pequenos e portátiles con resposta en tempo real. Isto propón novos desafíos tanto no deseño hardware como software para o procesado de imaxe, buscando novas estruturas ou arquitecturas coa menor area e consumo de enerxía posibles sen comprometer a funcionalidade e o rendemento

A VLSI array processor architecture for emulating resistive network filtering

This thesis deals with silicon implementations of an all-transistor analogue parallel processor that emulates the functionality of a resistive network. The problems related to VLSI -implementations of parallel processors are the main concern of this thesis. These problems are first discussed and then to overcome these problems, a new system design is introduced, namely Reduced Cell-row System (RCS). The work started from a resistive network -type spatial filter that was part of a video image compression algorithm. The functionality of this algorithm, as well as the filter, was described in Cellular Neural/Nonlinear Network (CNN) notations and they will be used throughout this thesis in describing the filters and processing operations. In addition to the resistive network array processor, a gradient calculation block was included on the chips to fulfil the original algorithm requirements. Two different array processors were manufactured and measured. The processors had different objectives for their implementation: in the first implementation, the objective was to test the developed Reduced Cell-row System, while in the second implementation the goal was to obtain information on the large-scale implementation of such an array. During the research, a method to include some level of programmability in this type of filters was also developed. For the possible future implementation of such a system, system-level simulations were performed to locate the critical parts that have the most effect on the accuracy of the network.Tämä työ käsittelee vastusverkkojen toiminnallisuuden toteuttamista analogisena rinnakkaisprosessoritoteutuksena VLSI-piirillä käyttäen ainoastaan MOS-transistoreja. Työssä on ensin käsitelty rinnakkaisprosessorien toteutukseen liittyviä ongelmia, minkä jälkeen esitellään uusi toteutusmenetelmä ongelmien ratkaisuksi. Menetelmää kutsutaan nimellä "Reduced Cell-row System" (RCS). Työn lähtökohta oli vastusverkko-tyyppinen spatiaalisuodatin, joka oli esitetty osana videopakkausalgoritmia. Algoritmin toiminnallisuus, kuten myös suodattimen, oli kuvattu alkuperäisessä algoritmissa epälineaaristen soluverkkojen "Cellular Neural/Nonlinear Network" (CNN) merkintöjä käyttäen ja näitä merkintöjä tullaan käyttämään koko kirjan ajan kuvattaessa suodattimien toiminnallisuutta. Vastusverkkototeutuksen lisäksi myös gradientinlaskentalohko on lisätty piireihin alkuperäisen algoritmin toteuttamiseksi. Kaksi tällaista rinnakkaisprosessoriverkkoa suunniteltiin, valmistutettiin ja mitattiin. Näiden kahden toteutuksen tavoitteet olivat erilaiset: ensimmäinen toteutettiin, jotta voitaisiin todentaa kehitetyn verkon rivien lukumäärää vähentävän menetelmän (RCS) toimivuus, kun taas toisen toteutuksen tavoitteena oli tutkia laajamittaisen toteutuksen ongelmia. Työn aikana kehitettiin myös menetelmä, miten prosessoriverkkoon voidaan lisätä säädettävyyttä ja tällaisen prosessoriverkon toteutukselle suoritettiin simuloinnit, jotta voitaisiin selvittää toteuksen kannalta kriittiset kohdat.reviewe

Modeling multiple object scenarios for feature recognition and classification using cellular neural networks

Cellular neural networks (CNNs) have been adopted in the spatio-temporal processing research field as a paradigm of complexity. This is due to the ease of designs for complex spatio-temporal tasks introduced by these networks. This has led to an increase in the adoption of CNNs for on-chip VLSI implementations. This dissertation proposes the use of a Cellular Neural Network to model, detect and classify objects appearing in multiple object scenes. The algorithm proposed is based on image scene enhancement through anisotropic diffusion; object detection and extraction through binary edge detection and boundary tracing; and object classification through genetically optimised associative networks and texture histograms. The first classification method is based on optimizing the space-invariant feedback template of the zero-input network through genetic operators, while the second method is based on computing diffusion filtered and modified histograms for object classes to generate decision boundaries that can be used to classify the objects. The primary goal is to design analogic algorithms that can be used to perform these tasks. While the use of genetically optimized associative networks for object learning yield an efficiency of over 95%, the use texture histograms has been found very accurate though there is a need to develop a better technique for histogram comparisons. The results found using these analogic algorithms affirm CNNs as well-suited for image processing tasks

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition