Tamás Szirányi

A 0.8-/spl mu/m CMOS two-dimensional programmable mixed-signal focal-plane array processor with on-chip binary imaging and instructions storage

This paper presents a CMOS chip for the parallel acquisition and concurrent analog processing of two-dimensional (2-D) binary images. Its processing function is determined by a reduced set of 19 analog coefficients whose values are programmable with 7-b accuracy. The internal programming signals are analog, but the external control interface is fully digital. On-chip nonlinear digital-to-analog converters (DACs) map digitally coded weight values into analog control signals, using feedback to predistort their transfer characteristics in accordance to the response of the analog programming circuitry. This strategy cancels out the nonlinear dependence of the analog circuitry with the programming signal and reduces the influence of interchip technological parameters random fluctuations. The chip includes a small digital RAM memory to store eight sets of processing parameters in the periphery of the cell array and four 2-D binary images spatially distributed over the processing array. It also includes the necessary control circuitry to realize the stored instructions in any order and also to realize programmable logic operations among images. The chip architecture is based on the cellular neural/nonlinear network universal machine (CNN-UM). It has been fabricated in a 0.8-/spl mu/m single-poly double-metal technology and features 2-/spl mu/s operation speed (time required to process an image) and around 7-b accuracy in the analog processing operations.

Bayesian Foreground and Shadow Detection in Uncertain Frame Rate Surveillance Videos

In in this paper, we propose a new model regarding foreground and shadow detection in video sequences. The model works without detailed a priori object-shape information, and it is also appropriate for low and unstable frame rate video sources. Contribution is presented in three key issues: 1) we propose a novel adaptive shadow model, and show the improvements versus previous approaches in scenes with difficult lighting and coloring effects; 2) we give a novel description for the foreground based on spatial statistics of the neighboring pixel values, which enhances the detection of background or shadow-colored object parts; 3) we show how microstructure analysis can be used in the proposed framework as additional feature components improving the results. Finally, a Markov random field model is used to enhance the accuracy of the separation. We validate our method on outdoor and indoor sequences including real surveillance videos and well-known benchmark test sets.

User-adaptive hand gesture recognition system with interactive training

Our paper proposes a vision-based hand gesture recognition system with interactive training, aimed to achieve a user-independent application by on-line supervised training. Usual recognition systems involve a preliminary off-line training phase, separated from the recognition phase. If the system recognizes unknown (non-trainer) users the recognition rate of gesture classes could decrease. The recognition has to be suspended and all gestures need to be retrained with an improved training set, resulting in inconveniences. Our new approach introduces an on-line training method embedded into the recognition process, being interactively controlled by the user and adapting to his/her gestures. Our main goal is that any non-trainer users be able to use the system instantly and if the recognition accuracy decreases only the faulty detected gestures be retrained realizing fast adaptation. We implement the proposed system as a camera-projector system in which users can directly interact with the projected image by hand gestures, realizing an augmented reality tool in a multi-user environment. The emphasis is on the novel approach of dynamic and quick follow-up training capabilities instead of handling large pre-trained databases. We also conducted tests on several users in real environments for a practical application.

Texture Classification and Segmentation by Cellular Neural Networks Using Genetic Learning

We present a new single-chip texture classifier based on the cellular neural network (CNN) architecture. Exploiting the dynamics of a locally interconnected 2D cell array of CNNs we have developed a theoretically new method for texture classification and segmentation. This technique differs from other convolution-based feature extraction methods since we utilize feedback convolution, and we use a genetic learning algorithm to determine the optimal kernel matrices of the network. The CNN operators we have found for texture recognition may combine different early vision effects. We show how the kernel matrices can be derived from the state equations of the network for convolution/deconvolution and nonlinear effects. The whole process includes histogram equalization of the textured images, filtering with the trained kernel matrices, and decision-making based on average gray-scale or texture energy of the filtered images. We present experimental results using digital CNN simulation with sensitivity analysis for noise, rotation, and scale. We also report a tested application performed on a programmable 22 × 20 CNN chip with optical inputs and an execution time of a few microseconds. We have found that this CNN chip with a simple 3 × 3 CNN kernel can reliably classify four textures. Using more templates for decision-making, we believe that more textures can be separated and adequate texture segmentation (< 1% error) can be achieved.

Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model

In this paper, we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the conditional mixed Markov model, is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth.

Study on color space selection for detecting cast shadows in video surveillance

In this article, the authors address the color modeling problem of cast shadows in video sequences. It is illustrated that the performance of shadow detection can be improved significantly through appropriate color space selection, if for practical purposes, the number of free parameters of the method should be kept low. Hence, the authors compare several well known color spaces with a six‐parameter shadow model embedded into a globally optimal MRF framework. Experimental results are shown regarding the following questions: (1) What is the gain of using color images instead of grayscale ones? (2) What is the gain of using uncorrelated spaces instead of the standard RGB? (3) Chrominance (illumination invariant), luminance, or mixed spaces are more effective? (4) In which scenes are the differences significant? The authors qualified the metrics both in color based clustering of the individual pixels and in the case of Bayesian foreground‐background‐shadow segmentation. Experimental results on real‐life videos show that CIE L*u*v* color space is the most efficient.

Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest

We present an automatic focus area estimation method, working with a single image without a priori information about the image, the camera, or the scene. It produces relative focus maps by localized blind deconvolution and a new residual error-based classification. Evaluation and comparison is performed and applicability is shown through image indexing

Hand Gesture Recognition in Camera-Projector System*

Our paper proposes a vision-based hand gesture recognition system. It is implemented in a camera-projector system to achieve an augmented reality tool. In this configuration the main problem is that the hand surface reflects the projected background, thus we apply a robust hand segmentation method. Hand localizing is based on a background subtraction method, which adapts to the changes of the projected background. Hand poses are described by a method based on modified Fourier descriptors, which involves distance metric for the nearest neighbor classification. The proposed classification method is compared to other feature extraction methods. We also conducted tests on several users. Finally, the recognition efficiency is improved by the recognition probabilities of the consecutive detected gestures by maximum likelihood approach.

Image Segmentation Using Markov Random Field Model in Fully Parallel Cellular Network Architectures

Markovian approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. Herein, we show that the Markovian labeling approach can be implemented in fully parallel cellular network architectures, using simple functions and data representations. This makes possible to implement our model in parallel imaging VLSI chips.As an example, we have developed a simplified statistical image segmentation algorithm for the Cellular Neural/Nonlinear Networks Universal Machine (CNN-UM), which is a new image processing tool, containing thousands of cells with analog dynamics, local memories and processing units. The Modified Metropolis Dynamics (MMD) optimization method can be implemented into the raw analog architecture of the CNN-UM. We can introduce the whole pseudo-stochastic segmentation process in the CNN architecture using 8 memories/cell. We use simple arithmetic functions (addition, multiplication), equality-test between neighboring pixels and very simple nonlinear output functions (step, jigsaw). With this architecture, the proposed VLSI CNN chip can execute a pseudo-stochastic relaxation algorithm of about 100 iterations in about 100 ?s.In the suggested solution the segmentation is unsupervised, where a pixel-level statistical estimation model is used. We have tested different monogrid and multigrid architectures.In our CNN-UM model several complex preprocessing steps can be involved, such as texture-classification or anisotropic diffusion. With these preprocessing steps, our fully parallel cellular system may work as a high-level image segmentation machine, using only simple functions based on the close-neighborhood of a pixel.

Detection of Gait Characteristics for Scene Registration in Video Surveillance System

This paper presents a robust walk-detection algorithm, based on our symmetry approach which can be used to extract gait characteristics from video-image sequences. To obtain a useful descriptor of a walking person, we temporally track the symmetries of a persons legs. Our method is suitable for use in indoor or outdoor surveillance scenes. Determining the leading leg of the walking subject is important, and the presented method can identify this from two successive walk steps (one walk cycle). We tested the accuracy of the presented walk-detection method in a possible application: Image registration methods are presented which are applicable to multicamera systems viewing human subjects in motion