Accurate and robust registration of image pairs is of interest in many fields that use computer vision such as surveillance and medical diagnostics. In each of these fields the area-based (or voxel-based) approach to image registration is popular, however it is known that these methods are sensitive to illumination change where incorrect results are common. Past work in applying chaos theory to computer vision has demonstrated that the underlying physics of illumination change versus contextual change result in very different behavior when analyzed in phase space. Illumination is deterministic and results in non-fractal phase space behavior, while contextual change is chaos-like and results in complex fractal regions in phase space. A chaos-theoretic approach to image registration is presented with favorable results compared to the traditional and very popular Mutual Information measure.

Accurate and robust attention direction has been of substantial interest in the computer vision community, particularly for industrial surveillance systems that initiate recording at the onset of motion or an interesting contextual event. One key issue is minimizing false alarms to limit video record bandwidth and capacity. One issue that these systems face is high false alarm rates under sudden illumination change. In this paper we propose a system which applies measures from chaos theory and fractal analysis to provide a robust pre-attentive processing engine for motion detection. Results compare quite favorably in terms of probability of detection versus false detection rate against traditional methods for low-level change detection, namely Sum of Absolute Differences, and Gaussian Mixture Models. The proposed chaos-based method is shown to have superior performance. Additionally the proposed approach has an intuitive justification based on creation and flow of information between image frames, and consequently a very intuitive and problem-based threshold determination.

Accurate and robust image motion detection has been of substantial interest in the image processing and computer vision communities. Unfortunately, no single motion detection algorithm has been universally superior; while biological vision systems are adept at motion detection. Recent research in neural signals have shown biological neural systems are highly responsive to chaotic signals. In this paper, we analyze image sequences using frame-wise phase plots and demonstrate that the changes in pixel amplitudes due to the motion of objects in an image sequence, results in apparently chaotic behavior in phase space. We explore these chaotic phenomena in a variety of image datasets to show their repeatability, to validate the assumption of ergodicity, and to demonstrate their uniqueness from the changes due to illumination, particularly spatio-temporally varying illumination.

Recent research in neural signals have shown biological neural systems are highly responsive to chaotic signals. In this paper, we analyze image sequences using tools from chaos theory associated with the phase plot of time series signals. We demonstrate that the changes in pixel amplitudes due to the motion of objects in an image sequence result in interesting behavior, visible as fractal patterns in the phase space, reminiscent of chaotic strange attractors. Likewise we demonstrate that changes in image amplitude due to spatio-temporally varying illumination are non-chaotic and well structured in phase space. We use the Lambertian reflectance model to explain the source of the non-linearities responsible for this chaotic behavior.

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest. This is challenging without any prior knowledge about the object that is being extracted from the scene. We previously proposed a method of segmentation that uses the classification subsystem as an integral part of the segmentation, which provides contextual information regarding the objects to be segmented. Our approach integrated segmentation and classification in a manner analogous to wrapper methods in feature selection. We initially perform low-level segmentation to label the image as a set of non-overlapping blobs. We then use the wrapper framework to select the blobs that comprise the final segmentation based on the classification performance of the wrapper. In this paper, the process of combining the blobs and then evaluating these combinations is performed with a genetic algorithm. We show the performance of the Genetic Algorithm based wrapper segmentation on real-world complex images of automotive vehicle occupants, where our overall classification accuracy is roughly 88% and the resultant segmentations are extremely accurate.

Accurate and robust image change detection and motion segmentation has been of substantial interest in the image processing and computer vision communities. Spatio-temporal illumination effects such as complex lighting effects involving moving light bands or moving shadow bands can confuse existing motion segmentation algorithms, which are based on analysing the greyscale image directly. While efforts have been made to improve the robustness of motion segmentation algorithms under varying illumination, the results to date are still not completely satisfactory. We propose using the joint scattergram between two images as the representational space for analyzing motion. These scattergrams have properties which can distinguish motion from complex illumination changes. We verify these properties of the scattergram on image datasets captured in the laboratory as well as outdoors and show its utility as a robust representational domain.

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest from the background image. It is extremely difficult to obtain a reliable segmentation without any prior knowledge about the object that is being extracted from the scene. This is further complicated by the lack of any clearly defined metrics for evaluating the quality of segmentation or for comparing segmentation algorithms. We propose a method of segmentation that addresses both of these issues, by using the object classification subsystem as an integral part of the segmentation. This will provide contextual information regarding the objects to be segmented, as well as allow us to use the probability of correct classification as a metric to determine the quality of the segmentation. We view traditional segmentation as a filter operating on the image that is independent of the classifier, much like the filter methods for feature selection. We propose a new paradigm for segmentation and classification that follows the wrapper methods of feature selection. Our method wraps the segmentation and classification together, and uses the classification accuracy as the metric to determine the best segmentation. By using shape as the classification feature, we are able to develop a segmentation algorithm that relaxes the requirement that the object of interest to be segmented must be homogeneous in some low-level image parameter, such as texture, color, or grayscale. This represents an improvement over other segmentation methods that have used classification information only to modify the segmenter parameters, since these algorithms still require an underlying homogeneity in some parameter space. Rather than considering our method as, yet, another segmentation algorithm, we propose that our wrapper method can be considered as an image segmentation framework, within which existing image segmentation algorithms may be executed. We show the performance of our proposed wrapper-based segmenter on real-world and complex images of automotive vehicle occupants for the purpose of recognizing infants on the passenger seat and disabling the vehicle airbag. This is an interesting application for testing the robustness of our approach, due to the complexity of the images, and, consequently, we believe the algorithm will be suitable for many other real-world applications.

To support real-time tracking of objects in video sequences, there has been considerable effort directed at developing optical flow and general motion-based image segmentation algorithms. The goal is to segment multiple moving objects in the image based on their relative motion. This task can be complicated by the presence of lighting variations. Furthermore, a combination of multiple motions and complex lighting effects can lead to dramatic image variations that may not be adequately accounted for by a single motion-based segmentation algorithm. We propose to fuse the results of multiple motion segmentation algorithms to improve the system robustness. Our approach uses the expectation maximization (EM) algorithm as a fusion engine. It also uses principal components analysis (PCA) to perform dimensionality reduction to improve the performance of the EM algorithm and reduce the processing burden. The performance of the proposed fusion algorithm has been demonstrated in the "smart airbag" application of monitoring occupants in a moving automobile to determine if they are too close to the instrument panel (airbag). Through fusion of the outputs of multiple algorithms we are able to reduce the percentage of pixels missed on the target by 35.

The introduction of airbags into automobiles has significantly improved the safety of the occupants. Unfortunately, airbags can also cause fatal injuries if the occupant is a child smaller (in weight) than a typical 6 year old. In response to this, The National Highway Transportation and Safety Administration (NHTSA) has mandated that starting in the 2006 model year all automobiles be equipped with an automatic suppression system to detect the presence of a child or infant and suppress the airbag. The classification problem we address is a four-class problem with the classes being rear-facing infant seat, child, adult, and empty seat. We describe a machine vision-based occupant classification system using a single grayscale camera and a digital signal processor that can perform this function in "real time" (< 5 seconds). The system has been extensively tested on a database of over 21,000 real-world images collected over a period of 4 months in moderate lighting conditions with a wide variety of passengers in eight different vehicles. We have achieved a classification accuracy of /spl sim/ 95%. We believe this system serves the need for a low-cost, high reliability embedded real-time airbag suppression system. Additional testing and improvements of the classification system are currently underway.

The use of airbags into automobiles has significantly improved the safety of the occupants. Unfortunately, when airbags are deployed in the case of a crash, they can also cause fatal injuries if the occupant is a child smaller (in weight) than a typical 6 year old. In response to this, The National Highway Transportation and Safety Administration (NHTSA) has mandated that, starting in the 2006 model year, all automobiles be equipped with an automatic suppression system. These systems are supposed to suppress the airbag if a child or an infant is occupying the front passenger seat. We are investigating the use of machine vision to classify front-seat passenger occupants into four classes: (i) adult, (ii) empty, (iii) RFIS (rear facing infant seat), and child. The design and integration of such a vision system into automobiles is very difficult due to (i) occupant variability (e.g., different types of infant seats and children clothing), and (ii) extreme lighting variability (e.g., bright sunny days, and night time operation). Our approach is based on recognition-driven segmentation. Preliminary results show that by integrating the segmentation and classification stages of processing, we are able to more reliably recognize the various occupant classes.