As the cost/performance Ratio of vision systems improves with time, new classes of applications become feasible. One such area, automotive applications, is currently being investigated. Applications include occupant detection, collision avoidance and lane tracking. Interest in occupant detection has been spurred by federal automotive safety rules in response to injuries and fatalities caused by deployment of occupant-side air bags. In principle, a vision system could control airbag deployment to prevent this type of mishap. Employing vision technology here, however, presents a variety of challenges, which include controlling costs, inability to control illumination, developing and training a reliable classification system and loss of performance due to production variations due to manufacturing tolerances and customer options. This paper describes the measures that have been developed to evaluate the sensitivity of an occupant detection system to these types of variations. Two procedures are described for evaluating how sensitive the classifier is to camera variations. The first procedure is based on classification accuracy while the second evaluates feature differences.
With the cost of image acquisition and processing hardware decreasing substantially, consumer applications utilizing machine vision are becoming more feasible. Automotive vision systems represent one emerging application area and offer the potential of significant enhancements to automotive safety. However, the relative lack of lower-cost and higher-performance cameras limits the use of vision technology in cars. Camera acquisition speed, sensitivity and dynamic range issues are especially critical due to the totally unconstrained illumination for this type of application. A successful vision system must be highly reliable under direct sunlight and near-total darkness. Conditions of extreme contrast occur primarily during the day when deep shadows are cast across part of a scene being imaged by the camera. This paper provides a survey of existing camera hardware and discusses the limitations of existing hardware. Performance criteria requirements for different automotive applications will also be presented.
Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.
The delineation of brain lesion boundaries in computerized tomography (CT) or magnetic resonance imaging (MRI) sequences is important in many medical research environments and clinical applications. For example, computer-aided neurosurgery requires the extraction of boundaries of lesions in a series of CT or MRI images in order to design the surgical trajectory and complete the surgical planning. Currently, in many clinical applications, the boundaries of lesions are traced manually. Manual methods are not only tedious but also subjective, leading to substantial inter- and intraobserver variability, and confusions between lesions and coexisting normal structures pose serious problems. Automatic detection of lesions is a nontrivial problem. Because of the low resolution, the border regions between lesions and normal tissues are typically of single-pixel width in CT images, and the intensity gradient at the lesion boundary varies considerably. These characteristics of lesions within CT images, in conjunction with the generally low signal-to-noise ratio of CT images, render simple boundary detection techniques inadequate. Recent work in the field of computer vision has shown multiscale analysis of objects in gray scale images to be effective in many applications. This paper describes and illustrates the application of multiscale morphological techniques to the delineation of brain tumors.
An important computational step in computer-aided neurosurgery is the extraction of boundaries of lesions in a series of images. Currently in many clinical applications, the boundaries of lesions are traced manually. Manual methods are not only tedious but also subjective, leading to substantial inter- and intraobserver variability. Furthermore, recent studies show that human observation of a lesion is not sufficient to guarantee accurate localization. With clinical images, possible confusion between lesions and coexisting normal structures (like blood vessels) is a serious constraint on an observer's performance. Automatic detection of lesions is a non-trivial problem. Typically the boundaries of lesions in CT images are of single-pixel width, and the gradient at the lesion boundary varies considerably. As many studies show, these characteristics of lesions within CT images, in conjunction with the generally low signal-to-ratio of CT images, render simple boundary detection techniques ineffective. In this paper we characterize the brain lesions in CT images, and describe a knowledge-guided boundary detection algorithm. The algorithm is both data- and goal-driven.
This paper presents the results of a comparative study of various Fourier descriptor representations and their use in the recognition of unconstrained handwritten digits. Certain characteristics of five Fourier descriptor representations of handwritten digits are discussed, and illustrations of ambiguous digit classes introduced by use of these Fourier descriptor representations are presented. It is concluded that Fourier descriptors are practically effective only within the framework of an intelligent system capable of reasoning about digit hypotheses. We describe a hypothesis-generating algorithm based on Fourier descriptors which allows the classifier to associate more than one possible digit class with each input. Such hypothesis-generating schemes can be very effective in systems employing multiple classifiers. We compare the performance of the five Fourier descriptor representations based on experiment results produced by the hypothesis-generating classifier for a test set of 14,000 handwritten digits. It is found that some Fourier descriptor formulations are more successful than others for handwritten digit recognition.
In stereotactic surgery, the need exists for means of relating intraoperatively the position and orientation of the surgical instrument used by the neurosurgeon to a known frame of reference. An articulated arm is proposed which would provide the neurosurgeon with on-line information for position, and orientation of the surgical tools being moved by the neurosurgeon. The articulated arm has six degrees of freedom, with five revolute and one prismatic joints. The design features include no obstruction to the field of view, lightweight, good balance against gravity, an accuracy of 1 mm spherical error probability (SEP), and a solvable kinematic structure making it capable of fitting the operating room environment. The arm can be mounted on either the surgical table or the stereotactic frame. A graphical simulation of the arm was created using the IGRIP simulation package created by Deneb Robotics. The simulation demonstrates the use of the arm, mounted on several positions of the ring reaching various target points within the cranium.
In the field of computer vision, multiscale analysis has received much attention in the past decade. In particular, Gaussian scale space has been studied extensively and has proven to be effective in multiscale analysis. Recent research has shown that morphological openings or openings or closings with isotropic structuring elements such as disks define a scale space, where the radius of a disk r is the scale parameter which changes continuously from 0 to infinity. The behaviors of objects described by the morphological scale space provide strong knowledge for multiscale analysis. Based on the theory of morphological scale space, we address in this paper the two fundamental problems in multiscale analysis: (1) how to select proper scale parameters for various applications, and (2) how to integrate the information filtered at multiscales. We propose two algorithms, binary morphological multiscale analysis (BMMA) and gray-scale morphological multiscale analysis (GMMA), for extracting desired regions from binary and gray images.