Showing papers on "Object-class detection published in 1990"

Object detection by step-wise analysis of spectral, spatial, and topographic features

Abstract: In many computer vision systems accurate identification of various objects appearing in a scene is required. In this paper we address the problem of object detection in analyzing high resolution multispectral aerial images. Development of a practical object detection approach should consider issues of speed, accuracy, robustness, and amount of supervision allowed. The approach is based upon extraction of information from images and their systematic analysis utilizing available prior knowledge of various physical attributes of the objects. The step-wise approach examines spectral, spatial, and topographic features in making the object vs background decision. Techniques for the analysis of the spectral, spatial, and topographic features tend to be of increasing levels of computational complexity. The computationally simpler spectral feature analysis is performed for the entire image to detect candidate object regions. Only these regions are considered in the spatial feature analysis step to further reduce the number of candidate regions which need to be analyzed in the topographic feature analysis step. Such step-wise analysis makes the entire object detection process efficient by incorporating the process of “focus of attention” to identify regions of interest thus eliminating a relatively large portion of image from further detailed examination at every stage. Results of the experiments performed using several high resolution multispectral images have demonstrated the basic feasibility of the approach. The images utilized in the experiments are acquired from geographically different locations, at different times, with different types of background, and are of different resolution. Successful object detection with high accuracy and low false alarm rates indicate the robustness of this approach.

A complete and efficient real time system for line segment detection based on the binary Hough transform

Abstract: A complete and efficient system for detection of line segments in real-time (video rate) based on the binary Hough transform is described. This system is able to operate in video rate for images with dimensions up to 1024*1024 for a clock of 32 ns. Special attention is paid to the descriptions of the main processes (image acquisition, edge detection. Hough transform, peak detection, and connectivity analysis) as well as to their balancing, interrelation, and implementation in dedicated systolic hardware. Expressions are derived for the total number of basic operators, as well as the execution rate for each main process. >

Object detection using scale space

Abstract: Scale-space representation is a topic of active research in computer vision. Most of the work so far has concentrated on image reconstruction from the scale-space representation. In this paper we discuss the use of scale- space representation for object detection. We have proposed a model based approach and developed an algorithm to implement it. Channel integration is the heart of the algorithm and there are a number of unresolved issues in it. Object detection is possible only if the objects of interest are different from the noise and clutter in certain features. We have used two different images, one with good signal to noise ratio and the other with poor signal to noise ratio In the first image the distinguishing feature of the object is its signal strength and in the second image it is its size. Accordingly we have studied two approaches to the channel integration : (i) based on the contrast value and (ii) based on edge focusing and splitting. The results of both approaches are presented and discussed.