Showing papers on "Object-class detection published in 1991"

Recognition in Face Space

Abstract: Individual facial features such as the eyes or nose may not be as important to human face recognition as the overall pattern capturing a more holistic encoding of the face. This paper describes " face space" a subspace of the space of all possible images which can be described as linear combinations of a small number of characteristic face-like images. The construction of face space and its use in the detection and identification of faces is explained in the context of a working face recognition system. The effects of illumination changes scale orientation and the image background are discussed.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

PREMIO: an overview (object recognition)

Abstract: A model-based vision system attempts to find a correspondence between features of an object model and features detected in an image. Most feature-based matching schemes assume that all the features that are potentially visible in a view of all object will appear with equal probability. The resultant matching algorithms have to allow for 'errors' without really understanding what they mean. PREMIO is an object recognition/localization system under construction at the University of Washington that attempts to model some of the physical processes that can cause these 'errors'. PREMIO combines techniques of analytic graphics and computer vision to predict how features of the object will appear in images under various assumptions of lighting, viewpoint, sensor, and image processing operators. These analytic predictions are used in a probabilistic matching algorithm to guide the search and to greatly reduce the search space. >

Minimum resolution for human face detection and identification

Abstract: Our goal is to build an automated system for face recognition. Such a system for a realistic application is likely to have thousands, possibly miffions of faces. Hence, it is essential to have a compact representation for a face. So an important issue is the minimum spatial and grayscale resolutions necessary for a pattern to be detected as a face and then identified. Several experiments were performed to estimate these limits using a collection of 64 faces imaged under very different conditions. All experiments were performed using human observers. The results indicate that there is enough information in 32 x32 x 4bpp images for human eyes to detect and identify the faces. Thus an automated system could represent a face using only 512 bytes.

Object detection using contrast based scale-space

Abstract: The authors propose four scale-space object detection algorithms for separating objects from the background. These algorithms do not need thresholding at any of the scales. The different algorithms are applicable to images with different noise and clutter characteristics. Statistical analysis of the four algorithms is conducted for noisy and cluttered backgrounds. >

Moving edges detection in time-varying image sequences

Abstract: A new computational paradigm for the detection of moving edges in time-varying image sequences is presented. The frames need not be contiguous. The detector includes both motion and edge detection in a way that improves overall performance. >

Scale-space features for object detection

Abstract: In many applications, such as remote sensing or target detection, the target objects are small, compact blobs. In the images discussed in this paper these objects are only 6 or fewer pixels across, and the images contain noise and clutter which is similar in appearance to the targets. Since so few pixels comprise an object, the object shape is uncertain, so common shape features are unreliable. To distinguish targets from clutter, features which make use of scale-space have proven useful. The scale-space of an image is a sequence of Gauss-filtered versions of the image, using increasing scales from one image to the next. Experiments show that object features calculated at a single scale. Various moments of the value of the Laplacian at the centroid of a blob were particularly effective for some targets.