We present an approach for extraction of minutiae features from fingerprint images. The proposed approach is based on the use of logical templates for minutiae extraction in the presence of data distortion. A logical template is an expression that is applied to the binary ridge (valley) image at selected potential locations to detect the presence of minutiae at these locations. It is adapted to local ridge orientation and frequency. We discuss the proposed technique in detail, and present experimental results on low-resolution images of various qualities.

We present a method for predicting fundamental performance of object recognition. We assume that both scene data and model objects are represented by 2D point features and a data/model match is evaluated using a vote-based criterion. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, in addition to model similarity. This is unlike previous approaches, which consider only a subset of these factors. Performance is predicted in two stages. In the first stage, the similarity between every pair of model objects is captured by comparing their structures as a function of the relative transformation between them. In the second stage, the similarity information is used along with statistical models of the data-distortion factors to determine an upper bound on the probability of recognition error. This bound is directly used to determine a lower bound on the probability of correct recognition. The validity of the method is experimentally demonstrated using real synthetic aperture radar (SAR) data.

This paper addresses the problem of predicting fundamental performance of vote-based object recognition using 2-D point features. It presents a method for predicting a tight lower bound on performance. Unlike previous approaches, the proposed method considers data-distortion factors, namely uncertainty, occlusion, and clutter, in addition to model similarity, simultaneously. The similarity between every pair of model objects is captured by comparing their structures as a function of the relative transformation between them. This information is used along with statistical models of the data-distortion factors to determine an upper bound on the probability of recognition error. This bound is directly used to determine a lower bound on the probability of correct recognition. The validity of the method is experimentally demonstrated using synthetic aperture radar (SAR) data obtained under different depression angles and target configurations.

The technique proceeds in three steps: firstly, the visible-edge image of the hypothesized model object is constructed; secondly, this image is superimposed on the scene edge image; finally, corresponding pixels in the two images are compared to gather votes for the validity of the hypothesis. Uncertainty in estimating the locations of both scene and model edge pixels is handled by dilating the scene edge image. An analytical method is presented for determining the extent of dilation, assuming some error bound on the object pose. The proposed technique has several important advantages over the common feature-based techniques. Firstly, it is much simpler to implement. Secondly, the time complexity of verifying a hypothesis is made independent of the scene complexity (number and types of scene features). Thirdly, it is insensitive to imperfections of the feature extraction process. Finally the technique is easy to implement on the existing parallel vision/graphics hardware; thus it is suitable for real-time applications.

We present a technique for recognizing polyhedral objects by integrating visual and tactile data. The problem is formulated as a constraint-satisfaction problem (CSP) to provide a unified framework for integrating different types of sensory data. To make use of the scene perceptual structures early in the recognition process, we enforce local consistency of the CSP. The process of local-consistency enforcing (LCE) reduces the correspondence uncertainty between scene and model features, which can lead to significant reductions in the computational load on subsequent recognition modules. LCE can also eliminate many erroneous model objects efficiently, without explicitly generating or verifying any object/pose hypotheses.

Most existing 3-D object recognition/localization systems rely on a single type of sensory data, although several sensors may be available in a robot task to provide information about the objects to be recognized. In this paper, the authors present a technique to localize polyhedral objects by integrating visual and tactile data. It is assumed that visual data is provided by a monocular visual sensor, while tactile data by a planar-array tactile sensor in contact with the object to be localized. The authors focus on using tactile data in the hypothesis generation phase to reduce the requirements of visual features for localization to a V-junction only. The main concept of this technique is to compute a set of partial pose hypotheses off-line by utilizing tactile data, and then complement these partial hypotheses on-line using visual data. The technique presented is tested using simulated and real data.>