Michael Boshra

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ÐWe 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(More)
We present a novel 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(More)
{ We present a pixel-based technique for visual veriication of 3-D object hypotheses. 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 pix-els in the two images are compared to gather votes for the(More)
Occlusion remains a major hindrance for automatic recognition of 3-D objects. In this paper, we address the occlusion problem in the context of polyhedral object recognition from range data. A novel approach is presented for object recognition based on sound occlusion-guided reasoning for feature distortion analysis and perceptual organization. This type of(More)
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 uniied framework for integrating diierent types of sensory data. To make use of the scene perceptual structures early in the recognition process , we enforce local consistency of(More)
We present a novel method for predicting the performance of an object recognition approach in the presence of data uncertainty, occlusion and clutter. The recognition approach uses a vote-based decision criterion , which selects the object/pose hypothesis that has the maximum number of consistent features (votes) with the scene data. The prediction method(More)
{ We present a technique for integrating 2-D and 3-D sensory data in the context of 3-D object recognition. The 3-D object recognition problem can be stated as follows. Given a set of model objects, and a combination of 2-D and 3-D sensory data of one of these objects, our objective is to identify such an object and determine its 3-D pose. In this work, the(More)