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)
Similarity between model targets plays a fundamental role in determining the performance of target recognition. We analyze the eeect of model similarity on the performance of a vote-based approach for target recognition from SAR images. In such an approach, each model target is represented by a set of SAR views sampled at a variety of azimuth angles and a(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 novel technique for verifying 3-D object hypotheses using an intensity image. Veriication is performed through pixel-wise comparison of edge images corresponding to scene data and hypothesized model object. We accommodate the uncertainties involved in this process, which correspond to bounded positional errors of scene and model edge pixels, by(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)
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 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)