Alter, Tao D.

Recognizing 3D objects from 2D images : an error analysis by William Eric Leifur Grimson ( Book )
1 edition published in 1992 in English and held by 4 libraries worldwide
Abstract: "Many recent object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. In the case of perfect image data, these systems seem to work well. With uncertain image data, however, the performance of such methods is less well understood. In this paper, we examine the effects of two- dimensional sensor uncertainty on the computation of three-dimensional model transformations. We use this analysis to bound the uncertainty in the transformation parameters, as well as the uncertainty associated with applying the transformation to map other model features into the image. We also examine the effects of the transformation uncertainty on the effectiveness of recognition methods."

Extracting salient curves from images : an analysis of the saliency network by Tao D Alter ( Book )
1 edition published in 1996 in English and held by 3 libraries worldwide
Abstract: "The Saliency Network proposed by Shashua and Ullman is a well-known approach to the problem of extracting salient curves from images while performing gap completion. This paper analyzes the Saliency Network. The Saliency Network is attractive for several reasons. First, the network generally prefers long and smooth curves over short or wiggly ones. While computing saliencies, the network also fills in gaps with smooth completions and tolerates noise. Finally, the network is locally connected, and its size is proportional to the size of the image. Nevertheless, our analysis reveals certain weaknesses with the method. In particular, we show cases in which the most salient element does not lie on the perceptually most salient curve. Furthermore, in some cases the saliency measure changes its preferences when curves are scaled uniformly. Also, we show that for certain fragmented curves the measure prefers large gaps over a few small gaps of the same total size. In addition, we analyze the time complexity required by the method. We show that the number of steps required for convergence in serial implementations is quadratic in the size of the network, and in parallel implementations is linear in the size of the network. We discuss problems due to coarse sampling of the range of possible orientations. We show that with proper sampling the complexity of the network becomes at least cubic in the size of the network. Finally, we consider the possibility of using the Saliency Network for grouping. We show that the Saliency Network recovers the most salient curve efficiently, but it has problems with identifying any salient curve other than the most salient one."

3D pose from 3 corresponding points under weak-perspective projection by Tao D Alter ( Book )
1 edition published in 1992 in English and held by 3 libraries worldwide
Abstract: "Model-based object recognition commonly involves using a minimal set of matched model and image points to compute the pose of the model in image coordinates. Furthermore, recognition systems often rely on the 'weak-perspective' imaging model in place of the perspective imaging model. This paper discusses computing the pose of a model from three corresponding points under weak-perspective projection. A new solution to the problem is proposed which, like previous solutions, involves solving a biquadratic equation. Here the biquadratic is motivated geometrically and its solutions, comprised of an actual and a false solution, are interpreted graphically. The final equations take a new form, which lead to a simple expression for the image position of any unmatched model point."

Robust and Efficient 3D Recognition by Alignment ( Book )
1 edition published in 1992 in English and held by 1 library worldwide
Alignment is a prevalent approach for recognizing three-dimensional objects in two-dimensional images. Current implementations handle errors that are inherent in images in ad hoc ways. This thesis shows that these errors can propagate and magnify through the alignment computations, such that the ad hoc approaches may not work. In addition, a technique is given for tightly bounding the propagated error, which can be used to make the recognition robust while still being efficient. Further, the error bounds can be used to formally compute the likelihood that a set of hypothesized matches between model and image features is correct. The technique for bounding the propagated error makes use of a new solution to a fundamental problem in computer recognition, namely, the solution for 3D pose from three corresponding points under weak-perspective projection. The new solution is intended to provide a fast means of computing the error bounds. In deriving the new solution, this thesis gives a geometrical interpretation to the problem, from which the situations are inferred where the solution does not exist and is unstable.

Robust and efficient 3D recognition by alignment by Massachusetts Institute of Technology ( Book )
1 edition published in 1992 in English and held by 1 library worldwide
Alignment is a prevalent approach for recognizing 3D objects in 2D images. A major problem with current implementations is how to robustly handle errors that propagate from uncertainties in the locations of image features. This thesis gives a technique for bounding these errors. The technique makes use of a new solution to the problem of recovering 3D pose from three matching point pairs under weak-perspective projection. Furthermore, the error bounds are used to demonstrate that using line segments for features instead of points significantly reduces the false positive rate, to the extent that alignment can remain reliable even in cluttered scenes.