Showing papers by "Mongi A. Abidi published in 1995"

A new efficient and direct solution for pose estimation using quadrangular targets: algorithm and evaluation

Abstract: Pose estimation is an important operation for many vision tasks. In this paper, the authors propose an algorithm for pose estimation based on the volume measurement of tetrahedra composed of feature-point triplets extracted from an arbitrary quadrangular target and the lens center of the vision system. The inputs to this algorithm are the six distances joining all feature pairs and the image coordinates of the quadrangular target. The outputs of this algorithm are the effective focal length of the vision system, the interior orientation parameters of the target, the exterior orientation parameters of the camera with respect to an arbitrary coordinate system if the target coordinates are known in this frame, and the final pose of the camera. The authors have also developed a shape restoration technique which is applied prior to pose recovery in order to reduce the effects of inaccuracies caused by image projection. An evaluation of the method has shown that this pose estimation technique is accurate and robust. Because it is based on a unique and closed form solution, its speed makes it a potential candidate for solving a variety of landmark-based tracking problems. >

Best-next-view algorithm for three-dimensional scene reconstruction using range images

Abstract: The primary focus of the research detailed in this paper is to develop an intelligent sensing module capable of automatically determining the optimal next sensor position and orientation during scene reconstruction. To facilitate a solution to this problem, we have assembled a system for reconstructing a 3D model of an object or scene from a sequence of range images. Candidates for the best-next-view position are determined by detecting and measuring occlusions to the range camera's view in an image. Ultimately, the candidate which will reveal the greatest amount of unknown scene information is selected as the best-next-view position. Our algorithm uses ray tracing to determine how much new information a given sensor perspective will reveal. We have tested our algorithm successfully on several synthetic range data streams, and found the system's results to be consistent with an intuitive human search. The models recovered by our system from range data compared well with the ideal models. Essentially, we have proven that range information of physical objects can be employed to automatically reconstruct a satisfactory dynamic 3D computer model at a minimal computational expense. This has obvious implications in the contexts of robot navigation, manufacturing, and hazardous materials handling. The algorithm we developed takes advantage of no a priori information in finding the best-next-view position.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Segmentation of range images using morphological operations: review and examples

Abstract: Image segmentation involves calculating the position of object boundaries For scene analysis, the intent is to differentiate objects from clutter by means of preprocessing The object of this paper is to examine and discuss two morphological techniques for preprocessing and segmenting range images A Morphological Watershed Algorithm has been studied in detail for segmenting range images This algorithm uses a unique approach for defining the boundaries of objects from a morphological gradient Several sets of range images are used as input to the algorithm to demonstrate the flexibility of the watershed technique and the experimental results support this approach as an effective method for segmenting range images Morphological image operators present another means for segmenting range images In particular, the results from implementing gray-scale morphological techniques indicate that these operators are useful for segmentation This is made possible by converting a range image of a scene to a gray-scale image representation The result represents the umbra of the surface of the objects within the scene By applying morphological operations to the gray values of the image, the operations are applied to the umbra Each pixel represents a point of the object's umbra, thereby yielding scene segmentation The techniques that are discussed are found to be useful for preprocessing and segmenting range images which are direct extensions to object recognition, scene analysis, and image understanding© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering Downloading of the abstract is permitted for personal use only

Dynamic adjustment of regularization parameters for the fusion of edge features and noisy dense surfaces

Abstract: Data fusion provides tools for solving problems which are characterized by distributed and diverse information sources. Many robotic applications need to retrieve particular properties from a scene; so it is necessary to use multiple knowledge sources since a single sensory modality cannot capture all of the physical causes of a given edge feature. In this paper we focus on the problem of extracting features such as image discontinuities from both synthetic and real images. Since edge detection and surface reconstruction are ill-posed problems according to Hadamard, Tikhonov's regularization paradigm is proposed as the basic tool for solving this inversion problem and restoring well-posedness. The proposed framework includes (1) a review of 2D regularization, (2) extension of the standard Tikhonov regularization method by allowing space-variant regularization parameters, and (3) further extension of the regularization paradigm by adding multiple data sources for different sensing modalities. The theoretical approach is complemented by developing a regularized hybrid fusion algorithm for solving the early vision problems of edge detection and surface reconstruction. An evaluation of these methods reveals that this new analytical data fusion technique reconstructs a smooth filtered surface in noisy regions while preserving edge characteristics needed for extracting object features. Results indicate the fusion technique is beneficial for combining edge features from different types of sensory data to locate and identify objects of interest.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Robotic sensory data fusion: a regularized color edge detection approach

Abstract: Data fusion provides tools for solving problems which are characterized by distributed and diverse information sources. In this paper we focus on the problem of extracting features such as image discontinuities from both synthetic and real color images. Since edge detection is an ill-posed problem in the sense of Hadamard, Tikhonov's regularization paradigm is proposed as a tool for solving this inversion problem and restoring well-posedness. The proposed framework includes (1) a systematic view of one-dimensional as well as two-dimensional regularization, (2) extension of the standard Tikhonov regularization method by allowing space-variant regularization parameters, and (3) further extension of the regularization paradigm by adding multiple data sources to allow for data fusion. The theoretical approach is complemented by developing a series of algorithms and then solving the early vision problem of color edge detection. An evaluation of this method shows this new analytical data fusion technique output is consistently better than each of the individual RGB edge maps.