Mel Siegel

Physically Based Simulation Model for Acoustic Sensor Robot Navigation

A computer model is described that combines concepts from the fields of acoustics, linear system theory, and digital signal processing to simulate an acoustic sensor navigation system using time-of-flight ranging. By separating the transmitter/receiver into separate components and assuming mirror-like reflectors, closed-form solutions for the reflections from corners, edges, and walls are determined as a function of transducer size, location, and orientation. A floor plan consisting of corners, walls, and edges is efficiently encoded to indicate which of these elements contribute to a particular pulse-echo response. Sonar maps produced by transducers having different resonant frequencies and transmitted pulse waveforms can then be simulated efficiently. Examples of simulated sonar maps of two floor plans illustrate the performance of the model. Actual sonar maps are presented to verify the simulation results.

Sensor fusion using Dempster-Shafer theory [for context-aware HCI]

Context-sensing for context-aware HCI challenges the traditional sensor fusion methods with dynamic sensor configuration and measurement requirements commensurate with human perception. The Dempster-Shafer theory of evidence has uncertainty management and inference mechanisms analogous to our human reasoning process. Our Sensor Fusion for Context-aware Computing Project aims to build a generalizable sensor fusion architecture in a systematic way. This naturally leads us to choose the Dempster-Shafer approach as our first sensor fusion implementation algorithm This paper discusses the relationship between Dempster-Shafer theory and the classical Bayesian method, describes our sensor fusion research work using Dempster-Shafer theory in comparison with the weighted sum of probability method The experimental approach is to track a users focus of attention from multiple cues. Our experiments show promising, thought-provoking results encouraging further research.

Sensor fusion for context understanding

To answer the challenge of context-understanding for HCI, we propose and test experimentally a top-down sensor fusion approach. We seek to systematize the sensing process in two steps. first, decompose relevant context information in such a way that it can be described in a model of discrete facts and quantitative measurements, second, we build a generalizable sensor fusion architecture to deal with highly distributed sensors in a dynamic configuration to collect, fuse and populate our context information model. This paper describes our information model, system architecture, and preliminary experimental results.

Intermediate view synthesis considering occluded and ambiguously referenced image regions

Abstract In this paper, we present an algorithm for synthesizing intermediate views from a single stereo-pair. The key contributions of this algorithm are the incorporation of scene assumptions and a disparity estimation confidence measure that lead to the accurate synthesis of occluded and ambiguously referenced regions. The synthesized views have been displayed on a multi-view binocular imaging system, with subjectively effective motion parallax and diminished eye strain.

Image understanding algorithms for remote visual inspection of aircraft surfaces

Visual inspection is, by far, the most widely used method in aircraft surface inspection. We are currently developing a prototype remote visual inspection system, designed to facilitate testing the hypothesized feasibility and advantages of remote visual inspection of aircraft surfaces. In this paper, we describe several experiments with image understanding algorithms that were developed to aid remote visual inspection, in enhancing and recognizing surface cracks and corrosion from the live imagery of an aircraft surface. Also described in this paper are the supporting mobile robot platform that delivers the live imagery, and the inspection console through which the inspector accesses the imagery for remote inspection. We discuss preliminary results of the image understanding algorithms and speculate on their future use in aircraft surface inspection.

A multiresolution framework for stereoscopic image sequence compression

Stereoscopic sequence compression typically involves the exploitation of the spatial redundancy between the left and right streams to achieve higher compressions than are possible with the independent compression of the two streams. In this paper the psychophysical property of the human visual system, that only one high resolution image in a stereo image pair is sufficient for satisfactory depth perception, has been used to further reduce the bit rates. Thus, one of the streams is independently coded along the lines of the MPEG standards, while the other stream is estimated at a lower resolution from this stream. A multiresolution framework has been adopted to facilitate such an estimation of motion and disparity vectors at different resolutions. Experimental results on typical sequences indicate that the additional stream can be compressed to about one-fifth of a highly compressed independently coded stream, without any significant loss in depth perception or perceived image quality.<<ETX>>

Mobile robots for difficult measurements in difficult environments: Application to aging aircraft inspection

Abstract This paper describes a systematic approach to using robots to automate making difficult measurements in real-world environments. The approach is illustrated by a detailed description of the architectures actual application to inspection of commercial aircraft for skin cracks and skin joint delamination. Several robot designs are evaluated; a small mobile robot with suction cup feet is found to be best in the practical context of commercial aircraft maintenance and inspection. Approaches are discussed for primary eddy-current flaw detection sensor signal understanding and for secondary vision-based guidance image understanding. The prospects for vision-based inspection are discussed.

Algorithm for automated eye-strain reduction in real stereoscopic images and sequences

Eye strain is often experienced when viewing a stereoscopic image pair on a flat display device (e.g., a computer monitor). Violations of two relationships that contribute to this eye strain are: (1) the accommodation/convergence breakdown and (2) the conflict between interposition and disparity depth cues. We describe a simple algorithm that reduces eye strain through horizontal image translation and corresponding image cropping, based on a statistical description of the estimated disparity within a stereoscopi image pair. The desired amount of translation is based on the given stereoscopic image pair, and, therefore, requires no user intervention. In this paper, we first develop a statistical model of the estimated disparity that incorporates the possibility of erroneous estimates. An estimate of the actual disparity range is obtained by thresholding the disparity histogram to avoid the contribution of false disparity values. Based on the estimated disparity range, the image pair is translated to force all points to lie on, or behind, the screen surface. This algorithm has been applied to diverse real stereoscopic images and sequences. Stereoscopic image pairs, which were often characterized as producing eye strain and confusion, produced comfortable stereoscopy after the automated translation.

Robotic assistants for aircraft inspectors

Various alternative aircraft inspection methods are first discussed and advantages of using robots are analysed. ANDI (Automated NonDestructive Inspector) and CIMP (Crown Inspection Mobile Platform) are then described. Remote 3D stereoscopic visual inspection is outlined and algorithms developed for crack detection and surface and subsurface corrosion detection are described. Future development trends are also outlined.

Compression and interpolation of 3D stereoscopic and multiview video

Compression and interpolation each require, given part of an image, or part of a collection or stream of images, being able to predict other parts. Compression is achieved by transmitting part of the imagery along with instructions for predicting the rest of it; of course, the instructions are usually much shorter than the unsent data. Interpolation is just a matter of predicting part of the way between two extreme images; however, whereas in compression the original image is known at the encoder, and thus the residual can be calculated, compressed, and transmitted, in interpolation the actual intermediate image is not known, so it is not possible to improve the final image quality by adding back the residual image. Practical 3D-video compression methods typically use a system with four modules: (1) coding one of the streams (the main stream) using a conventional method (e.g., MPEG), (2) calculating the disparity map(s) between corresponding points in the main stream and the auxiliary stream(s), (3) coding the disparity maps, and (4) coding the residuals. It is natural and usually advantageous to integrate motion compensation with the disparity calculation and coding. The efficient coding and transmission of the residuals is usually the only practical way to handle occlusions, and the ultimate performance of beginning-to-end systems is usually dominated by the cost of this coding. In this paper we summarize the background principles, explain the innovative features of our implementation steps, and provide quantitative measures of component and system performance.