A Relaxation Scheme for Mesh Locality in Computer Vision.

Parallel processing has been considered as the key to build computer systems of the future and has become a mainstream subject in Computer Science. Computer Vision applications are computationally intensive that require parallel approaches to exploit the intrinsic parallelism. This research addresses this problem for low-level and intermediate-level vision problems. The contributions of this dissertation are a unified scheme based on probabilistic relaxation labeling that captures localities of image data and the ability of using this scheme to develop efficient parallel algorithms for Computer Vision problems. We begin with investigating the problem of skeletonization. The technique of pattern match that exhausts all the possible interaction patterns between a pixel and its neighboring pixels captures the locality of this problem, and leads to an efficient One-pass Parallel Asymmetric Thinning Algorithm (OPATA\sb8). The use of 8-distance in this algorithm, or chessboard distance, not only improves the quality of the resulting skeletons, but also improves the efficiency of the computation. This new algorithm plays an important role in a hierarchical route planning system to extract high level typological information of cross-country mobility maps which greatly speeds up the route searching over large areas. We generalize the neighborhood interaction description method to include more complicated applications such as edge detection and image restoration. The proposed probabilistic relaxation labeling scheme exploit parallelism by discovering local interactions in neighboring areas and by describing them effectively. The proposed scheme consists of a transformation function and a dictionary construction method. The non-linear transformation function is derived from Markov Random Field theory. It efficiently combines evidences from neighborhood interactions. The dictionary construction method provides an efficient way to encode these localities. A case study applies the scheme to the problem of edge detection. The relaxation step of this edge-detection algorithm greatly reduces noise effects, gets better edge localization such as line ends and corners, and plays a crucial rule in refining edge outputs. The experiments on both synthetic and natural images show that our algorithm converges quickly, and is robust in noisy environment

Proceedings of the NASA Workshop on Image Analysis

Three major topics of image analysis are addressed: segmentation, shape and texture analysis, and structural analysis

Multi-Scale Integral Invariants for Robust Character Extraction from Irregular Polygon Mesh Data

Hunderttausende von antiken Dokumenten in Keilschrift befinden sich in Museen, und täglich werden weitere bei archäologischen Grabungen gefunden. Die Auswertung dieser Dokumente ist wesentlich für das Verständnis der Herkunft von Kultur, Gesetzgebung und Religion. Die Keilschrift ist eine Handschrift und wurde in den Jahrtausenden vor Christi Geburt im gesamten alten Orient benutzt. Der Name leitet sich von den keilförmigen Eindrücken eines Schreibgriffels in den weichen Beschreibstoff Ton ab. Das Anfertigen von Handzeichnungen und Transkriptionen dieser Tontafeln ist eine langwierige Aufgabe und verlangt nach Unterstützung mittels automatisierter rechnergestützter Verfahren. Das Ziel dieser Arbeit ist die präzise Extraktion von Schriftzeichen mit variablen Formen in 3D. Die für die Merkmalsextraktion aus 2D-Mannigfaltigkeiten in 3D entscheidenden Schritte sind Kantenerkennung und Segmentierung. Robuste Techniken in der Signalverarbeitung und dem Shape Matching benutzen hierfür Integralinvarianten in 2D. In aktuellen Arbeiten werden die Integralinvarianten grob geschätzt, um wenige prägnante Merkmale zu finden, mit denen sich zerbrochene 3D-Objekte zusammensetzen lassen. Mit dem Ziel der exakten Bestimmung der 3D-Formen von Zeichen, wurde die aus der Bildverarbeitung und Mustererkennung bekannte Verarbeitungskette an 3D-Modelle angepasst. Diese Modelle bestehen aus Millionen von Messpunkten, die mit optischen 3D-Scannern aufgenommen werden. Die Punkte approximieren Mannigfaltigkeiten durch ein irreguläres Dreiecksnetz. Verschiedene Typen von integralinvarianten Filtern in mehreren Skalen führen zu verschiedenen hochdimensionalen Merkmalsräumen. Faltungen und kombinierte Metriken werden auf die Merkmalsräume angewandt, um Zusammenhangskomponenten zu bestimmen. Diese Komponenten stellen die Zeichen genauer als die Messauflösung dar. Parallel zum Design der Algorithmen werden die Eigenschaften der verschiedenen Integralinvarianten analysiert. Die Interpretation der Filterergebnisse sind von großem Nutzen zur Bestimmung von robusten Krümmungsmaßen und zur Segmentierung. Die Extraktion von Keilschriftzeichen wird mit einer Voronoi basierten Berechnung von minimalen normalisierbaren Vektordarstellungen vervollständigt. Diese Darstellung ist eine wichtige Grundlage für die Paläographie. Weitere Abstraktion und Normalisierung der Darstellung führt zur Zeichenerkennung. Die Einbettung der Algorithmen in das neu entworfene mehrschichtige GigaMesh Software Framework erlaubt eine Vielzahl von Anwendungen. Die Algorithmen nutzen den Speicher effektiv und die Verarbeitungskette ist parallelisiert. Die konfigurierbare Verarbeitungskette hat nur einen relevanten Parameter, nämlich die maximale Größe der zu erwartenden Merkmale. Die vorgestellten Verfahren wurden an Hunderten von Keilschrifttafeln, so wie weiteren realen und synthetischen Objekten getestet.Repräsentative Ergebnisse sowie Aufwands- und Genauigkeitsabschätzung der Algorithmen werden gezeigt. Ein Ausblick auf künftige Erweiterungen und Integralinvarianten in höheren Dimensionen gegeben

The statistical properties and coding of handwriting and hand-drawn graphics in tutorial classes

This research investigates compression techniques as applied to input from a digitizing tablet. The representations we consider are either drawn from the literature or developed within this thesis; they involve selecting relevant points along the trajectory of the writing pen, and using an interpolating function for reconstruction. The two dimensional parametric polynomial interpolator (e.g linear, quadratic, cubic ) is used for regenerating the trajectory of the pen. Several techniques for selecting relevant points , i.e control points are discussed. We determine our best representation by weighting the criteria, analyzing the experimental results, examining the theoretical considerations and, making where necessary, justifiable tradeoffs. The techniques tested are objectively evaluated in terms of accuracy, efficiency and compactness. The information characteristics (i.e signal entropy) of the handwriting and drawing signals, are estimated from the original and approximated data. Results are discussed and conclusions drawn. The research has been carried out on static data

Spectral-domain optical coherence phase microscopy for quantitative biological studies

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references.Conventional phase-contrast and differential interference contrast microscopy produce high contrast images of transparent specimens such as cells. However, they do not provide quantitative information or do not have enough sensitivity to detect nanometerlevel structural alterations. We have developed spectral-domain optical coherence phase microscopy (SD-OCPM) for highly sensitive quantitative phase imaging in 3D. This technique employs common-path spectral-domain optical coherence reflectometry to produce depth-resolved reflectance and quantitative phase images with high phase stability. The phase sensitivity of SD-OCPM was measured as nanometer-level for cellular specimens, demonstrating the capability for detecting small structural variation within the specimens. We applied SD-OCPM to the studies of intracellular dynamics in living cells and the detection of molecular interactions on activated surfaces as a sensor application.In the study of intracellular dynamics, we measured fluctuation of localized field-based dynamic light scattering within cellular specimens. With its high sensitivity to amplitude and phase fluctuations, SD-OCPM could observe the existence of two different regimes in intracellular dynamics. We also investigated the effect of an anti-cancer drug, Colchicine and ATP-depletion on the intracellular dynamics of human ovarian cancer cells, and observed the modification in diffusion characteristics inside the cells. Based on the optical sectioning capability of SD-OCPM, quantitative phase imaging was performed to examine slow dynamics of living cells. Through time-lapsed imaging and spectral analysis on the dynamics at the vicinity of cell membrane, we observed the existence of dynamic and independent sub-domains inside the cells that fluctuate at various dominant frequencies with different frequency contents and magnitudes.SD-OCPM was further utilized to measure molecular interactions on activated sensor surfaces.(cont) The method is based on the fact that phase varies as analyte molecules bind to the immobilized probe molecules at the sensing surface and SD-OCPM can measure small phase alteration at any surface with high sensitivity. We have measured a -1.3 nm increase in optical thickness due to the binding of streptavidin on a biotin-activated substrate in a micro-fluidic device. Moreover, SD-OCPM was extended to image protein array chips, demonstrating its potential as a multiplexed protein array scanner.by Chulmin Joo.Ph.D

Information resources management, 1984-1989: A bibliography with indexes

This bibliography contains 768 annotated references to reports and journal articles entered into the NASA scientific and technical information database 1984 to 1989