No description available.

No description available.

If reliable and general computer vision techniques are to be developed it is crucial that we find ways of characterizing low-level image features with invariant quantities. For example, if edge significance could be measured in a way that was invariant to image illumination and contrast, higher-level image processing operations could be conducted with much greater confidence. However, despite their importance, little attention has been paid to the need for invariant quantities in low-level vision for tasks such as feature detection or feature matching. This thesis develops a number of invariant low-level image measures for feature detection, local symmetry/asymmetry detection, and for signal matching. These invariant quantities are developed from representations of the image in the frequency domain. In particular, phase data is used as the fundamental building block for constructing these measures. Phase congruency is developed as an illumination and contrast invariant measure of feature significance. This allows edges, lines and other features to be detected reliably, and fixed thresholds can be applied over wide classes of images. Points of local symmetry and asymmetry in images give rise to special arrangements of phase, and these too can be characterized by invariant measures. Finally, a new approach to signal matching that uses correlation of local phase and amplitude information is developed. This approach allows reliable phase based disparity measurements to be made, overcoming many of the difficulties associated with scale-space singularities.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Yucel Altunbasak; Committee Member: Ghassan Alregib; Committee Member: Marcus Spruill; Committee Member: Patricio A. Vela; Committee Member: Russell M. Mersereau.

No description available.

No description available.

W. Eric L. Grimson ; with contributions from Tomá³ Lozano-Pé²¥z, Daniel P. Huttenlocher. / Includes bibliographical references (p. 350-504) and indexes.

Thesis (MScEng)--Stellenbosch University, 2003. / ENGLISH ABSTRACT: We investigate object detection against cluttered backgrounds, based on the MINACE (Minimum Noise and Correlation Energy) filter. Application of the filter is followed by a suitable segmentation algorithm, and the standard techniques of global and local thresholding are compared to watershed-based segmentation. The aim of this approach is to provide a custom region-based object detection algorithm with a concise set of regions of interest. Two industrial case studies are examined: diamond detection in X-ray images, and the reading of a dynamic, and ink stamped, 2D barcode on packaging clutter. We demonstrate the robustness of our approach on these two diverse applications, and develop a complete algorithmic prototype for an automatic stamped code reader. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die herkenning van voorwerpe teen onduidelike agtergronde. Ons benadering maak staat op die MINACE (" Minimum Noise and Correlation Energy") korrelasiefilter. Die filter word aangewend saam met 'n gepaste segmenteringsalgoritme, en die standaard tegnieke van globale en lokale drumpelingsalgoritmes word vergelyk met 'n waterskeidingsgebaseerde segmenteringsalgoritme. Die doel van hierdie deteksiebenadering is om 'n klein stel moontlike voorwerpe te kan verskaf aan enige klassifikasie-algoritme wat fokus op die voorwerpe self. Twee industriële toepassings word ondersoek: die opsporing van diamante in X-straal beelde, en die lees van 'n dinamiese, inkgedrukte, 2D balkieskode op verpakkingsmateriaal. Ons demonstreer die robuustheid van ons benadering met hierdie twee uiteenlopende voorbeelde, en ontwikkel 'n volledige algoritmiese prototipe vir 'n outomatiese stempelkode leser.

In this thesis, we study modellings and numerical algorithms to data analysis with applications to image processing and financial forecast. The thesis is composed of two parts, namely the tensor regression and data assimilation methods for image restoration.;We start with investigating the tensor regression problem in Chapter 2. It is a generalization of a classical regression in order to adopt and analyze much more information by using multi-dimensional arrays. Since the regression problem is subject to multiple solutions, we propose a regularized tensor regression model to the problem. By imposing a low-rank property of the solution and considering the structure of the tensor product, we develop an algorithm which is suitable for scalable implementations. The regularization method is used to select useful solutions which depend on applications. The proposed model is solved by the alternating minimization method and we prove the convergence of the objective function values and iterates by the maximization-minimization (MM) technique. We study different factors which affects the performance of the algorithm, including sample sizes, solution ranks and the noise levels. Applications include image compressing and financial forecast.;In Chapter 3, we apply filtering methods in data assimilation to image restoration problems. Traditionally, data assimilation methods optimally combine a predictive state from a dynamical system with real partially observations. The motivation is to improve the model forecast by real observation. We construct an artificial dynamics to the non-blind deblurring problems. By making use of spatial information of a single image, a span of ensemble members is constructed. A two-stage use of ensemble transform Kalman filter (ETKF) is adopted to deblur corrupted images. The theoretical background of ETKF and the use of artificial dynamics by stage augmentation method are provided. Numerical experiments include image and video processing.;Concluding remarks and discussion on future extensions are included in Chapter 4.

Chan, Man Leung Dunco. / Thesis submitted in: Nov 2008. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 87-91). / Abstracts in English and Chinese. / Abstract --- p.2 / 摘要 --- p.3 / Acknowledgement --- p.4 / Content --- p.5 / List of figures --- p.6 / Chapter Chapter 1 --- Introduction --- p.9 / Chapter 1.1 --- Background --- p.9 / Chapter 1.2 --- Prior work --- p.11 / Chapter 1.3 --- Objectives --- p.13 / Chapter 1.4 --- Proposed method --- p.16 / Chapter 1.5 --- Thesis outline --- p.18 / Chapter Chapter 2 --- Axial curve-pair deformation --- p.19 / Chapter 2.1 --- Axial deformation technique --- p.20 / Chapter 2.1.1 --- Representing objects in axial space --- p.21 / Chapter 2.1.2 --- Defining the frame --- p.23 / Chapter 2.2 --- Axial curve-pair deformation technique --- p.24 / Chapter 2.2.1 --- Framing the curve-pair --- p.25 / Chapter 2.2.2 --- Construction of orientation curve --- p.26 / Chapter 2.2.3 --- Manipulation of the axial curve-pair --- p.28 / Chapter Chapter 3 --- Dynamic axial curve-pair based deformation --- p.32 / Chapter 3.1 --- The dynamic mass spring model --- p.34 / Chapter 3.1.1 --- Dynamic NURBS curve --- p.35 / Chapter 3.1.2 --- Dynamic Free-form deformation --- p.37 / Chapter 3.1.3 --- Dynamic Axial Curve-pair deformation --- p.38 / Chapter 3.2 --- The dynamic mass spring model --- p.41 / Chapter 3.2.1 --- Curve-pair Fitting --- p.41 / Chapter 3.2.2 --- Construction of dynamic curve-pair --- p.44 / Chapter 3.2.3 --- The three-degree torsional spring --- p.48 / Chapter 3.2.4 --- Conserving feature in a twisting deformation --- p.50 / Chapter 3.2.5 --- Comparison of mass spring model --- p.51 / Chapter 3.3 --- Internal and external forces --- p.54 / Chapter 3.3.1 --- Tensile stress --- p.54 / Chapter 3.3.2 --- Torsional stress --- p.55 / Chapter 3.3.3 --- External forces --- p.59 / Chapter 3.4 --- Equations of motion --- p.60 / Chapter 3.5 --- System solver --- p.63 / Chapter 3.6 --- Hierarchical representation --- p.67 / Chapter 3.7 --- Collision detection --- p.72 / Chapter Chapter 4 --- Implementation and experimental result --- p.75 / Chapter 4.1 --- Comparison with original mass-spring system --- p.76 / Chapter 4.2 --- Comparison with dynamic free form deformation --- p.77 / Chapter 4.3 --- Comparison with the axial curve-pair deformation --- p.78 / Chapter 4.4 --- Shape restoring power --- p.80 / Chapter 4.5 --- Applications --- p.81 / Chapter Chapter 5 --- Conclusion --- p.84 / Reference --- p.86