Virtual Visual Hulls: Example-Based 3D Shape Estimation from a Single Silhouette

Recovering a volumetric model of a person, car, or other object of interest from a single snapshot would be useful for many computer graphics applications. 3D model estimation in general is hard, and currently requires active sensors, multiple views, or integration over time. For a known object class, however, 3D shape can be successfully inferred from a single snapshot. We present a method for generating a ``virtual visual hull''-- an estimate of the 3D shape of an object from a known class, given a single silhouette observed from an unknown viewpoint. For a given class, a large database of multi-view silhouette examples from calibrated, though possibly varied, camera rigs are collected. To infer a novel single view input silhouette's virtual visual hull, we search for 3D shapes in the database which are most consistent with the observed contour. The input is matched to component single views of the multi-view training examples. A set of viewpoint-aligned virtual views are generated from the visual hulls corresponding to these examples. The 3D shape estimate for the input is then found by interpolating between the contours of these aligned views. When the underlying shape is ambiguous given a single view silhouette, we produce multiple visual hull hypotheses; if a sequence of input images is available, a dynamic programming approach is applied to find the maximum likelihood path through the feasible hypotheses over time. We show results of our algorithm on real and synthetic images of people.

Sharing visual features for multiclass and multiview object detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (run-time) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. It seems unlikely that such an approach will scale up to allow recognition of hundreds or thousands of objects. We present a multi-class boosting procedure (joint boosting) that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the computational cost, is observed to scale approximately logarithmically with the number of classes. The features selected jointly are closer to edges and generic features typical of many natural structures instead of finding specific object parts. Those generic features generalize better and reduce considerably the computational cost of an algorithm for multi-class object detection.

Contextual Influences on Saliency

This article describes a model for including scene/context priors in attention guidance. In the proposed scheme, visual context information can be available early in the visual processing chain, in order to modulate the saliency of image regions and to provide an efficient short cut for object detection and recognition. The scene is represented by means of a low-dimensional global description obtained from low-level features. The global scene features are then used to predict the probability of presence of the target object in the scene, and its location and scale, before exploring the image. Scene information can then be used to modulate the saliency of image regions early during the visual processing in order to provide an efficient short cut for object detection and recognition.

The Role of Fixation and Visual Attention in Object Recognition

This research project is a study of the role of fixation and visual attention in object recognition. In this project, we build an active vision system which can recognize a target object in a cluttered scene efficiently and reliably. Our system integrates visual cues like color and stereo to perform figure/ground separation, yielding candidate regions on which to focus attention. Within each image region, we use stereo to extract features that lie within a narrow disparity range about the fixation position. These selected features are then used as input to an alignment-style recognition system. We show that visual attention and fixation significantly reduce the complexity and the false identifications in model-based recognition using Alignment methods. We also demonstrate that stereo can be used effectively as a figure/ground separator without the need for accurate camera calibration.

Visual Navigation: Constructing and Utilizing Simple Maps of an Indoor Environment

The goal of this work is to navigate through an office environmentsusing only visual information gathered from four cameras placed onboard a mobile robot. The method is insensitive to physical changes within the room it is inspecting, such as moving objects. Forward and rotational motion vision are used to find doors and rooms, and these can be used to build topological maps. The map is built without the use of odometry or trajectory integration. The long term goal of the project described here is for the robot to build simple maps of its environment and to localize itself within this framework.

The Role of Knowledge in Visual Shape Representation

This report shows how knowledge about the visual world can be built into a shape representation in the form of a descriptive vocabulary making explicit the important geometrical relationships comprising objects' shapes. Two computational tools are offered: (1) Shapestokens are placed on a Scale-Space Blackboard, (2) Dimensionality-reduction captures deformation classes in configurations of tokens. Knowledge lies in the token types and deformation classes tailored to the constraints and regularities ofparticular shape worlds. A hierarchical shape vocabulary has been implemented supporting several later visual tasks in the two-dimensional shape domain of the dorsal fins of fishes.

Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects

We present the results of an implemented system for learning structural prototypes from grey-scale images. We show how to divide an object into subparts and how to encode the properties of these subparts and the relations between them. We discuss the importance of hierarchy and grouping in representing objects and show how a notion of visual similarities can be embedded in the description language. Finally we exhibit a learning algorithm that forms class models from the descriptions produced and uses these models to recognize new members of the class.

Computer System for Visual Recognition Using Active Knowledge

A system for visual recognition is described, with implications for the general problem of representation of knowledge to assist control. The immediate objective is a computer system that will recognize objects in a visual scene, specifically hammers. The computer receives an array of light intensities from a device like a television camera. It is to locate and identify the hammer if one is present. The computer must produce from the numerical "sensory data" a symbolic description that constitutes its perception of the scene. Of primary concern is the control of the recognition process. Control decisions should be guided by the partial results obtained on the scene. If a hammer handle is observed this should suggest that the handle is part of a hammer and advise where to look for the hammer head. The particular knowledge that a handle has been found combines with general knowledge about hammers to influence the recognition process. This use of knowledge to direct control is denoted here by the term "active knowledge". A descriptive formalism is presented for visual knowledge which identifies the relationships relevant to the active use of the knowledge. A control structure is provided which can apply knowledge organized in this fashion actively to the processing of a given scene.

Computer Recognition of Three-Dimensional Objects in a Visual Scene

Methods are presented (1) to partition or decompose a visual scene into the bodies forming it; (2) to position these bodies in three-dimensional space, by combining two scenes that make a stereoscopic pair; (3) to find the regions or zones of a visual scene that belong to its background; (4) to carry out the isolation of objects in (1) when the input has inaccuracies. Running computer programs implement the methods, and many examples illustrate their behavior. The input is a two-dimensional line-drawing of the scene, assumed to contain three-dimensional bodies possessing flat faces (polyhedra); some of them may be partially occluded. Suggestions are made for extending the work to curved objects. Some comparisons are made with human visual perception. The main conclusion is that it is possible to separate a picture or scene into the constituent objects exclusively on the basis of monocular geometric properties (on the basis of pure form); in fact, successful methods are shown.

Purification of human tissue prokallikrein excreted from insect cells by liquid chromatography

Tissue kallikrein, generally existing in living bodies as prokallikrein, is a serine proteinase that has proven of great significance to treat hypertension, cardiopathy and nephropathy. Although the extraction of tissue kallikrein from human urine is the most commonly used method to obtain such a protein, not only the yield is very little, but also the procedure is rather complex. Furthermore, the biological safety is uncertain. Therefore, the preparation of such a protein by genetic engineering method, including gene expression, cell culture, separation and purification, is very important. In this paper, a new method to obtain purified tissue prokallikrein excreted from insect cells by liquid chromatography has been proposed. In contrast to the previously published papers, the purification procedure is simplified to only three steps with the final yield of 57% and the purity of 95%, which is not only convenient, but also low-cost and suitable for the large-scale preparation of such a protein. The purified protein is further validated as prokallikrein by high performance liquid chromatography-mass spectrometry and amino acid sequencing. (c) 2005 Elsevier B.V. All rights reserved.

Modelo para programação visual de matrizes (MVM): uma nova abordagem para visualização, manipulação e programação de algoritmos matriciais.

Para muitos usuários, a programação visual é uma alternativa atrativa às linguagens de programação textuais. Uma das razões para esta atração é que a representação visual de um problema está muito mais próxima com a forma pela qual a solução é obtida ou entendida se comparada à representação textual. Este trabalho apresenta um modelo para a programação visual de matrizes baseado nos paradigmas de fluxo de dados e planilhas eletrônicas. O fluxo de dados e a planilha forma a base semântica da linguagem, enquanto as representações gráficas do grafo direcionado e de uma planilha fundamentam sua base sintática. Este modelo consiste em um conjunto de diagramas bidimensionais e de regras de transformação. Os processos são implementados como redes de fluxo de dados e os dados são representados por planilhas. As planilhas podem ser vistas como variáveis do tipo matriz que armazenam dados bidimensionais, ou como funções, que recebem e produzem valores utilizados por outros processos. Neste caso, as planilhas são programadas seguindo o paradigma de programação por demonstrações que incorporam um poderoso construtor de interação, reduzindo significativamente a utilização de recursos e repetições. O modelo proposto pode ser utilizado em diversos domínios de aplicação, principalmente para simplificar a construção de modelos matemáticos de simulação e análise estatística.

Visual Segmentation-Based Data Record Extraction From Web Documents

Li, Longzhuang, Liu, Yonghuai, Obregon, A., Weatherston, M. Visual Segmentation-Based Data Record Extraction From Web Documents. Proceedings of IEEE International Conference on Information Reuse and Integration, 2007, pp. 502-507. Sponsorship: IEEE

MUES (Mid Wales - Users - Ethnic Services) Ethnic services provision 2007-08. Policy guidance for Libraries for Life: Delivering the entitlement agenda for library users in Wales 2007-09

Urquhart, C., Thomas, R., Crossan, S. & Hines, B. (2008). MUES (Mid Wales - Users - Ethnic Services) Ethnic services provision 2007-08. Policy guidance for Libraries for Life: Delivering the entitlement agenda for library users in Wales 2007-09. Aberystwyth: Department of Information Studies, Aberystwyth University. Relates to report of same title - http://hdl.handle.net/2160/609 Sponsorship: CyMAL

Assessing the impact of a health library service. Best practice guidance

Urquhart, C. & Weightman, A. (2008). Assessing the impact of a health library service. Best Practice Guidance. Based on research originally funded by LKDN, now sponsored by National Library for Health. Aberystwyth: Department of Information Studies, Aberystwyth University. The guidance relates to a project report, Developing a toolkit for assessing the impact of health library services on patient care (also available in CADAIR). A version of this item is available as an online appendix to a paper in Health Information and Libraries Journal entitled: The value and impact of information provided through library services for patient care: developing guidance for best practice (Weightman, A., Urquhart, C. et al) available electronically prepublication Sponsorship: LKDN/NLH

Constructing Visual Taxonomies by Shape

Cook, Anthony; Gibbens, M.J., (2006) 'Constructing Visual Taxonomies by Shape', 18th International Conference on Pattern Recognition (ICPR'06) Volume 2, pp. 732 - 735 RAE2008