Deconstruction of compound objects from image sets

We propose a method to recover the structure of a compound object from multiple silhouettes. Structure is expressed as a collection of 3D primitives chosen from a pre-defined library, each with an associated pose. This has several advantages over a volume or mesh representation both for estimation and the utility of the recovered model. The main challenge in recovering such a model is the combinatorial number of possible arrangements of parts. We address this issue by exploiting the sparse nature of the problem, and show that our method scales to objects constructed from large libraries of parts

CBCV: A CAD-based vision system

Journal ArticleThe CBCV system has been developed in order to provide the capability of automatically synthesizing executable vision modules for various functions like object recognition, pose determinaion, quality inspection, etc. A wide range of tools exist for both 2D and 3D vision, including not only software capabilities for various vision algorithms, but also a high-level frame-based system for describing knowledge about applications and the techniques for solving particular problems?

Multi-Scale Vector-Ridge-Detection for Perceptual Organization Without Edges

We present a novel ridge detector that finds ridges on vector fields. It is designed to automatically find the right scale of a ridge even in the presence of noise, multiple steps and narrow valleys. One of the key features of such ridge detector is that it has a zero response at discontinuities. The ridge detector can be applied to scalar and vector quantities such as color. We also present a parallel perceptual organization scheme based on such ridge detector that works without edges; in addition to perceptual groups, the scheme computes potential focus of attention points at which to direct future processing. The relation to human perception and several theoretical findings supporting the scheme are presented. We also show results of a Connection Machine implementation of the scheme for perceptual organization (without edges) using color

Smoothness assumptions in human and machine vision, and their implications for optimal surface interpolation

In this paper we shall examine what smoothness assumptions are made about object surfaces, object motion, and image intensities. We begin by looking into the physiological limits of vision and how these might influence our perception of smoothness. We then look at a sampling of the computer vision and psychology literature, inferring smoothness constraints from the mathematical assumptions tacitly presumed by researchers. This look at computer vision and psychology of vision is not meant to be an inclusive study, but rather representative of the assumptions made, and in part representative of the mathematical models used therein. We shall conclude that prevalent assumptions are that surfaces, motion, and intensity images are functions in C2, eland c2 respectively. In the latter portion of this paper we examine one use of explicit assumptions on smoothness in the definition of existing method for obtaining "optimal" surface interpolation. We briefly introduce the nomenclature of information-based complexity originated by Traub, Wozniakowski, and their colleagues, which is the mathematical machinery used in obtaining these "optimal" surfaces. This theory requires that we know the class of functions from which our desired surface comes, and part of the definition of a class is the degree of smoothness. We then survey many possible classes for the visual interpolation problem of two dimensional surfaces, and state formulas from which one can obtain the optimal surface interpolating given depth data

Application of Super Resolution Convolutional Neural Networks (SRCNNs) to enhance medical images resolution

The importance of resolution is crucial when working with medical images. The possibility to visualize details lead to a more accurate diagnosis and makes segmentation easier. However, obtention of high-resolution medical images requires of long acquisition times. In clinical environments, lack of time leads to the acquisition of low-resolution images. Super Resolution (SR) consist in post-processing images in order to enhance its resolution. During the last years, a branch of SR is getting promising results. This branch focuses in the application of Convolutional Neural Networks (CNNs) to the images. This project is intended to create a network able to enhance resolution of knee MR stored in DICOM format. Different networks are proposed, and evaluation is made by computing Peak Signal-to-Noise Ratio (PSNR) and normalized Cross-Correlation. One of the networks proposed, SR-DCNN, presented better results than the conventional method, bicubic interpolation. Finally, visual comparison of the SR-DCNN and bicubic interpolation also showed that the network proposed outperforms the conventional methods.Ingeniería Biomédic

A domain independent adaptive imaging system for visual inspection

Computer vision is a rapidly growing area. The range of applications is increasing very quickly, robotics, inspection, medicine, physics and document processing are all computer vision applications still in their infancy. All these applications are written with a specific task in mind and do not perform well unless there under a controlled environment. They do not deploy any knowledge to produce a meaningful description of the scene, or indeed aid in the analysis of the image. The construction of a symbolic description of a scene from a digitised image is a difficult problem. A symbolic interpretation of an image can be viewed as a mapping from the image pixels to an identification of the semantically relevant objects. Before symbolic reasoning can take place image processing and segmentation routines must produce the relevant information. This part of the imaging system inherently introduces many errors. The aim of this project is to reduce the error rate produced by such algorithms and make them adaptable to change in the manufacturing process. Thus a prior knowledge is needed about the image and the objects they contain as well as knowledge about how the image was acquired from the scene (image geometry, quality, object decomposition, lighting conditions etc,). Knowledge on algorithms must also be acquired. Such knowledge is collected by studying the algorithms and deciding in which areas of image analysis they work well in. In most existing image analysis systems, knowledge of this kind is implicitly embedded into the algorithms employed in the system. Such an approach assumes that all these parameters are invariant. However, in complex applications this may not be the case, so that adjustment must be made from time to time to ensure a satisfactory performance of the system. A system that allows for such adjustments to be made, must comprise the explicit representation of the knowledge utilised in the image analysis procedure. In addition to the use of a priori knowledge, rules are employed to improve the performance of the image processing and segmentation algorithms. These rules considerably enhance the correctness of the segmentation process. The most frequently given goal, if not the only one in industrial image analysis is to detect and locate objects of a given type in the image. That is, an image may contain objects of different types, and the goal is to identify parts of the image. The system developed here is driven by these goals, and thus by teaching the system a new object or fault in an object the system may adapt the algorithms to detect these new objects as well compromise for changes in the environment such as a change in lighting conditions. We have called this system the Visual Planner, this is due to the fact that we use techniques based on planning to achieve a given goal. As the Visual Planner learns the specific domain it is working in, appropriate algorithms are selected to segment the object. This makes the system domain independent, because different algorithms may be selected for different applications and objects under different environmental condition

The situated vision : a concept to facilitate the autonomy of the systems

This paper is about vision to make systems more autonomous. We parallel two aspects of the current evolution of System-Perception: more ambitious yet coherent tasks tightly rely on more abstract description and control. As vision is likely to be a major and complex sensory modality for machines as it is for most animals, we concentrate our development on it. In the first part we show how thinking to systems helped to better pose vision problems and solve them in a useful manner. That is the “active vision” trend that we explain and illustrate. Along the same line, the necessity for anticipation shows further, leading to a first definition of “situated vision”. The second part deals with how to design systems able to achieve such vision. We show from a few examples how architectural descriptions evolve and better fit important features to grasp – a model – in view of more efficient control towards intelligence. Inner communication flows are better be controlled than local tasks that should be assumed completed efficiently enough in all cases. We conclude with a plausible sketch of a system to be experimented on in situations that require some autonomy.Cet article présente notre approche des techniques par lesquelles la vision doit contribuer à l’autonomie d’un système. Il établit un parallèle entre deux aspects de l’évolution des systèmes de perception. La première partie explique comment une conception système des problèmes de la vision (dénommée vision active) aide à les résoudre efficacement. Il y apparaît la nécessité d’anticiper, conduisant à une première définition de la vision située et cataloguant un ensemble des situations qui constitue la description fonctionnelle de l’exo-système. La seconde partie montre comment les descriptions architecturales des systèmes de fusion s’adaptent aux caractéristiques à extraire, et donc comment un modèle émerge progressivement au service d’un contrôle opportuniste vers plus d’intelligence (au sens capacité à utiliser conjointement des renseignements issus de diverses modalités). Nous pensons que l’essentiel des développements à court et moyen terme proviendra des avancées autour de cette approche duale et du concept de «systèmes autonomes», dans lesquels les flots internes de communication apparaissent alors plus utiles à contrôler que les tâches locales, supposées accomplies de manière satisfaisante. Un schéma de système de contrôle de flux est enfin proposé pour mettre en oeuvre au plus haut niveau du modèle un contrôleur de commutations entre différentes situations. Viser des tâches plus ambitieuses s’appuie sur des descriptions et un contrôle plus abstraits