Showing papers by "Thierry Pun published in 1992"

Comparison of photodensitometric with high-resolution digital analysis of bone density from serial dental radiographs.

Abstract: Photodensitometry is known to provide high spatial resolution and continuous measurement of optical density for the analysis of dental radiographs, whereas digitization allows powerful image manipulations but, when using conventional video cameras, gives less spatial resolution and fewer grey levels. The aim of this study was therefore to develop a technique of high-resolution digital analysis for the measurement of bone density following the same principles as those of photodensitometry and based upon the use of a CCD Scanner Camera which provides up to 4096 grey levels and a spatial resolution of 4096 x 4096 pixels. Twenty-four zones were analysed with both techniques in five serial dental radiographs taken before and after periodontal therapy in eight patients. Statistical comparison of the results obtained by digital analysis and photodensitometry shows that the two techniques have the same accuracy.

Probabilistic approach to 3-D inference of geons from a 2-D view

Abstract: A new, probabilistic approach for inferring 3-D volumetric primitives from a single 2-D view is presented. This recognition relies on the assumption that every object can be decomposed into component parts that belong to a finite set or alphabet of volumetric primitives (geons). For each possible primitive from the permissible set, a conditional probability function is computed. This law specifies the probability of obtaining the primitive given an observable 2- D measure or feature. The distribution functions are determined by simulation, on the basis of a representative number of random projections of the primitives. The measures themselves are chosen in such a way that they can easily be extracted from real images and their discriminative power for the volumetric primitive inference is high. Examples illustrate the proposed approach.

Potato operation: computer vision for agricultural robotics

Abstract: Each year at harvest time millions of seed potatoes are checked for the presence of viruses by means of an Elisa test. The Potato Operation aims at automatizing the potato manipulation and pulp sampling procedure, starting from bunches of harvested potatoes and ending with the deposit of potato pulp into Elisa containers. Automatizing these manipulations addresses several issues, linking robotic and computer vision. The paper reports on the current status of this project. It first summarizes the robotic aspects, which consist of locating a potato in a bunch, grasping it, positioning it into the camera field of view, pumping the pulp sample and depositing it into a container. The computer vision aspects are then detailed. They concern locating particular potatoes in a bunch and finding the position of the best germ where the drill has to sample the pulp. The emphasis is put on the germ location problem. A general overview of the approach is given, which combines the processing of both frontal and silhouette views of the potato, together with movements of the robot arm (active vision). Frontal and silhouette analysis algorithms are then presented. Results are shown that confirm the feasibility of the approach.

Digital image processing of electron micrographs: the PIC System II

Abstract: The PIC system, an integrated package of Fortran programs and subroutines designed to run on the Digital Equipment Corporation VAX family of computers, has been developed for analysis of electron micrographs with emphasis on the particular requirements for structural analysis of biological macromolecules. The substantially improved VAX version of PIC reported here has been developed from an earlier PDP-11 version which was, in turn, developed from a set of IBM 370 programs called MDPP. PIC now encompasses over 150 commands or processing operations that afford a comprehensive range of image processing operations including image restoration, enhancement, Fourier analysis, correlation averaging, and multivariate statistical analysis including clustering and classification. In particular, we describe our software for correction of imperfect lattices, as well as programs for correlation alignment and averaging of \"single particle\" images.

LaboImage: portable window-based environment for research in image processing and analysis

Abstract: LaboImage provides scientists with general purpose as well as specific processing families and tools in a highly interactive environment The current software results from an evolution reflecting several years of development and experiences This paper first presents the new X Window / OSF Motif version of LaboImage, as it is seen by the user It also describes how an image is manipulated in the system, how processing methods are applied and results are visualized Multiple types of interaction between the user and the system are addressed The implementation aspects are then detailed They concern data structures as well as algorithms and interfaces The data file and descriptor file formate used for strong images is described The organization in memory of multiple data such as images, vectors, and macros is presented The source code organization is also discussed A clear separation between algorithmic and interface parts in the code appears to be very important, in order to allow easy further developments of the system

3d image analysis for agricultural robotics: the potato operation

Abstract: The Potato Operation aims to automate pulp sampling in potatoes in order to detect viral activity. This article presents the vision aspect of the project. The goal is to compute the 3D coordinates of the biggest germ. This is accomplished by means of active vision and image analysis methods. A test system has been installed and experiments have been performed on a large sample, yielding a low error rate.

Do I Need A Robot Or A Nonrobot Automated System

Abstract: In manufacturing automation, the current focus is on integration at enterprise level. This however has not reduced the need for automation on the shopfloor. On the contrary, the latter has become more pressing. On the shopfloor, major manufacturing costs are related to handling and assembly operations. Difficulties in automating those operations are twofold. First, there is usually a line beyond which manual operation remains the most economical solution today. And then for each application, the proper level of sophistication in terms of automation technology should be adopted. The paper concentrates on the second difficulty. Most often, products and processes can be designed so as to require fairly simple automated systems for their manufacturing (on/off devices, programmable logic controllers, independent actuators, etc.). But when the application features significant position and/or orientation uncertainties (mathematical space of dimension 3 or more), some kind of perception is required to cope with them, and this usually results in adaptive workpiece or tool trajectories. Robotics provide unmatched solutions for such multi-dimensional, coordinated motions. General guidelines are introduced in order t o select the appropriate type of component for automation. Then, two case studies follow. In both cases, the application is complex, in terms of parameter variability. Many examples are given of correspondence between given guidelines and concrete, low-level details. In particular, while in the first case automated solutions can be devised with traditional, multiple independent servoed motions and general-purpose computers, in the second one, industrial robotics with dedicated controllers provide the right answer.

3D computer vision for agrotics: the potato operation, an overview

Abstract: The potato operation is a project belonging to the domain of agricultural robotics, or agrotics. It aims to automate pulp sampling of potatoes in order to detect viral diseases. The difficulty of this problem lies in the high variability (shape, texture, colour) of natural objects. Three approaches have been studied, based on contour classification, on thermometry and on active vision and image analysis. Cooperation between robotics, active 3D computer vision and robust image analysis leads to a performant solution. >

Electromagnetic models for perceptual grouping

Abstract: The use of electromagnetic analogies for perceptual grouping of image primiti ves is presented. This approach assumes that grouping is a lo w-level, data-driven and global process, with all image tokens interacting in some way. Two computational models are introduced, which allo w determination of proximity and directionality of image primiti ves. With the second model, prominent image features are considered as being electrical charges. According to Poisson’s equation, they generate a scalar potential and an associated “electrical” v ector field. The potential and field, determined by the combined global influence of all image features, are well defined o ver the entire image. The scalar potential can be used for proximity grouping, while the local direction of the field allows grouping of primitives according to their common directional tendency. Implementation problems and solutions are presented. V arious results are sho wn and discussed.

Multisource and color lighting for detection of small protuberance

Abstract: In typical machine vision problems such as quality control or object location, it is often the case that elements of interest are small protuberances over a surface. We present an innovative and robust approach aiming at detecting such protuberances. Its basic ideas are to detect the shadows produced by the protuberances and to use several light sources simultaneously to enhance detection. Each light source produces a different set of shadows; combining the shadows produced by all light sources helps to locate the protuberance, because these shadows are the only significantly varying patterns between views. Rather than using several white light sources in sequence, it is possible to use simultaneous color sources with appropriate filters to separate the image into independent channels. The approach has been validated on a concrete problem with highly variable protuberances and nonplanar surfaces. The results confirm the robustness of this approach, which could be used for other problems as well.

Relaxation network for a feature-driven visual attention system

Abstract: In this paper an attention module is described, which can be used by an active vision system to generate gaze changes. This module is based on a bottom-up, feature-driven analysis of the image. The results are regions of the input image which contain strange features, i.e., locations of the most `interesting' and `important' information. The method proposed for detecting such regions is based on the decomposition of the input image into a set of independent retinotopic feature maps. Each map represents the value of a certain attribute computed on a set of low-level primitives such as contours and regions. Relevant objects can be detected if the corresponding primitives have a feature value strongly different from the neighboring ones. Local comparisons of feature values are used to compute such measures of `difference' for each feature map and give rise to a corresponding set of conspicuity maps. In order to obtain a single measure of interest for each location and to make the process robust to noise, a relaxation algorithm is run on the set of conspicuity maps. A dozen iterations are sufficient to detect a binary mask identifying the attention regions. Results on real scenes are presented.