An active 3D acquisition system is presented that projects a simple pattern of squares on a scene and views it from a different angle. This paper describes how the observed pattern can be extracted from the image data. The underlying algorithm automatically detects the lines and crossings of the projected pattern in the image. Experiments show that the algorithm is robust and provides accurate three-dimensional (3D) reconstructions. Its one-shot operation principle enables the system to retrieve the shape of moving objects.

To obtain a Euclidean reconstruction from images the cameras have to be calibrated. In recent years different approaches have been proposed to avoid explicit calibration. The problem with these methods is that several parameters have to be retrieved at once. Because of the non-linearity of the equations this is not an easy task and the methods often fail to converge. In the's paper a stratified approach is proposed which allows to first retrieve the affine calibration of the camera using the modulus constraint. Having the affine calibration it is easy to upgrade to Euclidean. The important advantage of this method is that only three parameters have to be evaluated at first. From a practical point of view, the major gain is that an affine reconstruction is obtained from arbitrary sequences of views, whereas so far affine reconstruction has been based on pairs of views with a pure translation in between. A short illustration of another application is also given. Once the affine calibration is known, the constraint can be used to retrieve the Euclidean calibration in the presence of a variable focal length.

An active 3D acquisition system is presented that is based on the projection a simple pattern of squares. Through the observation of the projected pattern, and assuming pseudo-orthographic projection, the 3D shape of the scene can be retrieved up to a single parameter, that can be determined through a particularly simple calibration. An algorithm has been developed to automatically detect the lines and crossings of the projected pattern in the image. Experiments on a variety of scenes show that the algorithm is robust and provides accurate 3D reconstructions. Its one-shot operation enables the system to retrieve the shape of moving objects.

The paper describes a systematic approach to the analysis of symmetries in planar shapes. The shapes can be observed from arbitrary viewpoints. Hence, the results encompass the case of skewed symmetries. In fact, skewing is assumed to arise from perspective distortions, whereas most of the literature restricts the analysis to affine skewing. The point of departure is the identification of structures that symmetries keep fixed in an image. These define subgroups of the projectivities, which in turn assume simpler invariants than their projective counterparts. These invariants allow both simpler and more selective detection and checking of symmetries.

How to obtain accurate and reliable motion fields is a key problem for motion-compensated (MC) interpolation in video coding. An improved pel-based motion estimation algorithm with smoothness constraints is presented. In order to cope with various moving objects and their complex motion, a hierarchical motion estimation algorithm is discussed. The experimental results show that the motion fields obtained are quite smooth and approximate to real motion.>

This paper describes a possible strategy to migrate from block-based coding towards object-based coding. We use the results of an intelligent motion estimation algorithm to define low resolution (block based) starting islands. The information from the motion vector field and the image data together with spatial relations are used to measure the cost to belong to a given object. Finally a selection procedure is used to generate the high resolution (pixel based) objects.>

A new method for scattered radiation compensation in 2D projection radiographic imaging is presented. By comparing the detected signal under a small partially transparent shield (e.g. aluminum disk or strip), positioned between the X-ray source and the object being imaged, with the detected signal near the border of the shadow of the shield, the radiation scatter signal in the location of the shield is calculated. For polychromatic X-radiation, calibration with two known basis materials allows accurate calculation of the radiation scatter. Shields are positioned at several locations between object and source. By an interpolation technique, the radiation scatter in every region of interest can be calculated. The radiation scatter image is then subtracted from the original image of the object. The method needs only one exposure of the object. The primary radiation signals at the locations of the shields have undergone an extra drop, but the information about the object under the shields is not lost. One could restore the image by compensating for the effect of the shields on the primary signal. The theory of PTS is presented and results of experiments are given.>

The potential of coupled nonlinear diffusion processes to provide an efficient and implementable mechanism capable of edge preserving smoothing and noise suppression is investigated. The basic idea is that several maps undergo coupled development towards an equilibrium state. These maps could contain intensity, local edge strength, range, or another quantity. All these maps, including the edge map, contain continuous rather than all-or-nothing information, following a strategy of least commitment. Each of the approaches is developed and tested on a parallel transputer network.>

Line drawings of 3D environments are often sufficient for scene interpretation. The VLSI implementation of an algorithm for extracting edge information from grey level images is presented. It yields one pixel wide connected edge point strings and is applicable in real-time.>

An investigation is carried out on employing subjective redundancy to improve DCT coding respecting the non-trackable motion in video signals. The human visual system (HVS) is exploited through the two aspects of its behavior, the sensitivity to a visual stimulus and visual masking. Some measures possible to adopt those aspects are discussed and tested. The result showed that: (1) during the non-trackable motion, a reduction of the HVSs response to the reconstruction error occurs because of the loss of sensitivity and the increase of the masking effect; (2) as far as the sensitivity is concerned, adjusting the quantization weighting function with respect to the motion is unlikely to bring substantial improvement; (3) the magnitude of the visual masking by the image content over the reconstruction error is positively related to the function of spatial masking and the relative speed between the eyes and the image. An example of application on motion compensated DCT coding is presented.

This paper introduces a number of systems of coupled, non-linear diffusion equations and investigates their role in edge-preserving smoothing and noise suppression. The basic idea is that several maps describing the image undergo coupled development towards an equilibrium state representing the enhanced image. These maps could contain: e.g., intensity, local edge strength, range or another quantity. All these maps, including the edge map, contain continuous rather than all-or-nothing information, following a strategy of least commitment. Each of the approaches has been developed and tested on a parallel transputer network.

It is shown how object recognition and optical flow can be captured within a single framework. These examples have been selected because they illustrate two complementary problems which can be tackled using the same unified approach based on Lie theory. The object recognition work referred to is based on the extraction of shape invariants and has been reported elsewhere. The present study focuses on using the same framework for the calculation of the optical flow. Besides the introduction of some new methods, it is shown that several well-known schemes can be derived following the same principles.>

Semidifferential invariants, combining coordinates in different points together with their derivatives, are used for the description of planar contours. Their use can be seen as a tradeoff between two extreme strategies currently used in shape recognition: (invariant) feature extraction methods, involving high-order derivatives, and invariant coordinate descriptions, leading to the correspondence problem of reference points. The method for the derivation of such invariants, based on Lie group theory and applicable to a wide spectrum of transformation groups, is described. As an example, invariant curve parameterizations are developed for affine and projective transformations. The usefulness of the approach is illustrated with two examples: (1) recognition of a test set of 12 planar objects viewed under conditions allowing affine approximations, and (2) the detection of symmetry in perspective projections of curves.>

Human subjects easily perceive and extensively use shape regularities such as symmetry or periodicity when they are confronted with tile task of object description and recognition. A computer vision algorithm is presented that emulates such behavior in that it similarly makes use of shape redundancies for the concise description and meaningful segmentation of planar object contours. The contours are analyzed in so-called arc length space. This parameter space facilitates the detection of regularities under a broad range of viewing conditions. Several of the irregularities which have traditionally been treated in isolation, are given a unified substrate for their detection and use in building compact models. Regularity consistency checks are made and, if necessary, altered versions are inferred.>

An optimized recursive binary nesting (RBN) algorithm for coding true color documents is presented. The RBN compression in which algorithm is a segmentation algorithm in which a picture is subdivided into regions with equal properties and for each region only the relevant information for the human eye is kept. Thus, the compressed image consists of segmentation information and the information of the picture behavior in those regions. The picture is subsampled on a quadtree based lattice (segmentation information). The inner pixels are approximated with the use of four lattice corner pixels (pictorial behavior). The subdivision in blocks has to be a function of the image contents. The size of the initial blocks is 65*65. Each pixel in the block is approximated as a weighted average of the four corner pixels (bilinear interpolation). The efficient VLSI architecture used to implement the algorithm is termed the lowly multiplexed cooperating data-path style. Several other designs under consideration are briefly reviewed.>

A vision-guided robot workstation is presented which picks up workpieces from a fast-moving conveyor belt. The role of computer vision as the feedback transducer strongly affects the closed-loop dynamics of the overall system, and a tracking controller with dynamic visual feedback is designed for achieving fast response and high control accuracy. In view of the long time delay and the heavy noise corruption embedded in visual data, the problem of visual controller design is posed in the framework of stochastic optimal control theory. The Kalman filter is chosen to estimate the state of the target motion and formulated as a joint detection and adaptive estimation method. The generalized predictive control strategy is utilized to compute the optimal path control data and implemented in a weighted version. Experimental results are given to show the effectiveness of the approach.>

A parallel, self-similar network is proposed for the analysis and identification of possibly complex objects. The system allows one to define objects, or even conceptions at more abstract hierarchical levels, as equivalence classes. Based on this hierarchical, parallel, and distributed definition, the system constructs its networks. The system considers the object as an assembly of subparts or stiff components. Each stiff component can be defined by its geometrical contour and by surface characteristics (smoothness, texture, color, . . .). The entire object is defined hierarchically by the spatial and structural interrelations among the components. The system is able to recognize and analyze complex objects. Examples on real images are presented. It is also shown that the analysis can lead to an identification of specific instances of a complex equivalence class, as specific components and features of a new instance can be compared with several known instances. The identification of human faces is discussed as a test case

Motion-compensated (MC) interframe prediction which has been used widely in low bit-rate television coding is an efficient method to reduce the temporal redundancy in a sequence of television signals. However, if there is a violent or complicated motion in the scene such as in the broadcast television, the performance of MC interframe prediction will degenerate seriously while the intraframe prediction is better. It is obvious that an adaptive prediction scheme will provide a lower and more stable transmission rate than that obtained by simple MC interframe prediction or intraframe prediction. In this paper, a motion-compensated adaptive inter/intra frame prediction scheme based on the block-based motion estimation algorithm we proposed earlier [11] is presented. Adaptivity is achieved block-wise by comparing prediction errors of MC interframe predictor and intraframe predictor, one of the predictors which gives the smaller error is chosen as the actual predictor. To further compress transmission bit-rate, a variable length encoding is designed to encode the prediction errors and the motion information according to their actual distributions. Simulation results have shown that this adaptive prediction scheme is very efficient for different kinds of pictures containing slow to very violent motion, and a'high compression rate can be archived with very good picture quality.

The problem of strike identification in range image acquisition systems based on triangulation with periodically structured illumination is discussed. A coding scheme is presented based on a single fixed binary encoded illumination pattern, which contains all the information required to identify the individual strikes visible in the camera image. Every sample point indicated by the light pattern is made identifiable by means of a binary signature, which is locally shared among its closest neighbors. The applied code is derived from pseudonoise sequences, and it is optimized so that it can make the identification fault-tolerant to the largest extent. A prototype measurement system based on this coding principle is presented. Experimental results obtained with the measurement system are also presented.>

The recognition of the coronary blood vessels on single angiograms is addressed. Recognizing blood vessels on digital subtraction angiograms is a prerequisite to automatic diagnosis and quantification of blood-vessel pathologies. It is shown how stereoscopic angiograms can improve the interpretation process. The computational strategy recommended to solve the problem has the following important characteristics. First, the model is hierarchically instantiated, starting from a partial model and working up to the final complete model. Second, the contact with the image is never broken during the interpretation process. Missing model attributes are instantiated by returning to the image. Third, delineation (segmentation and image partitioning), interpretation, and stereoscopic matching are not sequential but integrated processes. Fourth, the knowledge representation is partially iconic. The iconic memory is used to guide the search for missing or incomplete attributes in the image.>

A resolution-independent method for detection of imperfections in quasi-periodic textures is described. After image standardization, the period is estimated in the horizontal and vertical directions. This determines the size of a sparse convolution mask. Mask coefficients are determined by the well-known technique of eigenfilter extraction. The method thus offers a completely automated generation of a bank of suitable filters, the form and the coefficients of which are made dependent on the texture type to be inspected. After feature extraction in the filtered images, a Mahalanobis classifier is applied.>

Expert systems and image understanding have traditionally been considered as two separate application fields of artificial intelligence (AI). In this paper it is shown, however, that the idea of building an expert system for image understanding may be fruitful. Although this paper may serve as a framework for situating existing works on knowledge-based vision, it is not a review paper. The interested reader will therefore be referred to some recommended survey papers in the literature.

The use of new staining methods and the large amount of investigations for human chromosomes has created a need for a useful automated chromosome analysis. Because of the lack of accuracy of the existing systems, a new approach is given. The problems in the different stages of the classification process are described. A comparison of the decision-statistical, syntactical, and interpretative approach is discussed. A system realization for the total karyotyping of chromosomes, based on the interpretative approach, is described in more detail. Guided by one of the models in the world model structure, a current description is recognized in a three-phase interpreter.