Showing papers on "3D single-object recognition published in 1985"

Three-dimensional object recognition

Abstract: A general-purpose computer vision system must be capable of recognizing three-dimensional (3-D) objects. This paper proposes a precise definition of the 3-D object recognition problem, discusses basic concepts associated with this problem, and reviews the relevant literature. Because range images (or depth maps) are often used as sensor input instead of intensity images, techniques for obtaining, processing, and characterizing range data are also surveyed.

Identifying objects by touch: an "expert system".

Abstract: How good are we at recognizing objects by touch? Intuition may suggest that the haptic system is a poor recognition device, and previous research with nonsense shapes and tangible-graphics displays supports this opinion. We argue that the recognition capabilities of touch are best assessed with three-dimensional, familiar objects. The present study provides a baseline measure of recognition under those circumstances, and it indicates that haptic object recognition can be both rapid and accurate.

Object recognition using vision and touch

Abstract: A system is described that integrates vision and tactile sensing in a robotics environment to perform object recognition tasks. It uses multiple sensor systems (active touch and passive stereo vision) to compute three dimensional primitives that can be matched against a model data base of complex curved surface objects containing holes and cavities. The low level sensing elements provide local surface and feature matches which are constrained by relational criteria embedded in the models. Once a model has been invoked, a verification procedure establishes confidence measures for a correct recognition. The three dimen* sional nature of the sensed data makes the matching process more robust as does the system's ability to sense visually occluded areas with touch. The model is hierarchic in nature and allows matching at different levels to provide support or inhibition for recognition.

Pattern recognition device

Abstract: A pattern recognition device is arranged to have learning of a reference pattern vector carried out in a recognition unit by making use of the pause periods in the recognition processing, without particularly providing a learning section for learning the reference pattern vector. Namely, a part or the entirety of the arithmetic processing unit where the recognition result is obtained in the recognition unit by collating the input pattern with the recognition dictionary, can be utilized as the learning portion of the reference pattern vector. In concrete terms, the operation of multiplication-accumulation (inner product) which represents the main operation in the recognition processing and the learning processing, can be carried out by means of similar processes of handling. Therefore, by utilizing the processing section for sum of the products operation, of the recognition unit, which is in the idle state for pattern recognition, it becomes possible to carry out learning the reference pattern vector efficiently in time, without forcibly interrupting the pattern recognition processing by formally setting a learning condition.

Object recognition using multiple views

Abstract: A new approach to model based object recognition employing multiple views is described. The emphasis is given on the determination of camera viewpoints for succesive views looking for distinguishing features of objects. The distance and direction of the camera are determined separately. The distance is determined by the size of the object and the feature, while the direction is determined by the shape of the feature and the presence of the occluding objects.

Machine vision method and apparatus

Abstract: A first linear sensor tracks the movement of an object through an inspection station and a second linear sensor views the object transverse to the object path to acquire a line image of the object, and a synchronizing arrangement responds to the sensed position of the object to trigger the second sensor at spaced object positions to obtain a plurality of line images which cummulatively represent the object image. The image is compared, line-by-line, as acquired with a stored model of the object to determine the correlation between the object and the model.

Introduction to machine vision

Abstract: This paper reviews the basic steps in computer-based recognition of patterns in image data, with emphasis on industrial machine vision.

A vision system to identify occluded industrial parts

Abstract: A vision system is presented that recognizes occluded industrial parts. The unknown image may contain multiple objects that may touch or overlap giving rise to partial occlusion. The vision system uses stored models to locate and identify the objects in the scene. The models are based on the boundary of the object, since we assume that the objects are rigid and planar. From the polygon approximation of the boundary, vertices of high curvature are identified as "corners." These corners are used as features in detecting the model in the image. A globally consistent coordinate transform that takes the model into the image is found by using a Hough like transform and the corner features.

Three-Dimensional Object Representation In Imaging Systems

Abstract: In image-forming optical systems the image of a three-dimensional object consists of a superposition of focused and defocused object layers. For a quantitative evaluation of the object it is necessary to decompose the superposi-tion image into different images corresponding to single object layers. For this purpose the object radiation is measured with different optical transfer func-tions of the imaging system, for example, by simply changing the focus plane. Each image contains focused and defocused parts of the object and can be described as a linear equation of the object layers, assuming linear space-invariant imaging properties. From these images the real object distribution can be calculated by the evaluation of the resulting linear system of equations in the Fourier domain. Due to noise in the detected images it is only possible to get an estimate of the true object distribution. In our case this estimate is based on an integral minimal mean square error in the reconstructed object. The algorithm is presented and demonstrated by simulation experiments and reconstructions of real human cell images in optical microscopy.

Recognition algorithms for the connection machine

Abstract: This paper describes an object recognition algorithm both on a sequential machine and on a SIMD parallel processor such as the MIT Connection Machine. The parallel version is shown to run three to four orders of magnitude faster than the sequential version.

Method Of 2D And 3D Object Recognition In Nonoptimal Conditions

Abstract: This paper describes an object recognition method that works in nonoptimal conditions. The method does not require a fixed camera position and can be applied in cases of partially occluded objects and noisy image data. The method is based on matching local properties of the contours of the model with the corresponding properties of image contours using a Hough-method. A general scheme for three-dimensional recognition is developed. The special case of two-dimensional recognition is carefully studied and a two-phase, piece-wise algorithm tuned for hardware realization is designed. The performance of the algorithms is shown through experiments.

Efficient new techniques for identification and 3D attitude determination of space objects from a single image

Abstract: We present new algorithms for rapid identification and three-dimensional (3D) attitude determination of a solid object from a single image, using a model matching approach. Our scheme allows the observed object to be partially occluded. The object, as well as the model to which it is matched are represented by the 3D surface constituting their boundaries. We assume that these surfaces consist of flat faces, i.e. the object and the model are (not necessarily convex) polyhedra. We represent each by an attributed graph. The nodes of the graph denote faces on the surface and edges indicate the adjacency of faces. Attributes on the nodes are features invariant to 3D motion made up of 2D moment invariants. With this representation the recognition problem becomes a subgraph matching problem between the image of the observed object and the stored models. We present an algorithm for the matching process, furthermore the exact attitude is obtained as a byproduct of the matching procedure.

Recognition of Moving Objects Using Feature Signatures

Abstract: An important application of machine vision systems is the recognition of known three-dimensional objects. A major difficulty arises when two or more objects project the same or similar two-dimensional image, often resulting in misclassification and degradation of system performance. The changes in images which result from the motion of objects provide a source of three-dimensional information which can greatly aid the classification process, but this three-dimensional analysis is computationally complex and subject to many sources of error. This work develops a methodology which utilizes the information derived from the apparent changes in object features over time to facilitate the recognition task, without the need to actually recover the three-dimensional structure of the objects under view. The basic approach is to generate a ``feature signature'' by combining the feature measurements of the individual regions in a long sequence of images. The static information in the individual frames is analyzed along with the temporal information from the entire sequence. These techniques are particularly applicable in situations where static image processing methods cannot discriminate between ambiguous objects. Two example implementations are presented to illustrate the application of the techniques of object recognition using motion information.

A tensor technique for determining three-dimensional object orientation using images from several viewing angles (robot vision, object recognition, sequence analysis)

Abstract: The research documented in this thesis was directed towards finding a method for the determination of the 3-D geometric transformation that relates two 3-D object orientations where the only available information is a set of 2-D images describing the object from several viewing angles. A tensor technique which does not rely on any type of feature correspondence information or differential object motion has been developed and implemented. The basis for this technique is the property that the moments of an object in some arbitrary orientation form the components of a tensor associated with that orientation. Therefore, 3-D Cartesian absolute moment tensors may be constructed from projections for each 3-D object orientation and may be used to form a simple linear system of equations which determines a unique solution for the 3-D transformation relating two 3-D object orientations. The rule giving the minimum number of projections as a function of the highest order of the tensor used in the calculations has also been found. The technique is developed for the case of objects described by sets of point mass arbitrarily distributed in 3-Space, but has application to more general object types.

A theoretical study of recognition of moving objects by monocular vision

Abstract: It is assumed that a human recognizes the movement and 3D-structure of an object based on the feature quantities and their changes extracted from the retinal images which are the projection of the three-dimensional object onto the retina. Human recognition of a moving object works as follows. First, primitive recognition takes place based on the change of feature quantity at the local areas corresponding to each small portion on the retina independently. This is local parallel information processing. Second, attention is shifted to the higher level of recognition which integrates all the local primitive recognition. This paper considers the local linear feature of the object. In other words, we consider the infinitesimal plane of the surface of the three-dimensional object, and we clarify the transformation of the two-dimensional image caused by the motion of the object. This transformation depends on both the motion of the object and the three-dimensional structure. Also, we clarify the law of the transformation of the linear features obtained from the image. Based on the above, we show that recognition of the motion of the object and the three-dimensional structure is possible without the need for identifying the corresponding points. In addition, we demonstrate the condition which the characteristic functions should satisfy, and the concrete computation methods to obtain the motion and the three-dimensional structure are given.

Three dimensional recognition from depth images by detailed surface encoding (moment invariants)

Abstract: This thesis describes a new approach to object recognition in three dimensions. The problem of three dimensional recognition is often reduced to the two subproblems of recognizing object components and recognizing the spatial relations between the components. Most previous work has concentrated on recognizing the spatial relations between components while approximating the components with easily parameterized surfaces. This emphasis has been a matter of necessity rather than of choice because it was difficult or impossible to parameterize the actual variations of contoured surfaces. The approach in this thesis is to concentrate on the 3D details of object structure, where it is assumed that objects to be recognized have already been isolated from the remainder of the scene they appear in. This approach is based on using numeric features that remain invariant under a general rotation in 3-space for a fixed visible part of an object. The features are derived from two dimensional moment invariants using the density function in the moments to encode the deviations of a contoured three dimensional surface from a plane. Hidden or partially hidden object surfaces are handled by segmenting objects into their distinct faces and applying the recognition features to each face. A new technique for segmenting an object into its faces and the recognition procedures for both the individual faces and the entire object are discussed. While the results of an actual implementation demonstrate that these features alone suffice to implement a fast, reliable object recognition procedure it is expected that the present approach of concentrating on the detailed contouring of 3D objects will eventually complement techniques relying on component spatial relations to yield more powerful recognition systems than now exist.

Fuzzy sets in pattern recognition, image analysis and automatic speech recognition

Abstract: Fuzzy set theory, a recent generalization of classical set theory, has attracted the attention of researchers working in various areas including pattern recognition, which has had a seminal influence in the development of this new theory. This paper attempts to discuss some of the methodologies that have been suggested for pattern recognition, and techniques for image processing and speech recognition.

Fast Digital Image Processing Algorithms and Techniques for Object Recognition and Decomposition

Abstract: Fast digital algorithms are required in many applications of digital image processing, especially when many images of large si- ze are involved (as in astronomy data analysis) or real-time im- plementation is needed.