3 edition of Recognition of 3-D symmetric objects from range images in automated assembly tasks found in the catalog.
Recognition of 3-D symmetric objects from range images in automated assembly tasks
by Old Dominion University Research Foundation, Dept. of Electrical and Computer Engineering, College of Engineering and Technology, Old Dominion University, National Technical Information Service, distributor in Norfolk, Va, [Springfield, Va
Written in English
|Other titles||Recognition of three dimensional symmetric objects from range images in automated assembly tasks.|
|Statement||by Nicolas Alvertos, principal investigator and Ivan D" Cunha.|
|Series||NASA-CR -- 187664., NASA contractor report -- NASA CR-187664.|
|Contributions||United States. National Aeronautics and Space Administration.|
|The Physical Object|
3. Hierarchical symmetry recognition In this way, the symmetry cells will be clustered into different groups, each group with consistent 3-D geometry interpretation is called a symmetry complex. Finally, we may pass this hierarchy to all sorts of higher level applications. We focus on the problem of semantic segmentation based on RGB-D data, with emphasis on analyzing cluttered indoor scenes containing many instances from many visual categories. Our approach is based on a parametric figure-ground intensity and depth-constrained proposal process that generates spatial layout hypotheses at multiple locations and scales in the image .
a real-world database of 3-D range images of common ob-jects, acquired through an active stereo rig. 1. Introduction In recent years, shape completion has become a major re-search area in 3-D computer vision. In the shape completion problem, one is given a partial 3-D view of an object surface. The view might be acquired through a stereoscopic. ing symmetric curve and point pairs, getting the depth val-ues using symmetry, and reconstructing a depth map using these sparse depths. The reconstruction can then be used as a proxy geometry for various sketch processing tasks such as viewing the sketch from a novel view-point, or under a different perspective projection, stereoscopic viewing.
In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to 4/5(1). Assign unclassified objects with Mean Difference to unclassified (20) > 4 m; Merge buildings objects and assign small objects (area.
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Get this from a library. Recognition of 3-D symmetric objects from range images in automated assembly tasks. [Nicolas Alvertos; United States. Recognition of 3-D Symmetric Objects from Range Images in Automated Assembly Tasks by Nicolas Alvertos* and Ivan D'Cunha** _7 Abstract This research presents a new technique for the three-dimensional recognition of symmetric objects from range images.
Beginning from the implicit representation of quadrics, a set of ten coefficients is. T08iyasu L. Kunii PREFACE The primary aim of this book is to present a coherent and self-contained de scription of recent advances in three-dimensional object recognition from range images.
Three-dimensional object recognition concerns recognition and localiza tion of objects of interest in a scene from input images.
Results: In a first experiment we compared recognition of symmetric objects with nonsymmetric objects. The generalization ability from a given "model" view of an object to novel viewing directions (range ±90°) increased from 64 average recognition rate for nonsymmetric objects to 77 for symmetric by: 1.
In this paper we extend a recent approach for 3D object recognition in order to deal with rotationally symmetric objects, which are frequent in daily environments. We base our work in a recent method that represents objects using a hash table of shape features, which in the case of symmetric objects contains redundant by: 5.
eral symmetry assumptions to improve 3-D reconstruction from image se-quences; and Zabrodsky et al.  provided a good survey on studies of reﬂective symmetry and rotational symmetry in computer vision. In 3-D object and pose recognition, Rothwell et al.
 pointed out that the assumption of. Summary: We completed the second round of the symmetry detection competition and presented results at a dedicated workshop hosted at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) in Colorado Springs, competition was divided into three parts, each focusing on one of three types of symmetries: reflection, rotation and translation.
Under special conditions, a single non-accidental "model" view is theoretically sufficient for recognition of novel views, if the object is bilaterally symmetric, whereas the.
In this paper we extend a recent approach for 3D object recognition in order to deal with rotationally symmetric objects, which are frequent in daily environments. We base our work in a recent method that represents objects using a hash table of shape features, which in the case of symmetric objects contains redundant information.
We propose a way to remove redundant. ited number of object instances lying on a ﬂat surface on a given face, because of the cost of pose annotation. This article tries to address those issues. In section3, we propose automatic techniques for generating fully annotated real range image and synthetic RGBD datasets of many in-stances of objects in arbitrary poses, with no redundancy.
Figure 2: Automatic production of annotated range images of many object instances in bulk. Range images are pro-duced by active stereo matching (left), while ground truth poses are annotated by detecting, in intensity images, mar-kers placed on object instances (right).
tirely occluded – provided that the object is densely enough (RX. Symmetry: A Powerful Mid-Level Shape Prior • Many objects in our world can be modeled as configurations of symmetric parts, e.g., humans, animals, plants, and a vast array of man-made objects.
• This regularity has shaped the evolution of the human visual system, which can quickly detect symmetry as a non-accidental feature. The hypothesis of symmetry-induced virtual views together with a network model that successfully accounts for human recognition of generic 3D objects leads to.
The paper deals with the recognition of symmetric three-dimensional (3D) bodies that can be rotated and translated. We provide a complete list of all existing combinations of rotation and reflection symmetries in 3D.
We define 3D complex moments by means of spherical harmonics, and the influence of individual symmetry groups on complex moment values is studied. Thoretical arguments suggest and psychophysical experiments confirm that humans may be better in the recognition of symmetric objects.
The hypothesis of symmetry-induced virtual views together with a network model that successfully accounts for human recognition of generic 3D objects leads to predictions that we have verified with.
Given an image of a planar structure S with a reflective symmetry R in 3-D space, R ′ = R 0 RR 0 T, and N all known, if the origin of the object frame is chosen to be on the object plane, the initial pose g 0 can be determined up to an arbitrary translation of the frame along the intersection line of the object plane and the plane of reflection.
Tools for 3-D object location from geometrical features by monocular vision. Part-based modeling and qualitative recognition. Appearance-based vision and the automatic generation of object recognition programs. Recognizing 3-D objects using constrained search.
Recognition of superquadric models in dense range data. Recognition by alignment. It emphasizes that these methods only lead to extraction of 3-D shapes: they do not immediately lead to the recognition of 3-D objects.
Further analysis is presented demonstrating how object recognition can be tackled using 3-D geometry, in particular, by identification of junctions, familiar flat shapes such as circles, and straight boundaries. of 3-D sensing, range sensors provide 3-D points with reasonable quality and high sampling rates, su cient for e cient shape-based object recognition.
In recent past, Drost et al.  proposed an approach which extracts description from a given object model, using point pair features, encoding the geometric re-lation between oriented point pairs. To do this, we created an image by drawing check pairs from two of the three binarized images just constructed.
In the example shown, % of the check pairs of the symmetric image were mixed with % of the antisymmetric image, resulting in an image that was 75% symmetric (C =.
I assume the variable thresh is a binary image. In order to find symmetry for a non-uniform object, i suggest we compare the projection of the binary pixels in the X and Y axis.
Then compare the 2 histogram via histogram comparing method such as correlation, chi-square or Bhattacharyya distances.gle 2p=n.
Then we are looking for symmetry points of objects with a symmetry groupC 2m, because, when an object is invariant under a rotation by p=m, it is also invariant under a rotation by p.
A problem in symmetry detection is that the size of the symmetric object is generally not known be-forehand. For computing the symmetry score value.In this note we discuss how recognition can be achieved from a single 2D model view by exploiting prior knowledge of an object's symmetry.
We prove that for any bilaterally symmetric 3D object one non-accidental 2D model view is sufficient for recognition since it can be used to generate additional "virtual" views.