Journal Article10.1109/TPAMI.1987.4767935
Localizing Overlapping Parts by Searching the Interpretation Tree
TL;DR: The approach operates by examining all hypotheses about pairings between sensed data and object surfaces and efficiently discarding inconsistent ones by using local constraints on distances between faces, angles between face normals, and angles of vectors between sensed points.
read more
Abstract: This paper discusses how local measurements of positions and surface normals may be used to identify and locate overlapping objects. The objects are modeled as polyhedra (or polygons) having up to six degrees of positional freedom relative to the sensors. The approach operates by examining all hypotheses about pairings between sensed data and object surfaces and efficiently discarding inconsistent ones by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. The method described here is an extension of a method for recognition and localization of nonoverlapping parts previously described in [18] and [15].
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Detection of target models in 2D images by line-based matching and a genetic algorithm
Tsuyoshi Kawaguchi,Ryoichi Nagata,T. Sinozaki +2 more
- 24 Oct 1999
TL;DR: An algorithm to detect a 2D target model in cluttered environments and to find its scale s, translation (tx, ty) and orientation θ is proposed and the obtained transformation vector gives the scale, translation and orientation of the model in the image.
9
Edge replacement in the recognition of occluded objects
TL;DR: A preliminary Generalized Hough process is introduced that restores edges which were invisible because of occlusion or because of lack of contrast between occluding and occluded areas to recognize objects more successfully when all edges are visible.
9
Object recognition based on ORB and self-adaptive kernel clustering algorithm
Yazhong Zhang,Zhenjiang Miao +1 more
- 01 Oct 2014
TL;DR: ORB combine with Self-adaptive kernel clustering algorithm to implement object recognition using image matching and targets in test images can be recognized in a large degree, especially in the images contains of deformed targets.
9
Use neural networks to determine matching order for recognizing overlapping objects
Du-Ming Tsai,Ray-Yuan Tsai +1 more
TL;DR: Experimental results have shown that the proposed ANN approach succeeds in recognizing isolated objects, and achieves significant gain, in number of matches, over traditional model-based object recognition systems for identifying overlapping objects.
9
Recognizing partially occluded objects using markov model
Chau-Jin Chan,Shu-Yuan Chen +1 more
TL;DR: The effectiveness and practicability of the proposed approach have been proven by various experimental results and the solution of the method is useful for depth-search applications such as inspection of printed circuit board with multiple layers, underwater diving for searching objects, underground drilling for exploring mine, etc.
9
References
Theory of Edge Detection
David Marr,Ellen C. Hildreth +1 more
TL;DR: The theory of edge detection explains several basic psychophysical findings, and the operation of forming oriented zero-crossing segments from the output of centre-surround ∇2G filters acting on the image forms the basis for a physiological model of simple cells.
7.3K
•Journal Article
Computer vision
TL;DR: How the field of computer (and robot) vision has evolved, particularly over the past 20 years, is described, and its central methodological paradigms are introduced.
4K
Consistency in Networks of Relations
TL;DR: The primary aim is to provide an accessible, unified framework, within which to present the algorithms including a new path consistency algorithm, to discuss their relationships and the may applications, both realized and potential of network consistency algorithms.
2.8K
Understanding Line drawings of Scenes with Shadows
David L. Waltz
- 01 Jan 1975
TL;DR: A detailed discussion of the standard approach to computer interpretation of line drawings as three-dimensional scenes as well as some alternative approaches to this approach are discussed.
1.2K
Three-dimensional object recognition
Paul J. Besl,Ramesh Jain +1 more
TL;DR: In this paper, a precise definition of the 3D object recognition problem is proposed, and basic concepts associated with this problem are discussed, and a review of relevant literature is provided.
1.2K