Journal Article10.1109/34.61706
Partial shape classification using contour matching in distance transformation
H.-C. Liu,M.D. Srinath +1 more
216
TL;DR: An algorithm is presented to recognize and locate partially distorted 2D shapes without regard to their orientation, location, and size and works reasonably well in the presence of a moderate amount of noise.
read more
Abstract: An algorithm is presented to recognize and locate partially distorted 2D shapes without regard to their orientation, location, and size. The algorithm first calculates the curvature function from the digitized image of an object. The points of local maxima and minima extracted from the smooth curvature are used as control points to segment the boundary and to guide the boundary-matching procedure. The boundary-matching procedure considers two shapes at a time, one shape from the template databank, and the other from the object being classified. The procedure tries to match the control points in the unknown shape to those of a shape from the template databank, and estimates the translation, rotation, and scaling factors to be used to normalize the boundary of the unknown shape. The chamfer 3/4 distance transformation and a partial distance measurement scheme constitute the final step in measuring the similarity between the two shapes. The unknown shape is assigned to the class corresponding to the minimum distance. The algorithm has been successfully tested on partial shapes using two sets of data, one with sharp corners and the other with curve segments. This algorithm not only is computationally simple, but also works reasonably well in the presence of a moderate amount of noise. >
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
Axioms and fundamental equations of image processing
TL;DR: In this article, the authors classify image multiscale transforms into three categories: architectural requirements, structural requirements and morphological requirements, which correspond to shape-preserving properties (rotation invariance, scale invariance etc.).
1.1K
A survey of shape analysis techniques
TL;DR: This paper provides a review of shape analysis methods, which play an important role in systems for object recognition, matching, registration, and analysis.
1K
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
F. Zana,J.-C. Klein +1 more
TL;DR: An algorithm based on mathematical morphology and curvature evaluation for the detection of vessel-like patterns in a noisy environment is presented and its robustness and its accuracy with respect to noise are evaluated.
Symmetric phase-only matched filtering of Fourier-Mellin transforms for image registration and recognition
TL;DR: A new method to match a 2D image to a translated, rotated and scaled reference image using symmetric phase-only matched filtering to the FMI descriptors, which guarantees high discriminating power and excellent robustness in the presence of noise.
729
2D Euclidean distance transform algorithms: A comparative survey
TL;DR: In this paper, state-of-the-art sequential 2D EDT algorithms are reviewed and compared, in an effort to reach more solid conclusions regarding their differences in speed and their exactness.
502
References
Distance transformations in digital images
TL;DR: Six different distance transformations, both old and new, are used for a few different applications, which show both that the choice of distance transformation is important, and that any of the six transformations may be the right choice.
2.1K
Euclidean distance mapping
TL;DR: It is shown that skeletons can be produced by simple procedures and since these are based on Euclidean distances it is assumed that they are superior to skeletons based on d4−, d8−, and even octagonal metrics.
2K
Recognising and Locating Partially Visible Objects: The Local-Feature-Focus Method
Robert C. Bolles,Ronald A. Cain +1 more
TL;DR: In this paper, a new method of locating partially visible two-dimensional objects is presented, which is applicable to complex industrial parts that may contain several occurrences of local features, such as holes and corners.
412
A Model-Based Vision System for Industrial Parts
TL;DR: A vision system has been developed which can determine the position and orientation of complex curved objects in gray-level noisy scenes and organizes and reduces the image data from a digitized picture to a compact representation having the appearance of a line drawing.
337
Recognizing partially visible objects using feature indexed hypotheses
TL;DR: A new recognition method, the feature indexed hypotheses method, is described, which takes advantage of the similarities and differences between object types, and is able to handle cases, where there are a large number of possible objecttypes, in sub-linear computation time.