Paper
3 September 1993 Robust fast model-based recognition of partially occluded objects using normalized interval vertex descriptors
Ramon Parra-Loera, Hector Erives, Wiley E. Thompson
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Abstract
This paper deals with the problem of robust fast recognition of partially occluded or incomplete views of `flat' objects. Robustness is accomplished through hypothesis confirmation using complementary or supporting information available for the current hypothesis and by model-based hypothesis verification. Classification speed is obtained by pruning the hypothesis hierarchy using simple pruning procedures based on structural properties derived from current object representation. In addition, classification speed is also improved through the use of simple model-based decision making procedures instead of computationally expensive transformations. Normalized Interval Vertex Descriptors (NIVD) are used to represent objects. NIVDs are representations derived from the physical characteristics of an object (vertices and sides) that are easy to obtain, especially for polygon like shapes. They provide not only a compact representation, but they also allow the definition of features that can be used to speed up the classification process. Experimental results of this process are also included.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ramon Parra-Loera, Hector Erives, and Wiley E. Thompson "Robust fast model-based recognition of partially occluded objects using normalized interval vertex descriptors", Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); https://doi.org/10.1117/12.154979
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KEYWORDS
Model-based design

Process modeling

Image processing

Image segmentation

Object recognition

Image processing algorithms and systems

Binary data

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