Paper
8 March 1999 FLASH system for fast and accurate pattern localization
Shang-Hong Lai, Ming Fang
Author Affiliations +
Abstract
The needs for accurate and efficient object localization prevail in many industrial applications, such as automated visual inspection and factory automation. Image reference approach is very popular in automatic visual inspection due to its general applicability to a variety of inspection tasks. However, it requires very precise alignment of the inspection pattern in the image. To achieve very precise pattern alignment, traditional template matching is extremely time-consuming when the search space is large. In this paper, we present a new FLASH (Fast Localization with Advanced Search Hierarchy) algorithm for fast and accurate object localization in a large search space. This object localization algorithm is very useful for applications in automated visual inspection and pick-and-place systems for automatic factory assembly. It is based on the assumption that the surrounding regions of the pattern within the search range are always fixed, which is valid for most industrial inspection applications. The FLASH algorithm comprises a hierarchical nearest-neighbor search algorithm and an optical-flow based energy minimization algorithm. The hierarchical nearest-neighbor search algorithm produces a rough estimate of the transformation parameters for the initial guess of the iterative optical-flow based energy minimization algorithm, which provides very accurate estimation results and associated confidence measures. Experimental results demonstrate the accuracy and efficiency of the proposed FLASH algorithm.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shang-Hong Lai and Ming Fang "FLASH system for fast and accurate pattern localization", Proc. SPIE 3652, Machine Vision Applications in Industrial Inspection VII, (8 March 1999); https://doi.org/10.1117/12.341136
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Cited by 4 scholarly publications.
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KEYWORDS
Inspection

Optical inspection

Neural networks

Optical flow

Wavelets

Algorithm development

Factory automation

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