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PRACTICAL RECOGNITION SYSTEM FOR TEXT PRINTED ON CLEAR REFLECTED MATERIAL
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Khader Mohammad, Sos Agaian
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Text embedded in an image contains useful information for applications in the medical, industrial, commercial, and research fields.
While many systems have been designed to correctly identify text in images, no work addressing the recognition of degraded text
on clear plastic has been found. This paper posits novel methods and an apparatus for extracting text from an image with the
practical assumption: (a) poor background contrast, (b) white, curved, and/or differing fonts or character width between sets of
images, (c) dotted text printed on curved reflective material, and/or (d) touching characters.Methods were evaluated using a total
of 100 unique test images containing a variety of texts captured from water bottles. These tests averaged a processing time of ~10
seconds (using MATLAB R2008A on an HP 8510W with 4G of RAM and 2.3 GHz of processor speed), and experimental results
yielded an average recognition rate of 90 to 93% using customized systems generated by the proposed development....
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MULTIMODAL MARKOV RANDOM FIELD FOR IMAGE RERANKING BASED ON RELEVANCE FEEDBACK
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Ricardo Omar Chavez, Hugo Jair Escalante, Manuel Montes-y-Gomez, Luis Enrique Sucar
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This paper introduces a multimodal approach for reranking of image retrieval results based on relevance feedback. We consider
the problem of reordering the ranked list of images returned by an image retrieval system, in such a way that relevant images to
a query are moved to the first positions of the list. We propose a Markov random field (MRF) model that aims at classifying the
images in the initial retrieval-result list as relevant or irrelevant; the output of the MRF is used to generate a new list of ranked
images. The MRF takes into account (1) the rank information provided by the initial retrieval system, (2) similarities among images
in the list, and (3) relevance feedback information. Hence, the problem of image reranking is reduced to that of minimizing an
energy function that represents a trade-off between image relevance and interimage similarity.The proposed MRF is a multimodal
as it can take advantage of both visual and textual information by which images are described with.We report experimental results
in the IAPR TC12 collection using visual and textual features to represent images. Experimental results show that our method is
able to improve the ranking provided by the base retrieval system. Also, the multimodal MRF outperforms unimodal (i.e., either
text-based or image-based) MRFs that we have developed in previous work. Furthermore, the proposed MRF outperforms baseline
multimodal methods that combine information from unimodal MRFs....
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ON-LINE ESTIMATION OF LASER-DRILLED HOLE DEPTH USING A MACHINE VISION METHOD
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Chao-Ching Ho, Jun-Jia He, Te-Ying Liao
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The paper presents a novel method for monitoring and estimating the depth of
a laser-drilled hole using machine vision. Through on-line image acquisition and analysis
in laser machining processes, we could simultaneously obtain correlations between the
machining processes and analyzed images. Based on the machine vision method, the
depths of laser-machined holes could be estimated in real time. Therefore, a low cost
on-line inspection system is developed to increase productivity. All of the processing work
was performed in air under standard atmospheric conditions and gas assist was used. A
correlation between the cumulative size of the laser-induced plasma region and the depth
of the hole is presented. The result indicates that the estimated depths of the laser-drilled
holes were a linear function of the cumulative plasma size, with a high degree of
confidence. This research provides a novel machine vision-based method for estimating the
depths of laser-drilled holes in real time....
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