Current Issue : April - June Volume : 2013 Issue Number : 2 Articles : 4 Articles
This paper introduces a multimodal approach for reranking of image retrieval results based on relevance feedback. We consider\r\nthe problem of reordering the ranked list of images returned by an image retrieval system, in such a way that relevant images to\r\na query are moved to the first positions of the list. We propose a Markov random field (MRF) model that aims at classifying the\r\nimages in the initial retrieval-result list as relevant or irrelevant; the output of the MRF is used to generate a new list of ranked\r\nimages. The MRF takes into account (1) the rank information provided by the initial retrieval system, (2) similarities among images\r\nin the list, and (3) relevance feedback information. Hence, the problem of image reranking is reduced to that of minimizing an\r\nenergy function that represents a trade-off between image relevance and interimage similarity.The proposed MRF is a multimodal\r\nas it can take advantage of both visual and textual information by which images are described with.We report experimental results\r\nin the IAPR TC12 collection using visual and textual features to represent images. Experimental results show that our method is\r\nable to improve the ranking provided by the base retrieval system. Also, the multimodal MRF outperforms unimodal (i.e., either\r\ntext-based or image-based) MRFs that we have developed in previous work. Furthermore, the proposed MRF outperforms baseline\r\nmultimodal methods that combine information from unimodal MRFs....
The paper presents a novel method for monitoring and estimating the depth of\r\na laser-drilled hole using machine vision. Through on-line image acquisition and analysis\r\nin laser machining processes, we could simultaneously obtain correlations between the\r\nmachining processes and analyzed images. Based on the machine vision method, the\r\ndepths of laser-machined holes could be estimated in real time. Therefore, a low cost\r\non-line inspection system is developed to increase productivity. All of the processing work\r\nwas performed in air under standard atmospheric conditions and gas assist was used. A\r\ncorrelation between the cumulative size of the laser-induced plasma region and the depth\r\nof the hole is presented. The result indicates that the estimated depths of the laser-drilled\r\nholes were a linear function of the cumulative plasma size, with a high degree of\r\nconfidence. This research provides a novel machine vision-based method for estimating the\r\ndepths of laser-drilled holes in real time....
Potato (Solanum tuberosum) is cultivated as a major food resource in some countries that have moderate climate.\r\nManual sorting is labor intensive. Furthermore in mechanical sorting the crop damages is high, for this reason we\r\nmust operate a system in which the crop damages would be diminished. For sorting of potatoes fast, accurate and\r\nless labor intensive modern techniques such as Machine vision is created. Machine vision system is one of the\r\nmodern sorting techniques. The basis of this method is imaging of samples, analysis of the images, comparing\r\nthem with a standard and finally decision making in acceptance or rejection of samples. In this research 110\r\nnumbers of potatoes from Agria variety were prepared. Samples were pre-graded based on quantitative,\r\nqualitative and total factors manually before sorting. Quantitative, qualitative and total sorting in Machine vision\r\nsystem was performed by improving images quality and extracting the best thresholds. The accuracy of total\r\nsorting was %96.823....
Text embedded in an image contains useful information for applications in the medical, industrial, commercial, and research fields.\r\nWhile many systems have been designed to correctly identify text in images, no work addressing the recognition of degraded text\r\non clear plastic has been found. This paper posits novel methods and an apparatus for extracting text from an image with the\r\npractical assumption: (a) poor background contrast, (b) white, curved, and/or differing fonts or character width between sets of\r\nimages, (c) dotted text printed on curved reflective material, and/or (d) touching characters.Methods were evaluated using a total\r\nof 100 unique test images containing a variety of texts captured from water bottles. These tests averaged a processing time of ~10\r\nseconds (using MATLAB R2008A on an HP 8510W with 4G of RAM and 2.3 GHz of processor speed), and experimental results\r\nyielded an average recognition rate of 90 to 93% using customized systems generated by the proposed development....
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