Topic
Standard test image
About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.
Papers published on a yearly basis
Papers
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25 Jan 2000
TL;DR: In this article, a method is proposed to infer a scene from a test image using a set of images and corresponding scenes, where each of the images and scenes are partitioned respectively into a plurality of image patches and scene patches, and probabilities of the compatibility matrices are propagated in the network until convergence.
Abstract: A method infers a scene from a test image During a training phase, a plurality of images and corresponding scenes are acquired Each of the images and corresponding scenes are partitioned respectively into a plurality of image patches and scene patches Each image patch is represented as an image vector, and each scene patch is represented as a scene vector The image vectors and scene vectors are modeled as a network During an inference phase, the test image is acquired The test image is partitioned into a plurality of test image patches Each test image patch is represented as a test image vector Candidate scene vectors corresponding to the test image vectors are located in the network Compatibility matrices for the candidate scene vectors are determined, and probabilities of the compatibility matrices are propagated in the network until convergence to infer the scene from the test image
31 citations
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16 Jan 2002TL;DR: In this article, a process and system for automatic image quality inspection and correction for scanned document which previously required a human operator is presented. But the quality of the scanned and thresholded image is not automatically evaluated.
Abstract: A process and system for automating the image quality inspection and correction for scanned document which previously required a human operator. For every scanned and thresholded image, the process and system performs an automatic evaluation through a binary image quality detection system which generates an image noise index indicative of the amount of image artifacts or image loss. When a poor quality scanned page is detected, for example, too much speckle noise, the gray scale image is retrieved or the image is rescanned. The gray scale image then automatically undergoes an image quality correction process to produce a clean, readable binary image.
31 citations
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28 May 1999TL;DR: An image processing apparatus which obtains an output result faithful to an original image independently of characteristics of a device to optically read the original image is described in this paper, where the image data is stored with Profile (color characteristic information) unique to the image scanner.
Abstract: An image processing apparatus which obtains an output result faithful to an original image independently of characteristics of a device to optically read the original image. When the original image is read by an image scanner, layout and the like of the read image are analyzed, recognition is performed on characters, and compression is performed on an image area. The image data is stored with Profile (color characteristic information) unique to the image scanner. When the image is displayed or print-outputted, color matching is performed in accordance with Profile of a display device or printer and the Profile of the image scanner, and the image is reproduced.
31 citations
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01 Jan 2006
TL;DR: Experimental results show that the proposed multi-cue combination scheme significantly increases detection performance compared to any of its constituent cues alone, and provides an interesting evaluation tool to analyze the complementarity of local feature detectors and descriptors.
Abstract: This paper proposes a novel method for integrating multiple local cues, i.e. local region detectors as well as descriptors, in the context of object detection. Rather than to fuse the outputs of several distinct classifiers in a fixed setup, our approach implements a highly flexible combination scheme, where the contributions of all individual cues are flexibly recombined depending on their explanatory power for each new test image. The key idea behind our approach is to integrate the cues over an estimated top-down segmentation, which allows to quantify how much each of them contributed to the object hypothesis. By combining those contributions on a per-pixel level, our approach ensures that each cue only contributes to object regions for which it is confident and that potential correlations between cues are effectively factored out. Experimental results on several benchmark data sets show that the proposed multi-cue combination scheme significantly increases detection performance compared to any of its constituent cues alone. Moreover, it provides an interesting evaluation tool to analyze the complementarity of local feature detectors and descriptors.
31 citations
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27 Oct 2009TL;DR: A framework for ROI based compression of medical images using wavelet based compression techniques (i.e. JPEG2000 and SPIHT) is proposed and performance is evaluated using various image quality metrics like PSNR, SSIM and Correlation.
Abstract: Ultrasound, computed tomography (CT), magnetic resonance imaging (MRI) medical imaging produce human body pictures in digital form. These medical applications have already been integrated into mobile devices and are being used by medical personnel in treatment centers, for retrieving and examining patient data and medical images. Storage and transmission are key issues in such platforms, due to the significant image file sizes. Wavelet transform has been considered to be a highly efficient technique of image compression resulting in both lossless and lossy compression of images with great accuracy, enabling its use on medical images. On the other hand, in some areas in medicine, it may be sufficient to maintain high image quality only in the region of interest i.e. in diagnostically important regions. This paper proposes a framework for ROI based compression of medical images using wavelet based compression techniques (i.e. JPEG2000 and SPIHT). Results are analyzed by conducting the experiments on a number of medical images by taking different region of interests. The performance is evaluated using various image quality metrics like PSNR, SSIM and Correlation.
31 citations