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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
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Journal ArticleDOI
TL;DR: The authors describe current research and development on a robotic visual servoing system for assembly of LIGA (lithography galvonoforming abforming) parts and can visually servo a 100 micron outside diameter LigA gear to a desired x,y reference position as determined from a synthetic image of the gear.
Abstract: The authors describe current research and development on a robotic visual servoing system for assembly of LIGA (lithography galvonoforming abforming) parts. The workcell consists of an AMTI robot, precision stage, long working distance microscope, and LIGA fabricated tweezers for picking up the parts. Fourier optics methods are used to generate synthetic microscope images from CAD drawings. These synthetic images are used off-line to test image processing routines under varying magnifications and depths of field. They also provide reference image features which are used to visually servo the part to the desired position. Currently, we can visually servo a 100 micron outside diameter LIGA gear to a desired x,y reference position as determined from a synthetic image of the gear.

55 citations

Journal ArticleDOI
TL;DR: Extensive experiments on real-world image data sets suggest that the proposed framework is able to predict the label and annotation for testing images successfully, and is computationally efficient and effective for image classification and annotation.
Abstract: In this paper, we propose a novel supervised nonnegative matrix factorization-based framework for both image classification and annotation. The framework consists of two phases: training and prediction. In the training phase, two supervised nonnegative matrix factorizations for image descriptors and annotation terms are combined to identify the latent image bases, and to represent the training images in the bases space. These latent bases can capture the representation of the images in terms of both descriptors and annotation terms. Based on the new representation of training images, classifiers can be learnt and built. In the prediction phase, a test image is first represented by the latent bases via solving a linear least squares problem, and then its class label and annotation can be predicted via the trained classifiers and the proposed annotation mapping model. In the algorithm, we develop a three-block proximal alternating nonnegative least squares algorithm to determine the latent image bases, and show its convergent property. Extensive experiments on real-world image data sets suggest that the proposed framework is able to predict the label and annotation for testing images successfully. Experimental results have also shown that our algorithm is computationally efficient and effective for image classification and annotation.

55 citations

Journal ArticleDOI
TL;DR: The experimental results show that the proposed scheme is effective, especially for the multi-objects splicing, and is proven to be superior to the existing state-of-the-art method.

54 citations

Journal ArticleDOI
T. Ida1, Y. Sambonsugi
TL;DR: Fractal coding was applied to image segmentation and contour detection and the proposed methods are expected to enable compressed codes to be used directly for image processing.
Abstract: Fractal coding was applied to image segmentation and contour detection. The encoding method was the same as in conventional fractal coding, and the compressed code, which we call the fractal code, was used for image segmentation and contour detection instead of image reconstruction. An image can be segmented by calculating the basin of attraction on a mapping that is a set of local maps from the domain block to the range block. The local maps are parameterized using the fractal code, and contours of the objects in the image are detected by the inverse mapping from the range block to the domain block. Some objects in the test image Lena were segmented, and the contours were detected well. The proposed methods are expected to enable compressed codes to be used directly for image processing.

54 citations

Patent
18 Apr 2005
TL;DR: In this article, a blur checking region is determined by selecting a region in which a blur tends to clearly appear in a digital photograph image as the blur-checking region, and an image of a region corresponding to the blur checking regions is obtained as a checking image in a corrected image obtained by performing blur correction processing on the digital image.
Abstract: A blur checking region is determined by selecting a region in which a blur tends to clearly appear in a digital photograph image as the blur checking region. Then, an image of a region corresponding to the blur checking region is obtained as a checking image in a corrected image obtained by performing blur correction processing on the digital photograph image. Then, the obtained checking image is displayed in a size appropriate for the resolution of a display device.

54 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231
20228
2021130
2020232
2019321
2018293