scispace - formally typeset
Search or ask a question
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
More filters
Journal ArticleDOI
TL;DR: In this paper, various gamut mapping algorithms were evaluated using simple rendered images and artificial gamut boundaries using a paired comparison paradigm, which consisted of combinations of clipping and mapping the original gamut in linear piecewise segments.
Abstract: Using a paired comparison paradigm, various gamut mapping algorithms were evaluated using simple rendered images and artificial gamut boundaries. The test images consisted of simple rendered spheres floating in front of a gray background. Using CIELAB as our device-independent color space, cut-off values for lightness and chroma, based on the statistics of the images, were chosen to reduce the gamuts for the test images. The gamut mapping algorithms consisted of combinations of clipping and mapping the original gamut in linear piecewise segments. Complete color space compression in RGB and CIELAB was also tested. Each of the colored originals (R,G,B,C,M,Y, and Skin) were mapped separately in lightness and chroma. In addition, each algorithm was implemented with saturation (C/sup *//L/sup */) allowed to vary or retain the same values as in the original image. Pairs of test images with reduced color gamuts were presented to twenty subjects along with the original image. For each pair the subjects chose the test image that better reproduced the original. Rank orders and interval scales of algorithm performance with confidence limits were then derived. Clipping all out-of-gamut colors was the best method for mapping chroma. For lightness mapping at low lightness levels and high lightness levels particular gamut mapping algorithms consistently produced images chosen as most like the original. The choice of device-independent color space may also influence which gamut mapping algorithms are best.

62 citations

Book ChapterDOI
07 May 1994
TL;DR: This contribution addresses the problem of detection and tracking of moving vehicles in image sequences from traffic scenes recorded by a stationary camera by replacing the low level vision system component for the estimation of displacement vectors by an optical flow estimation module.
Abstract: This contribution addresses the problem of detection and tracking of moving vehicles in image sequences from traffic scenes recorded by a stationary camera. By replacing the low level vision system component for the estimation of displacement vectors by an optical flow estimation module we are able to detect all moving vehicles in our test image sequence. By replacing the edge detector and by doubling the sampling rate we improve the model-based object tracking system significantly compared to an earlier system. The trajectories of vehicles are characterized by motion verbs and verb phrases. Results from various experiments with real world traffic scenes are presented.

62 citations

Proceedings ArticleDOI
24 Oct 1999
TL;DR: A segmentation method and associated file format for storing images of color documents that can produce very highly-compressed document files that nonetheless retain excellent image quality.
Abstract: We describe a segmentation method and associated file format for storing images of color documents. We separate each page of the document into three layers, containing the background (usually one or more photographic images), the text, and the color of the text. Each of these layers has different properties, making it desirable to use different compression methods to represent the three layers. The background layers are compressed using any method designed for photographic images, the text layers are compressed using a token-based representation, and the text color layers are compressed by augmenting the representation used for the text layers. We also describe an algorithm for segmenting images into these three layers. This representation and algorithm can produce very highly-compressed document files that nonetheless retain excellent image quality.

62 citations

Patent
31 Mar 1994
TL;DR: In this paper, a color separation of the input image data is determined according to geometrical data for each image portion of the image data, and color data for the image portion and a background of input data.
Abstract: An image processing system capable of facilitating the highly accurate character recognition on the colored input images. In this system, an input image data to be processed is entered, and a color separation of the input image data is determined according to geometrical data for each image portion of the input image data, and color data for each image portion and a background of the input image data. Then, the input image data is appropriately processed according to the determined color separation. Also, in this system, at least one of color image data and gray scale image data according to the input image data are stored along with and binary image data according to the input image data, and the binary image data are processed by looking up the at least one of the color image data and the gray scale image data.

62 citations

Patent
04 Aug 2003
TL;DR: In this article, a simple but powerful image stack is employed in creating an enhanced image from a stack of registered images, which combines pixels using multi-image operations on the image stack.
Abstract: A system and method for editing images. A simple but powerful image stack is employed in creating an enhanced image from a stack of registered images. This paradigm combines pixels using multi-image operations on the image stack. Image Stacks can help create group photographs, create high dynamic range images, combine images captured under different lighting conditions, remove unwanted objects from images, and combine images captured at different times and with different focal lengths.

62 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
91% related
Image segmentation
79.6K papers, 1.8M citations
91% related
Image processing
229.9K papers, 3.5M citations
90% related
Convolutional neural network
74.7K papers, 2M citations
90% related
Support vector machine
73.6K papers, 1.7M citations
90% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231
20228
2021130
2020232
2019321
2018293