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: The presented approach is used for object recognition in images: by matching images that contain certain objects to a test image, it can detect whether the test image contains an object of that class or not.
Abstract: We present an efficient method to determine the optimal matching of two patch-based image object representations under rotation, scaling, and translation (RST). This use of patches is equivalent to a fullyconnected part-based model, for which the presented approach offers an efficient procedure to determine the best fit. While other approaches that use fully connected models have a high complexity in the number of parts used, we achieve linear complexity in that variable, because we only allow RST-matchings. The presented approach is used for object recognition in images: by matching images that contain certain objects to a test image, we can detect whether the test image contains an object of that class or not. We evaluate this approach on the Caltech data and obtain very competitive results.

25 citations

Journal ArticleDOI
TL;DR: A new gray-scale image coding technique has been developed, in which an extended DPCM approach has been combined with entropy coding, which has been implemented in a freeze-frame videoconferencing system which is now operational at IBM sites throughout the world.
Abstract: A new gray-scale image coding technique has been developed, in which an extended DPCM approach has been combined with entropy coding This technique has been implemented in a freeze-frame videoconferencing system which is now operational at IBM sites throughout the world Following image preprocessing, the two fields of the interlaced 512 x 480 pixel video frame are compressed sequentially with different algorithms The reconstructed image quality is improved by subsequent image postprocessing, the final reconstructed image being almost indistinguishable from the original image Typical gray-scale video images compress to about a half bit per pixel and transmit over 48 kbit/s dial-up telephone lines in about a half minute The gray-scale image processing and compression algorithms are described in this paper

25 citations

Proceedings ArticleDOI
28 May 2000
TL;DR: The proposed method is robust in high quality lossy image compression and provides the user not only with a measure for the authenticity of the test image but also with an image map that highlights the unaltered image regions when selective tampering has been made.
Abstract: A novel method for image authentication is proposed. A watermark signal is embedded in a grayscale or a color host image. The watermark key controls a set of parameters of a chaotic system used for the watermark generation. The use of chaotic mixing increases the security of the proposed method and provides the additional feature of imperceptible encryption of the image owner logo in the host image. The method succeeds in detecting any alteration made in a watermarked image. The proposed method is robust in high quality lossy image compression. It provides the user not only with a measure for the authenticity of the test image but also with an image map that highlights the unaltered image regions when selective tampering has been made.

25 citations

Patent
21 May 2014
TL;DR: In this article, a depth study method for detecting salient regions in a natural image is proposed, where a certain number of pictures are selected from a natural images database, basic features of the images are extracted to form a training sample, subsequently, the extracted features are studied by using a depth-study model so as to obtain enhanced advanced features which are more abstractive and more distinguishable, and finally, a classifier is trained by using studied features.
Abstract: The invention relates to a depth study method for detecting salient regions in a natural image. During a training phase, a certain number of pictures are selected from a natural image database, basic features of the images are extracted to form a training sample, subsequently, the extracted features are studied by using a depth study model so as to obtain enhanced advanced features which are more abstractive and more distinguishable, and finally, a classifier is trained by using studied features. During a testing phase, as to any test image, firstly, the base features are extracted, secondly, the enhanced advanced features are extracted by using the trained depth model, finally, salience is predicted by using the classifier, and a predicted value of each pixel point serves as a salient value of the point. In such a way, a salient image of the whole image can be obtained, and the higher the salient value is, the more salient the image is.

25 citations

Proceedings ArticleDOI
18 Sep 2011
TL;DR: This paper proposes a script identification method that works for unknown orientation for all 11 official Indian scripts by a multi-stage tree classifier using features invariant to 0°/180° orientation.
Abstract: A major preprocessing step in a multi-script OCR is to identify the script type of the test document image. The published papers on script identification usually assume that the test image is in correct i.e. 0° orientation. But by mistake a document may be fed to the system in wrong orientation, say at an angle of nearly 180° or ±90°. In this method we propose a script identification method that works for unknown orientation for all 11 official Indian scripts. Here, we first find the skew and counter-rotate the document by the skew angle. This will lead to correct (0°) or upside down (180°) orientation. Then script identification is done by a multi-stage tree classifier using features invariant to 0°/180° orientation. Next we go to find the orientation of the image by a two class classifier for each script. Performance of the proposed method has been tested on a variety of documents and promising results have been obtained.

25 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