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.
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01 Nov 2007
TL;DR: An efficient depth image based rendering (DIBR) for generating arbitrary novel views is proposed by preprocessing depth images and merging two desired images to solve the visibility problem.
Abstract: An efficient depth image based rendering (DIBR) for generating arbitrary novel views is proposed. The proposed method fills the holes by preprocessing depth images and merging two desired images. In order to solve the visibility problem, the pixels in reference image are processed in an occlusion-compatible order. Furthermore, the reference images are corrected before 3D image warping so as to reduce the edge artifacts. Experimental results have show that the proposed method can provide a satisfactory image quality.
41 citations
TL;DR: Experimental results demonstrate that the proposed algorithm is superior to the state of the art of tamper detection and tamper recovery and could keep high security strength.
40 citations
10 Sep 2001
TL;DR: A new connected component based segmentation algorithm which automatically extracts text regions from natural scene images is proposed in this paper, utilizing a multichannel decomposition method to locate text blocks in complex backgrounds.
Abstract: A new connected component based segmentation algorithm which automatically extracts text regions from natural scene images is proposed in this paper. This approach utilizes a multichannel decomposition method to locate text blocks in complex backgrounds. Block alignment analysis and recognition confidence values are used in the combination and identification of the connected components. The algorithm is applied to a test image database and shows promising results.
40 citations
20 Apr 2018
TL;DR: A python based application is being developed to recognize faces in all conditions and the final step is to train a classifier that can take in the measurements from a new test image and tells which known person is the closest match.
Abstract: Facial recognition systems are commonly used for verification and security purposes but the levels of accuracy are still being improved. Errors occurring in facial feature detection due to occlusions, pose and illumination changes can be compensated by the use of hog descriptors. The most reliable way to measure a face is by employing deep learning techniques. The final step is to train a classifier that can take in the measurements from a new test image and tells which known person is the closest match. A python based application is being developed to recognize faces in all conditions.
40 citations