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.

Performance of double phase encoding encryption technique using binarized encrypted images

Abstract: We investigate the performance of an image encryption tech- nique that uses random-phase encoding in both the input plane and the Fourier plane, using partial information of the encrypted image. We first investigate the phase-only information of the encrypted data for decryp- tion. A binary version of the phase-only information is also considered for decryption. Binary images are well suited for optical display and practical implementation. Using partial information of the encrypted image, a re- constructed complex image is generated, which is used for decryption. Tests are performed for both gray-scale and binary images. We show that the phase information of the encrypted image is very important in the reconstruction of the decrypted image. Computer simulations show that for the images tested here, binarization of the encrypted image can re- cover the original image with low mean squared error. © 1998 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(98)04302-5) noise. 3 The fault-tolerance properties of this technique were investigated in Ref. 4. In this paper, we evaluate the phase information of the encrypted image and investigate the ef- fect of its binarization on the recovered image. We give the reasons for the binarization of the encrypted image, and tackle the problems raised by it. It is shown that postpro- cessing of the decrypted image can improve the quality of the recovered images. We show, for the images tested here, that recovered images of very good quality can be obtained.

Content and quality: Interpretation-based estimation of image quality

Abstract: Test image contents affect subjective image-quality evaluations. Psychometric methods might show that contents have an influence on image quality, but they do not tell what this influence is like, i.e., how the contents influence image quality. To obtain a holistic description of subjective image quality, we have used an interpretation-based quality (IBQ) estimation approach, which combines qualitative and quantitative methodology. The method enables simultaneous examination of psychometric results and the subjective meanings related to the perceived image-quality changes. In this way, the relationship between subjective feature detection, subjective preferences, and interpretations are revealed. We report a study that shows that different impressions are conveyed in five test image contents after similar sharpness variations. Thirty naive observers classified and freely described the images after which magnitude estimation was used to verify that they distinguished the changes in the images. The data suggest that in the case of high image quality, the test image selection is crucial. If subjective evaluation is limited only to technical defects in test images, important subjective information of image-quality experience is lost. The approach described here can be used to examine image quality and it will help image scientists to evaluate their test images.

Higher order statistical learning for vehicle detection in images

Abstract: The paper describes a scheme for detecting vehicles in images. The proposed method approximately models the unknown distribution of the images of vehicles by learning higher order statistics (HOS) information of the 'vehicle class' from sample images. Given a test image, statistical information about the background is learnt 'on the fly'. An HOS-based decision measure then classifies test patterns as vehicles or otherwise. When tested on real images of aerial views of vehicular activity, the method gives good results even on complicated scenes. It does not require any a priori information about the site. However, it is amenable to augmentation with contextual information. The method can serve as an important step towards building an automated roadway monitoring system.

Classifying Computer Generated Charts

Abstract: We present an approach for classifying images of charts based on the shape and spatial relationships of their primitives. Five categories are considered: bar-charts, curve-plots, pie-charts, scatter-plots and surface-plots. We introduce two novel features to represent the structural information based on (a) region segmentation and (b) curve saliency. The local shape is characterized using the Histograms of Oriented Gradients (HOG) and the Scale Invariant Feature Transform (SIFT) descriptors. Each image is represented by sets of feature vectors of each modality. The similarity between two images is measured by the overlap in the distribution of the features -measured using the Pyramid Match algorithm. A test image is classified based on its similarity with training images from the categories. The approach is tested with a database of images collected from the Internet.