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.

Image manipulation detection with Binary Similarity Measures

Abstract: Since extremely powerful technologies are now available to generate and process digital images, there is a concomitant need for developing techniques to distinguish the original images from the altered ones, the genuine ones from the doctored ones In this paper we focus on this problem and propose a method based on the neighbor bit planes of the image The basic idea is that, the correlation between the bit planes as well the binary texture characteristics within the bit planes will differ between an original and a doctored image This change in the intrinsic characteristics of the image can be monitored via the quantal-spatial moments of the bit planes These so-called Binary Similarity Measures are used as features in classifier design It has been shown that the linear classifiers based on BSM features can detect with satisfactory reliability most of the image doctoring executed via Photoshop tool

Method and system for automatic processing and evaluation of images, particularly diagnostic images

Abstract: Method for automatic processing and evaluation of images, particularly diagnostic images, comprising an image processing tool in the form of a software program which is executable by the computer hardware and which image processing tool processes image data of a digital input image generating a modified digital output image whose image data are outputted in a graphical and/or alphanumerical format highlighting certain predetermined features or qualities of the corresponding regions of an imaged body or object, characterized in that the image processing tool comprises a first image detecting module which is an image processing module based on image processing non expert algorithms and which furnishes at its output a modified image file which modified image data are further processed by a classification or evaluation module which is a second image processing module comprising an image processing tool consisting in an expert image processing algorithm such as a classification or prediction algorithm the output of which is a further modified image file in which the pixels or voxels are highlighted corresponding to imaged object having a predetermined feature or quality

A hybrid face recognition method using Markov random fields

Abstract: We propose a hybrid face recognition method that combines holistic and feature analysis-based approaches using a Markov random field (MRF) model. The face images are divided into small patches, and the MRF model is used to represent the relationship between the image patches and the patch ID's. The MRF model is first learned from the training image patches, given a test image. The most probable patch ID's is then inferred using the belief propagation (BP) algorithm. Finally, the ID of the test image is determined by a voting scheme from the estimated patch ID's. Experimental results on several face datasets indicate the significant potential of our method.

Image recognition in an augmented reality application

Abstract: A computer-implemented augmented reality method includes obtaining an image acquired by a computing device running an augmented reality application, identifying image characterizing data in the obtained image, the data identifying characteristic points in the image, comparing the image characterizing data with image characterizing data for a plurality of geo-coded images stored by a computer server system, identifying locations of items in the obtained image using the comparison, and providing, for display on the computing device at the identified locations, data for textual or graphical annotations that correspond to each of the items in the obtained image, and formatted to be displayed with the obtained image or a subsequently acquired image.

Methods and systems for binning defects detected on a specimen

Abstract: Methods and systems for binning defects detected on a specimen are provided. One method includes comparing a test image to reference images. The test image includes an image of one or more patterned features formed on the specimen proximate to a defect detected on the specimen. The reference images include images of one or more patterned features associated with different regions of interest within a device being formed on the specimen. If the one or more patterned features of the test image match the one or more patterned features of one of the reference images, the method includes assigning the defect to a bin corresponding to the region of interest associated with the reference image.