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.

From Where and How to What We See

Abstract: Eye movement studies have confirmed that overt attention is highly biased towards faces and text regions in images. In this paper we explore a novel problem of predicting face and text regions in images using eye tracking data from multiple subjects. The problem is challenging as we aim to predict the semantics (face/text/background) only from eye tracking data without utilizing any image information. The proposed algorithm spatially clusters eye tracking data obtained in an image into different coherent groups and subsequently models the likelihood of the clusters containing faces and text using a fully connected Markov Random Field (MRF). Given the eye tracking data from a test image, it predicts potential face/head (humans, dogs and cats) and text locations reliably. Furthermore, the approach can be used to select regions of interest for further analysis by object detectors for faces and text. The hybrid eye position/object detector approach achieves better detection performance and reduced computation time compared to using only the object detection algorithm. We also present a new eye tracking dataset on 300 images selected from ICDAR, Street-view, Flickr and Oxford-IIIT Pet Dataset from 15 subjects.

Device and method for detecting optical-axis offset of lens in equipment

Abstract: The invention discloses a device and a method for detecting optical-axis offset of a lens in equipment. The device comprises a standard image acquiring module, a reference coordinate system setup module, a test image acquiring module, a test cursor position determining module and an optical-axis offset detecting module, wherein the standard image acquiring module is used for focusing a standard lens assembled in the equipment at a pickup position, picking up an image sample and acquiring a standard image of the image sample; the reference coordinate system setup module is used for taking the center of the standard image as coordinate origin and setting up a reference coordinate system; the test image acquiring module is used for focusing a to-be-detected lens assembled in the equipment at the pickup position, picking up the image sample and acquiring a test image of the image sample; the test cursor position determining module is used for taking the center of the test image as test cursor and determining the position of the test cursor in the reference coordinate system; and the optical-axis offset detecting module is used for determining optical-axis offset and/or optical-axis offset angle of the to-be-detected lens according the position. The device and the method for detecting optical-axis offset of the lens can solve the technical problem that the optical-axis offset of the lens in the equipment cannot be detected during assembly.

Automatic Cardiac Segmentation Using Semantic Information from Random Forests

Abstract: We propose a fully automated method for segmenting the cardiac right ventricle (RV) from magnetic resonance (MR) images. Given a MR test image, it is first oversegmented into superpixels and each superpixel is analyzed to detect the presence of RV regions using random forest (RF) classifiers. The superpixels containing RV regions constitute the region of interest (ROI) which is used to segment the actual RV. Probability maps are generated for each ROI pixel using a second set of RF classifiers which give the probabilities of each pixel belonging to RV or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low-level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that compared to conventional method our algorithm achieves superior performance due to the inclusion of semantic knowledge and context information.

Image recognition and processing using Artificial Neural Network

Abstract: There are several techniques for image recognition. Among those methods, application of soft computing models on digital image has been considered to be an approach for a better result. The main objective of the present work is to provide a new approach for image recognition using Artificial Neural Networks. Initially an original gray scale intensity image has been taken for transformation. The Input image has been added with Salt and Peeper noise. Adaptive median Filter has been applied on noisy image such that the noise can be removed and the output image would be considered as filtered Image. The estimated Error and average error of the values stored in filtered image matrix have been calculated with reference to the values stored in original data matrix for the purpose of checking of proper noise removal. Now each pixel data has been converted into binary number (8 bit) from decimal values. A set of four pixels has been taken together to form a new binary number with 32 bits and it has been converted into a decimal. This process continues to produce new data matrix with new different set of values. This data matrix has been taken as original data matrix and saved in data bank. Now for recognition, a new test image has been taken and the same steps as salt & pepper noise insertion, removal of noise using adaptive median filter as mentioned earlier have been applied to get a new test matrix. Now the average error of the second image with respect to original image has been calculated based on the both generated matrices. If the average error is more than 45% then a conclusion can be made that the images are different and cannot be matched. But if the value of average error has been found to be less than or equal to 45%, an effort has been made to use the artificial neural network on test data matrix with reference to original data matrix thereby producing a new matrix of the second image(test image). The total average error has been calculated on generated data matrix produced after the application of artificial neural networks on test data matrix to check whether proper identification can be made or not. It has been observed that the value of average error is less than that of test image without application of artificial neural network. Further it has been observed that the test image is matching and recognized with respect to original image.

System and method of measuring fidelity of decompressed video signals and images

Abstract: A method of quantitatively measuring fidelity of a reproduced image reconstructed from a compressed data representation of an original image is disclosed. The method comprises, responsive to user selection, for establishing a global assessment mode or a local assessment mode. In the global assessment mode changes in luminance of the reproduced image from the original image and changes in color in first and second color difference values of the reproduced image from the original image are used score fidelity. Changes in luminance are measured using a dynamic range, nonlinear transform equation. In the local assessment mode, and responsive to user selection, the reproduced image and the original image are segmented and corresponding pairs of segments from the reproduced image and the original image are identified. Scoring of fidelity of the reproduced image to the original image is done by comparing corresponding pairs of segments in color, luminance, shape, displacement and texture.