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.

Natural Image Statistics in Digital Image Forensics

Abstract: We describe a set of natural image statistics that are built upon two multi-scale image decompositions, the quadrature mirror filter pyramid decomposition and the local angular harmonic decomposition. These image statistics consist of first- and higher-order statistics that capture certain statistical regularities of natural images. We propose to apply these image statistics, together with classification techniques, to three problems in digital image forensics: (1) differentiating photographic images from computer-generated photorealistic images, (2) generic steganalysis; (3) rebroadcast image detection. We also apply these image statistics to the traditional art authentication for forgery detection and identification of artists in an art work. For each application we show the effectiveness of these image statistics and analyze their sensitivity and robustness.

Dense population estimation method based on deep learning

Abstract: The invention relates to a dense population estimation method based on deep learning. The method comprises the following steps: a dense-scene image is selected to be as a test image, block segmentation operation is performed on the test image then, and proportions of segmented blocks are guaranteed to be approximately the same as the width-height ratio of the original image; normalized operation is performed on the segmented image blocks, the image blocks are normalized into 32*32 pixel blocks as test samples, and corresponding actual population labels are attached to the test samples; the pixel blocks are sent into a trained depth network in a batched manner, and, for each pixel block, the network feeds back a prediction result; and the prediction results of the pixel blocks are summed, and an obtained result is the total number of people in the test image needing to be estimated. The method is advantageous in that a deep learning method is introduced into a specific problem of people number statistics, and a constructed regression mode containing two paths of signals reduces the possibility of over-fitting occurrence at some extent.

Image processing method and apparatus and storage medium

Abstract: Software of a simple configuration checks whether an original image to be corrected is an image picture, and an image correction process based upon a formed histogram of the original image is not performed for an image different from the image picture. An image processing condition is set in accordance with the formed histogram to perform the image correction process for the original image. An image processing method judges from a shape of the formed histogram whether the original image is an image picture, and does not perform not perform the image correction process for the original image if it is judged that the original image is not an image picture.

A new reference-free image quality index for blur estimation in the frequency domain

Abstract: A new reference-free image quality index based on spectral analysis is proposed. The main idea is based on exploiting the limitations of the Human Visual System (HVS) in blur detection,. The proposed method consists of adding blur to the test image and measuring its impact. The impact is measured using radial analysis in the frequency domain. The efficiency of the proposed method is tested objectively by comparing it to some well known algorithms and in terms of correlation with subjective scores.

Image quality based adaptive optimization of computer aided detection schemes

Abstract: A computerized method of detecting regions of interest in a digital image optimizes and adapts a computer aided scheme for detecting regions of interest in images. The optimization is based on global image characteristics. For each image in a database of images having known regions of interest, global image features are measured and an image characteristic index is established based on these global image features. All the images in the database are divided into a number of image groups based on the image characteristic index of each image in the database and the CAD scheme is optimized for each image group. Once the CAD scheme is optimized, to process a digital image, an image characteristics based classification criteria is established for that image, and then global image features of the digitized image are determined. The digitized image is then assigned an image characteristics rating based on the determined global image features, and the image is assigned to an image group based on the image rating. Then regions of interest depicted in the image are determined using a detection scheme adapted for the assigned image group.