Journal of Imaging Science and Technology 

About: Journal of Imaging Science and Technology is an academic journal published by Society for Imaging Science and Technology. The journal publishes majorly in the area(s): Computer science & Silver halide. It has an ISSN identifier of 1062-3701. Over the lifetime, 1491 publications have been published receiving 14471 citations. The journal is also known as: J. Imaging Sci. Technol. & JIST.

Visual Discomfort and Visual Fatigue of Stereoscopic Displays: A Review

Abstract: Visual discomfort has been the subject of considerable research in relation to stereoscopic and autostereoscopic displays. In this paper, the importance of various causes and aspects of visual discomfort is clarified. When disparity values do not surpass a limit of 1°, which still provides sufficient range to allow satisfactory depth perception in stereoscopic television, classical determinants such as excessive binocular parallax and accommodation-vergence conflict appear to be of minor importance. Visual discomfort, however, may still occur within this limit and we believe the following factors to be the most pertinent in contributing to this: (1) temporally changing demand of accommodation-vergence linkage, e.g., by fast motion in depth; (2) three-dimensional artifacts resulting from insufficient depth information in the incoming data signal yielding spatial and temporal inconsistencies; and (3) unnatural blur. In order to ad- equately characterize and understand visual discomfort, multiple types of measurements, both objective and subjective, are required. © 2009 Society for Imaging Science and Technology. DOI: 10.2352/J.ImagingSci.Technol.2009.53.3.030201

Progress and trends in ink-jet printing technology

Abstract: This paper provides a brief review of the various paths undertaken in the development of ink-jet printing. Highlights of recent progress and trends in this technology are discussed. The technologies embedded in the latest ink-jet products from current industry leaders in both thermal and piezoelectric drop-on-demand ink-jet methods are also described. Finally, this article presents a list of the potential ink-jet technology applications that have emerged in the past few years.

The fundamentals of gamut mapping : A survey

Abstract: This article aims to give a survey of the fundamentals of gamut mapping by describing the cross-media color reproduction context in which it occurs, by giving definitions of terms used in conjunction with it, by describing its aims and by giving an overview of parameters that influence it. These parameters are primarily the choice of color space used, the category into which a gamut mapping algorithm belongs and whether the approach is image or medium dependent. A succinct summary is then given of the principal trends in gamut mapping studies conducted to date.

Medical Image Segmentation based on U-Net: A Review

Abstract: Abstract Medical image analysis is performed by analyzing images obtained by medical imaging systems to solve clinical problems. The purpose is to extract effective information and improve the level of clinical diagnosis. In recent years, automatic segmentation based on deep learning (DL) methods has been widely used, where a neural network can automatically learn image features, which is in sharp contrast with the traditional manual learning method. U-net is one of the most important semantic segmentation frameworks for a convolutional neural network (CNN). It is widely used in the medical image analysis domain for lesion segmentation, anatomical segmentation, and classification. The advantage of this network framework is that it can not only accurately segment the desired feature target and effectively process and objectively evaluate medical images but also help to improve accuracy in the diagnosis by medical images. Therefore, this article presents a literature review of medical image segmentation based on U-net, focusing on the successful segmentation experience of U-net for different lesion regions in six medical imaging systems. Along with the latest advances in DL, this article introduces the method of combining the original U-net architecture with deep learning and a method for improving the U-net network.