本文是对至今已连续发表25年的中国图像工程年度文献综述系列的概括回顾。近25年来，为了使国内广大从事图像工程研究和图像技术应用的科技人员能够较全面地了解图像工程研究和发展的现状，能够有针对性地查询有关文献，并向期刊编者和作者提供有用的参考，笔者每年都对上一年度图像工程的相关文献进行统计和分析。25年间，该综述系列从国内15种有关图像工程重要期刊所发行的共2 964期上所发表的65 040篇学术研究和技术应用文献中，选取出15 856篇属于图像工程领域的文献，并根据各文献的主要内容将其分别归入图像处理、图像分析、图像理解、技术应用和综述评论5个大类，然后进一步分入23个专业小类，并在此基础上分别进行各期刊各类文献的统计和分析。此次回顾，除汇总了25年的统计分类情况，还着重对一些主要的研究方向进行了分析和讨论。这样，不仅可从中了解最近四分之一个世纪图像工程相关文献的发表情况，还可以提供全面和可信的各研究方向发展趋势的信息。

图像工程(IE); 图像处理(IP); 图像分析(IA); 图像理解(IU); 技术应用(TA); 文献综述; 文献分类; 文献计量学

This is an overview of the annual survey series of bibliographies on image engineering in China for the past 25 years. Images are an important medium for human beings to observe the information of the real world around. In its general sense, the word "image" could include all entities that can be visualized by human eyes, such as a still image or picture, a clip of video, as well as graphics, animations, cartoons, charts, drawings, paintings, even also text, etc. Nowadays, with the progress of information science and society, "image" rather than "picture" is used because computers store numerical images of a picture or scene. Image techniques are those techniques that have been invented, designed, implemented, developed, and utilized to treat various types of images for different and specified purposes. They are expanding over wider and wider application areas. They have attracted more and more attention in recent years with the fast advances of mathematic theories and physical principles, as well as the progress of computer and electronic devices, etc. Image engineering (IE) is an integrated discipline/subject comprising the study of all the different branches of image techniques, which has been formally proposed and defined 25 years ago to cover the whole domain. Image engineering, from a perspective more oriented to techniques for treating images, could be referred to as the collection of three related and partially overlapped groups of image techniques, that is, image processing (IP) techniques (in its narrow sense), image analysis (IA) techniques and image understanding (IU) techniques. In a structural sense, IP, IA and IU build up three inter-connected layers of IE. The three layers follow a progression of increasing abstractness and of decreasing compactness from IP to IA to IU. Each of them operates on different elements (IP's operand is pixel, IA's operand is object, and IU's operand is symbol) and works with altered semantic levels (from low level for IP, via middle level for IA, and to high level for IU). The evolving of image engineering is very quickly, its advances are closely related to the development of biomedical engineering, office automation, industrial inspection, intelligent transportation, remote sensing, surveying and mapping, and telecommunications, etc. To follow the development and to record the progress of image engineering, a bibliography series have been started since the propose of image engineering 25 years ago, and this work has been made consecutively till this year. With a set of carefully selected journals and the thoroughly reading on the papers published, several hundreds of papers related to image engineering are chosen each year for further classification and statistical analysis. The motivations and purposes for this work are three folds. 1) to enable the vast number of scientific and technical personnel engaged in image engineering research and image technology applications for grasping the current status of image engineering research and development, 2) to help them for searching relevant literature in a targeted manner, 3) to provide useful information for journal editors and authors. Based on these three points, the statistics and analysis of the previous year's literature related to image engineering are carried out in every year. In particular, three works are mainly conducted. 1) Forming a classification scheme for literatures. The coverage of research and technology in image engineering is quite large. For the analysis to be general for the whole domain and specific for particular direction, a classification scheme in two levels for literatures is formed. The top level is for the research domains in general (main-class), and the bottom level is for the specific research directions (sub-class). With the development of technology, the sub-classes have been adjusted and increased, from the initial 18 sub-classes to the current 23 sub-classes. 2) Analyzing the statistics for main classes of literatures. This analysis could provide a general picture over the different research domains along the years. 3) Analyzing the statistics for sub-classes of literatures. This analysis could deliver a specific figure to the different research directions at the current year. In the past 25 years, a total of 2 964 issues of 15 major Chinese journals on image engineering in China have been selected for this annual survey series. From 65 014 academic research and technical application papers published in these issues, 15 850 papers in the field of image engineering have been chosen, and classified according to the contents, into five broad classes: (A) image processing, (B) image analysis, (C) image understanding, (D) technology application and (E) review. Then they are further divided into 23 professional sub-classes, namely in brief: (A1) image capturing; (A2) image reconstruction from projections; (A3) image filtering, transformation, enhancement, restoration; (A4) image and/or video coding; (A5) image safety and security; (A6) image multiple-resolutions; (B1) image segmentation; (B2) representation, description, and measurement of objects; (B3) analysis of color, shape, texture, structure, motion, spatial relation; (B4) object extraction, tracking, and recognition; (B5) human biometrics (face, organ, etc.) identification; (C1) image registration, matching and fusion; (C2) 3-D modeling and real world/scene recovery; (C3) image perception, interpretation and reasoning; (C4) content-based image and video retrieval; (C5) spatial-temporal technology; (D1) system and hardware; (D2) telecommunication application; (D3) document application; (D4) bio-medical imaging and applications; (D5) remote sensing, radar, surveying and mapping; (D6) other application domains; and (E1) cross category summary and survey. In this paper, these classified data for all 25 years are integrated and analyzed. The foremost intention is to show a progression of image engineering, and to provide a vivid current picture of image engineering. An overview of the literature survey series on image engineering made in the last 25 years is supplied. The idea behind as well as a thorough summary of obtained statistics for this survey are illustrated and discussed. Many useful information regarding the tendency of fast progresses of image engineering can be obtained. According to the statistics collected and analyses performed, it is seen that the field of image engineering has changed enormously in recent years. It is seen that techniques for image engineering being developed, implemented and utilized on a large scale no one would have predicted a few years ago. One interesting point should be mentioned here is that the fast growth and relative increase of image analysis publications over image processing publications are clearly observed. This gives an indication of a general tendency for the image engineering toward to higher layer. After image analysis, image understanding will catch up and come from behind. This trend has already made an appearance, for example, in the International Conference on Image Processing (ICIP 2017), the new research papers related to topic of "Image & Video Interpretation & Understanding" are much more than those related to topic of "Image & Video Analysis" (http://2017.ieeeicip.org/). In addition to the statistical information for publication in these 25 years, some main research directions are analyzed and deliberated, especially those in image processing, image analysis and image understanding, to present more comprehensive and credible information on the development trends of each technique class. Some insights from it are also pointed and discussed. It is evident that through the statistical analysis of the published papers in important journals of image engineering, it cannot only help people understand the general situation of researches and applications, but also provide scientific basis for the development of relevant disciplines and research strategies.

image engineering(IE); image processing(IP); image analysis(IA); image understanding(IU); technique application(TA); literature survey; literature classification; bibliometrics