王培元,关欣(海军航空大学, 烟台 264001)
Hybrid enhanced visual cognition framework and its key technologies
Wang Peiyuan,Guan Xin(Naval Aviation University, Yantai 264001, China)
Although the current intelligent vision system has certain advantages in feature detection, the extraction and matching of large-scale visual information and the cognition of deep-seated visual information remain uncertain and fragile. How to mine and understand the connotation of visual information efficiently, and make cognitive decisions is an engaging research field in computer vision. Especially for the visual cognitive task based on visual perception, the related mathematical logic and image processing methods have not achieved a qualitative breakthrough at present due to limitations by the western philosophy system. It makes the development of computer vision processing intelligent algorithm enter a bottleneck period and completely replacing human to perform more complex operations such as understanding, reasoning, decision making, and learning difficult. The basic framework of hybrid enhanced visual cognition and the application fields and key technologies that can be included in the framework to promote the development of intelligent visual perception and cognitive technology based on the application status of hybrid enhanced intelligence in the field of visual cognition are summarized in this paper. First, on the basis of analyzing the connotation and basic category of intelligent visual perception, human visual perception and psychological cognition are integrated; the definition, category, and deepening of hybrid enhanced visual cognition are discussed; different visual information processing stages are compared and analyzed; and then the basic framework of hybrid enhanced visual cognition on analyzing the development status of relevant cognitive models is constructed. The framework can rely on intelligent algorithms for rapid detection, recognition, understanding, and other processing to maximize the computational potential of "machine"; can effectively enhance the accuracy and reliability of system cognition with timely, appropriate artificial reasoning, prediction, and decision making; and give full play to human cognitive advantages. Second, the representative applications and existing problems of the framework are discussed from four fields, namely, hybrid enhanced visual monitoring, hybrid enhanced visual driving, hybrid enhanced visual decision making, and hybrid enhanced visual sharing, and the hybrid enhanced visual cognitive framework is identified as an expedient measure to enhance computer efficiency and reduce the pressure on people to process information under existing technical conditions. Then, based on high, medium, and low computer vision processing technology systems, the macro and micro relationships of several medium- and high-level visual processing technologies in a hybrid enhanced visual cognition framework are analyzed, focusing on key technologies such as visual analysis, visual enhancement, visual attention, visual understanding, visual reasoning, interactive learning, and cognitive evaluation. This framework will help break through the bottleneck of "weak artificial intelligence" in current visual information cognition and effectively promote the further development of intelligent vision system toward the direction of human-computer deep integration. Next, more indepth research must be carried out on pure basic innovation, efficient human-computer interaction, and flexible connection path.