Examples of state-of-the-art vision systems, which include some of the levels of representation and processing mechanisms, are presented.Ĭomputer vision holds great promise for helping persons with blindness or visual impairments (VI) to interpret and explore the visual world.
A computational visual processing model is proposed and its architecture and operation are described. Consideration is given to early vision, the recovery of intrinsic surface characteristics, higher levels of interpretation, and system integration and control. Theoretical and experimental data on the formation of a computer vision system are discussed. Vision is examined in terms of a computational process, and the competence, structure, and control of computer vision systems are analyzed. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control.
Solutions of these tasks are used for making decisions about possible future actions. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks.
Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. Reinforcement learning in computer vision The results obtained have been satisfactory in the application of different image processing algorithms. The developed scheduling algorithm has been tested in one application of quality control using computer vision. The scheduling algorithm is based on the use of two matrices, obtained from the precedence relationships between tasks, and the data obtained from the two matrices. This condition is very important in some applications of computer vision in which the time to finish the total process is particularly critical - quality control in industrial inspection, real- time computer vision, guided robots. The developed algorithm not only minimizes the total elapsed time but also reduces the idle time and waiting time of in-process tasks. This paper presents a method for minimizing the total elapsed time spent by n tasks running on m differents processors working in parallel. Torres-Medina, Fernando Aracil, Rafael Reinoso, Oscar Jimenez, Luis M. Job-shop scheduling applied to computer vision
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