No abstract

In this paper a new method for contour detection in grayscale images is proposed. The pro-posed method is based on the use of an image weight model, which allows one to estimate its pix-els from the point of view of their significance for perception. In this case, the most significant pixels are those that contain characteristic features of the image, including brightness differences at the boundaries of the regions. To assess the significance of pixels, we propose a procedure for analyzing the contribution of the corresponding wavelet coefficients at different scale levels to the total energy of the image. The described method of contour detection involves building an image weight model, determining the directions of linear segments along the edges on the weight image, analyzing the significance of pixels and linking significant pixels. The advantage of the method is the high operation speed (the corresponding loop detector works on average four times faster than the Canny edge detector). In addition, the paper describes a detector of significant image areas, which is also based on the weight model. The proposed approach can be used in various systems of information processing and control based on methods and tools of computer vision, including control and navigation systems of unmanned vehicles, remote sensing of the Earth, systems for pavement defect detection, biometric systems, etc.

The article deals with the creation of intelligent tractor driver support systems based on computer vision technologies for analyzing the direction of movement and detecting obstacles when performing specified operations, such as plowing, harrowing, weeding, and fertilizing. Electric power poles, trees, rocks, bird nests, animals, people, and field roads are identified as obstacles. The solution of functional problems in the system is based on the extraction of information from images using methods for detecting and recognizing objects in images. The analysis of existing approaches to solving the problems under consideration is carried out and it is shown that the use of deep neural networks is effective. The practical use of the methods based on the chosen approach is based on the performance of the computing system, the availability of sufficient training data and the optimality of the training method. It is shown that these factors are important when implementing an intelligent tractor driver support system.

No abstract

No abstract