The Properties of Computing Processes in Image Analysis and Machine Learning Tasks

Abstract

The solving process of any computer vision or machine learning task can be represented in form of some sequence of computational operations on input data. The feature of intelligent data analysis is significant input data heterogeneity which includes emissions, measurement uncertainty, and multimodality. Different types of computing operations respond differently to the presented types of mismatches. The quality of problem solution to a large extent depends on the properties and data mismatch stability of the basic operations. The article describes main types of computational operations, used in computer vision and machine learning algorithms, the analysis of their resistance to various types of mismatch in the data is provided. The information will be useful in designing visual objects descriptors, in the development of detection and tracking algorithms. Of particular value is using of the information in design and analysis of deep convolutional neural networks.