IMAGE SEGMENTATION AND OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY

Abstract

Background. An analysis of the processes of image segmentation is being carried out. An original method of image segmentation using a convolutional neural network is proposed. Materials and methods. A comparative assessment of existing segmentation methods such as threshold segmentation methods: Otsu, Niblack, Bernsen, Savola, as well as the method of image segmentation using a convolutional neural network is carried out. Their advantages and disadvantages are evaluated. Examples of image segmentation by various methods are given. Algorithmic descriptions of segmentation methods are presented. Experiments were carried out to study the segmentation of frames (images) from a given video sequence. Results and conclusions. The results of the experiment, showing the operation of one or another segmentation method, are presented.

Keywords

• adaptive methods
• threshold methods
• segmentation
• otsu's method
• niblack's method
• bernsen's method
• savol's method
• convolutional neural network