Robust object detection in images corrupted by impulse noise

This paper proposes two effective normalized similarity functions for robust object detection in very high density impulse noisy images. These functions form an integral similarity estimate based on relations of minimum by maximum values for all pairs of analyzed image features. To provide invariance under the constant brightness changes, zero-mean additive modification is used. We explore properties of our functions and compare them with other commonly used for object detection in images corrupted by impulse noise. The efficiency of our approach is illustrated and confirmed by experimental results

Effective Object Localization in Images by Calculating Ratio and Distance Between Pixels

Bohush, Rykhard & Ablameyko, Sergey & Adamovsky, Egor. (2020). Effective Object Localization in Images by Calculating Ratio and Distance Between Pixels.In this paper, two novel similarity functions which consider the spatial and brightness relations between pixels for object localization in images are presented. We explore different advantages of our functions and compare them to others that use only spatial connection between pixels. It is shown, that one of them is robust to linear change in pixel brightness levels of the compared images. Comparison of computational cost and localization accuracy of shifted object for our similarity functions with others is given in the paper. The presented experimental results confirm the effectiveness of the proposed approach for object localization

Review of Methods for Smartphone Application Foot Size Estimation From Images

The task of obtaining real-world coordinates of an object is quite challenging. In this paper proposed brief explanation of algorithms dedicated to overcome this challenge. They were analyzed including their advantages and disadvantages in application to possible implementation in smartphone app

Analysis of motion detection in video for early forest fire detection

Reliable and early detection of forest fire can significantly reduce the damage caused to forestry. Using of machine vision systems, namely video fire detectors, belongs to promising area forest fire recognition. Motion detection is the key step for smoke and flame monitoring in video. This document describes some methods of movement detection in video sequences, including comparative analysis of opportunities to find moving regions, their advantages and disadvantages

Metallographic studies

The composition of the fractions of the heavier cut of pyrolysis gas oil production was investigated by gas chromatography. In order to increase the profitability of pyrolysis units, it is recommended to organize complex technological schemes for the processing of the heavier cut of pyrolysis gas oil

Object localization and classification based on convolutional neural networks

The paper deals with the basic structural elements of the convolution neural network as well as methods for describing and tested methods of localization of objects in the neural network. Effective approaches are proposed for the construction of algorithm of objects localization. The results of the implementation of the algorithm are presented

Алгоритм сопровождения людей на видеопоследовательностях с использованием свёрточных нейронных сетей для видеонаблюдения внутри помещений

Богуш, Р. П. Алгоритм сопровождения людей на видеопоследовательностях с использованием свёрточных нейронных сетей для видеонаблюдения внутри помещений / Р.П. Богуш, И.Ю. Захарова // Компьютерная оптика. – 2020. – Т. 44, № 1. – С. 109-116. – DOI: 10.18287/2412-6179-CO-565.In this paper, a person tracking algorithm for indoor video surveillance is presented. The algo-rithm contains the following steps: person detection, person features formation, features similarity calculation for the detected objects, postprocessing, person indexing, and person visibility deter-mination in the current frame. Convolutional Neural Network (CNN) YOLO v3 is used for person detection. Person features are formed based on H channel in HSV color space histograms and a modified CNN ResNet. The proposed architecture includes 29 convolutional and one fully connected layer. As the output, it forms a 128-feature vector for every input image. CNN model was trained to perform feature extraction. Experiments were conducted using MOT methodology on stable camera videos in indoor environment. Main characteristics of the presented algorithm are calculated and discussed, confirming its effectiveness in comparison with the current approaches for person tracking in an indoor environment. Our algorithm performs real time processing for object detection and tracking using CUDA technology and a graphics card NVIDIA GTX 1060. = Рассматривается алгоритм сопровождения людей в помещениях, который состоит из следующих основных этапов: обнаружение людей, формирование их признаков, установление соответствия между ними на кадрах, постобработка, индексация сопровождаемых объектов и определение их видимости на кадре. Для детектирования используется свёрточная нейронная сеть YOLO v3. Признаки людей формируются на основе гистограммы канала цветового тона пространства HSV и модифицированной СНС ResNet34. Предлагаемая структура свёрточной нейронной сети состоит из 29 свёрточных и одного полносвязного слоёв и формирует вектор из 128 значений признаков для входного изображения. Выполнено обучение данной модели свёрточной нейронной сети. Определены и представлены основные характеристики разработанного алгоритма, которые подтвердили его эффективность для видеонаблюдения внутри помещений. Эксперименты проведены по методике МОТ на тестовых видеопоследовательностях, снятых в помещениях неподвижной видеокамерой. При решении задач обнаружения и сопровождения предложенный алгоритм работает в режиме реального времени с использованием технологии CUDA и видеокарты NVIDIA GTX 1060