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

Joint Dataset for CNN-based Person Re-identification

In this paper, we propose a joint dataset for person re-identification task that includes the existing public datasets CUHK02, CUHK03, Market, Duke, LPW and our collected PolReID. We investigate the training dataset size and composition effect on the re-identification accuracy. We carried out a number of experiments with different size of dataset to solve re-identification task. The results of experiments are presented

Formalization of People and Crowd Detection and Tracking for Smart Video Surveillance

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the problem of detection and tracking of people and crowd in video. At first, we defined person, group of persons and crowd motion detection types and formalized them. For crowd, we defined three main types of its motion: direct motion, aggregation and dispersion. Then, we formalised the task of tracking for these three groups of people (single person, group of persons and crowd). Based on these formalizations, we developed algorithms for detection and tracking people and crowd in video sequences for indoor and outdoor environment. The results of experiments for video sequences obtained using a stationary and moving video camera are presented

Распознавание образов и обработка изображений в Беларуси: краткая история ассоциации и проведения конференций PRIP

To the 30th anniversary of the establishment of the Belarusian Association for Analysis and Image Recognition К 30-летию создания Белорусской ассоциации по анализу и распознаванию изображени

Evolutionary Design of the Classifier Ensemble

This paper presents two novel approaches to evolutionary design of the classifier ensemble. The first one presents the task of one-objective optimization of feature set partitioning together with feature weighting for the construction of the inividual classifiers. The second approach deals with multi-objective optimization of classifier ensemble design. The proposed approaches have been tested on two data sets from the machine learning repository and one real data set on transient ischemic attack. The experiments show the advantages of the feature weighting in terms of classification accuracy when dealing with multivariate data sets and the possibility in one run of multi-objective genetic algorithm to get the non-dominated ensembles of different sizes and thereby skip the tedious process of iterative search for the best ensemble of fixed size.У статті запропоновано два нові підходи до еволюційної побудови ансамблю класифікаторів. Перший підхід є завданням одинкритерійної оптимізації розбиття безлічі ознак на окремі підмножини, які використовуються для побудови класифікаторів ансамблю. Другий підхід здійснює багатокритеріальну оптимізацію структури ансамблю класифікаторів.В статье предложены два новых подхода к эволюционному построению ансамбля классификаторов. Первый подход представляет собой задачу однокритериальной оптимизации разбиения множества признаков на отдельные подмножества, которые используются для построения классификаторов ансамбля. Второй подход осуществляет многокритериальную оптимизацию структуры ансамбля классификаторов

Formalization of People and Crowd Detection and Tracking in Video

Оne of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of people and crowd in video. The approach for tracking multiple people on video sequences for indoor and outdoor is described. The results of experiments for video sequences obtained using a stationary and moving video camera are presented