Wyniki wyszukiwania - BazTech

1

Smartphone-Based Color Measurement of Tooth Shade Guide in Clinical Lighting Conditions

Radziun Agnieszka, Doniec Rafał, Sieciński Szymon, Piaseczna Natalia, Mocny-Pachońska Katarzyna, Cieślik-Wegemund Marta, Duraj Konrad, Tanasiewicz Marta, Tkacz Ewaryst

Annals of Computer Science and Information Systems

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2021

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Vol. 25

409--413

EN

Caries is a common disease of hard tissues of teeth which results in dental cavities, which are usually replaced by dental filling. Matching the color of a dental filling is usually a subjective assessment. In this study we conducted a color analysis of GC Gradia Direct shade guide in the lighting conditions of the dental office. Color measurement was performed using Color Grab mobile app and the results were acquired as values of RGB and HSV values. The results indicate the possibility of identifying each shade of tooth by the most prominent changes in RGB, and/or HSV components.

2

Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit

Janusz Andrzej, Przyborowski Mateusz, Biczyk Piotr, Ślęzak Dominik

Annals of Computer Science and Information Systems

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2020

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Vol. 21

77--80

EN

FedCSIS 2020 Data Mining Challenge: Network Device Workload Prediction was the seventh edition of the international data mining competition organized at Knowledge Pit, in association with the Conference on Computer Science and Information Systems. The main goal was to answer the question of whether it is possible to reliably predict workload-related characteristics of monitored network devices based on historical readings. We describe the scope and explain the motivation for this challenge. We also analyze solutions uploaded by the most successful participants and investigate prediction errors which had the greatest influence on the results. Finally, we describe our baseline solution to the considered problem, which turned out to be the most reliable in the final evaluation.

3

Counting instances of objects in color images using U-net network on example of honey bees

Westwańska Weronika W., Respondek Jerzy S.

Annals of Computer Science and Information Systems

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2019

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Vol. 18

87--90

EN

This article presents a novel approach to segmentation and counting of objects in color digital images. The objects belong to a certain class, which in this case are honey bees. The authors briefly present existing approaches which use Convolutional Neural Networks to solve the problem of image segmentation and object recognition. The focus however is on application of U-Net convolutional neural network in an environment where knowledge about the object of interest is only limited to its rough, single pixel location. The authors provide full access to the details of the code used to implement the algorithms, as well as the data sets used and results obtained. The results show an encouraging low level of counting error at 14.27% for the best experiment.

4

A two-pass median-like filter for impulse noise removal in multi-channel images

Grabowski S., Bieniecki W.

Image Processing & Communications

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2003

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Vol. 9, no 3-4

19-26

EN

We propose a two-pass median filter for impulse noise suppression in color images. The first pass is our previously presented algorithm, PNN-VMF [2]. The second pass routine is chosen dynamically on the basis of the number of pixel modifications performed by the first pass filtering. Such an approach is likely to avoid harm (e.g., blurring edges) resulting from excessive filtering. The effectiveness of the algorithm has been experimentally verified on 15 standard images.

5

A color image segmentation method based on seeded region growing and visual attention

Ouerhani N., Archip N., Huegli H., Erard P.

Image Processing & Communications

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2002

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Vol. 8, no 1

3-11

EN

This paper reports a color image segmentation method based on a seeded region growing technique (SRG) and guided by a saliency-based visual attention algorithm. Inspired by biological vision the purely data-driven model of visual attention is built around the feature, conspicuity and saliency maps. Using chromatic as well as unchromatic scene features, it, automatically, generates a set of regions-of-interest (ROIs), which represent the most visually-salient locations of the image. The automatically selected points are then used as seeds by the region growing algorithm to segment the conspicuous parts of the scene, using a color homogeneity criterion. A snakes-based technique is then used to improve the contours of the segmented regions. The results reported in this paper clearly shoe the effectiveness of the considered model of visual attention to detect the salient locations in color images.