Wyniki wyszukiwania - Biblioteka Nauki

1

Generating Layout for Complex Cave-like Levels with Schematic Maps and Cellular Automata

100%

Antoniuk I.

Machine Graphics and Vision

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2023

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tom Vol. 32, No. 2

45--65

EN

Abstract. In this paper an algorithm for creating cave-like, user-guided layout is presented. In applications such as computer games, underground structures offer unique challenges and interesting space for player actions. Preparation of such areas can be time consuming and tiresome, especially during the design process, when many ideas are often scrapped. Presented approach aims at improving this process. Schematic input is used so the user can quickly define the general layout. Cave system is divided into levels and tiles - easily-parallelizable modules for the following method stages. Cellular automata are used to extend initial system sketch with interesting shapes while the diamond-square algorithm spreads the final terrain heights. Each stage uses the results of the previously performed operations as input, providing space for alterations. Input maps can be reused to obtain different variations of the same system. The final structure is represented as a 3D point cloud. Chosen representation supports multilevel systems and can be used either as a base for further algorithms, or as a final mesh. The presented approach can be easily incorporated into game design process, while visualizing initial layouts and speeding up preparation of unique, interesting and challenging game spaces for the players to traverse.

2

Method for the Player Profiling in the Turn-based Computer Games

51%

Bilski P. , Antoniuk I. , Łabędzki R.

International Journal of Electronics and Telecommunications

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2023

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tom Vol. 69, No. 3

461--468

EN

The following paper presents the players profiling methodology applied to the turn-based computer game in the audience-driven system. The general scope are mobile games where the players compete against each other and are able to tackle challenges presented by the game engine. As the aim of the game producer is to make the gameplay as attractive as possible, the players should be paired in a way that makes their duel the most exciting. This requires the proper player profiling based on their previous games. The paper presents the general structure of the system, the method for extracting information about each duel and storing them in the data vector form and the method for classifying different players through the clustering or predefined category assignment. The obtained results show the applied method is suitable for the simulated data of the gameplay model and clustering of players may be used to effectively group them and pair for the duels.

3

Text area detection in handwritten documents scanned for further processing

51%

Pach J. L. , Krupa A. , Antoniuk I.

Machine Graphics and Vision

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2020

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tom Vol. 29, No. 1/4

21--31

EN

In this paper we present an approach to text area detection using binary images, Constrained Run Length Algorithm and other noise reduction methods of removing the artefacts. Text processing includes various activities, most of which are related to preparing input data for further operations in the best possible way, that will not hinder the OCR algorithms. This is especially the case when handwritten manuscripts are considered, and even more so with very old documents. We present our methodology for text area detection problem, which is capable of removing most of irrelevant objects, including elements such as page edges, stains, folds etc. At the same time the presented method can handle multi-column texts or varying line thickness. The generated mask can accurately mark the actual text area, so that the output image can be easily used in further text processing steps.

4

Normal Patch Retinex robust algorithm for white balancing in digital microscopy

51%

Roszczyk R. , Krupa A. , Antoniuk I.

Machine Graphics and Vision

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2020

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tom Vol. 29, No. 1/4

79--94

EN

The acquisition of accurately coloured, balanced images in an optical microscope can be a challenge even for experienced microscope operators. This article presents an entirely automatic mechanism for balancing the white level that allows the correction of the microscopic colour images adequately. The results of the algorithm have been confirmed experimentally on a set of two hundred microscopic images. The images contained scans of three microscopic specimens commonly used in pathomorphology. Also, the results achieved were compared with other commonly used white balance algorithms in digital photography. The algorithm applied in this work is more effective than the classical algorithms used in colour photography for microscopic images stained with hematoxylin-phloxine-saffron and for immunohistochemical staining images.

5

Improved efficient capsule network for Kuzushiji-MNIST benchmark dataset classification

51%

Bukowski M. , Antoniuk I. , Kurek J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2023

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tom Vol. 71, nr 6

art. no. e147338

EN

In this paper, we present an improved efficient capsule network (CN) model for the classification of the Kuzushiji-MNIST and Kuzushiji-49 benchmark datasets. CNs are a promising approach in the field of deep learning, offering advantages such as robustness, better generalization, and a simpler network structure compared to traditional convolutional neural networks (CNNs). Proposed model, based on the Efficient CapsNet architecture, incorporates the self-attention routing mechanism, resulting in improved efficiency and reduced parameter count. The experiments conducted on the Kuzushiji-MNIST and Kuzushiji-49 datasets demonstrate that the model achieves competitive performance, ranking within the top ten solutions for both benchmarks. Despite using significantly fewer parameters compared to higher-rated competitors, presented model achieves comparable accuracy, with overall differences of only 0.91% and 1.97% for the Kuzushiji-MNIST and Kuzushiji- 49 datasets, respectively. Furthermore, the training time required to achieve these results is substantially reduced, enabling training on nonspecialized workstations. The proposed novelties of capsule architecture, including the integration of the self-attention mechanism and the efficient network structure, contribute to the improved efficiency and performance of presented model. These findings highlight the potential of CNs as a more efficient and effective approach for character classification tasks, with broader applications in various domains.

6

BCT Boost Segmentation with U-net in TensorFlow

38%

Wieczorek G. , Antoniuk I. , Kurek J. , Świderski B. , Kruk M. , Pach J. , Orłowski A.

Machine Graphics and Vision

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2019

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tom Vol. 28, No. 1/4

25--34

EN

In this paper we present a new segmentation method meant for boost area that remains after removing the tumour using BCT (breast conserving therapy). The selected area is a region on which radiation treatment will later be made. Consequently, an inaccurate designation of this region can result in a treatment missing its target or focusing on healthy breast tissue that otherwise could be spared. Needless to say that exact indication of boost area is an extremely important aspect of the entire medical procedure, where a better definition can lead to optimizing of the coverage of the target volume and, in result, can save normal breast tissue. Precise definition of this area has a potential to both improve the local control of the disease and to ensure better cosmetic outcome for the patient. In our approach we use U-net along with Keras and TensorFlow systems to tailor a precise solution for the indication of the boost area. During the training process we utilize a set of CT images, where each of them came with a contour assigned by an expert. We wanted to achieve a segmentation result as close to given contour as possible. With a rather small initial data set we used data augmentation techniques to increase the number of training examples, while the final outcomes were evaluated according to their similarity to the ones produced by experts, by calculating the mean square error and the structural similarity index (SSIM).

7

Textural features based on run length encoding in the classification of furniture surfaces with the orange skin defect

32%

Pach J. , Chmielewski L. J. , Orłowski A. , Kruk M. , Kurek J. , Świderski B. , Antoniuk I. , Wieczorek G. , Śmietańska K. , Górski J.

Machine Graphics and Vision

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2019

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tom Vol. 28, No. 1/4

35--45

EN

Textural features based upon thresholding and run length encoding have been successfully applied to the problem of classification of the quality of lacquered surfaces in furniture exhibiting the surface defect known as orange skin. The set of features for one surface patch consists of 12 real numbers. The classifier used was the one nearest neighbour classifier without feature selection. The classification quality was tested on 808 images 300 by 300 pixels, made under controlled, close-to-tangential lighting, with three classes: good, acceptable and bad, in close to balanced numbers. The classification accuracy was not smaller than 98% when the tested surface was not rotated with respect to the training samples, 97% for rotations up to 20 degrees and 95.5% in the worst case for arbitrary rotations.

8

Data augmentation techniques for transfer learning improvement in drill wear classification using convolutional neural network

32%

Kurek J. , Antoniuk I. , Górski J. , Jegorowa A. , Świderski B. , Kruk M. , Wieczorek G. , Pach J. , Orłowski A. , Aleksiejuk-Gawron J.

Machine Graphics and Vision

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2019

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tom Vol. 28, No. 1/4

3--12

EN

This paper presents an improved method for recognizing the drill state on the basisof hole images drilled in a laminated chipboard, using convolutional neural network (CNN) and data augmentation techniques. Three classes were used to describe the drill state: red - for drill that is worn out and should be replaced, yellow - for state in which the system should send a warning to the operator, indicating that this element should be checked manually, and green - denoting the drill that is still in good condition, which allows for further use in the production process. The presented method combines the advantages of transfer learning and data augmentation methods to improve the accuracy of the received evaluations. In contrast to the classical deep learning methods, transfer learning requires much smaller training data sets to achieve acceptable results. At the same time, data augmentation customized for drill wear recognition makes it possible to expand the original dataset and to improve the overall accuracy. The experiments performed have confirmed the suitability of the presented approach to accurate class recognition in the given problem, even while using a small original dataset.

9

Context-based segmentation of the longissimus muscle in beef with a deep neural network

32%

Talacha K. , Świderski B. , Kurek J. , Kruk M. , Półtorak A. , Chmielewski L. J. , Wieczorek G. , Antoniuk I. , Pach J. , Orłowski A.

Machine Graphics and Vision

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2019

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tom Vol. 28, No. 1/4

47--57

EN

The problem of segmenting the cross-section through the longissimus muscle in beef carcasses with computer vision methods was investigated. The available data were 111 images of cross-sections coming from 28 cows (typically four images per cow). Training data were the pixels of the muscles, marked manually. The AlexNet deep convolutional neural network was used as the classifier, and single pixels were the classified objects. Each pixel was presented to the network together with its small circular neighbourhood, and with its context represented by the further neighbourhood, darkened by halving the image intensity. The average classification accuracy was 96%. The accuracy without darkening the context was found to be smaller, with a small but statistically significant difference. The segmentation of the longissimus muscle is the introductory stage for the next steps of assessing the quality of beef for the alimentary purposes.

10

Classifiers ensemble of transfer learning for improved drill wear classification using convolutional neural network

32%

Kurek J. , Antoniuk I. , Górski J. , Jegorowa A. , Świderski B. , Kruk M. , Wieczorek G. , Pach J. , Orłowski A. , Aleksiejuk-Gawron J.

Machine Graphics and Vision

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2019

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tom Vol. 28, No. 1/4

13--23

EN

In this paper we introduce the enhanced drill wear recognition method, based on classifiers ensemble, obtained using transfer learning and data augmentation methods. Red, green and yellow classes are used to describe the current drill state. The first one corresponds to the case when drill should be immediately replaced. The second one denotes a tool that is still in a good condition. The final class refers to the case when a drill is suspected of being worn out, and a human expert evaluation would be required. The proposed algorithm uses three different, pretrained network models and adjusts them to the drill wear classification problem. To ensure satisfactory results, each of the methods used was required to achieve accuracy above 90% for the given classification task. Final evaluation is achieved by voting of all three classifiers. Since the initial data set was small (242 instances), the data augmentation method was used to artificially increase the total number of drill hole images. The experiments performed confirmed that the presented approach can achieve high accuracy, even with such a limited set of training data.