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1
Content available remote Novel feature-based stereo matching method that employs tensor representation on local pixels neighborhoods in images
100%
Cyganek B.
Machine Graphics and Vision
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2001
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tom Vol. 10, No. 3
289-316
EN
Stereo matching techniques have evolved substantially throughout recent years. However, the problem of unambigous stereo points matching, especially in presence of object occlusions, as well as images noise and distortions, remains still open. In this paper, a novel feature-based stereo matching method, based on tensor representation of local structures in digital images, has been described. Application of a structural tensor enables more reliable matching of locally coherent structures, representing averaged dominant gradients in local neighborhoods rather than sparse points. The presented work has been completed with many experiments that confirmed its usefulness, especially in a case of real stereo images.
2
Content available remote A software framework for tensor representation and decompositions for object recognition
100%
Cyganek B.
Image Processing & Communications
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2010
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tom Vol. 15, no 2
5-16
EN
The paper presents a framework for object detection by the tensor decomposition, called Higher Order Singular Value Decomposition (HOSVD), of the space of the training patterns. This allows a direct control over a number of dimensions inherent to the pattern space. The pattern space can be build from the available prototypes, as well as their geometrically deformed versions. Such strategy allows recognition of shifted and rotated patterns. In the paper a software framework for efficient representation and manipulations of tensors is also discussed. Tensors are stored in the matricized form with simultaneous abstraction imposed on tensor indices thanks to the proxy design pattern. This allows minimization of data copying, e.g. in the process of tensor decomposition. Finally, the whole framework was tested in the system of driver drowsiness control in which it is used for eye recognition. The latter is called TensorEye processing.
3
Content available remote Hardware-software system for acceleration of image processing operations
100%
Cyganek B.
Machine Graphics and Vision
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2006
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tom Vol. 15, No. 3/4
329-337
EN
The paper presents design and architecture of a hybrid software/hardware system for acceleration of image processing. The front end consists of a software interface that defines the basic data structures and exchange mechanisms for connecting to external software. The back end consists of a hardware board which is responsible for acceleration of image computations. Thus, the two main components follow the handle/body concept, which allows modifications to the implementation without changes in interfaces. This flexibility allows for better resource usage, and faster development, and facilitates system extensions. In this paper we present the design and implementation issues for the system, as well as discuss its run-time performance for the selected image operations.
4
Content available remote Wprowadzenie do pomiaru głębi obrazu za pomocą stereoskopowego układu kamer
100%
Cyganek B.
Przegląd Spawalnictwa
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2013
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tom R. 85, nr 3
38-43
PL
W artykule zaprezentowano podstawy widzenia stereoskopowego zarówno w układzie wzrokowym człowieka, jak również w systemie komputerowym wyposażonym w parę kamer. Przedstawione zostały matematyczne podstawy widzenia stereoskopowego, które z wykorzystaniem dwóch przetworników wizyjnych umożliwiają ocenę nie tylko przesunięcia obiektów, ale również ich oddalenia od kamer. Zaprezentowane systemy znajdują zastosowanie również w spawalnictwie do pomiaru parametrów powierzchni jeziorka spawalniczego oraz wyznaczenia stopnia przetopu spoiny.
EN
The paper presents an introduction to stereoscopic vision in the human visual system as well as in computer system equipped with the two cameras. Application of the two cameras in the vision systems allows not only measurement of objects shifts but also measurement of their distance to the cameras. Mathematical foundations of this process are also discussed. Presented systems find application in welding for measurement of parameters of the weld pool and weld penetration.
5
Content available remote Computationally efficient methods of approximations of the S-shape functions for image processing and computer graphics tasks
63%
Cyganek B. , Socha K.
Image Processing & Communications
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2011
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tom Vol. 16, no 1-2
19-28
EN
The paper describes a number of methods for approximation of the S-shape functions, frequently used in computer graphics or image processing. The main focus is on efficient software and hardware implementations. We present original code for the high and low level languages which implement different approximations of the S-shape functions. Additionally we introduce the FixedFor <> template class which fills the gap of efficient representation of different length fixed-point data formats in C++.
6
Content available Impact of low resolution on image recognition with deep neural networks: An experimental study
63%
Koziarski M. , Cyganek B.
International Journal of Applied Mathematics and Computer Science
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2018
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tom Vol. 28, no. 4
735--744
EN
Due to the advances made in recent years, methods based on deep neural networks have been able to achieve a state-of-the-art performance in various computer vision problems. In some tasks, such as image recognition, neural-based approaches have even been able to surpass human performance. However, the benchmarks on which neural networks achieve these impressive results usually consist of fairly high quality data. On the other hand, in practical applications we are often faced with images of low quality, affected by factors such as low resolution, presence of noise or a small dynamic range. It is unclear how resilient deep neural networks are to the presence of such factors. In this paper we experimentally evaluate the impact of low resolution on the classification accuracy of several notable neural architectures of recent years. Furthermore, we examine the possibility of improving neural networks’ performance in the task of low resolution image recognition by applying super-resolution prior to classification. The results of our experiments indicate that contemporary neural architectures remain significantly affected by low image resolution. By applying super-resolution prior to classification we were able to alleviate this issue to a large extent as long as the resolution of the images did not decrease too severely. However, in the case of very low resolution images the classification accuracy remained considerably affected.
7
Content available Parallelized algorithms for finding similar images and object recognition
51%
Frączek R. , Cyganek B. , Wiatr K.
Computer Science
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2013
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tom Vol. 14 (1)
113--127
EN
The paper addresses the issue of searching for similar images and objects in arepository of information. The contained images are annotated with the help of the sparse descriptors. In the presented research, different color and edge histogram descriptors were used. To measure similarities among images,various color descriptors are compared. For this purpose different distance measures were employed. In order to decrease execution time, several code optimization and parallelization methods are proposed. Results of these experiments, as well as discussion of the advantages and limitations of different combinations of metods are presented.
8
Content available Automatic segmentation of brain tumors using tensor analysis and multimodal MRI
51%
Jackowski K. , Manhães-Savio A. , Cyganek B.
Journal of Medical Informatics & Technologies
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2015
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tom Vol. 24
165--172
EN
Glioma detection and classification is an critical step to diagnose and select the correct treatment for the brain tumours. There has been advances in glioma research and Magnetic Resonance Imaging (MRI) is the most accurate non-invasive medical tool to localize and analyse brain cancer.The scientific global community has been organizing challenges of open data analysis to push forward automatic algorithms to tackle this task. In this paper we analyse part of such challenge data, the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), with novel algorithms using partial learning to test an active learning methodology and tensor-based image modelling methods to deal with the fusion of the multimodal MRI data into one space. A Random Forest classifier is used for pixel classification. Our results show an error rates of 0.011 up to 0.057 for intra-subject classification. These results are promising compared to other studies. We plan to extend this method to use more than 3 MRI modalities and present a full active learning approach.
9
Teoria wyszkolenia fizycznego spawaczy i lutowaczy gazowych kluczem do kopiowania ruchów spawacza na ruchy robota
45%
Jastrzębski R. , Cyganek B. , Przytuła J. , Jastrzębska I. , Szczyrbak K.
Dozór Techniczny
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2014
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tom z. 4-5
74--82
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