Martina Zachariasova - Academia.edu (original) (raw)

Papers by Martina Zachariasova

Research paper thumbnail of Structural Health Monitoring of Helicopter Fuselage

Communications - Scientific letters of the University of Zilina, 2013

Research paper thumbnail of Simple comparison of image segmentation algorithms based on evaluation criterion

Proceedings of 21st International Conference Radioelektronika 2011, 2011

Page 1. Simple Comparison of Image Segmentation Algorithms Based on Evaluation Criterion Peter LU... more Page 1. Simple Comparison of Image Segmentation Algorithms Based on Evaluation Criterion Peter LUKAC, Robert HUDEC, Miroslav BENCO, Patrik KAMENCAY,Zuzana DUBCOVA, Martina ZACHARIASOVA Department ...

Research paper thumbnail of Utilization of electro-conductive blended Ag/PA textile rayon yarns as data and power wires in an intelligent textile structures

2014 ELEKTRO, 2014

ABSTRACT Nowadays, health application has growing market potential. In this paper, an investigati... more ABSTRACT Nowadays, health application has growing market potential. In this paper, an investigation of electro-conductive, parameters of blended silver coated polyamide yarn were measured. We focused on the several objectives important in electro-conductive yarns design and application into intelligent clothing. In detail, the effect of numbers of silver filaments, draft and twists, the effect of external heating source on electrical and temperature parameters were tested. Likewise, the possibilities of electro-conductive yarn as data bus were tested too.

Research paper thumbnail of 3D image reconstruction from 2D CT slices

2014 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), 2014

ABSTRACT In this paper, a 3D reconstruction algorithm using CT slices of human pelvis is presente... more ABSTRACT In this paper, a 3D reconstruction algorithm using CT slices of human pelvis is presented. We propose the method for 3D image reconstruction that is based on a combination of the SURF (Speeded-Up Robust Features) descriptor and SSD (Sum of Squared Differences) matching algorithm using image segmentation with aim to obtain accurate 3D model of human pelvis. Firstly, we apply image filtering for noise removing and smoothing. Next, the filtered image is split into segments using Mean- Shift segmentation algorithm. Secondly, the edges using Canny edge detector are extracted. Then, for each segment we look at the associated corresponding points. The best corresponding points of all the segments using SURF-SSD method were obtained. The smaller is the value of SSD at a particular pixel, the more similarity exists between the first image and the second image in the neighborhood of that pixel. Finally, we have integrated the Mean-Shift segmentation algorithm with the SURF-SSD method. The obtained experimental results demonstrate that the SURF-SSD algorithm in combination with image segmentation provides accurate 3D model of human pelvis.

Research paper thumbnail of 2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm

International Journal of Advanced Robotic Systems, 2014

This paper presents a proposed methodology for face recognition based on an information theory ap... more This paper presents a proposed methodology for face recognition based on an information theory approach to coding and decoding face images. In this paper, we propose a 2D-3D face-matching method based on a principal component analysis (PCA) algorithm using canonical correlation analysis (CCA) to learn the mapping between a 2D face image and 3D face data. This method makes it possible to match a 2D face image with enrolled 3D face data. Our proposed fusion algorithm is based on the PCA method, which is applied to extract base features. PCA feature-level fusion requires the extraction of different features from the source data before features are merged together. Experimental results on the TEXAS face image database have shown that the classification and recognition results based on the modified CCA-PCA method are superior to those based on the CCA method. Testing the 2D-3D face match results gave a recognition rate for the CCA method of a quite poor 55% while the modified CCA method ...

Research paper thumbnail of Novel Approach to Color Texture Retrieval Based on GLCM

Communications - Scientific letters of the University of Zilina

Research paper thumbnail of Image Segmentation and Feature Extraction Using Sift-Sad Algorithm for Disparity Map Generation

Communications - Scientific letters of the University of Zilina, 2013

Research paper thumbnail of The Effect of Metric Space on the Results of Graph Based Colour Image Segmentation

Communications - Scientific letters of the University of Zilina, 2012

Research paper thumbnail of Classification of animals to determine the migration potential at the construction of new infrastructure

Archives of Transport System Telematics, 2013

Research paper thumbnail of A New Approach for Disparity Map Estimation from Stereo Image Sequences using Hybrid Segmentation Algorithm

In this paper, a stereo matching algorithm based on image segments is presented. We propose the h... more In this paper, a stereo matching algorithm based on image segments is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and K-Means algorithms with aim to refine the final sparse disparity map by using a stereo pair of images. Firstly, a color based segmentation method is applied for segmenting the left image of the input stereo pair (reference image) into regions. The main aim of the segmentation is to simplify representation of the image into the form that is easier to analyze and is able to locate objects in images. Secondly, results of the segmentation are used as an input of the SIFT-SAD matching method to determine the disparity estimate of each image pixel. This matching algorithm is proposed by combining Scale Invariant Feature Transform (SIFT) with the Sum of Absolute Difference (SAD). Finally, the comparisons between the three robust feature detection methods: Scale Invariant Feature Transform (SIFT), Affine SI...

Research paper thumbnail of Estimation and Image Segmentation of a Sparse Disparity Map for 3 D Reconstruction

In this paper, a stereo matching algorithm based on image segments is presented. We propose the h... more In this paper, a stereo matching algorithm based on image segments is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and K-Means algorithms with aim to refine the final sparse disparity map by using a stereo pair of images. Firstly, a color based segmentation method is applied for segmenting the left image of the input stereo pair (reference image) into regions. The aim of the segmentation is to simplify representation of the image into the form that is easier to analyze and is able to locate objects in images. Secondly, results of the segmentation are used as an input of the SIFT-SAD matching method to determine the disparity estimate of each image pixel. This matching algorithm is proposed by combining Scale Invariant Feature Transform (SIFT) with the Sum of Absolute Difference (SAD). Finally, the comparisons between the three robust feature detection methods SIFT, Affine SIFT (ASIFT) and Speeded Up Robust Features ...

Research paper thumbnail of A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method

In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCAKNN) ... more In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCAKNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods.

Research paper thumbnail of Improved Depth Map Estimation from Stereo Images Based on Hybrid Method

In this paper, a stereo matching algorithm based on image segments is presented. We propose the h... more In this paper, a stereo matching algorithm based on image segments is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and Mean Shift algorithms with aim to refine the disparity and depth map by using a stereo pair of images. This algorithm utilizes image filtering and modified SAD (Sum of Absolute Differences) stereo matching method. Firstly, a color based segmentation method is applied for segmenting the left image of the input stereo pair (reference image) into regions. The aim of the segmentation is to simplify representation of the image into the form that is easier to analyze and is able to locate objects in images. Secondly, results of the segmentation are used as an input of the local window-based matching method to determine the disparity estimate of each image pixel. The obtained experimental results demonstrate that the final depth map can be obtained by application of seg-ment disparities to the original ima...

Research paper thumbnail of Sparse disparity map computation from stereo-view images using segment based algorithm

In this paper, we propose a novel disparity map estimation method based on image segmentation and... more In this paper, we propose a novel disparity map estimation method based on image segmentation and detection of feature points. Image segmentation and detection of feature points in images plays a very important role in stereo-view analysis. Firstly, K-Means based segmentation method is applied for segmenting the input images into regions. The aim of the segmentation is to simplify representation of an image into the form that is more suitable for analysis and further processing, yielding correct disparity estimates. Secondly, results of the image segmentation are used as an input of the SIFT-SAD algorithm to determine the disparity estimate. The proposed matching algorithm combines the Scale Invariant Feature Transform (SIFT) with the Sum of Absolute Difference (SAD). The obtained experimental results demonstrate that the performance of our method is competitive and the final disparity maps are close to the ground truth data.

Research paper thumbnail of Improved face recognition method based on segmentation algorithm using SIFT-PCA

2012 35th International Conference on Telecommunications and Signal Processing (TSP), 2012

ABSTRACT This paper provides an example of the face recognition using SIFT-PCA method and impact ... more ABSTRACT This paper provides an example of the face recognition using SIFT-PCA method and impact of Graph Based segmentation algorithm on recognition rate. Principle component analysis (PCA) is a multivariate technique that analyzes a face data in which observation are described by several inter-correlated dependent variables. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. The paper presents a proposed methodology for face recognition based on preprocessing face images using segmentation algorithm and SIFT (Scale Invariant Feature Transform) descriptor. The algorithm has been tested on 50 subjects (100 images). The proposed method first was tested on ESSEX face database and next on own segmented face database using SIFT-PCA. The experimental result shows that the segmentation in combination with SIFT-PCA has a positive effect for face recognition and accelerates the recognition PCA technique.

Research paper thumbnail of Automatic extraction of non-textual information in web document and their classification

2012 35th International Conference on Telecommunications and Signal Processing (TSP), 2012

Research paper thumbnail of The object recognition based on Scale-Invariant feature transform and hybrid segmentation

2012 ELEKTRO, 2012

This paper deals with research in the area of image analysis. Our approach is based on hybrid seg... more This paper deals with research in the area of image analysis. Our approach is based on hybrid segmentation and Scale-Invariant Feature Transform (SIFT) method. The main idea is to improve the process of object recognition and their classification into classes by Support Vector Machine (SVM) classifier. The fast and powerful hybrid segmentation algorithm based on Mean Shift and Believe Propagation

Research paper thumbnail of A Novel Imaging Approach of Web Documents based on Semantic Inclusion of Textual and Non–Textual Information

AASRI Procedia, 2014

ABSTRACT This paper deals with research in the area of a novel imaging approach of web documents ... more ABSTRACT This paper deals with research in the area of a novel imaging approach of web documents based on semantic inclusion of textual and non-textual informations. The main idea was to create a robust method for relevant display results into search engine based on search by keywords or images. Thus, we proposed method called Semantic Inclusion of Images and Textual (SIIT) segments. The output SIIT method is short web document. It contains image and textual segments, which are semantic linked. Creation of short web document to possible three steps was divided. Firstly, the all images and textual segments from main content web document were extracted. Secondly, extraction images were analyzed in order to obtain of semantic description objects into image. Finally, linked images and textual segments using linguistic analysis.

Research paper thumbnail of Classification of Wild Animals based on SVM and Local Descriptors

AASRI Procedia, 2014

ABSTRACT In this paper, a novel method for object recognition based on hybrid local descriptors i... more ABSTRACT In this paper, a novel method for object recognition based on hybrid local descriptors is presented. This method utilizes a combination of a few approaches (SIFT - Scale-invariant feature transform, SURF - Speeded Up Robust Features) and consists of second parts. The applicability of the presented hybrid methods are demonstrated on a few images from dataset. Dataset classes represent big animals situated in Slovak country, namely wolf, fox, brown bear, deer and wild boar. The presented method may be also used in other areas of image classification and feature extraction. The experimental results show, that the combination of local descriptors has a positive effect for object recognition.

Research paper thumbnail of Hand gesture recognition based on depth map

Research paper thumbnail of Structural Health Monitoring of Helicopter Fuselage

Communications - Scientific letters of the University of Zilina, 2013

Research paper thumbnail of Simple comparison of image segmentation algorithms based on evaluation criterion

Proceedings of 21st International Conference Radioelektronika 2011, 2011

Page 1. Simple Comparison of Image Segmentation Algorithms Based on Evaluation Criterion Peter LU... more Page 1. Simple Comparison of Image Segmentation Algorithms Based on Evaluation Criterion Peter LUKAC, Robert HUDEC, Miroslav BENCO, Patrik KAMENCAY,Zuzana DUBCOVA, Martina ZACHARIASOVA Department ...

Research paper thumbnail of Utilization of electro-conductive blended Ag/PA textile rayon yarns as data and power wires in an intelligent textile structures

2014 ELEKTRO, 2014

ABSTRACT Nowadays, health application has growing market potential. In this paper, an investigati... more ABSTRACT Nowadays, health application has growing market potential. In this paper, an investigation of electro-conductive, parameters of blended silver coated polyamide yarn were measured. We focused on the several objectives important in electro-conductive yarns design and application into intelligent clothing. In detail, the effect of numbers of silver filaments, draft and twists, the effect of external heating source on electrical and temperature parameters were tested. Likewise, the possibilities of electro-conductive yarn as data bus were tested too.

Research paper thumbnail of 3D image reconstruction from 2D CT slices

2014 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), 2014

ABSTRACT In this paper, a 3D reconstruction algorithm using CT slices of human pelvis is presente... more ABSTRACT In this paper, a 3D reconstruction algorithm using CT slices of human pelvis is presented. We propose the method for 3D image reconstruction that is based on a combination of the SURF (Speeded-Up Robust Features) descriptor and SSD (Sum of Squared Differences) matching algorithm using image segmentation with aim to obtain accurate 3D model of human pelvis. Firstly, we apply image filtering for noise removing and smoothing. Next, the filtered image is split into segments using Mean- Shift segmentation algorithm. Secondly, the edges using Canny edge detector are extracted. Then, for each segment we look at the associated corresponding points. The best corresponding points of all the segments using SURF-SSD method were obtained. The smaller is the value of SSD at a particular pixel, the more similarity exists between the first image and the second image in the neighborhood of that pixel. Finally, we have integrated the Mean-Shift segmentation algorithm with the SURF-SSD method. The obtained experimental results demonstrate that the SURF-SSD algorithm in combination with image segmentation provides accurate 3D model of human pelvis.

Research paper thumbnail of 2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm

International Journal of Advanced Robotic Systems, 2014

This paper presents a proposed methodology for face recognition based on an information theory ap... more This paper presents a proposed methodology for face recognition based on an information theory approach to coding and decoding face images. In this paper, we propose a 2D-3D face-matching method based on a principal component analysis (PCA) algorithm using canonical correlation analysis (CCA) to learn the mapping between a 2D face image and 3D face data. This method makes it possible to match a 2D face image with enrolled 3D face data. Our proposed fusion algorithm is based on the PCA method, which is applied to extract base features. PCA feature-level fusion requires the extraction of different features from the source data before features are merged together. Experimental results on the TEXAS face image database have shown that the classification and recognition results based on the modified CCA-PCA method are superior to those based on the CCA method. Testing the 2D-3D face match results gave a recognition rate for the CCA method of a quite poor 55% while the modified CCA method ...

Research paper thumbnail of Novel Approach to Color Texture Retrieval Based on GLCM

Communications - Scientific letters of the University of Zilina

Research paper thumbnail of Image Segmentation and Feature Extraction Using Sift-Sad Algorithm for Disparity Map Generation

Communications - Scientific letters of the University of Zilina, 2013

Research paper thumbnail of The Effect of Metric Space on the Results of Graph Based Colour Image Segmentation

Communications - Scientific letters of the University of Zilina, 2012

Research paper thumbnail of Classification of animals to determine the migration potential at the construction of new infrastructure

Archives of Transport System Telematics, 2013

Research paper thumbnail of A New Approach for Disparity Map Estimation from Stereo Image Sequences using Hybrid Segmentation Algorithm

In this paper, a stereo matching algorithm based on image segments is presented. We propose the h... more In this paper, a stereo matching algorithm based on image segments is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and K-Means algorithms with aim to refine the final sparse disparity map by using a stereo pair of images. Firstly, a color based segmentation method is applied for segmenting the left image of the input stereo pair (reference image) into regions. The main aim of the segmentation is to simplify representation of the image into the form that is easier to analyze and is able to locate objects in images. Secondly, results of the segmentation are used as an input of the SIFT-SAD matching method to determine the disparity estimate of each image pixel. This matching algorithm is proposed by combining Scale Invariant Feature Transform (SIFT) with the Sum of Absolute Difference (SAD). Finally, the comparisons between the three robust feature detection methods: Scale Invariant Feature Transform (SIFT), Affine SI...

Research paper thumbnail of Estimation and Image Segmentation of a Sparse Disparity Map for 3 D Reconstruction

In this paper, a stereo matching algorithm based on image segments is presented. We propose the h... more In this paper, a stereo matching algorithm based on image segments is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and K-Means algorithms with aim to refine the final sparse disparity map by using a stereo pair of images. Firstly, a color based segmentation method is applied for segmenting the left image of the input stereo pair (reference image) into regions. The aim of the segmentation is to simplify representation of the image into the form that is easier to analyze and is able to locate objects in images. Secondly, results of the segmentation are used as an input of the SIFT-SAD matching method to determine the disparity estimate of each image pixel. This matching algorithm is proposed by combining Scale Invariant Feature Transform (SIFT) with the Sum of Absolute Difference (SAD). Finally, the comparisons between the three robust feature detection methods SIFT, Affine SIFT (ASIFT) and Speeded Up Robust Features ...

Research paper thumbnail of A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method

In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCAKNN) ... more In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCAKNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods.

Research paper thumbnail of Improved Depth Map Estimation from Stereo Images Based on Hybrid Method

In this paper, a stereo matching algorithm based on image segments is presented. We propose the h... more In this paper, a stereo matching algorithm based on image segments is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and Mean Shift algorithms with aim to refine the disparity and depth map by using a stereo pair of images. This algorithm utilizes image filtering and modified SAD (Sum of Absolute Differences) stereo matching method. Firstly, a color based segmentation method is applied for segmenting the left image of the input stereo pair (reference image) into regions. The aim of the segmentation is to simplify representation of the image into the form that is easier to analyze and is able to locate objects in images. Secondly, results of the segmentation are used as an input of the local window-based matching method to determine the disparity estimate of each image pixel. The obtained experimental results demonstrate that the final depth map can be obtained by application of seg-ment disparities to the original ima...

Research paper thumbnail of Sparse disparity map computation from stereo-view images using segment based algorithm

In this paper, we propose a novel disparity map estimation method based on image segmentation and... more In this paper, we propose a novel disparity map estimation method based on image segmentation and detection of feature points. Image segmentation and detection of feature points in images plays a very important role in stereo-view analysis. Firstly, K-Means based segmentation method is applied for segmenting the input images into regions. The aim of the segmentation is to simplify representation of an image into the form that is more suitable for analysis and further processing, yielding correct disparity estimates. Secondly, results of the image segmentation are used as an input of the SIFT-SAD algorithm to determine the disparity estimate. The proposed matching algorithm combines the Scale Invariant Feature Transform (SIFT) with the Sum of Absolute Difference (SAD). The obtained experimental results demonstrate that the performance of our method is competitive and the final disparity maps are close to the ground truth data.

Research paper thumbnail of Improved face recognition method based on segmentation algorithm using SIFT-PCA

2012 35th International Conference on Telecommunications and Signal Processing (TSP), 2012

ABSTRACT This paper provides an example of the face recognition using SIFT-PCA method and impact ... more ABSTRACT This paper provides an example of the face recognition using SIFT-PCA method and impact of Graph Based segmentation algorithm on recognition rate. Principle component analysis (PCA) is a multivariate technique that analyzes a face data in which observation are described by several inter-correlated dependent variables. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. The paper presents a proposed methodology for face recognition based on preprocessing face images using segmentation algorithm and SIFT (Scale Invariant Feature Transform) descriptor. The algorithm has been tested on 50 subjects (100 images). The proposed method first was tested on ESSEX face database and next on own segmented face database using SIFT-PCA. The experimental result shows that the segmentation in combination with SIFT-PCA has a positive effect for face recognition and accelerates the recognition PCA technique.

Research paper thumbnail of Automatic extraction of non-textual information in web document and their classification

2012 35th International Conference on Telecommunications and Signal Processing (TSP), 2012

Research paper thumbnail of The object recognition based on Scale-Invariant feature transform and hybrid segmentation

2012 ELEKTRO, 2012

This paper deals with research in the area of image analysis. Our approach is based on hybrid seg... more This paper deals with research in the area of image analysis. Our approach is based on hybrid segmentation and Scale-Invariant Feature Transform (SIFT) method. The main idea is to improve the process of object recognition and their classification into classes by Support Vector Machine (SVM) classifier. The fast and powerful hybrid segmentation algorithm based on Mean Shift and Believe Propagation

Research paper thumbnail of A Novel Imaging Approach of Web Documents based on Semantic Inclusion of Textual and Non–Textual Information

AASRI Procedia, 2014

ABSTRACT This paper deals with research in the area of a novel imaging approach of web documents ... more ABSTRACT This paper deals with research in the area of a novel imaging approach of web documents based on semantic inclusion of textual and non-textual informations. The main idea was to create a robust method for relevant display results into search engine based on search by keywords or images. Thus, we proposed method called Semantic Inclusion of Images and Textual (SIIT) segments. The output SIIT method is short web document. It contains image and textual segments, which are semantic linked. Creation of short web document to possible three steps was divided. Firstly, the all images and textual segments from main content web document were extracted. Secondly, extraction images were analyzed in order to obtain of semantic description objects into image. Finally, linked images and textual segments using linguistic analysis.

Research paper thumbnail of Classification of Wild Animals based on SVM and Local Descriptors

AASRI Procedia, 2014

ABSTRACT In this paper, a novel method for object recognition based on hybrid local descriptors i... more ABSTRACT In this paper, a novel method for object recognition based on hybrid local descriptors is presented. This method utilizes a combination of a few approaches (SIFT - Scale-invariant feature transform, SURF - Speeded Up Robust Features) and consists of second parts. The applicability of the presented hybrid methods are demonstrated on a few images from dataset. Dataset classes represent big animals situated in Slovak country, namely wolf, fox, brown bear, deer and wild boar. The presented method may be also used in other areas of image classification and feature extraction. The experimental results show, that the combination of local descriptors has a positive effect for object recognition.

Research paper thumbnail of Hand gesture recognition based on depth map