Martina Zachariasova

2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm

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 based on PCA-level fusion achieved a very good recognition score of 85%.

Simple comparison of image segmentation algorithms based on evaluation criterion

In the image analysis, image segmentation is the operation that divides image into set of different segments. The paper deals about common color image segmentation techniques and methods. The advantages and disadvantage of each one will be described in this paper. At the end of the paper, the evaluation criterion will be introduced and applied on the algorithms results. Five most used image segmentation algorithms, namely, Efficient graph based, K-means, Mean shift, Expectation maximization and hybrid method are compared by designed criterion.

An Advanced Approach to Extraction of Colour Texture Features Based on GLCM

This paper discusses research in the area of texture image classification. More specifically, the combination of texture and colour features is researched. The principle objective is to create a robust descriptor for the extraction of colour texture features. The principles of two well-known methods for grey-level texture feature extraction, namely GLCM (grey-level co-occurrence matrix) and Gabor filters, are used in experiments. For the texture classification, the support vector machine is used. In the first approach, the methods are applied in separate channels in the colour image. The experimental results show the huge growth of precision for colour texture retrieval by GLCM. Therefore, the GLCM is modified for extracting probability matrices directly from the colour image. The method for 13 directions neighbourhood system is proposed and formulas for probability matrices computation are presented. The proposed method is called CLCM (colour-level co-occurrence matrices) and experimental results show that it is a powerful method for colour texture classification.

Improved face recognition method based on segmentation algorithm using SIFT-PCA

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.

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

This paper deals with research in the area of automatic extraction of textual and non-textual information and their classification. The main idea is to create a robust method for extraction of image and textual segments to obtain short web document. Thus, developed method consist of two data types extractions, where both image and text data extraction are using Document Object Model tree. Extracted objects are saved in separate databases followed the images analysis that define and describe image object from semantic point of view. Moreover, the semantic description of all modal objects are utilized to short web document creation. To accurate object classification, the fast and powerful hybrid segmentation algorithm based on Mean Shift and Believe Propagation principles are mentioned in this paper, too. Likewise, the image segmentation algorithm was integrated with SIFT descriptor. Finally, in order to obtain a semantic description of objects in static image, the SVM classification is used. The developed method was tested on real unsegmented and segmented images, too.

3D image reconstruction from 2D CT slices

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.

Feature extraction for object recognition using PCA-KNN with application to medical image analysis

This paper provides a new feature extraction method for object recognition using PCA-KNN algorithm with SIFT descriptor. The proposed method is divided into three steps. The first step is based on feature extraction from the input images using SIFT (Scale Invariant Feature Transform) descriptor. Each of the features is represented using one or more feature descriptors. In medical systems images used as patterns are also represented by feature vectors. In the second step eigen values and eigen vectors are extracted from each image. We apply PCA algorithm after we reduce the number of features by SIFT algorithm. The goal is to extract the important information as a set of new orthogonal variables called principal components. In the final step a nearest neighbor classifier is designed for classifying the images based on the extracted features. The algorithm is experimented in MATLAB and tested with the Caltech 101 database and the experimental results are shown.

One-dimensional Color-level Co-occurrence matrices

The texture feature extraction plays important role in image analysis. This paper deals with improvement of the one-dimensional version of GLCM (Gray Level Cooccurrence Matrix). In our approach, the color information of texture was taken into consideration. The novel One dimensional Color Level Co-occurrence Matrix (1D-CLCM) are designed. Performances of proposed method are verified on database of 2600 color images. Experimental results demonstrated that 1D-CLCM is more effective compared to one-dimensional and original GLCM for image retrieval.

A new approach to short web document creation based on textual and visual information

This paper deals with research in area of automatic semantic inclusion of textual and non-textual information of Web documents. The main idea is to create a robust method for extraction of images and textual segments to obtain short web document. Thus, developed method consist of two data types extractions, where both, image and text data extraction are using Document Object Model (DOM) tree. Extracted objects are saved in separated databases followed by the images analysis that defines and describes image object from semantic point of view. Moreover, the semantic descriptions of all modal objects are utilized to short web document creation. We implement our novel method using the Scale Invariant Feature Transform (SIFT) descriptor within a Support Vector Machine (SVM) classifier. Further, in order to obtain a semantic description of objects in static image, the Support Vector Machine (SVM) classification were applied. Finally, semantic inclusion textual and visual information was realized. The developed method has been tested on real and off-line web documents.

The Evaluation Criterion for Color Image Segmentation Algorithms

The Evaluation Criterion for Color Image Segmentation Algorithms Image segmentation is first and very important step in image analysis. The main idea of image segmentation is to simplify and change image into easier and meaningful form to analyze. Image segmentation is process, which locate objects in image. Many segmentation algorithms have been created for different applications. The algorithms are used in traffic applications, army applications, web applications, medical applications, studying and many others. In present time, do not exist restful objective methods to evaluate segmentation algorithms. This paper presents evaluation criterion based on measurement of precision of boundary segmentation. Moreover, the automatic segmentation algorithms in comparison with human segmentation results were tested. Four most used image segmentation algorithms, namely, Efficient graph based, K-means, Mean shift and Belief propagation are compared by designed criterion. The criterion computes three evaluation parameters like precision, recall and F1 and the results are presented in the tables and graphs at the end of the paper.