Slavomir Matuska

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.

The comparison of CPU time consumption for image processing algorithm in Matlab and OpenCV

In order to fill gap of growing demand for high efficient image and video processing, open source computer vision library (OpenCv) is way to deals with this task. Hence, this paper is about basic algorithm for image processing and their CPU time consumption in Matlab comparing with OpenCv. Algorithms are tested on images with resolution 3264×2448, 1920×1080, 1024×768 and 220×260. Multi-processors computer and multi-threading programs are used to improve processing efficiency.

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.

Real-Time Segmentation and Tracking Module of Target of Interest from Video Sequence in Object Recognition Systems

This paper proposes a real-time objects segmentation and tracking module from video sequences, which can be effectively used in real-time object recognition systems. The module is based on background subtraction method in combination with CAMshift (Continuously Adaptive Mean shift) algorithm. In the first step, background subtraction method is applied to determine pixels of moving objects in video stream. Then, foreground pixels are used as starting point for CAMshift algorithm. CAMshift finds optimal size, position and orientation of moving objects. After that, key frame extraction method is applied in order to choose only relevant frame in later objects classification.

A Video Camera Road Sign System of the Early Warning from Collision with the Wild Animals

Abstract This paper proposes a camera road sign system of the early warning, which can help to avoid from vehicle collision with the wild animals. The system consists of camera modules placed down the particularly chosen route and the intelligent road signs. The camera module consists of the camera device and the computing unit. The video stream is captured from video camera using computing unit. Then the algorithms of object detection are deployed. Afterwards, the machine learning algorithms will be used to classify the moving objects. If the moving object is classified as animal and this animal can be dangerous for safety of the vehicle, warning will be displayed on the intelligent road sings.

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

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.