Myanmar Warning Board Recognition System

In any country, warning text is described on the signboards or wallpapers to follow by everybody. This paper present Myanmar character recognition from various warning text signboards using block based pixel count and eight-directions chain code. Character recognition is the process of converting a printed or typewritten or handwritten text image file into editable and searchable text file. In this system, the characters on the warning signboard images are recognized using the hybrid eight direction chain code features and 16-blocks based pixel count features. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation, vertically cropping method and bounding box is used for connected component character segmentation. In the classification step, the performance accuracy is measured by two ways such as KNN (K’s Nearest Neivour) classifier and feature based approach of template matching on 150 warning text signboard images

Kernels Analysis in MRI Images Noise Removal Methods

With advanced imaging techniques, MagneticResonance Imaging (MRI) plays an important role inmedical environments to create high quality imagescontained in the human organs. In the processing ofmedical images, medical images are coordinated bydifferent types of noise. It is very important toacquire accurate images and observe specificapplications with precision. Currently, eliminatingnoise from medical images is a very difficult problemin the field of medical image processing. In thisdocument, three types of noise, Gaussian noise, andsalt & pepper noise, uniform noise are tested and thetested variances of Gaussian noise and uniform noiseare 0.02 and 10 respectively. We analyze the kernelvalue or the window size of the medium filter and theWiener filter. All experimental results are tested onMRI images of the BRATS data set, the DICOM dataset and TCIA data set. MRI brain images areobtained from the BRATS data set and the DICOMdata set, the MRI bone images are obtained from theTCIA data set. The quality of the output image ismeasured by statistical measurements, such as thepeak signal noise ratio (PSNR) and the root meansquare error (RMSE)

Background Subtraction and Foreground Detection based on Codebook Model with Kalman Filter

Foreground object extraction is an important subject for computer vision applications. The separation of foreground objects form the background is the crucial step in application such as video surveillance. In order to extract foreground object from a video scene, a background model which can represent dynamic changes in the scene is required. A robust, accurate and high performance approach is still a great challenge today. In this paper, the background modeling approach based on Codebook model with Kalman Filter is presented. This approach can be used to extract foreground objects from the video stream. The Lab color space is used in this approach to calculate color difference between two pixels using CIEDE2000 color difference formula. Extracted foreground object from video sequence using this approach is useful for object detection in video surveillance applications

Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images

Melanoma, one type of skin cancer is considered o the most dangerous form of skin cancer occurred in humans. However it is curable if the person detects early. To minimize the diagnostic error caused by the complexity of visual interpretation and subjectivity, it is important to develop a technology for computerized image analysis. This paper presents a methodological approach for the classification of pigmented skin lesions in dermoscopic images. Firstly, the image of the skin to remove unwanted hair and noise, and then the segmentation process is performed to extract the affected area. For detecting the melanoma skin cancer, the meanshift algorithm that segments the lesion from the entire image is used in this study. Feature extraction is then performed by underlying ABCD dermatology rules. After extracting the features from the lesion, feature selection algorithm has been used to get optimized features in order to feed for classification stage. Those selected optimized features are classified using kNN, decision tree and SVM classifiers. The performance of the system was tested and compare those accuracies and get promising results

Background Subtraction and Foreground Detection based on Codebook Model with Kalman Filter

Foreground object extraction is an importantsubject for computer vision applications. Theseparation of foreground objects form thebackground is the crucial step in application such asvideo surveillance. In order to extract foregroundobject from a video scene, a background model whichcan represent dynamic changes in the scene isrequired. A robust, accurate and high performanceapproach is still a great challenge today. In thispaper, the background modeling approach based onCodebook model with Kalman Filter is presented.This approach can be used to extract foregroundobjects from the video stream. The Lab color space isused in this approach to calculate color differencebetween two pixels using CIEDE2000 colordifference formula. extracted foreground object fromvideo sequence using this approach is useful forobject detection in video surveillance applications

Generating Relational Schema Depend on XML Document

XML has become the important standard for data denotation and exchange in the web. One of the most popular used of XML is as a data storage facility. The initial purpose is to store XML data into relational database without using DTD information. In order to store XML data into relational database, there are two major components: schema mapping and data mapping. In particular, we have developed an efficient algorithm which takes an XML DTD as input and produces a relational schema as output for storing and querying XML documents conforming to the input DTD. This paper proposed schema generation approach that generates relational schema depend on input XML document which need to store in relational database. By using this, DTD information is not needed to use to generate relational schema. Experimental results are presented to show this schema generation approach is efficient and scalable

Estimating Body Condition Score of Cows from Images with the Newly Developed Approach

The Body Condition Score (BCS) is the level of energy reserves in many species, including dairy cattle. For the exact management on dairy farms, the judgment process of BCS is critically important. In this study, the implementation of newly developed approach to estimate body condition score is proposed. Back view images of the cow were used in this system. The area around the tailhead and left and right hooks are segmented automatically and then classified that region for estimating the body condition score. The three main steps conducted are (1) segmentation of cows’ images, (2) extraction of region of interest (ROI) by using the convex hull method, and (3) calculation of parameter using moving average method. To confirm this new approach, back view images of various cow types are used and the experimental results confirm its effectiveness with accurate results