Browsing by Subject "image segmentation"
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Item Identifying Webpage Regions and Their Roles by Combining Image Processing and Markup Analysis(2014-05-22) Singh, Sanjeev KumarUnderstanding what are the regions of a webpage and the functions of those regions is important for many services over web pages, including screen readers, web search, and assessing web-page similarity. In this thesis, we present an approach to identify the regions of a webpage based on image processing techniques and to identify the portions of the DOM tree corresponding to these regions. We then present and compare a rule-based approach and a SVM-based approach using the visual and markup information to classify regions based on their roles. A corpus of 150 web pages exhibiting a wide variety of designs was collected. Each page was provided human-assigned regions and their roles to use in training and for evaluating results. The segmentation algorithm accurately identified 77.8% of the 1222 web page regions in the corpus but its performance was not even across different types of regions. Segmentation accuracy was above 80% for headers, footers, body regions, and top navigation bars. The algorithm had more difficulty with left, right, and bottom navigation bars and dynamic content, having lower than 70% accuracy for locating these segments. The correctly segmented web page components were used as a test collection to compare the rule-based and SVM-based approach to assigning the role of each segment. The SVM-based and the rule-based approach both achieved between 74 and 75% accuracy over 951 classifications. The SVM-based approach was better at classifying left and bottom navigation bars while the rule-based approach did better at recognizing dynamic content. Moreover, an accuracy of 81.3% is obtained when we used both the methods to identify regions correctly. In this case, we considered a region correctly identified if the region is identified correctly either by the rule-based or SVM-based method. Overall, these results are promising for incorporating these segmentation and segment role classifications into web services.Item Topics in living cell miultiphoton laser scanning microscopy (MPLSM) image analysis(Texas A&M University, 2006-10-30) Zhang, WeiminMultiphoton laser scanning microscopy (MPLSM) is an advanced fluorescence imaging technology which can produce a less noisy microscope image and minimize the damage in living tissue. The MPLSM image in this research is the dehydroergosterol (DHE, a fluorescent sterol which closely mimics those of cholesterol in lipoproteins and membranes) on living cell's plasma membrane area. The objective is to use a statistical image analysis method to describe how cholesterol is distributed on a living cell's membrane. Statistical image analysis methods applied in this research include image segmentation/classification and spatial analysis. In image segmentation analysis, we design a supervised learning method by using smoothing technique with rank statistics. This approach is especially useful in a situation where we have only very limited information of classes we want to segment. We also apply unsupervised leaning methods on the image data. In image data spatial analysis, we explore the spatial correlation of segmented data by a Monte Carlo test. Our research shows that the distributions of DHE exhibit a spatially aggregated pattern. We fit two aggregated point pattern models, an area-interaction process model and a Poisson cluster process model, to the data. For the area interaction process model, we design algorithms for maximum pseudo-likelihood estimator and Monte Carlo maximum likelihood estimator under lattice data setting. For the Poisson Cluster process parameter estimation, the method for implicit statistical model parameter estimate is used. A group of simulation studies shows that the Monte Carlo maximum estimation method produces consistent parameter estimates. The goodness-of-fit tests show that we cannot reject both models. We propose to use the area interaction process model in further research.