This thesis is about methods of image comparison. Goal is to create a mobile app that allows user to compare images in real time. In the first part there is a theoretical basis, in particular image similarity algorithms. The practical part contains the app implementation including use case analysis, user interface design and functional requirements. It is followed by source code samples a description of frameworks used. Last part is made of testing the implemented algorithms regarding speed and precision.

Automatic segmentation of human organs allows more accurate calculation of organ doses in radiationtreatment planning, as it adds prior information about the material composition of imaged tissues. For instance, the separation of tissues into bone, adipose tissue and remaining soft tissues allows to use tabulated material compositions of those tissues. This approximation is not perfect because of variability of tissue composition among patients, but is still better than no approximation at all. Another use for automated tissue segmentationis in model based iterative reconstruction algorithms. An example of such an algorithm is DIRA, which is developed at the Medical Radiation Physics and the Center for Medical Imaging Science and Visualization(CMIV) at Linköpings University. DIRA uses dual-energy computed tomography (DECT) data to decompose patient tissues into two or three base components. So far DIRA has used the MK2014 algorithm which segments human pelvis into bones, adipose tissue, gluteus maximus muscles and the prostate. One problem was that MK2014 was limited to 2D and it was not very robust. Aim: The aim of this thesis work was to extend the MK2014 to 3D as well as to improve it. The task was structured to the following activities: selection of suitable segmentation algorithms, evaluation of their results and combining of those to an automated segmentation algorithm. Of special interest was image registration usingthe Morphon. Methods: Several different algorithms were tested.  For instance: Otsu's method followed by threshold segmentation; histogram matching followed by threshold segmentation, region growing and hole-filling; affine phase-based registration and the Morphon. The best-performing algorithms were combined into the newly developed JJ2016. Results: For the segmentation of adipose tissue and the bones in the eight investigated data sets, the JJ2016 algorithm gave better results than the MK2014. The better results of the JJ2016 were achieved by: (i) a new segmentation algorithm for adipose tissue which was not affected by the amount of air surrounding the patient and segmented smaller regions of adipose tissue and (ii) a new filling algorithm for connecting segments of compact bone. The JJ2016 algorithm also estimates a likely position for the prostate and the rectum by combining linear and non-linear phase-based registration for atlas based segmentation. The estimated position (center point) was in most cases close to the true position of the organs. Several deficiencies of the MK2014 algorithm were removed but the improved version (MK2014v2) did not perform as well as the JJ2016. Conclusions: JJ2016 performed well for all data sets. The JJ2016 algorithm is usable for the intended application, but is (without further improvements) too slow for interactive usage. Additionally, a validation of the algorithm for clinical use should be performed on a larger number of data sets, covering the variability of patients in shape and size.

This thesis deals with the segmentation of microscopic images from plant sections. It describes the importance of the histogram for obtaining information from the image, and the utilization of the wavelet transformation for the preprocessing of the images. The thesis describes and categorizes different segmentation methods. In the thesis we use MATLAB for the validation of the presented theories and as the interface for creating a software model. The created software application automatically analyzes and evaluates microscopic images.

This diploma thesis is focused on detection in images using extended set of Haar-like features and histogram-based method. At first is introduced a basic concept of extraction and classification image features. The next part bring own concept of image features based on Diffusion distance. Result of this work is implementation this methods in Rapidminer.

This diploma thesis deals with image similarity based on colour. There are discussed necessary theoretical basis for better understanding of this topic. These basis are color models, that are implemented in work, principle of creating the histogram and its comparing. Next chapter deals with summary of recent progress in the field of image comparison and overview of several most used methods. Practical part introduces training image database, which gives results of success for each created method. These methods are separately described, including their principles and achieved results. In the very end of this work, user interface is described. This interface provides a transparent presentation of the results for the chosen method.

The aim of this thesis is a description and implementation of algorithms of the tracked objects in the video feed. This thesis introduces Mean shift and Continuously adaptive mean shift algorithms which represent category based on kernel tracking. For construction of a model is used a threedimensional color histogram whose construction is described in this thesis as well. The achievements of described algorithms are compared in the testing images sequences and evaluated in details.

In this thesis I dealt with the Six Sigma method and uses in practice in Miele technica s.r.o. company. The first part deals with the theoretical description of Six Sigma, DMAIC methodology and used tools as SIPOC, Ishikawa diagram, Pareto diagram, or regression or correlation. The second part describes the application of Six Sigma methods, which I had the opportunity to perform as a team leader in the assembly process of washing machines and dryers.

Online shopping is becoming a popular option for consumers. Currently, the most common product searching method that online shopping websites provide is keyword search. Most shoppers have to carefully select relevant keywords to search for their favorite products. Finding desired products using a query image for online shopping is currently not available. Image has been used for searching similar images in the database but they are usually not well annotated. Research effort has been devoted to developing reliable image-based retrieval systems for applications such as medical image retrieval and trademark search. None of these developments focuses on improving online shopping experiences for consumers. This thesis reports the development of an image retrieval system to provide better online shopping experience for consumers. The system searches products with similar appearance such as shape and textures to the query images the user provides. Turn angle is a contour based shape descriptor. It has many unique properties that make it a perfect shape matching method for image retrieval. The best matching image has the shortest shape distance to the query shape. Turn angle, however, could fail with slightly stretched shapes. Dynamic programming is used to help turn angle match slightly deformed shapes. Another technique called centroid distance is also included as a restriction for shape matching in order to avoid retrieving irrelevant or disparate shapes. With a well-built database, the enhanced turn angle descriptor that includes dynamic programming and centroid distance is able to reach a high accuracy rate.Shape matching alone is usually not sufficient for a powerful retrieval system. Products with similar shape but very different textures will not be distinguished based solely on shape matching. Edge histogram is a robust shape descriptor for texture matching. It can be implemented to construct either global or local histogram for this purpose. Global edge histogram uses only 5 bins, which is simple but ignores detail texture information. Local and semi-global edge histograms are more complex but retains detail texture information. A hierarchical matching system is built to combine the shape and texture descriptors for better retrieval accuracy.Easy access to the shopping system is desired. An Android Application is developed to provide consumers a convenient and friendly tool to use the system. Grab cut is applied to the captured image to segment the object from the background. The segmentation provides the retrieval system the required contour information for shape matching. The Android Application submits the captured image along with the segmented contour to the server. After the retrieval process is completed, the server sends retrieved images of similar products back to the Android App for the user to consider. Using the retrieval system via a handheld device provides a user-friendly online shopping experience.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19417号 / 情博第596号 / 新制||情||104(附属図書館) / 32442 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 山本 章博, 教授 黒橋 禎夫, 教授 鹿島 久嗣, 准教授 Cuturi Marco / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第13032号 / 論医博第2114号 / 新制||医||1016(附属図書館) / 32990 / 横浜市立大学大学院医科学専攻 / (主査)教授 増永 慎一郎, 教授 坂井 義治, 教授 小川 修 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM