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Facial expression recognition
Vránová, Markéta ; Odstrčilík, Jan (referee) ; Mézl, Martin (advisor)
This project deals with automatic recognition of facial expression in colour pictures. At first, the colour-based face detection is accomplished, three colour spaces are used: RGB, HSV and YCbcCr. As next, the pictures are automatically cropped so that only the face region is present. It is accomplished by computing the borders of the face region based on knowledge of position of eyes, nose and mouth. From the face region, the feature vector is obtained using a bank of Gabor filters. The project introduces two different kinds of Gabor filters and proposes a new bank of filters. The feature vector is used as an input to the neural network. The neural network was trained on a set of pictures from AR database created for facial expression recognition. The output of the network is the facial expression the input picture was assigned to. This project mentions the testing for different settings of the neural network and presents and discuss the recognition results of the network.
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Interactive web applications for image processing education
Krolop, Filip ; Záviška, Pavel (referee) ; Rajmic, Pavel (advisor)
Introduction to theory related to applets that will be used for educational purposes, creation of custom design, draft of their basic functionality and control elements, followed by own implementation and optimization. This is an interactive submission of image processing algorithms specifically, applets are focused on color models, their relationships and calculations between each other: 1 / Pallet (index) image representation, 2 / Colormixing in different models, 3 / Color image conversion to grayscale image, 4 / Conversion of a grayscale image to color image (false colors). Applets implemented using HTMLand JavaScript to demonstrate the theory and encourage students to interact.
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Face detection system
Karásek, Miroslav ; Horák, Karel (referee) ; Petyovský, Petr (advisor)
This work deals with methods of computer vision for localization faces in an image segmentation of individual parts of face and its comparison with the face in the reference frame. The paper gives a brief overview of biometric methods useful to identify people. It describes the various methods of locating faces in images and their features. It also deals with the editing of digital images, with the design of my own methods of localization and verification of faces and the subsequent implementation of these methods.
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Detection of Objects on Belt Conveyer
Láník, Aleš ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
In this master thesis, object's detection in image and tracking these objects in temporal area will be presented. First, theoretical background of the image's preprocessing, image filtration, the foreground extraction, and many others various image's features will be described. Next, design and implementation of detector will be processed. This part of my master thesis containes mainly information about detection of objects on belt conveyer Finally,results, conclusion and many supplementary data such as a photography camera's location will be shown.
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Methods for texture analysis in ophthalmologic images
Hanyášová, Lucie ; Szabó, Zoltán (referee) ; Kolář, Radim (advisor)
This thesis is focused on texture analysis methods. The project contains an overview of widely used methods. The main aim of the thesis is to develop a method for texture analysis of retinal images, which will be used for distinction of two patient groups, one with glaucoma eyes and one healthy. It is observed that glaucoma patients don´t have a texture on the eye ground. Preprocessing of the images is found by transfer of the image to different color spaces to achieve the best emphasis of the eye ground texture. Co-occurrence matrix is chosen for texture analysis of this data. The thesis contains detail description of the chosen solutions and feature discussion and the result is a list of features, which can be used for distinction between glaucoma and healthy eyes. The method is implemented in Matlab environment.
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Spectral Analysis in Microscopy
Sýs, Michal ; Procházková, Jana (referee) ; Štarha, Pavel (advisor)
The aim of this work is to develop a mathematical apparatus for the accurate description of the colour of objects in a microscope image. This problem is solved by extracting the brightness intensities of pollen grains at fourteen wavelength bands and representing them in color space. The developed apparatus has been successfully applied to two series of images, allowing comparison of the color of individual objects and simplifying its description. The results obtained allow accurate investigation of the colour of objects in the image and form a simple tool for spectral analysis.
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Analysis of cytology images
Pavlík, Jan ; Blaha, Milan (referee) ; Kolář, Radim (advisor)
This master’s thesis is focused on automating the process of differential leukocyte count in peripherial blood using image processing. It deals with the design of the processing of digital images - from scanning and image preprocessing, segmentation nucleus and cytoplasm, feature selection and classifier, including testing on a set of images that were scanned in the context of this work. This work introduces used segmentation methods and classification procedures which separate nucleus and the cytoplasm of leukocytes. A statistical analysis is performed on the basis of these structures. Following adequate statistical parameters, a set of features has been chosen. This data then go through a classification process realized by three artificial neural networks. Overall were classified 5 types of leukocytes: neutropfiles, lymphocytes, monocytes, eosinophiles and basophiles. The sensitivity and specificity of the classification made for 4 out of 5 leukocyte types (neutropfiles, lymphocytes, monocytes, eosinophiles) is higher than 90 %. Sensitivity of classiffication basophiles was evaluated at 75 % and specificity at 67 %. The total ability of classification has been tested on 111 leukocytes and was approximately 91% successful. All algorithms were created in the MATLAB program.
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Spectral Analysis in Microscopy
Sýs, Michal ; Procházková, Jana (referee) ; Štarha, Pavel (advisor)
The aim of this work is to develop a mathematical apparatus for the accurate description of the colour of objects in a microscope image. This problem is solved by extracting the brightness intensities of pollen grains at fourteen wavelength bands and representing them in color space. The developed apparatus has been successfully applied to two series of images, allowing comparison of the color of individual objects and simplifying its description. The results obtained allow accurate investigation of the colour of objects in the image and form a simple tool for spectral analysis.
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Interactive web applications for image processing education
Krolop, Filip ; Záviška, Pavel (referee) ; Rajmic, Pavel (advisor)
Introduction to theory related to applets that will be used for educational purposes, creation of custom design, draft of their basic functionality and control elements, followed by own implementation and optimization. This is an interactive submission of image processing algorithms specifically, applets are focused on color models, their relationships and calculations between each other: 1 / Pallet (index) image representation, 2 / Colormixing in different models, 3 / Color image conversion to grayscale image, 4 / Conversion of a grayscale image to color image (false colors). Applets implemented using HTMLand JavaScript to demonstrate the theory and encourage students to interact.
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Facial expression recognition
Vránová, Markéta ; Odstrčilík, Jan (referee) ; Mézl, Martin (advisor)
This project deals with automatic recognition of facial expression in colour pictures. At first, the colour-based face detection is accomplished, three colour spaces are used: RGB, HSV and YCbcCr. As next, the pictures are automatically cropped so that only the face region is present. It is accomplished by computing the borders of the face region based on knowledge of position of eyes, nose and mouth. From the face region, the feature vector is obtained using a bank of Gabor filters. The project introduces two different kinds of Gabor filters and proposes a new bank of filters. The feature vector is used as an input to the neural network. The neural network was trained on a set of pictures from AR database created for facial expression recognition. The output of the network is the facial expression the input picture was assigned to. This project mentions the testing for different settings of the neural network and presents and discuss the recognition results of the network.
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