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Design of the application for the camera control and machine learning
Lukaszczyk, Jakub ; Richter, Miloslav (referee) ; Bilík, Šimon (advisor)
This bachelor thesis deals with the design of a program for controlling industrial cameras. The first part deals with current applications, their design and shortcomings. In the practical part, a similar application is then developed using Python. Compared to currently available applications, the developed application provides a modular and open design and can therefore be further extended and modified. The application is further complemented with a link to the Tensorflow library to enable image classification and training of artificial neural network models. The application has been tested and appears to be functional. The thesis concludes by evaluating the results and outlining possibilities for further development.
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Polygonal Mesh Segmentation
Bezděčík, Ladislav ; Polášek, Tomáš (referee) ; Španěl, Michal (advisor)
This bachelor's thesis deals with the issues of segmentating 3D models of human jaws. It analyzes currently used methods and proposes, implements and tests possible improvement to these methods from user perspective. The proposal consists of using neural networks for topology recognition on jaw models, and possibly combining this topology with currently used segmentation methods. This thesis also analyzes and implements the possibility of automated expnansion of 3D model datasets converted to depth maps, used for neural network training.
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Utilization of Robotic Operating System (ROS) for control of collaborative robot UR3
Juříček, Martin ; Matoušek, Radomil (referee) ; Parák, Roman (advisor)
The aim of the bachelor's thesis is to create a control program, its subsequent testing and verification of functionality for the collaborative robot UR3 from the company Universal Robots. The control program is written in python and integrates control options through the Robotic Operating System, where a defined point can be reached using pre-simulated trajectories of Q-learning, SARSA, Deep Q-learning, Deep SARSA, or using only the MoveIT framework. The thesis deals with a cross-section of the topics of collaborative robotics, Robotic Operating System, Gazebo simulation environment, feedback and deep feedback learning. Finally, the design and implementation of the control program with partial parts is described.
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Crowd Density Estimation from a Photo
Ferencz, Adam ; Herout, Adam (referee) ; Beran, Vítězslav (advisor)
The aim of this thesis is to develop an aplication estimating the total number of people at a demonstration or at other public events. Input is a serie of photos from a drone or some other photos. The output are couloured maps according to people density in the place. Photos are placed in a topological map. Convolutional neural network MCNN is used for the crowd counting, which can generate a density map from the photo. Special method was proposed to correct the total amount of counted people when photographs overlap. The application is divided into server and web client. The server part generates density maps, saves data and runs an overlap correction algorithm. Client handles user inputs and provides an interactiv map with visualization.
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Recognition of Vehicle Class in Image
Čabala, Roman ; Kodym, Oldřich (referee) ; Špaňhel, Jakub (advisor)
The goal of this bachelor thesis is to recognize the type of vehicle from the image using neural networks. Vehicles are divided into 6 types, namely a car, a small van, a van, a mini truck, a truck and a bus. The data set was picked from videos that record the trajectory of the vehicles. Subsequently, an image annotation tool was built. The following architectures were used for network training: VGG16, ResNet50, Xception, InceptionResNet-v2. The result of the work is a comparison of architectures. All architectures were trained and achieved a result above 90%.
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Detection Of Collapse By Android Smartphone
Repčík, Tomáš
The bachelor’s study is focused to design and build an Android application for the detection of collapse, which is enhanced by new techniques coming from a sphere of the artificial intelligence modified for smartphones. The application uses accelerometer outputs which are in suspicious moments analysed by the neural network. The artificial intelligence is based on simulated events of collapse and events which resemble a fall of a person. The study describes data collected from 20 people. To provide the best results of training, the most convenient and useful features were selected by multiple approaches. Total accuracy of the collapse detection reached 93 %, with 9 % and 13 % of false positive and false negative detections, respectively.
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Detection of printing defects
Boček, Václav ; Boštík, Ondřej (referee) ; Honec, Peter (advisor)
This thesis deals with the design and subsequent implementation of a unit inspecting a printed logos on the pen surface. A line-scan camera is used to capture the object. Whole the unit including acquited data processing is controlled by Raspberry Pi 4 platform extended by perifery board. The control of the hardware parts is implemented in C++, the detection algorithms in Python using OpenCV and TensorFlow libraries. The unit has a graphical user interface for control of the inspection process. In the end of the thesis test of the unit reliability is shown.
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Classification of arteries and veins in retinal image data
Černohorská, Lucie ; Jakubíček, Roman (referee) ; Kolář, Radim (advisor)
This master's thesis deals with the classification of the retinal blood vessels in retinal image data. The thesis contains a description of anatomy of the human eye with focus on the blood circulation, and imaging and diagnostic methods of the retina are briefly mentioned further. The thesis also summarizes methods of the blood circulation classification with emphasis on the deep learning. The practical section was implemented in Python programming language and describes the pre-processing of the data with determination of AV ratio. Based on a literature search, the U-net architecture was chosen for the classification of the retinal blood vessels. The architecture was modified using the open-source Keras library and tested on images from the experimental video-ophthalmoscope. The modified architecture was initially used for classification of vessels into the corresponding classes and because of unsatisfying results was modified another architecture segmenting retinal vessels, arteries or veins and a proposition of a method of the blood vessels classification.
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Smartphone Game Using Recognition of Face Features
Skoták, Jiří ; Szőke, Igor (referee) ; Herout, Adam (advisor)
This master's thesis focuses on smartphone game for iOS, which uses recognition of face features and other information, which can be obtained from a smartphone's camera and sensors. This work describes a few approaches for real-time face detection and then introduces and compares possibilities for such task on iOS. Moreover, the thesis contains a draft of the final game and its levels. The game showcases various technologies in its levels such as object detection, processing an image color and others. Finally, the thesis introduces the final form of the game that is released and available on the App Store.
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