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Fast Detection of Traffic Signs in Image
Sochor, Jakub ; Španěl, Michal (referee) ; Herout, Adam (advisor)
This bachelor thesis focuses on detection of traffic signs in real-time. First of all, algorithms used for traffic signs detection will be presented. Description of approach used in this thesis based on shapes of traffic signs and modifications of this algorithm will follow. Evaluation of accomplished results with this algorithm will be also presented.
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Vehicle Following Distance Estimation from Mobile Phone in Vehicle
Zemánek, Ondřej ; Špaňhel, Jakub (referee) ; Sochor, Jakub (advisor)
The aim of this bachelor thesis is to create a mobile application for Android that estimates the distance of vehicles based on vehicle size in the camera image of the mobile phone. The estimation of the following distance is evaluated based on known camera parameters, the average vehicle width and the size of image area that represents detected car. Vehicles and their licences plates are detected in the image using cascade classifiers. Licence plate is detected only in the area of the detected vehicle. A training dataset for cascade classifier was created as part of this work. The cascade classifier is designed for vehicle detection. This work is extended with feature that tracks following distance in time and warns you with an acoustic signal on sudden distances change. This thesis is divided into five main parts - comparison of existing solutions for distance estimation, review of object detection methods, application design, implementation and evaluation of detectors, distance evaluation.
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Library for Multiplatform Development of Mobile Apps
Kovařík, Michal ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This thesis addresses the issues when developing mobile applications for multiple operating systems and development environments, with the target being to create the ideal user interface library. A framework for HTML app development has been designed and implemented that is built on top of modern web standards, allowing the developer to create applications with a single codebase that will, when deployed, intelligently adapt to the device and operating system that they are being run on. Released as an open-source project and currently supporting Windows 10, Android, Chrome OS and Web. Flexus, a framework for building user interfaces, is in live use and active development.
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License Plate Detection and Recognition
Řepka, Michal ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This paper addresses the problem of object detection and recognition from still images using methods of computer vision. The objects of detection are czech license plates and the goal of this paper was to create an automatic license plate anotation tool. Suggested solution uses edge detection and machine learned cascading classifiers. Created application was then tested on dataset taken by the author.
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Deep Learning for Facial Recognition in Video
Jeřábek, Vladimír ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This work deals with face recognition in video using neural networks. In the beginning, there is described the process of selection and verification of convolution neural network to generate feature vectors from images of different identities. In the next part, this work deals with the aggregation of feature vectors from video frames. Aggregation takes place through aggregation neural networks. At the end of this work, the results obtained by the aggregation methods are discussed.
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Deep Learning for Facial Recognition in Video
Stratil, Jan ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This bachelor's thesis deals with facial recognition in video using deep neural networks. This task is split into 2 parts. The first part deals with training network that produces compact feature vector which represents the face identity from a video frame. The second part deals with training aggregation network that aggregates those feature vectors into one. This aggregation is fast and it has shown that its results are better than naive pooling methods. Results are tested on the LFW dataset, where it achieves 92.8% accuracy and on the YTF dataset, where the accuracy is 84.06%.
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Braille Reader on Mobile Device
Krušina, Jan ; Maršík, Lukáš (referee) ; Sochor, Jakub (advisor)
This bachelor thesis deals with detection and translation of Braille characters from images taken by a camera on a mobile phone. Firstly, different approaches of solution of this issue are mentioned, and their results are described. Secondly, the chosen method that deals with this problem is introduced, and implementation of this technique on a chosen mobile device is presented. Finally, evaluation of the algorithm is described and results are analyzed.
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Recognition of Pieces on Chessboard from Photography on Mobile Device
Tábi, Roman ; Polok, Lukáš (referee) ; Sochor, Jakub (advisor)
The aim of this bachelor thesis is a mobile application for Android platform, which recognises chessboard pieces from photography of the chessboard. Application uses photography from gallery and determine the content of every single chessboard cell, thus if it contains black or white piece, or if it is empty. The task is solved in two stages. First is the detection of chessboard lines to determine the position of chessboard cells, second is the detection of chessboard pieces. The result of this thesis is working Android application.
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Deep Learning for Facial Recognition in Video
Mihalčin, Tomáš ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This diploma thesis focuses on a face recognition from a video, specifically how to aggregate feature vectors into a single discriminatory vector also called a template. It examines the issue of the extremely angled faces with respect to the accuracy of the verification. Also compares the relationship between templates made from vectors extracted from video frames and vectors from photos. Suggested hypothesis is tested by two deep convolutional neural networks, namely the well-known VGG-16 network model and a model called Fingera provided by company Innovatrics. Several experiments were carried out in the course of the work and the results of which confirm the success of proposed technique. As an accuracy metric was chosen the ROC curve. For work with neural networks was used framework Caffe.
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Mobile System for Text Recognition on iOS
Bobák, Petr ; Sochor, Jakub (referee) ; Zemčík, Pavel (advisor)
This thesis describes a development of a modern client-server application for text recognition on iOS platform. The reader is acquainted with common principles of a client-server model, including its known architecture styles, and with a distribution of logical layers between both sides of the model. After that the thesis depicts current trends and examples of suitable technologies for creating an application programming interface of a web server. Possible ways of text recognition on the server side are discussed as well. In context of a client side, the thesis provides an insight into iOS platform and a few important concepts in iOS application development. Following implementation of the server side is stressed to be reusable as much as possible for different kinds of use cases. Last but not least, the thesis provides a simple iOS framework for a direct communication with the recognition server. Finally, an application for evaluation of food ingredients from a packaging material is implemented as an example of usage.
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