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Detection of Graffiti Tags in Image
Pavlica, Jan ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The thesis is focused on the possible utilization of current methods in the area of computer vision with the purpose of automatic detection of graffiti tags in the image. Graffiti tagsare the most common expression of graffiti, which serves as the author’s signature. In the thesis, state-of-the-art detection systems were tested; the most effective one is the Single Shot MultiBox Detector. The result has reached 75.7% AP.
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Deep Neural Networks for Defect Detection
Juřica, Tomáš ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
The goal of this work is to bring automatic defect detection to the manufacturing process of plastic cards. A card is considered defective when it is contaminated with a dust particle or a hair. The main challenges I am facing to accomplish this task are a very few training data samples (214 images), small area of target defects in context of an entire card (average defect area is 0.0068 \% of the card) and also very complex background the detection task is performed on. In order to accomplish the task, I decided to use Mask R-CNN detection algorithm combined with augmentation techniques such as synthetic dataset generation. I trained the model on the synthetic dataset consisting of 20 000 images. This way I was able to create a model performing 0.83 AP at 0.1 IoU on the original data test set.
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Optical Character Recognition Using Convolutional Networks
Csóka, Pavel ; Behúň, Kamil (referee) ; Hradiš, Michal (advisor)
This thesis aims at creation of new datasets for text recognition machine learning tasks and experiments with convolutional neural networks on these datasets. It describes architecture of convolutional nets, difficulties of recognizing text from photographs and contemporary works using these networks. Next, creation of annotation, using Tesseract OCR, for dataset comprised from photos of document pages, taken by mobile phones, named Mobile Page Photos. From this dataset two additional are created by cropping characters out of its photos formatted as Street View House Numbers dataset. Dataset Mobile Nice Page Photos Characters contains readable characters and Mobile Page Photos Characters adds hardly readable and unreadable ones. Three models of convolutional nets are created and used for text recognition experiments on these datasets, which are also used for estimation of annotation error.
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Automatic Industrial Quality Control from Image
Kruták, Martin ; Hradiš, Michal (referee) ; Zemčík, Pavel (advisor)
The goal of this thesis is to create overall, automatic and non-contact quality control of a pellet. The issue is divided into two separate parts. The first part deals with precise dimensional measuring of pellet - its length and head diameter so that it is precise and reasonably fast. Precise measuring is achieved with help of algorithms which achieve the sub-pixel precision by polynomial approximation of the edges extracted from the image gradients. The second part deals with the defects of a pellet. Detecting defects like longitudinal furrows or skirt cuts is achieved with convolutional neural networks. The measurement modules work with the resulting precision up to 0.025 mm in case of length measuring and up to 0.01 mm in case of head diameter measuring. In case of defect detections, neural network shows very high classification success rate. The contribution of this thesis is a presentation of innovative approaches in automatic quality control of pellets with use of neural networks and a demonstration of its usage in real manufacturing process.
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Detection and Classification of Road Users in Aerial Imagery Based on Deep Neural Networks
Hlavoň, David ; Hradiš, Michal (referee) ; Rozman, Jaroslav (advisor)
This master's thesis deals with a vehicle detector based on the convolutional neural network and scene captured by drone. Dataset is described at the beginning, because the main aim of this thesis is to create practicly usable detector. Architectures of the forward neural networks which detector was created from are described in the next chapter. Techniques for building a detector based on the naive methods and current the most successful meta architectures follow the neural network architectures. An implementation of the detector is described in the second part of this thesis. The final detector was built on meta architecture Faster R-CNN and PVA neural network on which the detector achieved score over 90 % and 45 full HD frames per seconds.
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Object Tracking in Panoramic Video
Ambrož, Vít ; Hradiš, Michal (referee) ; Čadík, Martin (advisor)
The master thesis maps the state of the art of visual object tracking in panoramic 360° video. The thesis aims to reveal the main problems related to visual object tracking and moreover focuses on their solution in panoramic videos. In the study of the existing approaches was found that very few solutions of visual object tracking in equirectangular projection of panoramic video have been implemented so far. This thesis therefore presents two improvements of object tracking methods that are based on the adaptation of equirectangular frames. In addition, this thesis brings the manually created dataset of panoramic videos with more than 9900 annotations. Finally the detailed evaluation of 12 well known and state of the art trackers has been performed for this new dataset.
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Pedestrian Attribute Analysis
Studená, Zuzana ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This work deals with obtaining pedestrian information, which are captured by static, external cameras located in public, outdoor or indoor spaces. The aim is to obtain as much information as possible. Information such as gender, age and type of clothing, accessories, fashion style, or overall personality are obtained using using convolutional neural networks. One part of the work consists of creating a new dataset that captures pedestrians and includes information about the person's sex, age, and fashion style. Another part of the thesis is the design and implementation of convolutional neural networks, which classify the mentioned pedestrian characteristics. Neural networks evaluate pedestrian input images in PETA, FashionStyle14 and BUT Pedestrian Attributes datasets. Experiments performed over the PETA and FashionStyle datasets compare my results to various convolutional neural networks described in publications. Further experiments are shown on created BUT data set of pedestrian attributes.
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User Interface for 3D Applications (and also 2D Applications in 3D)
Švasta, Michael ; Hradiš, Michal (referee) ; Zemčík, Pavel (advisor)
The goal of this bachelor's thesis is to design and implement a software, which can be controlled by a using hands free gestures. This thesis contains a description of the Leap motion technology and possibilities of development for this device. Further, there is introduction of graphical user interface and then there is detailed description of its individual parts. Last section of thesis presented methods of testing and results of these tests.
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Text to Audio Alignment
Šuba, Adam ; Hradiš, Michal (referee) ; Szőke, Igor (advisor)
This bachelor thesis studies a tool for automatic text to audio alignment at the level of single phonemes and graphemes. It also discusses possible techniques used in alignment and possible limitations and difficulties that need to be taken into account. Studied tool uses approach based on grapheme-to-phoneme conversion using joint-sequence models. Data used in experiments are TV broadcast recordings from Multi-Genre Broadcast Challenge 2015.
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Face Recognition
Benda, Tomáš ; Hradiš, Michal (referee) ; Smrž, Pavel (advisor)
This thesis deals with human recognition on a videorecording. Convolution neural network was used for face recognition, from which we will get multidimensional vector, which will allow to determine person’s identity. There are demands imposed on the system, for it to be able to work in real time and could be used for example for person recognition at various conferences, or as a part of security system. Whole system is written in Python language. Part of this thesis is dataset in form of videorecords with persons.
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