Semantic segmentation as an approach to recognizing and localizing objects within an image is a major research area in computer vision. Now that convolutional neural networks are being increasingly used for such tasks, there have been many improve- ments in grand challenge results, and many new research opportunities in previously untennable areas. Using fully convolutional networks, we have developed a semantic segmentation pipeline for the identification of melanocytic tumor regions, epidermis, and dermis lay- ers in whole slide microscopy images of cutaneous melanoma or cutaneous metastatic melanoma. This pipeline includes processes for annotating and preparing a dataset from the output of a tissue slide scanner to the patch-based training and inference by an artificial neural network. We have curated a large dataset of 50 whole slide images containing cutaneous melanoma or cutaneous metastatic melanoma that are fully annotated at 40× ob- jective resolution by an expert pathologist. We will publish the source images of this dataset online. We also present two new FCN architectures that fuse multiple deconvolutional strides, combining coarse and fine predictions to improve accuracy over similar networks without multi-stride information. Our results show that the system performs better than our comparators. We include inference results on thousands of patches from four whole slide images, reassembling them into whole slide segmentation masks to demonstrate how our system generalizes on novel cases.

In the scheme of Non Destructive Testing (NDT), defect detection is an important process. Traditional image processing techniques have successfully been used for defect recognition. Usage of machine learning techniques is still in the initial stages of development. Convolution Neural Networks (CNN) is widely used for object classification one such scenario is defect classification in weld tubes. With the advent of deep learning techniques such as transfer learning, we can transfer knowledge gained in one domain successfully into other. Pre-trained models successfully learn features from large scale datasets that can be used for in domains having sparse data and smaller datasets. The aim of this work is to help a manual inspector in recognition of defects on the weld tubes. With a given set of images, we proceed by forming unique pipeline architecture for automatic defect recognition. The research in this thesis focuses on extraction of welds using image segmentation techniques, creating a dataset of defects and using it to on pre-trained Convolution Neural Networks of VGG16, VGG19, Inception V3 and ResNet101. We evaluate the models on different metrics finding the best suited model for the created dataset. Further a prototype sliding window solution is used to find defects over the extracted weld region. We also present the limitations of this approach and suggest modifications that can be implemented in the future.

In Storytel’s application on which a user can read and listen to digitalized literature, a user is displayed a list of books where the first thing the user encounters is the book title and cover. A book cover is therefore essential to attract a consumer’s attention. In this study, we take a data-driven approach to investigate the design principles for book covers through deep learning models and explainable AI. The first aim is to explore how well a Convolutional Neural Network (CNN) can interpret and classify a book cover image according to its genre in a multi-class classification task. The second aim is to increase model interpretability and investigate model feature to genre correlations. With the help of the explanatory artificial intelligence method Gradient-weighted Class Activation Map (Grad-CAM), we analyze the pixel-wise contribution to the model prediction. In addition, object detection by YOLOv3 was implemented to investigate which objects are detectable and reoccurring in the book covers. An interplay between Grad-CAM and YOLOv3 was used to investigate how identified objects and features correlate to a specific book genre and ultimately answer what makes a good book cover. Using a State-of-the-Art CNN model architecture we achieve an accuracy of 48% with the best class-wise accuracies for genres Erotica, Economy & Business and Children with accuracies 73%, 67% and 66%. Quantitative results from the Grad-CAM and YOLOv3 interplay show some strong associations between objects and genres, while indicating weak associations between abstract design principles and genres. Furthermore, a qualitative analysis of Grad-CAM visualizations show strong relevance of certain objects and text fonts for specific book genres. It was also observed that the portrayal of a feature was relevant for the model prediction of certain genres.

Biomechanical Big Data is of great significance to precision health applications, among which we take special interest in Physical Activity Detection (PAD). In this study, we have performed extensive research on deep learning-based PAD from biomechanical big data, focusing on the challenges raised by the need of real-time edge inference. First, considering there are many places we can place the motion sensors, we have thoroughly compared and analyzed the location difference in terms of deep learning-based PAD performance. We have further compared the difference among six sensor channels (3-axis accelerometer and 3-axis gyroscope). Second, we have selected the optimal sensor and the optimal sensor channel, which can not only provide sensor usage suggestions but also enable ultra-low-power application on the edge. Third, we have investigated innovative methods to minimize the training effort of the deep learning model, leveraging the transfer learning strategy. More specifically, we propose to pre-train a transferable deep learning model using the data from other subjects and then fine-tune the model using limited data from the target-user. In such a way, we have found that, for single-channel case, the transfer learning can effectively increase the deep model performance even when the fine-tuning effort is very small. This research, demonstrated by comprehensive experimental evaluation, have shown the potential of ultra-low-power PAD with minimized sensor stream and minimized training effort.

This thesis describes the design and implementation of system for vehicle speed estimation from on-board camera recording. Speed estimation is based on optical flow estimation and convolutional neural network. Designed system is able to estimate speed with average error of 20% on created data set where actual speed is greater than 35 kilometers per hour.

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.

This thesis deals with convolutional neural networks. It is a kind of deep neural networks that are presently widely used mainly for image recognition and natural language processing. The thesis describes specifics of convolutional neural networks in comparison with traditional neural networks and is focused on inner computations in the process of learning. Convolutional neural networks typically consist of a different types of layers of neurons and the core part of this thesis is to demonstrate computations of individual types of layers. Learning demonstrating program of a simple convolutional network was designed and implemented using own implementation of neural network. Validity of the implementation was tested by training models for solving a classification task. Experiments with different types of architectures were conducted and their performance was compared.

The goal of this thesis is to design and implement a multiplatform multilingual mobile application for detecting leukocoria in an image of human face for iOS and Android platforms. Leukocoria is a whitish light of the pupil, which can be seen on the photo when the flash is used. Early detection of this symptom can save human eyesight. The application itself allows to analyze a user's photo and detect the presence of leukocoria. The goal of the application is to analyze eyes of the human, from which the mobile application name - Eye Check is derived. React Native framework was used to create a multiplatform mobile application. The Dlib library was chosen for human face and eye detection, the OpenCV library for working with the photo. The convolutional neural network was used to classify the eyes for the possible presence of leukocoria. Client-Server communication is solved using the REST architecture. The result is a mobile application that detects leukocoria and allerts the user to visit his doctor if leukocoria is detected.

This master's thesis is focused on segmentation of the scene from traffic environment. The solution to this problem is segmentation neural networks, which enables classification of every pixel in the image. In this thesis is created segmentation neural network, that has reached better results than present state-of-the-art architectures. This work is also focused on the segmentation of the top view of the road, as there are no freely available annotated datasets. For this purpose, there was created automatic tool for generation of synthetic datasets by using PC game Grand Theft Auto V. The work compares the networks, that have been trained solely on synthetic data and the networks that have been trained on both real and synthetic data. Experiments prove, that the synthetic data can be used for segmentation of the data from the real environment. There has been implemented a system, that enables work with segmentation neural networks.

This diploma thesis deals with automatic detection and measurement of the electron beam in the images from a transmission electron microscope (TEM). The introduction provides a description of the construction and the main parts of the electron microscope. In the theoretical part, there are summarized modes of illumination from the fluorescent screen. Machine learning, specifically convolution neural network U-Net is used for automatic detection of the electron beam in the image. The measurement of the beam is based on ellipse approximation, which defines the size and dimension of the beam. Neural network learning requires an extensive database of images. For this purpose, the own augmentation approach is proposed, which applies a specific combination of geometric transformations for each mode of illumination. In the conclusion of this thesis, the results are evaluated and summarized. This proposed algorithm achieves 0.815 of the DICE coefficient, which describes an overlap between two sets. The thesis was designed in Python programming language.