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The thesis deals with digital image processing, from the initial acquisition of digital picture frames, subsequent processing segmentation and algorithms to detect visual shapes on the scene. Image processing is a very broad topic, so here are analyzed for more understanding of the fundamental principles of perception and processing of video signals, image representation, his starting shooting through filters governing digital image processing methods to detect the objects in an image. It is also demonstrated by the size dependence of the object in the image on the distance from the camera, whereby we can determine the speed of approaching or moving away from the object. We will show you the specific determination of the distance we need to know the actual result size of the object. This is because the ratio between the size of the object depending on the distance is the same for each object. Finally, this work presents the resulting image frames for implementation using OpenCV library.

This thesis focuses on object tracking in real-time. Tracked object is defined by bounding rectangle. The thesis works on issue of image processing and using histogram for real-time object tracking. The main contribution of the work is the extension of the provided program to track object in real-time with changing bounding rectangle. Size of the rectangle is changing as the object moves closer of further from camera. Furthemore the detection behavior in different scenarios is analyzed. In addition, various weight calculations were tested. The program is written in C++ using OpenCV library.

Background. The view of the Earth from above can offer a lot of data and with technological advancements in image sensors and high-resolution satellite images there is more quantity and quality of data which can be useful in research and applications like military, monitoring climate, etc. Deep neural networks have been successful in object detection and it is seen that their learning process can be improved with using right hyperparameters when configuring the networks. This can be done hyperparameter optimization by the use of genetic algorithms. Objectives. The thesis focuses on obtaining deep learning techniques with optimal hyperparameters using genetic algorithm to detect aircraft, vehicles and ships from satellite and aerial images and compare the optimal models with the original deep learning models. Methods. The study uses literature review to obtain the appropriate deep learning techniques for object detection in satellite and aerial images, followed by conducting experiments in order to implement a genetic algorithm to find the right hyperparameters and then followed by another experiment which compares the performance between optimal and original deep learning model on basis of performance metrics mentioned in the study. Results. The literature review results depict that deep learning techniques for object detection in satellite and aerial images are Faster R-CNN, SSD and YOLO. The results of experiments show that the genetic algorithm was successful in finding optimal hyperparameters. The accuracy achieved by optimized models was higher than the original models in the case of aircraft, vehicles and ship detection. The results also show that the training times for the models have been reduced with the use of optimal hyperparameters with slight decrease in precision when detecting ships. Conclusions. After analyzing all the results carefully, the best deep learning techniques to detect aircraft, vehicles and ships are found and stated. The implementation of the genetic algorithm has been successful as it provided a set of hyperparameters which resulted in the improvement of accuracy, precision and recall in all scenarios except for values of precision in ship detection as well as improvement in training times.

This work is devoted to problem of detection of cardiac cells in microscopic picture. All possible means of preprocessing and segmentation were considered with the aim to choose the most suitable method for further classification. Different methods of classification were be testing: method of objects attributes and classifier based on neural network. As a result was obtained the number of living and dead cardiac cells and percentage of them. The electivity of classification methods was calculated by sensitivity and specificity. The user’s interface was created for improvement of clearness classification in MATLAB environment.

This thesis deals with applying methods on object detection in image. Separation of objects off the background is often needed during the image processing. It isolates the region of interest that can be worked with. The main purpose of this paper is the explanation of principles of pre-processing and segmentation of image, resulting in object detection using the wavelet transformation. This wavelet transformation is described more in detail, because it is the base of the primary used method. In the practical part of this thesis the main method was implemented to MATLAB environment and tested on set of images. The method was tested for robustness against noise and blur of image. It was compared with commonly used methods, using the edge detectors and thresholding. A simulation program was created for comparison of methods efficiency, including user interface.

This thesis deal with problem of object detection. Object may be of different shapes and colors. The aim of this thesis is to determine the location, direction of movement, angle and proximity.

This thesis deals with tracking a predefined object in the movie. After a brief introduction describes the procedure suitable for the detection of an object in a video sequence, where the methods are also discussed in detail. There is dealt with issues of image preprocessing, image segmentation and object detection in the image. The main emphasis is laid on using detectors of interest points and descriptors of areas - SURF and SIFT. The second part deals with the practical implementation of a program suitable to monitor predefined object in the movie. First are analyzed libraries suitable for object tracking in a video sequence in an environment of Java, followed by a detailed description of the selected library OpenCV along with wrapper JavaCV. Further described is own application in terms of control and functionality are described key method. Outputs along with discussion and evaluation are presented at the end of work.

Application of computational methods in classification of glass stones Bc. Matěj Lébl Abstrakt: The goal of this thesis is to employ mathematical image processing methods in automatic quality control of glass jewellery stones. The main math- ematical subject is a matrix of specific attributes representing digital image of the studied products. First, the thesis summarizes mathematical definition of digital image and some standard image processing methods. Then, a complete solution to the considered problem is presented. The solution consists of stone localization within the image followed by analysis of the localized area. Two lo- calization approaches are presented. The first is based on the matrix convolution and optimized through the Fourier transform. The second uses mathematical methods of thresholding and median filtering, and data projection into one di- mension. The localized area is analyzed based on statistical distribution of the stone brightness. All methods are implemented in the MATLAB environment. 1

<div>An Autonomous vehicle depends on the combination of latest technology or the ADAS safety features such as Adaptive cruise control (ACC), Autonomous Emergency Braking (AEB), Automatic Parking, Blind Spot Monitor, Forward Collision Warning or Avoidance (FCW or FCA), Lane Departure Warning. The current trend follows incorporation of these technologies using the Artificial neural network or Deep neural network, as an imitation of the traditionally used algorithms. Recent research in the field of deep learning and development of competent processors for autonomous or self driving car have shown amplitude of prospect, but there are many complexities for hardware deployment because of limited resources such as memory, computational power, and energy. Deployment of several mentioned ADAS safety feature using multiple sensors and individual processors, increases the integration complexity and also results in the distribution of the system, which is very pivotal for autonomous vehicles.</div><div><br></div><div>This thesis attempts to tackle two important adas safety feature: Forward collision Warning, and Object Detection using the machine learning and Deep Neural Networks and there deployment in the autonomous embedded platform.</div><div><br></div><div><div>This thesis proposes the following: </div><div>1. A machine learning based approach for the forward collision warning system in an autonomous vehicle.<br></div><div>2.3-D object detection using Lidar and Camera which is primarily based on Lidar Point Clouds. </div><div><br></div><div>The proposed forward collision warning model is based on the forward facing automotive radar providing the sensed input values such as acceleration, velocity and separation distance to a classifier algorithm which on the basis of supervised learning model, alerts the driver of possible collision. Decision Tress, Linear Regression, Support Vector Machine, Stochastic Gradient Descent, and a Fully Connected Neural Network is used for the prediction purpose.</div><div><br></div><div>The second proposed methods uses object detection architecture, which combines the 2D object detectors and a contemporary 3D deep learning techniques. For this approach, the 2D object detectors is used first, which proposes a 2D bounding box on the images or video frames. Additionally a 3D object detection technique is used where the point clouds are instance segmented and based on raw point clouds density a 3D bounding box is predicted across the previously segmented objects.</div></div>