Národní úložiště šedé literatury Nalezeno 5 záznamů.  Hledání trvalo 0.00 vteřin. 
Compensation of distortions caused by movements of objects scanned by a line scan camera
Szabó, Michal ; Shehadeh, Mhd Ali (oponent) ; Škrabánek, Pavel (vedoucí práce)
This master’s thesis focuses on compensating for distortions in data of lizards obtained via a line scan camera (LSC) that captures ultraviolet (UV) wavelengths. Breathing movements during scanning cause distortions in the lizard’s trunk width, affecting the Ąnal LSC image. The thesis proposes image processing methods to adjust these distortions, including contour extraction, interpolation, and evaluation using a reference image. The methodology aims to minimize the difference between the adjusted LSC image and the reference image.
3D point cloud segmentation for industrial bin-picking
Šooš, Marek ; Škrabánek, Pavel (oponent) ; Shehadeh, Mhd Ali (vedoucí práce)
This thesis deals with 3D point cloud segmentation for industrial bin-picking, a key challenge in the field of industrial robotics. The aim of the thesis is to develop and deploy a highly effective algorithm for segmenting and registering 3D point clouds, thereby improving the accuracy, speed, and efficiency of bin-picking operations. The contribution of the thesis is the presentation of the researcher's solution to create artificially generated data needed for training. The thesis results in a symbiosis of advantages of a fast-segmentation algorithm based on machine learning, and a high quality, robust but slow algorithm based on geometric principles. Functionality, reliability and quality of the developed algorithm were also experimentally verified on different types of objects and different datasets. Results of the work show that the proposed algorithm yields a fast, reliable, and comprehensive solution to the bin-picking problem. Customized data generation reduces the time and cost of applying such a system. In conjunction with a comprehensive problem solving system we are able to accurately and easily generate applications for diverse and specialized bin-picking tasks. Achieved results contribute to the development of point cloud segmentation methods and their applications in various industrial and scientific fields. By putting the proposed system into practice we significantly contribute to performance and reliability of the proposed automatic line.
Computer Vision for Autonomous Vehicles
Lečbych, Michal ; Škrabánek, Pavel (oponent) ; Shehadeh, Mhd Ali (vedoucí práce)
Perceptive systems in autonomous cars are a heavily researched topic these days and an essential part of making fully autonomous vehicles possible. First, we make a short summary of the development of such a system, then we explain different approaches to make these systems possible, and we focus on object detection, as this will be the main part of our own created perceptive system. A new model for object detection is implemented, and some additional parts like distance estimation and lane detection are added.
Segmentation of cardiac muscle images acquired using confocal microscopy
Kadlec, Filip ; Shehadeh, Mhd Ali (oponent) ; Škrabánek, Pavel (vedoucí práce)
Automating data acquisition and processing is common practice in both microscopy and computer vision fields. To classify and localize objects of interest (cardiomyocytes in this case) in microscopy images, segmentation can be performed. In this particular case, semantic segmentation by using deep neural networks was used as the core mean to perform mentioned task and software providing possibility of processing unlabeled data or training neural network architectures on labeled data was implemented. This work does a brief introduction to optical microscopy, inspects segmentation and deep learning in detail and finally describes the process from preparing data, implementing and training neural networks, to design of the final software. This software will ease the work of researchers by providing them with only relevant data, automate microscopy data acquisition, and with minor changes it can be applied to any similar segmentation task.
Geometrically controlled snake-like robot model
Shehadeh, Mhd Ali ; Návrat, Aleš (oponent) ; Vašík, Petr (vedoucí práce)
This master’s thesis describes equations of motion for dynamic model of nonholonomic constrained system, namely the trident robotic snakes. The model is studied in the form of Lagrange's equations and D’Alembert’s principle is applied. Actually this thesis is a continuation of the study going at VUT about the simulations of non-holonomic mechanisms, specifically robotic snakes. The kinematics model was well-examined in the work of of Byrtus, Roman and Vechetová, Jana. So here we provide equations of motion and address the motion planning problem regarding dynamics of the trident snake equipped with active joints through basic examples and propose a feedback linearization algorithm.

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