Implementation of Methods for 3D Object Detection for Machine Vision Purposes

Tato diplomová práce pojednává o různých metodách získání 3D projekce reálného světa pro účely strojového vidění. Konkrétně se jedná o algoritmy triangulace a stereometrie, tedy měření vzdálenosti pomocí vícebodového systému, a „Time of Flight“ kamer, které měří absolutní dobu letu světla k cíli a zpět. Na základě této vzdálenosti poté kamera vypočte skutečnou vzdálenost k předmětu. Součástí práce je i matematický rozbor spolu s navržením algoritmů pro jednotlivé úlohy, a hlubší porovnání výpočetních metod pro vyhodnocení stereometrických snímků. Praktické provedení je implementováno v grafickém vývojovém prostředí LabVIEW, jež je nazýváno programovacím jazykem typu G. Výsledky metod jsou následně srovnány podle vstupních kritérií a výpočetních časových komplexností.This thesis describes different methods which are used to acquire a 3D image projection of real word, with a main goal to use this image for a machine vision processing. Specifically, the thesis is about algorithms of triangulation, stereometry, that are based on a multipoint system, and a Time of Flight camera. Those cameras are specially designed to measure the time of traveling light to and from object, and later evaluate the real distance of the object in the space. Part of the thesis is dedicated to a mathematical model of the image processing used in computation algorithms of stereometry and triangulation, as well as a general theory of image processing. This is concluded with a deeper explanation of methods that are used for stereometry image processing. Practical part is created in development environment LabVIEW, that represent graphical programing or G language, and results of different methods are concluded based on the input arguments, and mathematically modeled complexity of evaluation process.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Automating the creation of programs in the LabVIEW environment using genetic algorithms with the use of artificial intelligence elements

Tato disertační práce řeší problematiku automatického vytváření kódu, na základě uživatelem definovaných požadavků. Cílem bylo využití algoritmů umělé inteligence, konkrétně evolučních algoritmů, k automatické tvorbě částí programů tak, aby výstupy vytvořeného algoritmu plně korespondovaly s požadavky uživatele, který nemusí v tomto případě mít ani základní programátorské znalosti. V teoretické části jsou pospány základní předpoklady a principy hledání optimálních řešení za použití genetiky, genetického programování a evolučních algoritmů. Jsou zde obsaženy principy fungování základních operátorů v reálném světě, a jejich následné převedení do matematických modelů a světa informačních technologií. Práce taktéž podává matematický důkaz konvergence dílčích řešených problémů za použití genetického programování. Samotné vypracování automatické tvorby bylo provedeno v programovém prostředí LabVIEW. Práce se tedy dále zabývá hlavní myšlenkou fungování tohoto programového prostředí, a popisuje, proč je LabVIEW vhodným nástrojem k řešení této problematiky. Kapitola taktéž předkládá rozdílnosti tohoto programovacího prostředí oproti textově orientovaným jazykům. Praktická část práce poté popisuje navržený koncept vhodný k dosažení požadovaných výsledků, a aplikuje uvedené teoretické postupy. Finální část práce se věnuje validaci získaných výsledků, a to konkrétně pro automatické vytváření kódů na základě uživatelem definovaných vstupů a jejich požadovaných výstupů pro systémové proměnné typu Bool, Numeric a String. Současně je tato problematika rozšířena o jejich vzájemné kombinace a cykly. Výsledný blok práce se věnuje výsledkům při generovaní sekvencí.This dissertation deals with the issue of automatic code generation, based on user-defined requirements. The aim was to use artificial intelligence algorithms, specifically evolutionary algorithms, to automatically create parts of programs so that the outputs of the created algorithm fully correspond to the requirements of the user, who does not need to have any basic programming knowledge in this case. In the theoretical part, the basic assumptions and principles of finding optimal solutions using genetics, genetic programming and evolutionary algorithms are summarized. The principles of operation in the real world and their subsequent transfer to mathematical models and the world of information technology are contained. The work also provides a mathematical proof of the convergence of partial solutions of problems using genetic programming. The actual automatic code generation was performed in the LabVIEW programming environment. The work therefore further deals with the main idea of the operation of this programming environment and describes why LabVIEW is a suitable tool for solving this issue. The practical part of the work then describes the proposed concept suitable for achieving the desired results and applies the theoretical procedures mentioned. The final part of the work is devoted to the validation of the acquired results, specifically for automatic code generation based on user-defined inputs and their desired outputs for Boolean, Numeric and String system variables. At the same time, this issue is extended to their mutual combinations and cycles. The resulting part of the work is devoted to the results when generating sequences.450 - Katedra kybernetiky a biomedicínského inženýrstvívyhově

Individual Professional Practice in the Company

Import 03/11/2016Tato bakalářské práce pojednává o absolvování mé bakalářské praxe ve společnosti ATEsystem s.r.o., kde jsem se věnoval návrhu, realizaci a ověření funkcionality modulární, rozšířitelné aplikace pro strojové vidění. Koncepce realizovaného systému je postavena na bázi virtuální instrumentace s využitím vývojového prostředí LabVIEW. V teoretické části bakalářské práce je představena firma ATEsystem s.r.o., a dále jsou diskutovány současné trendy v oblasti strojového vidění. Nejobsáhlejší celek této práce tvoří experimentální část, která popisuje vlastní návrh a implementaci navržené aplikace na dostupnou hardwarovou platformu. Výstupem práce je funkční aplikace (sběr dat, zpracování obrazu a vizualizaci dat) určená pro automatickou kontrolu zapalovacích svíček na bázi strojivého vidění. Navržený systém je využitelný v praxi (např. automatizovaná kontrola výrobků na výrobní lince)i na akademické půdě (začlenění do výuky jako demonstrace strojového vidění).This thesis is about my internship at the ATEsystem s.r.o. company, where I worked on the design, implementation and verification of the machine vision application (which can be expanded with the inclusion of new VIs). Concept of the created system is based on virtual instrumentalization through the LabVIEW software. Theoretical part of this thesis presents company ATEsystem s.r.o., it also contains information about actual trends in the field of machine vision. Biggest part of the thesis discusses experimental part, which describes design and implementation of the application for the hardware module. Final product of the thesis is a functioning application (data acquisition, image processing and data visualization) for automatic control of spark plugs based on machine vision. Designed system is applicable in the industry (for example automatic control of products on the production line) and on the academic field (inclusion to the education process, for example demonstration of the machine vision).450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

GP-based automated LabVIEW code generation framework

<p><span>Automated code development framework based on genetic programming in graphical programming language LabVIEW.</span></p&gt

A large-scale high-resolution WBC image dataset

The dataset comprises 16 027 single cell images of normal and pathological white blood cells, including neutrophil segments, neutrophil bands, eosinophiles, basophiles, lymphocytes, monocytes, normoblasts and blast cells of myeloid and lymphoid lineage

A high-resolution large-scale dataset of pathological and normal white blood cells

Microscopic examination plays a significant role in the initial screening for a variety of hematological, as well as non-hematological, diagnoses. Microscopic blood smear examination that is considered a key diagnostic technique, is in recent clinical practice still performed manually, which is not only time consuming, but can lead to human errors. Although automated and semi-automated systems have been developed in recent years, their high purchasing and maintenance costs make them unaffordable for many medical institutions. Even though much research has been conducted lately to explore more accurate and feasible solutions, most researchers had to deal with a lack of medical data. To address the lack of large-scale databases in this field, we created a high-resolution dataset containing a total of 16027 annotated white blood cells. Moreover, the dataset covers overall 9 types of white blood cells, including clinically significant pathological findings. Since we used high-quality acquisition equipment, the dataset provides one of the highest quality images of blood cells, achieving an approximate resolution of 42 pixels per 1 μm.Web of Science101art. no. 46

Automated detection of acute lymphoblastic leukemia from microscopic images based on human visual perception

Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that hematological experts do, which significantly increased the capability of the classifiers to recognize leukemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31%, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100%, respectively.Web of Science8art. no. 100

Automated code development based on genetic programming in graphical programming language: A pilot study.

Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software development and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field could, therefore, improve software engineers' common workflow and decrease the time to market. Even though many code-generating algorithms have been proposed in textual-based programming languages, to the best of the authors' knowledge, none of the studies deals with the implementation of such algorithms in graphical programming environments, especially LabVIEW. Due to this fact, the main goal of this study is to conduct a proof-of-concept for a requirement-based automated code-developing system within the graphical programming environment LabVIEW. The proposed framework was evaluated on four basic benchmark problems, encompassing a string model, a numeric model, a boolean model and a mixed-type problem model, which covers fundamental programming scenarios. In all tested cases, the algorithm demonstrated an ability to create satisfying functional and errorless solutions that met all user-defined requirements. Even though the generated programs were burdened with redundant objects and were much more complex compared to programmer-developed codes, this fact has no effect on the code's execution speed or accuracy. Based on the achieved results, we can conclude that this pilot study not only proved the feasibility and viability of the proposed concept, but also showed promising results in solving linear and binary programming tasks. Furthermore, the results revealed that with further research, this poorly explored field could become a powerful tool not only for application developers but also for non-programmers and low-skilled users

Wood recognition and quality imaging inspection systems

Forestry is an undoubtedly crucial part of today's industry; thus, automation of certain visual tasks could lead to a significant increase in productivity and reduction of labor costs. Eye fatigue or lack of attention during manual visual inspections can lead to falsely categorized wood, thus leading to major loss of earnings. These mistakes could be eliminated using automated vision inspection systems. This article focuses on the comparison of researched methodologies related to wood type classification and wood defect detection/identification; hence, readers with an intention of building a similar vision-based system have summarized review to build upon.Web of Science2020art. no. 321712