Stanje razvoja strojnog vida

Machine vision (system visional) it\u27s a apply computer vision in industry. While computer vision is focused mainly on image processing at the level of hardware, machine vision most often requires the use of additional hardware I/O (input/output) and computer networks to transmit information generated by the other process components, such as a robot arm. Machine vision is a subcategory of engineering machinery, dealing with issues of information technology, optics, mechanics and industrial automation. One of the most common applications of machine vision is inspection of the products such as microprocessors, cars, food and pharmaceuticals. Machine vision systems are used increasingly to solve problems of industrial inspection, allowing for complete automation of the inspection process and to increase its accuracy and efficiency. As is the case for inspection of products on the production line, made by people, so in case of application for that purpose machine vision systems are used digital cameras, smart cameras and image processing software. This paper presents the principle of image processing, the components of the system and possible applications of machine vision in the present.Strojni vid (vidni sustav) je primjena računalnog vida u industriji. Dok je računalni vid usmjeren uglavnom na obradu slike na razini hardvera, strojni vid najčešće zahtijeva korištenje dodatnog hardvera I / O (input / output) i računalnih mreža za prijenos podataka generiranih od strane drugih komponenata procesa, kao što je robotska ruka. Strojni vid je pod kategorija inženjerskog projektiranja, a bavi se pitanjima informatičke tehnologije, optike, mehanike i industrijske automatizacije. Jedna od najčešćih primjena strojnog vida je inspekcija proizvoda kao što su mikroprocesori, automobili, hrana i farmaceutski proizvodi. Sustavi strojnog vida koriste se sve više za rješavanje problema industrijske inspekcije, omogućujući potpunu automatizaciju procesa inspekcije i povećanje njezine točnosti i efikasnosti. Kao što je slučaj kod kontrole proizvoda na proizvodnoj liniji koju provode ljudi, tako se i u slučaju primjene sustava strojnog vida koriste digitalne kamere, pametne kamere i programi za obradu slike. U radu su prikazani princip obrade slike, komponente sustava i moguće primjene strojnog vida u sadašnjosti

The essence and applications of machine vision

Pojam strojne vizije (vizijskih sustava) obuhvaća industrijsku primjenu računalnih vizijskih sustava. Dok je računalna vizija usmjerena uglavnom na obradu slikovnih zapisa na hardverskoj razini, sustavi strojne vizije najčešće zahtijevaju uporabu dodatnog izlazno/ulaznog sučelja i računalnih mreža za prijenos podataka generiranih od strane drugih procesnih komponenti, primjerice robota, manipulatora itd. Jedan od najčešćih primjena strojne vizije jest kontrola kvalitete proizvoda, primjerice mikroprocesora, automobila, hrane i farmaceutskih proizvoda. Sustavi strojne vizije učestalo se upotrebljavaju za rješavanje problema industrijske kontrole, te omogućuju potpunu automatizaciju procesa i povećanje pouzdanosti i učinkovitosti. Takvi sustavi rabe digitalne fotoaparate, kamere i odgovarajući softver za obradu slikovnih zapisa kako kod ručne, tako i kod automatske kontrole na proizvodnoj liniji. U radu su opisana temeljna načela obrade slikovnih zapisa, dijelovi sustava i današnje mogućnosti primjene sustava strojne vizualizacije.Machine vision (system vision) comprises using computer vision in industry. While computer vision is focused mainly on image processing at the level of hardware, machine vision most often requires the use of additional hardware I/O (input/output) and computer networks to transmit information generated by the other process components, such as a robot arm. One of the most common applications of machine vision is inspection of the products such as microprocessors, cars, food and pharmaceuticals. Machine vision systems are used increasingly to solve problems of industrial inspection, allowing for complete automation of the inspection process and to increase its accuracy and efficiency. In the case of manual inspection on the production line as well as in the case of application of machine vision systems, digital cameras, smart cameras and image processing software have been used. This paper presents the principle of image processing, the components of the system and possible applications of machine vision in the present

Automated Evaluation of Surface Roughness using Machine Vision based Intelligent Systems

Machine vision systems play a vital role in entirely automating the evaluation of surface roughness due to the hitches in the conformist system. Machine vision systems significantly abridged the ideal time and human errors for evaluation of the surface roughness in a nondestructive way. In this work, face milling operations are performed on aluminum and a total of 60 diverse cutting experiments are conducted. Surface images of machined components are captured for the development of machine vision systems. Images captured are processed for texture features namely RGB (Red Green Blue), GLCM (Grey Level Co-occurrence Matrix) and an advanced wavelet known as curvelet transforms. Curvelet transforms are developed to study the curved textured lines present in the captured images and this module is capable to unite the discontinuous curved lines present in images. The CNC machined components consists of visible lay patterns in the curved form, so this novel machine vision technique is developed to identify the texture well over the other two extensively researched methods. Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) intelligent models are developed to evaluate the surface roughness from texture features. The model average error attained using RGB, GLCM, Curvelet transform-based machine vision systems are 12.68, 7.8 and 3.57 respectively. In comparison, the results proved that computer vision system based on curvelet transforms outperformed the other two existing systems. This curvelet based machine vision system can be used for the evaluation of surface roughness. Here, image processing might be crucial in identifying certain information. One crucial issue is that, even as performance improves, cameras continue to get smaller and more affordable. The possibility for new applications in Industry 4.0 is made possible by this technological advancement and the promise of ever-expanding networking

Authenticating Users with 3D Passwords Captured by Motion Sensors

Authentication plays a key role in securing various resources including corporate facilities or electronic assets. As the most used authentication scheme, knowledgebased authentication is easy to use but its security is bounded by how much a user can remember. Biometrics-based authentication requires no memorization but ‘resetting’ a biometric password may not always be possible. Thus, we propose study several behavioral biometrics (i.e., mid-air gestures) for authentication which does not have the same privacy or availability concerns as of physiological biometrics. In this dissertation, we first propose a user-friendly authentication system Kin- Write that allows users to choose arbitrary, short and easy-to-memorize passwords while providing resilience to password cracking and password theft. Specifically, we let users write their passwords (i.e., signatures in the 3D space), and verify a user’s identity with similarities between the user’s password and enrolled password templates. Dynamic time warping distance is used for similarity calculation between 3D passwords samples. In the second part of the dissertation, we design an authentication scheme that does not depend on the handwriting contents, i.e., regardless of the written words or symbols, and adapt challenge-response mechanism to avoid possible eavesdropping, man-in-the-middle attacks, and reply attacks. We design a MoCRA system that utilizes Leap Motion to capture users’ writing movements and use writing style to verify users, even if what they write during the verification is completely different from what they write during the enrollment. Specifically, MoCRA leverages co-occurrence matrices to model the handwriting styles, and use a Support Vector Machine (SVM) to accept a legitimate user and reject the rest. In the third part, we study both security and usability performance on multiple types of mid-air gestures that used as passwords, including writing signatures in the air. We objectively quantify the usability performance by metrics related to the enroll time and the complexity of the gestures, and evaluate the security performance by the authentication performance. In addition, we subjectively evaluate the gestures by survey responses from both field subjects who participated in gesture experiments and on-line subjects who watched a short video on gesture introducing. Finally, we study the consistency of gestures over samples collected in a two-month period, and evaluate their security under shoulder surfing attacks

Modelo de processamento de imagem, com múltiplas fontes de aquisição, para manipulação aplicada à domótica

Este trabalho foca-se em modelos de processamento de imagem para utilização na visão por computador. Modelos de processamento de imagem com multi-aquisição e/ou em multi-perspectiva, para um conhecimento do meio circundante, com possibilidade de comando e controlo na área da domótica e/ou robótica móvel. Os algoritmos desenvolvidos têm a capacidade de serem implementados em blocos de software ou hardware, de forma independente (autónomos), ou integrados como componentes de um sistema mais complexo. O desenvolvimento dos algoritmos privilegiou o seu elevado desempenho, constrangido pela minimização da carga computacional. Nos modelos de processamento de imagem desenvolvidos foram focados 4 tópicos fundamentais de investigação: a) detecção de movimento de objectos e seres humano em ambiente não controlado; b) detecção da face humana, a ser usada como variável de controlo (entre outras aplicações); c) capacidade de utilização de multi-fontes de aquisição e processamento de imagem, com diferentes condições de iluminação não controladas, integradas num sistema complexo com diversas topologias; d) capacidade de funcionamento de forma autónoma ou em rede distribuída, apenas comunicando resultados finais, ou integrados modularmente na solução final de sistemas complexos de aquisição de imagem. A implementação laboratorial, com teste em protótipos, foi ferramenta decisiva no melhoramento de todos os algoritmos desenvolvidos neste trabalho; IMAGE PROCESSING MODELS, WITH MULTIPLE ACQUISITION SOURCES, FOR MANIPULATION IN DOMOTICS Abstract: This work focuses on image processing models for computer vision. Image processing models with multi-acquisition and/or multi-perspective models were developed to acquire knowledge over the surrounding environment, allowing system control in the field of domotics and/or mobile robotics. The developed algorithms have the capacity to be implemented in software or hardware blocks, independently (autonomous), or integrated as a component in more complex systems. The development of the algorithms was focused on high performance constrained by the computational burden minimization. In the developed image processing models it were addressed 4 main research topics: a) movement detection of objects and human beings in an uncontrolled environment; b) detection of the human face to be used as a control variable (among other applications); c) possibility of using multi-sources of acquisition and image processing, with different uncontrolled lighting conditions, integrated into a complex system with different topologies; d) ability to work as an autonomous entity or as a node integrated on a distributed network, only transmitting final results, or integrated as a link in a complex image processing system. The laboratorial implementation, with prototype tests, was the main tool for the improvement of all developed algorithms, discussed in the present wor

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems