Field investigation of contactless displacement measurement using computer vision systems for civil engineering applications

Much of the bridge stock on major transport links in North America and Europe was constructed in the 1950s and 1960s and has since deteriorated or is carrying loads far in excess of the original design loads. Structural Health Monitoring Systems (SHM) can provide valuable information on the bridge capacity but the application of such systems is currently limited by access and bridge type. This paper investigates the use of computer vision systems for SHM. A series of field tests have been carried out to test the accuracy of displacement measurements using contactless methods. A video image of each test was processed using a modified version of the optical flow tracking method to track displacement. These results have been validated with an established measurement method using linear variable differential transformers (LVDTs). The results obtained from the algorithm provided an accurate comparison with the validation measurements. The calculated displacements agree within 2% of the verified LVDT measurements, a number of post processing methods were then applied to attempt to reduce this error.

Laboratory investigation of contactless displacement measurement using computer vision systems

Much of the bridge stock on major transport links in North America and Europe was constructed in the 1950’s and 1960’s and has since deteriorated or is carrying loads far in excess of the original design loads. Structural Health Monitoring Systems (SHM) can provide valuable information on the bridge capacity but the application of such systems is currently limited by access and system cost. This paper investigates the development of a low cost portable SHM system using commercially available cameras and computer vision techniques. A series of laboratory tests have been carried out to test the accuracy of displacement measurements using contactless methods. The results from each of the tests have been validated with established measurement methods, such as linear variable differential transformers (LVDTs). A video image of each test was processed using two different digital image correlation programs. The results obtained from the digital image correlation methods provided an accurate comparison with the validation measurements. The calculated displacements agree within 4% of the verified measurements LVDT measurements in most cases confirming the suitability full camera based SHM systems

Continual learning for computer vision applications

One of the most visionary goals of Artificial Intelligence is to create a system able to mimic and eventually surpass the intelligence observed in biological systems including, ambitiously, the one observed in humans. The main distinctive strength of humans is their ability to build a deep understanding of the world by learning continuously and drawing from their experiences. This ability, which is found in various degrees in all intelligent biological beings, allows them to adapt and properly react to changes by incrementally expanding and refining their knowledge. Arguably, achieving this ability is one of the main goals of Artificial Intelligence and a cornerstone towards the creation of intelligent artificial agents. Modern Deep Learning approaches allowed researchers and industries to achieve great advancements towards the resolution of many long-standing problems in areas like Computer Vision and Natural Language Processing. However, while this current age of renewed interest in AI allowed for the creation of extremely useful applications, a concerningly limited effort is being directed towards the design of systems able to learn continuously. The biggest problem that hinders an AI system from learning incrementally is the catastrophic forgetting phenomenon. This phenomenon, which was discovered in the 90s, naturally occurs in Deep Learning architectures where classic learning paradigms are applied when learning incrementally from a stream of experiences. This dissertation revolves around the Continual Learning field, a sub-field of Machine Learning research that has recently made a comeback following the renewed interest in Deep Learning approaches. This work will focus on a comprehensive view of continual learning by considering algorithmic, benchmarking, and applicative aspects of this field. This dissertation will also touch on community aspects such as the design and creation of research tools aimed at supporting Continual Learning research, and the theoretical and practical aspects concerning public competitions in this field.

Robótica móvel: sistema de condução autónoma

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo Automação e Electrónica Industrial

A Real Time Vision System for Autonomous Systems: Characterization during a Middle Size Match

in RoboCup 2007: Robot Soccer World Cup XI

Aquisição por Visão Artificial e Medição Automática de Micro Dureza Vickers

O ensaio de dureza, e mais concretamente o ensaio de micro dureza Vickers, é no universo dos ensaios mecânicos um dos mais utilizados quer seja na indústria, no ensino ou na investigação e desenvolvimento de produto no âmbito das ciências dos materiais. Na grande maioria dos casos, a utilização deste ensaio tem como principal aplicação a caracterização ou controlo da qualidade de fabrico de materiais metálicos. Sendo um ensaio de relativa simplicidade de execução, rapidez e com resultados comparáveis e relacionáveis a outras grandezas físicas das propriedades dos materiais. Contudo, e tratando-se de um método de ensaio cuja intervenção humana é importante, na medição da indentação gerada por penetração mecânica através de um sistema ótico, não deixa de exibir algumas debilidades que daí advêm, como sendo o treino dos técnicos e respetivas acuidades visuais, fenómenos de fadiga visual que afetam os resultados ao longo de um turno de trabalho; ora estes fenómenos afetam a repetibilidade e reprodutibilidade dos resultados obtidos no ensaio. O CINFU possui um micro durómetro Vickers, cuja realização dos ensaios depende de um técnico treinado para a execução do mesmo, apresentando todas as debilidades já mencionadas e que o tornou elegível para o estudo e aplicação de uma solução alternativa. Assim, esta dissertação apresenta o desenvolvimento de uma solução alternativa ao método ótico convencional na medição de micro dureza Vickers. Utilizando programação em LabVIEW da National Instruments, juntamente com as ferramentas de visão computacional (NI Vision), o programa começa por solicitar ao técnico a seleção da câmara para aquisição da imagem digital acoplada ao micro durómetro, seleção do método de ensaio (Força de ensaio); posteriormente o programa efetua o tratamento da imagem (aplicação de filtros para eliminação do ruído de fundo da imagem original), segue-se, por indicação do operador, a zona de interesse (ROI) e por sua vez são identificadas automaticamente os vértices da calote e respetivas distâncias das diagonais geradas concluindo, após aceitação das mesmas, com o respetivo cálculo de micro dureza resultante. Para validação dos resultados foram utilizados blocos-padrão de dureza certificada (CRM), cujos resultados foram satisfatórios, tendo-se obtido um elevado nível de exatidão nas medições efetuadas. Por fim, desenvolveu-se uma folha de cálculo em Excel com a determinação da incerteza associada às medições de micro dureza Vickers. Foram então comparados os resultados nas duas metodologias possíveis, pelo método ótico convencional e pela utilização das ferramentas de visão computacional, tendo-se obtido bons resultados com a solução proposta.

Vision-based hand Wheel-chair control

Several studies have shown that people with disabilities benefit substantially from access to a means of independent mobility and assistive technology. Researchers are using technology originally developed for mobile robots to create easier to use wheelchairs. With this kind of technology people with disabilities can gain a degree of independence in performing daily life activities. In this work a computer vision system is presented, able to drive a wheelchair with a minimum number of finger commands. The user hand is detected and segmented with the use of a kinect camera, and fingertips are extracted from depth information, and used as wheelchair commands.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

Accuracy estimation of a new omnidirectional 3D vision sensor

We present a computer vision system that associates omnidirectional vision with structured light with the aim of obtaining depth information for a 360 degrees field of view. The approach proposed in this article combines an omnidirectional camera with a panoramic laser projector. The article shows how the sensor is modelled and its accuracy is proved by means of experimental results. The proposed sensor provides useful information for robot navigation applications, pipe inspection, 3D scene modelling etc

Behaviours implemented using a vision system based on coded light projection

In a search for new sensor systems and new methods for underwater vehicle positioning based on visual observation, this paper presents a computer vision system based on coded light projection. 3D information is taken from an underwater scene. This information is used to test obstacle avoidance behaviour. In addition, the main ideas for achieving stabilisation of the vehicle in front of an object are presented

Real-time Imaging and Mosaicking of Planar Surfaces

Laajojen pintojen kuvaaminen rajoitetussa työskentelytilassa riittävällä kuvatarkkuudella voi olla vaikeaa. Kuvaaminen on suoritettava osissa ja osat koottava saumattomaksi kokonaisnäkymäksi eli mosaiikkikuvaksi. Kuvauslaitetta käsin siirtelevän käyttäjän on saatava välitöntä palautetta, jotta mosaiikkiin ei jäisi aukkoja ja työ olisi nopeaa. Työn tarkoituksena oli rakentaa pieni, kannettava ja tarkka kuvauslaite paperi- ja painoteollisuuden tarpeisiin sekä kehittää palautteen antamiseen menetelmä, joka koostaaja esittää karkeaa mosaiikkikuvaa tosiajassa. Työssä rakennettiin kaksi kuvauslaitetta: ensimmäinen kuluttajille ja toinen teollisuuteen tarkoitetuista osista. Kuvamateriaali käsiteltiin tavallisella pöytätietokoneella. Videokuvien välinen liike laskettiin yksinkertaisella seurantamenetelmällä ja mosaiikkikuvaa koottiin kameroiden kuvanopeudella. Laskennallista valaistuksenkorjausta tutkittiin ja kehitetty menetelmä otettiin käyttöön. Ensimmäisessä kuvauslaitteessa on ongelmia valaistuksen ja linssivääristymien kanssa tuottaen huonolaatuisia mosaiikkikuvia. Toisessa kuvauslaitteessa nämä ongelmat on korjattu. Seurantamenetelmä toimii hyvin ottaen huomioon sen yksinkertaisuuden ja siihen ehdotetaan monia parannuksia. Työn tulokset osoittavat, että tosiaikainen mosaiikkikuvan koostaminen megapikselin kuvamateriaalista on mahdollista kuluttajille tarkoitetulla tietokonelaitteistolla.

Keraamisten laattojen luokittelu väripiirteiden avulla

Vaikka keraamisten laattojen valmistusprosessi onkin täysin automatisoitu, viimeinen vaihe eli laaduntarkistus ja luokittelu tehdään yleensä ihmisvoimin. Automaattinen laaduntarkastus laattojen valmistuksessa voidaan perustella taloudellisuus- ja turvallisuusnäkökohtien avulla. Tämän työn tarkoituksena on kuvata tutkimusprojektia keraamisten laattojen luokittelusta erilaisten väripiirteiden avulla. Oleellisena osana tutkittiin RGB- ja spektrikuvien välistä eroa. Työn teoreettinen osuus käy läpi aiemmin aiheesta tehdyn tutkimuksen sekä antaa taustatietoa konenäöstä, hahmontunnistuksesta, luokittelijoista sekä väriteoriasta. Käytännön osan aineistona oli 25 keraamista laattaa, jotka olivat viidestä eri luokasta. Luokittelussa käytettiin apuna k:n lähimmän naapurin (k-NN) luokittelijaa sekä itseorganisoituvaa karttaa (SOM). Saatuja tuloksia verrattiin myös ihmisten tekemään luokitteluun. Neuraalilaskenta huomattiin tärkeäksi työkaluksi spektrianalyysissä. SOM:n ja spektraalisten piirteiden avulla saadut tulokset olivat lupaavia ja ainoastaan kromatisoidut RGB-piirteet olivat luokittelussa parempia kuin nämä.