Detta examensarbete har genomförts på Nobel Biocare i Karlskoga under cirka två månaders tid. Målet med detta projekt är att utvärdera och implementera visionsystem som kan ersätta de manuella kontroller och inspektioner som finns inom fabriken. Företaget är verksamma inom medicinindustrin där de tillverkar diverse produkter bl.a. tandimplantat, skruvar och distanser. Både företaget och lagstiftning ställer höga krav på produkter som tillverkas inom denna sorts industri. Detta leder till att flertal kvalitetskontroller äger rum inom processerna. Dessa olika kvalitetskontroller utvärderas ifall de kan ersättas med ett visionsystem. Dessa manuella kontroller bidrar till ett varierande utfall då operatörerna bedömer olika. Detta leder till att godkända produkter kasseras även fast de är godkända. Momenten som utförs under en kvalitetskontroll är tidskrävande och påfrestande som bidrar till både dålig ergonomi och dålig arbetsmiljö. Detta examensarbete syftar till att undersöka den nuvarande fabriken för att eventuellt kunna ersätta kvalitetskontrollerna med kompatibla visionsystem. När väl problemområdena var definierade undersöktes marknaden för visionsystem i förväntan att redovisa diverse lösningar för Nobel Biocare.  Resultatet inom detta examensarbete var att tre olika problemområden definierades. Därefter framtogs det två visionsystem som kan ersätta dessa problemområden. Visionsystemen som framtogs var båda från samma företag men uppfyller olika syften vid kvalitetskontrollerna. Arbetet gick ut på att skapa en förstudie vilket leder till att det inte utförs några praktiska tester. / This thesis has been carried out at Nobel Biocare in Karlskoga for about two months. The goal of this project is to evaluate and implement vision systems that can replace the manual controls and inspections that exist within the factory. The company is active in the medical industry where they manufacture various products including dental implants, screws, and spacers. Both the company and the legislation have high demands on products manufactured in this type of industry. This leads to several quality controls taking place within the processes. These different quality controls are evaluated if they can be replaced with a vision system. These manual checks contribute to a varying outcome as the operators assess differently. This leads to approved products being discarded even though they are approved. The steps performed during a quality control are time-consuming and stressful, which contributes to both poor ergonomics and a poor working environment. This thesis aims to investigate the current factory to possibly replace the quality controls with compatible vision systems. Once the problem areas were defined, the market for vision systems was examined in the expectation of presenting various solutions for Nobel Biocare. The result of this thesis was that three different problem areas were defined. Subsequently, two vision systems were developed that can replace these problem areas. The vision systems that were developed were both from the same company but fulfil different purposes during the quality controls. The work involved creating a feasibility study, which means that no practical tests are performed. Key words: Vision system,

This dissertation addresses the problem of human detection and tracking in surveillance videos. Even though this is a well-explored topic, many challenges remain when confronted with data from real world situations. These challenges include appearance variation, illumination changes, camera motion, cluttered scenes and occlusion. In this dissertation several novel methods for improving on the current state of human detection and tracking based on learning scene-specific information in video feeds are proposed. Firstly, we propose a novel method for human detection which employs unsupervised learning and superpixel segmentation. The performance of generic human detectors is usually degraded in unconstrained video environments due to varying lighting conditions, backgrounds and camera viewpoints. To handle this problem, we employ an unsupervised learning framework that improves the detection performance of a generic detector when it is applied to a particular video. In our approach, a generic DPM human detector is employed to collect initial detection examples. These examples are segmented into superpixels and then represented using Bag-of-Words (BoW) framework. The superpixel-based BoW feature encodes useful color features of the scene, which provides additional information. Finally a new scene-specific classifier is trained using the BoW features extracted from the new examples. Compared to previous work, our method learns scene-specific information through superpixel-based features, hence it can avoid many false detections typically obtained by a generic detector. We are able to demonstrate a significant improvement in the performance of the state-of-the-art detector. Given robust human detection, we propose a robust multiple-human tracking framework using a part-based model. Human detection using part models has become quite popular, yet its extension in tracking has not been fully explored. Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. We address such problems by developing an online-learning tracking-by-detection method. Our approach learns part-based person-specific Support Vector Machine (SVM) classifiers which capture articulations of moving human bodies with dynamically changing backgrounds. With the part-based model, our approach is able to handle partial occlusions in both the detection and the tracking stages. In the detection stage, we select the subset of parts which maximizes the probability of detection. This leads to a significant improvement in detection performance in cluttered scenes. In the tracking stage, we dynamically handle occlusions by distributing the score of the learned person classifier among its corresponding parts, which allows us to detect and predict partial occlusions and prevent the performance of the classifiers from being degraded. Extensive experiments using the proposed method on several challenging sequences demonstrate state-of-the-art performance in multiple-people tracking. Next, in order to obtain precise boundaries of humans, we propose a novel method for multiple human segmentation in videos by incorporating human detection and part-based detection potential into a multi-frame optimization framework. In the first stage, after obtaining the superpixel segmentation for each detection window, we separate superpixels corresponding to a human and background by minimizing an energy function using Conditional Random Field (CRF). We use the part detection potentials from the DPM detector, which provides useful information for human shape. In the second stage, the spatio-temporal constraints of the video is leveraged to build a tracklet-based Gaussian Mixture Model for each person, and the boundaries are smoothed by multi-frame graph optimization. Compared to previous work, our method could automatically segment multiple people in videos with accurate boundaries, and it is robust to camera motion. Experimental results show that our method achieves better segmentation performance than previous methods in terms of segmentation accuracy on several challenging video sequences. Most of the work in Computer Vision deals with point solution; a specific algorithm for a specific problem. However, putting different algorithms into one real world integrated system is a big challenge. Finally, we introduce an efficient tracking system, NONA, for high-definition surveillance video. We implement the system using a multi-threaded architecture (Intel Threading Building Blocks (TBB)), which executes video ingestion, tracking, and video output in parallel. To improve tracking accuracy without sacrificing efficiency, we employ several useful techniques. Adaptive Template Scaling is used to handle the scale change due to objects moving towards a camera. Incremental Searching and Local Frame Differencing are used to resolve challenging issues such as scale change, occlusion and cluttered backgrounds. We tested our tracking system on a high-definition video dataset and achieved acceptable tracking accuracy while maintaining real-time performance.

Nobel Biocare är världsledande inom tillverkning av tandimplantat. Implantaten tillverkas av titan och måste efter tillverkning sterilförpackas för att skyddas från oönskade partiklar. Förpackningen som används är en blisterförpackning som limmas ihop i en blistermaskin. För att kontrollera att förpackningen uppfyller de krav som ställs på medicinsk tillverkning kontrolleras tätheten med hjälp av tre olika kvalitetskontroller. Dessa kontroller är tidskrävande och innebär onödig kassation då de blisterark som kontrolleras måste kasseras.  Syftet med detta arbete var att undersöka möjligheten att automatisera de manuella kvalitetskontrollerna. Automation skulle innebära besparingar i både tid och material. Det skulle också innebära att en relativt stor golvyta i förpackningsrummet skulle kunna frigöras, ytan rymmer i dagsläget två bord med diverse hjälpmedel som behövs för att utföra de manuella kvalitetskontrollerna. I samråd med företaget riktades arbetet in på att undersöka om automatiseringen skulle kunna göras med hjälp av visionsystem.  Eftersom blisterförpackningarna till större delen består av plast ansågs belysningen för visionsystemet vara den största utmaningen. Tester genomfördes på företaget Prevas i Karlstad där olika typer av ljussättning provades tillsammans med en visionkamera. Testerna visar att limmet på blisterförpackningarna är fluorescerande och blev tydligt i UV-ljus. Detta innebär att UV-ljus skulle kunna användas vid en automatisering med visionsystem.   Slutligen kontaktades olika företag, bland annat Karlskoga Automation och Neurolearn. Företagskontakt var en viktig del i arbetet då samtliga företag som kontaktades arbetar med automationslösningar. De företag som projektet presenterades för ansåg att det är möjligt att ersätta de manuella kvalitetskontrollerna med ett visionsystem och de hade möjlighet att leverera en sådan lösning till Nobel Biocare. / Nobel Biocare is a world leading company in the manufacture of dental implants. The implants are made of titanium and must be packed sterile to protect the implants from unwanted particles. The package used is a blister pack that is glued together in a blister machine. Three different quality controls are done manually to ensure that the packaging meets the requirements for medical manufacture. The quality controls are time-consuming and involve unnecessary disposal.  The purpose of this thesis was to investigate the possibility of this process being automated. Automation would mean that savings can be made for time and materials. It would also mean that a relatively large floorspace could be freed up in the packing room. The surface currently holds two tables that contains various equipment used to perform the manual quality controls. In consultation with the company the investigation was focused on using a vision system for automation. Since the blister packs mostly consists of plastic the lighting for the vision system was considered to be the biggest challenge. Lighting tests were performed at Prevas in Karlstad where a vision camera and different types of lights were used. The result shows that the blister packs contain fluorescent glue that became visible in UV light. This means that UV light can be used to automate the process with a vision system.  Finally, various companies were contacted, including Karlskoga Automation and Neurolearn. All companies that were contacted work with creating automation solutions and that made the contact important for this thesis. The companies to which the project was presented, they all considered it to be achievable. They also could deliver such a solution for Nobel Biocare.

This thesis is based on a research project to evaluate a quality control system for car component stamping lines. The quality control system measures the abrasion of the stamping tools by measuring the surface of the products. A 3D vision system is developed for the real time online measurement of the product surface. In this thesis, there are three main research themes. First is to produce an industrial application. All the components of this vision system are selected from industrial products and user application software is developed. A rich human machine interface for interaction with the vision system is developed along with a link between the vision system and a control unit which is established for interaction with a production line. The second research theme is to enhance the robustness of the 3D measurement. As an industrial product, this system will be deployed in different factories. It should be robust against environmental uncertainties. For this purpose, a high signal to noise ratio is required with the light pattern being produced by a laser projector. Additionally, multiple height calculation methods and a spatial Kalman filter are proposed for optimal height estimation. The final research theme is to achieve real time 3D measurement. The vision system is expected to be installed on production lines for online quality inspection. A new 3D measurement method is developed. It combines the spatial binary coded method with phase shift methods with a single image needs to be captured. / SHRIS (Shanghai Ro-Intelligent System,co.,Ltd.)

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In the forest products manufacturing industry, recent price increases in the cost of high-quality lumber together with the reduced availability of this resource have forced manufacturers to utilize lower grade hardwood lumber in their manufacturing operations. This use of low quality lumber means that the labor involved in converting this lumber to usable parts is also increased because it takes more time to remove the additional defects that occur in the lower grade material. Simultaneously, labor costs have gone up and availability of skilled workers capable of getting a high yield of usable parts has markedly decreased. To face this increasingly complex and competitive environment, the industry has a critical need for efficient and cost-effective new processing equipment that can replace human operators who locate and identify defects that need to be removed in lumber and then remove these defects when cutting the lumber into rough parts. This human inspection process is laborious, inconsistent and subjective in nature due to the demands of making decisions very rapidly in a noisy and tiring environment. Hence, an automatic sawing system that could remove defects in lumber while creating maximum yield, offers significant opportunities for increasing profits of this industry. The difficult part in designing an automatic sawing system is creating an automatic inspection system that can detect critical features in wood that affect the quality of the rough parts. Many automatic inspection systems have been proposed and studied for the inspection of wood or wood products. But, most of these systems utilize a single sensing modality, e.g., a single optical sensor or an X-ray imaging system. These systems cannot detect all critical defects in wood. This research work reported in this dissertation is the first aimed at creating a vision system utilizes three imaging modalities: a color imaging system, a laser range profiling system and an X-ray imaging system. The objective of in designing this vision system is to detect and identify: 1) surface features such as knots, splits, stains; 2) geometry features such as wane, thin board; and 3) internal features such as voids, knots. The laser range profiling system is used to locate and identify geometry features. The X-ray imaging system is primarily used to detect features such as knots, splits and interior voids. The color imaging system is mainly employed to identify surface features. In this vision system a number of methodologies are used to improve processing speed and identification accuracy. The images from different sensing modalities are analyzed in a special order to offset the larger amount of image data that comes from the multiple sensors and that must be analyzed. The analysis of laser image is performed first. It is used to find defects that have insufficient thickness. These defects are then removed from consideration in the subsequent analysis of the X-ray image. Removing these defects from consideration in the analysis of the X-ray image not only improves the accuracy of detecting and identifying defects but also reduces the amount of time needed to analyze the X-ray image. Similarly, defect areas such as knot and mineral streak that are found in the analysis of the X-ray image are removed from consideration in the analysis of the color image. A fuzzy logic algorithm -- the approaching degree method-- is used to assign defect labels. The fuzzy logic approach is used to mimic human behavior in identifying defects in hardwood lumber. The initial results obtained from this vision system demonstrate the feasibility of locating and identifying all the major defects that occur in hardwood lumber. This was even true during the initial hardware development phase when only images of unsatisfactory quality from a limited lumber of samples were available. The vision system is capable of locating and identifying defects at the production speed of two linear feet per second that is typical in most hardwood secondary manufacturing plants. This vision system software was designed to run on a relative slow computer (200 MHz Pentium processor) with aid of special image processing hardware, i.e., the MORRPH board that was also designed at Virginia Tech. / Ph. D.

This thesis presents online 3D reconstruction and ground segmentation using unmanned aerial vehicle (UAV) based stereo vision. For this purpose, a long baseline stereo vision system has been designed and built. Application of this system is to work as part of an air and ground based multi-robot autonomous terrain surveying project at Unmanned Systems Lab (USL), Virginia Tech, to act as a first responder robotic system in disaster situations. Areas covered by this thesis are design of long baseline stereo vision system, study of stereo vision raw output, techniques to filter out outliers from raw stereo vision output, a 3D reconstruction method and a study to improve running time by controlling the density of point clouds. Presented work makes use of filtering methods and implementations in Point Cloud Library (PCL) and feature matching on graphics processing unit (GPU) using OpenCV with CUDA. Besides 3D reconstruction, the challenge in the project was speed and several steps and ideas are presented to achieve it. Presented 3D reconstruction algorithm uses feature matching in 2D images, converts keypoints to 3D using disparity images, estimates rigid body transformation between matched 3D keypoints and fits point clouds. To correct and control orientation and localization errors, it fits re-projected UAV positions on GPS recorded UAV positions using iterative closest point (ICP) algorithm as the correction step. A new but computationally intensive process of use of superpixel clustering and plane fitting to increase resolution of disparity images to sub-pixel resolution is also presented. Results section provides accuracy of 3D reconstruction results. The presented process is able to generate application acceptable semi-dense 3D reconstruction and ground segmentation at 8-12 frames per second (fps). In 3D reconstruction of an area of size 25 x 40 m2, with UAV flight altitude of 23 m, average obstacle localization error and average obstacle size/dimension error is found to be of 17 cm and 3 cm, respectively. / MS / This thesis presents near real-time, called online, visual reconstruction in 3-dimensions (3D) using ground facing camera system on an unmanned aerial vehicle. Another result of this thesis is separating ground from obstacles on the ground. To do this the camera system using two cameras, called stereo vision system, with the cameras being positioned comparatively far away from each other at 60 cm was designed as well as an algorithm and software to do the visual 3D reconstruction was developed. Application of this system is to work as part of an air and ground based multi-robot autonomous terrain surveying project at Unmanned Systems Lab, Virginia Tech, to act as a first responder robotic system in disaster situations. Presented work makes use of Point Cloud Library and library functions on graphics processing unit using OpenCV with CUDA, which are popular Computer Vision libraries. Besides 3D reconstruction, the challenge in the project was speed and several steps and ideas are presented to achieve it. Presented 3D reconstruction algorithm is based on feature matching, which is a popular way to mathematically identify unique pixels in an image. Besides using image features in 3D reconstruction, the algorithm also presents a correction step to correct and control orientation and localization errors using iterative closest point algorithm. A new but computationally intensive process to improve resolution of disparity images, which is an output of the developed stereo vision system, from single pixel accuracy to sub-pixel accuracy is also presented. Results section provides accuracy of 3D reconstruction results. The presented process is able to generate application acceptable 3D reconstruction and ground segmentation at 8-12 frames per second. In 3D reconstruction of an area of size 25 x 40 m2 , with UAV flight altitude of 23 m, average obstacle localization error and average obstacle size/dimension error is found to be of 17 cm and 3 cm, respectively.

A pesquisa visou avaliar a metodologia do projeto Tree Vis para determinar a nutrição de ferro, boro, zinco e cobre em plantas de milho submetidas a doses desses nutrientes. Foram utilizados tratamentos constituídos pela omissão, 1/5, 2/5 e a dose completa dos elementos com quatro repetições em cada fase de coleta, sendo essas V4, V7 e R1. Os experimentos foram realizados em casa de vegetação, em cultivo hidropônico, conduzidos em vasos com solução nutritiva. Foi determinada a produção de massa seca da parte aérea e do sistema radicular, além da determinação dos teores dos nutrientes nas folhas indicativas dos estádios fenológicos de cada época de coleta. Em cada estádio foram coletadas imagens das folhas indicativas e novas através de um scanner para as análises de visão artificial. As doses crescentes dos nutrientes promoveram maior produção de massa seca na parte aérea e nas raízes e reduziram a produção quando utilizada a dose máxima do nutriente. O sistema de visão artificial mostrou-se promissor na identificação de deficiência de ferro com 77,5% de acerto, boro com 81,7% de acerto, zinco com 81,0% e cobre com 57,2 % de acerto, tendo identificado as com boa confiabilidade. / The research aimed to evaluate the methodology of the Pr oject Tree Vis for determining nutrition iron, boron, zinc and copper in maize plants subjected to doses of these nutrients. Treatments used were made by omission, 1/5, 2/5 and the full dose of the elements with four replicates at each stage of collection, these are V4, V7 and R1. The experiments ware conducted in a greenhouse in hydroponics, conducted in pots with nutrient solution. Was determined the dry mass production of the aerial part and roots, besides the determ ination of nutritional content in the leaves indicative of phenological stages of each harvest time. At each stage were collected images of indicative and new leaves through with a scanner for the analyzes of artificial vision. The increasing doses of nutr ients promoted higher dry mass production in the aerial part and roots and reduced the production when using the highest dose of the nutrient. The artificial vision system showed promise in identifying of deficiency of iron with 77.5% accuracy, of boron with 81.7% of correct, of zinc with 81.0% accuracy and copper with 57.2% accuracy, with a good reliability in the identifi.

Cette thèse s’inscrit dans le cadre d’une convention industrielle de formation par la recherche (CIFRE) entre le laboratoire Hubert Curien et l’entreprise Pattyn Bakery Division. L’objectif de ces travaux est le développement d’un système de détection de trait laser sur FPGA (Field Programmable Gate Array) qui soit plus performant que système actuel de l’entreprise. Dans l’industrie, les concepteurs de systèmes de vision doivent pouvoir créer et modifier facilement leurs systèmes afin de pouvoir les adapter aux besoins de leurs clients et aux évolutions technologiques. Ainsi les opérateurs développés doivent être génériques afin de permettre aux concepteurs de modifier le système de vision sans nécessairement avoir de compétences matérielles. Les concepteurs doivent également pouvoir être en mesure d’estimer quelles seront les ressources utilisées par l’opérateur en cas modifications du système : paramètres de l’application, capteur, famille de FPGA... Dans ce manuscrit, les principaux algorithmes de détection de trait laser ainsi que leurs propriétés ont été étudiés. Un opérateur de détection de trait laser a été choisi et développé. L’implantation de cet opérateur sur une caméra-FPGA du marché a permis d’obtenir un premier prototype fonctionnel. Les performances temporelles de ce nouveau système sont quatre fois supérieures à celles du système actuellement utilisé par l’entreprise. Le nouveau système est capable de traiter jusqu’à 2500 images par seconde. Enfin, les modèles de la consommation des ressources permettent de dimensionner une architecture à partir d’un ensemble de paramètres prédéfinis de manière rapide et sans faire de synthèses. Le paramètre auquel les concepteurs doivent prêter le plus d’attention est le niveau de parallélisme des données. Ce paramètre permet d’exploiter les capacités de parallélisme du FPGA en consommant plus de ressources. Cependant, les ressources du FPGA sont limitées et augmenter le niveau de parallélisme peut induire la nécessité de changer de FPGA. Le système et les données fournies permettront à l’entreprise d’adapter le système de vision selon les besoins futurs des clients en les guidant vers le choix du matériel / This thesis is part of an industrial research training agreement (CIFRE) between the Hubert Curien laboratory and the company Pattyn Bakery Division. The goal of this work is the development of an FPGA laser line detection system that is more efficient than the current system of the company. In the industry, vision system designers need to be able to easily create and modify their systems in order to adapt them to their customers’ needs and technological developments. Thus developed operators must be generic to allow designers to change the vision system without necessarily having material skills. Designers must also be able to estimate what resources will be used by the operator in case of system changes : application parameters, sensor, family of FPGAs ... In this manuscript, the main laser line detection algorithms and their properties have been studied. A laser line detection operator was chosen and developed. The implementation of this operator on an FPGA-camera from market has resulted in a first functional prototype. The time performance of this new system is four times that of the system currently used by the company. The new system is able to process up to 2500 frames per second. Finally, resource consumption models makes it possible to size an architecture from a set of predefined parameters quickly and without synthesizing. The parameter to which designers must pay the most attention is the level of parallelism of the data. This parameter makes it possible to exploit the parallelism capabilities of the FPGA by consuming more resources. However, the resources of the FPGA are limited and increasing the level of parallelism can induce the need to change the family of FPGAs. The system and the data provided will enable the company to adapt the vision system to the future needs of customers by guiding the choice of equipment.