Image processing plays a key role in vision systems. Its function is to extract and enhance pertinent information from raw data. In robotics, processing of real-time data is constrained by limited resources. Thus, it is important to understand and analyse image processing algorithms for accuracy, speed, and quality. The theme of this thesis is an implementation and comparative study of algorithms related to various image processing techniques like edge detection, corner detection and thinning. A re-interpretation of a standard technique, non-maxima suppression for corner detectors was attempted. In addition, a thinning filter, Hall-Guo, was modified to achieve better results. Generally, real time data is corrupted with noise. This thesis also incorporates few smoothing filters that help in noise reduction. Apart from comparing and analysing algorithms for these techniques, an attempt was made to implement correlation-based optic flow

L’interprétation du contenu de l’image est un objectif très important dans le traitement de l’image et la vision par ordinateur. Par conséquent, plusieurs chercheurs y sont intéressés. Une image contient des informations multiples qui peuvent être étudiés, telles que la couleur, les formes, les arêtes, les angles, la taille et l’orientation. En outre, les contours contiennent les structures les plus importantes de l’image. Afin d’extraire les caractéristiques du contour d’un objet, nous devons détecter les bords de cet objet. La détection de bords est un point clé dans plusieurs applications, telles que :la restauration, l’amélioration de l’image, la stéganographie, le filigrane, la récupération, la reconnaissance et la compression de l’image, etc. Toutefois, l’évaluation de la performance de la méthode de détection de bords reste un grand défi. Les images numériques sont parfois modifiées par une procédure légale ou illégale afin d’envoyer des données secrètes ou spéciales. Afin d’être moins visibles, la plupart des méthodes stéganographiques modifient les valeurs de pixels dans les bords/textures de parties de l’image. Par conséquent, il est important de détecter la présence de données cachées dans les images numériques. Cette thèse est divisée principalement en deux parties.La première partie discute l’évaluation des méthodes de détection des bords du filtrage, des contours et des angles. En effet, cinq contributions sont présentées dans cette partie : d’abord, nous avons proposé un nouveau plan de surveillance normalisée de mesure de la qualité. En second lieu, nous avons proposé une nouvelle technique pour évaluer les méthodes de détection des bords de filtrage impliquant le score minimal des mesures considérées. En plus, nous avons construit une nouvelle vérité terrain de la carte de bords étiquetée d’une manière semi-automatique pour des images réelles.En troisième lieu, nous avons proposé une nouvelle mesure prenant en compte les distances de faux points positifs pour évaluer un détecteur de bords d’une manière objective. Enfin, nous avons proposé une nouvelle approche de détection de bords qui combine la dérivée directionnelle et l’homogénéité des grains. Notre approche proposée est plus stable et robuste au bruit que dix autres méthodes célèbres de détection. La seconde partie discute la stéganalyse de l’image en couleurs, basée sur l’apprentissage automatique (machine learning). En effet, trois contributions sont présentées dans cette partie : d’abord, nous avons proposé une nouvelle méthode de stéganalyse de l’image en couleurs, basée sur l’extraction de caractéristiques de couleurs à partir de corrélations entre les gradients de canaux rouge, vert et bleu. En fait, ces caractéristiques donnent le cosinus des angles entre les gradients. En second lieu, nous avons proposé une nouvelle méthode de stéganalyse de l’image en couleurs, basée sur des mesures géométriques obtenues par le sinus et le cosinus des angles de gradients entre tous les canaux de couleurs. Enfin, nous avons proposé une nouvelle méthode de stéganalyse de l’image en couleurs, basée sur une banque de filtres gaussiens orientables. Toutes les trois méthodes proposées présentent des résultats intéressants et prometteur en devançant l’état de l’art de la stéganalyse en couleurs. / Interpretation of image contents is very important objective in image processing and computer vision. Wherefore, it has received much attention of researchers. An image contains a lot of information which can be studied such as color, shapes, edges, corners, size, and orientation. Moreover, contours include the most important structures in the image. In order to extract features contour of an object, we must detect the edges of that object. Edge detection results, remains a key point and very important step in wide range of applications such as: image restoration, enhancement, steganography, watermarking, image retrieval, recognition, compression, and etc. An efficient boundary detection method should create a contour image containing edges at their correct locations with a minimum of misclassified pixels. However, the performance evaluationof the edge detection results is still a challenging problem. The digital images are sometimes modify by a legal or illegal data in order to send special or secret data. These changes modify slight coefficient values of the image. In order to be less visible, most of the steganography methods modify the pixel values in the edge/texture image areas. Therefore, it is important to detect the presence of hidden data in digital images. This thesis is divided mainly into two main parts. The first part, deals with filtering edge detection, contours evaluation and corners detection methods. More deeply, there are five contributions are presented in this part: first, proposed a new normalized supervised edge map quality measure. The strategy to normalize the evaluation enables to consider a score close to 0 as a good edge map, whereas a score 1 translates a poor segmentation. Second, proposed a new technique to evaluate filtering edge detection methods involving the minimum score of the considerate measures. Moreover, build a new ground truth edge map labelled in semi-automatic way in real images. Third, proposed a new measure takes into account the distances of false positive points to evaluate an edge detector in an objective way. Finally, proposed a new approach for corner detection based on the combination of directional derivative and homogeneity kernels. The proposed approach remains more stable and robust to noise than ten famous corner detection methods. The second part, deals with color image steganalysis, based on a machine learning classification. More deeply, there are three contributionsare presented in this part: first, proposed a new color image steganalysis method based on extract color features from correlations between the gradients of red, green and blue channels. Since these features give the cosine of angles between gradients. Second, proposed a new color steganalysis method based on geometric measures obtained by the sine and cosine of gradient angles between all the color channels. Finally, proposed a new approach for color image steganalysisbased on steerable Gaussian filters Bank.All the three proposed methods in this part, provide interesting and promising results by outperforming the state-of-art color image steganalysis.

A detecção de bordas é um primeiro passo importante no processamento digital de imagens, pois permite separar blocos distinto presentes em uma imagem. O desenvolvimento de sistemas autônomos que realizem tarefas a partir de informações visuais necessitam que o processamento destas seja realizado em tempo real. Este trabalho descreve a implementação de um processador, baseado numa arquitetura pipeline e no operador de máscara chamado Cruz de Roberts, dedicado para a extração de bordas em tempo real de imagens de vídeo. Tanto a entrada como a saída dos dados são em formato de vídeo composto monocromático padrão e foram utilizados circuitos digitais discretos para a implementação do processador. Os resultados são apresentados em forma de imagens e estas são comparadas com os resultados obtidos através de programas que realizam a detecção de bordas. / Edge detection is the first important step in digital image processing which allows to separate the distinct blocks present in an image. The development of automatic systemswhich perform operations from visual information need real time processing. This work describes the implementation of a pipeline processor based in the Robert\'s Cross mask operator, dedicated to extract edges from video images in real time. Both input and output video signals are monochromatic. Common digital circuits have been used to implement the processor. The results obtained are presented as images and are compared with edge detected images obtained from comercial software.

The goals of this work are a design and implementation of a new vision system, integrated with the waterjet machine. This system combines two commercial webcams applied on an industrial dedicated platform. A main purpose of the vision system is to detect the position and rotation of a workpiece placed on the machine table. The used object recognition algorithm consists of edge detection, standard math processing functions and noise filters. The Hough transform technique is used to extract lines and their intersections of a workpiece. Metric rectification method is used, in order to obtain a top view of the workspace and to adjust an image coordinate system, accordingly to the waterjet machine coordinates. In situ calibration procedures of the booth webcams are developed and implemented. Experimental results of the proposed new vision system prototype confirm required performance and precision of the element detection.

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This diploma thesis summarizes the knowledge about microalgae, their use, cultivation methods and obstacles that prevent their wider use. In the practical part of the work, a device was designed, constructed, and programmed. This device can analyze the bubbles of the tubular photobioreactor and, based on the obtained data, control its aeration. The Python programming language was used to create the program and the OpenCV library was used to analyze the photographs. The bubble detection is based on the edge detection and the subsequent refinement. The data obtained from the analysis are displayed on the device screen and the data are also stored in a csv file. The discussion lists possible improvements and lessons learned during the creation of this device.

Quality control is an important part of any production line. It can be done manually but is most efficient if automated. Inspecting qualitycan include many different processes but this thesisisfocusedon the visual inspection for cracks and scratches. The best way of doingthis at the time of writing is with the help of Artificial Intelligence (AI), more specifically Deep Learning (DL).However, these need a training datasetbeforehand to train on and for some smaller companies, this mightnotbean option. This study triesto find an alternative visual inspection method,that does notrelyon atrained deep learning modelfor when trainingdata is severely limited. Our method is to use edge detection algorithmsin combination with a template to find any edge that doesn’t belong. These include scratches, cracks, or misaligned stickers. These anomalies arethen highlighted in the original picture to show where the defect is. Since deep learningis stateof the art ofvisual inspection, it is expected to outperform template matching when sufficiently trained.To find where this occurs,the accuracy of template matching iscompared to the accuracy of adeep learning modelat different training levels. The deep learning modelisto be trained onimage augmenteddatasets of size: 6, 12, 24, 48, 84, 126, 180, 210, 315, and 423. Both template matching and the deep learning modelwas tested on the samebalanceddataset of size 216. Half of the dataset was images of scratched units,and the other half was of unscratched units. This gave a baseline of 50% where anything under would be worse thanjust guessing. Template matching achieved an accuracy of 88%, and the deep learning modelaccuracyrose from 51% to 100%as the training setincreased. This makes template matching have better accuracy then AI trained on dataset of 84imagesor smaller. But a deep learning modeltrained on 126 images doesstart to outperform template matching. Template matching did perform well where no data was available and training adeep learning modelis no option. But unlike a deep learning model, template matching would not need retraining to find other kinds of surface defects. Template matching could also be used to find for example, misplaced stickers. Due to the use of a template, any edge that doesnot match isdetected.  The ways to train deep learning modelis highly customizable to the users need. Due to resourceand knowledge restrictions, a deep dive into this subject was not conducted.For template matching, only Canny edge detection was used whenmeasuringaccuracy. Other edge detection methodssuch as, Sobel, and Prewitt was ruledoutearlier in this study.

The thesis deals with the edge detection in the magnetic resonance images and their segmentation. There are adduced the gradient based methods, methods based on zero-crossing in the Laplacian images and also methods combined both of the methods adduced above. These methods are compared to find the best one for the temporo-mandibular joint detection. Consequently sufficient segmentation method for particular parts of the temporo-mandibular joint (the condyle, the acetabulum and the articular disk) separation is chosen.

Diploma work is focused on basic characteristics of optical measuring methods commonly used to rate contactions of isolated heart cell and describes basic actions, that are connected with it. A camera record made by a microscope served as basis for optical measuring methods of contractions chosen by us, with using appropriate method elaborating and analysis of pictures used for detection of important part of cell structure. The suggested user application built-up in Matlab environment allows analysis and interpretation of contractional functions in two methodical ways.

This thesis dissert on traffic monitoring. There are couple of different methods of background extraction and four methods vehicle detection described here. Furthermore there is one method that describes vehicle counting. All of these methods was realized in Matlab where was created graphical user interface. One whole chapter is dedicated to process of practical realization. All methods are compared by set of testing videos. These videos are resulting in statistics which diagnoses about efficiency of single one method.