This master’s thesis deals with an important part of computer vision field. Main focus of this thesis is on feature detectors and descriptors in an image. Throughout the thesis the simplest feature detectors like Moravec detector will be presented, building up to more complex detectors like MSER or FAST. The purpose of feature descriptors is in a mathematical description of these points. We begin with the oldest ones like SIFT and move on to newest and best performing descriptors like FREAK or ORB. The major objective of the thesis is comparison of presented methods on licence plate localization task.

This paper addresses the problem of mobile robot localization based on current 2D and 3D data and previous records. Focusing on practical loop detection in the trajectory of a robot. The objective of this work was to evaluate current methods of image processing and depth data for issues of localization in environment. This work uses Bag of Words for 2D data and environment of point cloud with Viewpoint Feature Histogram for 3D data. Designed system was implemented and evaluated.

<div> Planar homography estimation is foundational to many computer vision problems, such as Simultaneous Localization and Mapping (SLAM) and Augmented Reality (AR). However, conditions of high variance confound even the state-of-the-art algorithms. In this report, we analyze the performance of two recently published methods using Convolutional Neural Networks (CNNs) that are meant to replace the more traditional feature-matching based approaches to the estimation of homography. Our evaluation of the CNN based methods focuses particularly on measuring the performance under conditions of significant noise, illumination shift, and occlusion. We also measure the benefits of training CNNs to varying degrees of noise. Additionally, we compare the effect of using color images instead of grayscale images for inputs to CNNs. Finally, we compare the results against baseline feature-matching based homography estimation methods using SIFT, SURF, and ORB. We find that CNNs can be trained to be more robust against noise, but at a small cost to accuracy in the noiseless case. Additionally, CNNs perform significantly better in conditions of extreme variance than their feature-matching based counterparts. With regard to color inputs, we conclude that with no change in the CNN architecture to take advantage of the additional information in the color planes, the difference in performance using color inputs or grayscale inputs is negligible. About the CNNs trained with noise-corrupted inputs, we show that training a CNN to a specific magnitude of noise leads to a ``Goldilocks Zone'' with regard to the noise levels where that CNN performs best.</div>

In first part thesis contains known methods of license plate detection. Preprocessing-based methods, AdaBoost-based methods and extremal region detection methods are described.Finally, there is a described and implemented own access using local detectors to creating visual vocabulary, which is used to plate recognition. All measurements are summarized on the end.

In the area of automation, object localization stands as a fundamental functionalitywith widespread applicability. This master’s thesis focuses on a specificapplication involving robot object picking. Given recent advancements in depthcamera technology, there is a high interest in exploring the synergistic integrationof both 2D and 3D data to address challenges such as missing data, occlusion,varying viewing angles, and diverse lighting conditions. This master’s thesis presents the development of two distinct algorithms for arbitraryshaped template matching using 2D image features. Both algorithms leveragefeatures detected by the GoodFeaturesToTrack algorithm and described withScale-invariant feature transform (SIFT) descriptors. While an initial sliding windowmatcher was developed, it was ultimately discarded due to extensive timerequirements. Instead, a correspondence matcher was created, offering two variations:one exclusively employing 2D image data for matching and another utilizing3D coordinates to enhance matching accuracy. The correspondence matchingalgorithms showed similar strengths and weaknesses. They demonstrated proficiencyin handling scenarios characterized by occlusion, minor tilt, and varyingscaling. Both variations struggled with objects 90-degrees rotated and could inmany cases not find them. The findings suggest that the developed feature-based correspondence matchingalgorithm holds promise for object localization in industrial picking applications,although with limitations concerning objects with substantial rotationdifferences. Addressing the challenge of large rotations is recommended for enhancingthe algorithm’s robustness, followed by comprehensive testing to ascertainits efficacy in diverse scenarios.iii

This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The idea is to move one point set coherently to align with the second point set. The CPD method finds both the non-rigid transformation and the correspondence distance between two point sets at the same time without having to use a-priori declaration of the transformation model used. The first part of this thesis is focused on speaker identification in video conferencing. A real-time, audio-coupled video based approach is presented, which focuses more on the video analysis side, rather than the audio analysis that is known to be prone to errors. CPD is effectively utilised for lip movement detection and a temporal face detection approach is used to minimise false positives if face detection algorithm fails to perform. The second part of the thesis is focused on multi-exposure and multi-focus image fusion with compensation for camera shake. Scale Invariant Feature Transforms (SIFT) are first used to detect keypoints in images being fused. Subsequently this point set is reduced to remove outliers, using RANSAC (RANdom Sample Consensus) and finally the point sets are registered using CPD with non-rigid transformations. The registered images are then fused with a Contourlet based image fusion algorithm that makes use of a novel alpha blending and filtering technique to minimise artefacts. The thesis evaluates the performance of the algorithm in comparison to a number of state-of-the-art approaches, including the key commercial products available in the market at present, showing significantly improved subjective quality in the fused images. The final part of the thesis presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR task and may capture vehicles at different approaching angles. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximise the reliability of the final outcome. Experimental results are provided to prove that the proposed system demonstrates an accuracy in excess of 95% when tested on real CCTV footage with no prior camera calibration.

Availability of digital cameras in various forms and user-friendly image editing softwares has enabled people to create and manipulate digital images easily. While image editing can be used for enhancing the quality of the images, it can also be used to tamper the images for malicious purposes. In this context, it is important to question the originality of digital images. Digital image forensics deals with the development of algorithms and systems to detect tampering in digital images. This thesis presents some simple algorithms which can be used to detect tampering in digital images. Out of the various kinds of image forgeries possible, the discussion is restricted to photo compositing (Photo montaging) and copy-paste forgeries. While creating photomontage, it is very likely that one of the images needs to be resampled and hence there will be an inconsistency in some of its underlying characteristics. So, detection of resampling in an image will give a clue to decide whether the image is tampered or not. Two pixel domain techniques to detect resampling have been presented. The rest of them exploits the property of periodic zeros that occur in the second divergences due to interpolation during resembling. It requires a special condition on the resembling factor to be met. The second technique is based on the periodic zero-crossings that occur in the second divergences, which does not require any special condition on the resembling factor. It has been noted that this is an important property of revamping and hence the decay of this technique against mild counter attacks such as JPEG compression and additive noise has been studied. This property has been repeatedly used throughout this thesis. It is a well known fact that interpolation is essentially low-pass filtering. In case of photomontage image which consists of resample and non resample portions, there will be an in consistency in the high-frequency content of the image. This can be demonstrated by a simple high-pass filtering of the image. This fact has also been exploited to detect photomontaging. One approach involves performing block wise DCT and reconstructing the image using only a few high-frequency coercions. Another elegant approach is to decompose the image using wavelets and reconstruct the image using only the diagonal detail coefficients. In both the cases mere visual inspection will reveal the forgery. The second part of the thesis is related to tamper detection in colour filter array (CFA) interpolated images. Digital cameras employ Bayer filters to efficiently capture the RGB components of an image. The output of Bayer filter are sub-sampled versions of R, G and B components and they are completed by using demosaicing algorithms. It has been shown that demos icing of the color components is equivalent to resembling the image by a factor of two. Hence, CFA interpolated images contain periodic zero-crossings in its second differences. Experimental demonstration of the presence of periodic zero-crossings in images captured using four digital cameras of deferent brands has been done. When such an image is tampered, these periodic zero-crossings are destroyed and hence the tampering can be detected. The utility of zero-crossings in detecting various kinds of forgeries on CFA interpolated images has been discussed. The next part of the thesis is a technique to detect copy-paste forgery in images. Generally, while an object or a portion if an image has to be erased from an image, the easiest way to do it is to copy a portion of background from the same image and paste it over the object. In such a case, there are two pixel wise identical regions in the same image, which when detected can serve as a clue of tampering. The use of Scale-Invariant-Feature-Transform (SIFT) in detecting this kind of forgery has been studied. Also certain modifications that can to be done to the image in order to get the SIFT working effectively has been proposed. Throughout the thesis, the importance of human intervention in making the final decision about the authenticity of an image has been highlighted and it has been concluded that the techniques presented in the thesis can effectively help the decision making process.

Les dernières années, la taille des collections vidéo a connu une forte augmentation. La recherche et la navigation efficaces dans des telles collections demande une indexation avec des termes pertinents, ce qui nous amène au sujet de cette thèse, l'indexation sémantique des vidéos. Dans ce contexte, le modèle Sac de Mots (BoW), utilisant souvent des caractéristiques SIFT ou SURF, donne de bons résultats sur les images statiques. Notre première contribution est d'améliorer les résultats des descripteurs SIFT/SURF BoW sur les vidéos en pré-traitant les vidéos avec un modèle de rétine humaine, ce qui rend les descripteurs SIFT/SURF BoW plus robustes aux dégradations vidéo et qui leurs donne une sensitivité à l'information spatio-temporelle. Notre deuxième contribution est un ensemble de descripteurs BoW basés sur les trajectoires. Ceux-ci apportent une information de mouvement et contribuent vers une description plus riche des vidéos. Notre troisième contribution, motivée par la disponibilité de descripteurs complémentaires, est une fusion tardive qui détermine automatiquement comment combiner un grand ensemble de descripteurs et améliore significativement la précision moyenne des concepts détectés. Toutes ces approches sont validées sur les bases vidéo du challenge TRECVid, dont le but est la détection de concepts sémantiques visuels dans un contenu multimédia très riche et non contrôlé.

This thesis deals with mining of relevant information from documents and automatic splitting of multiple documents merged together. Moreover, it describes the design and implementation of software for data mining from documents and for automatic splitting of multiple documents. Methods for acquiring textual data from scanned documents, named entity recognition, document clustering, their supportive algorithms and metrics for automatic splitting of documents are described in this thesis. Furthermore, an algorithm of implemented software is explained and tools and techniques used by this software are described. Lastly, the success rate of the implemented software is evaluated. In conclusion, possible extensions and further development of this thesis are discussed at the end.

The Master thesis is focused on analyzing stereo camera ZED in the outdoor environment. There is compared ZEDfu visual odometry with commonly used methods like GPS or wheel odometry. Moreover, the thesis includes analyses of SLAM in the changeable outdoor environment, too. The simultaneous mapping and localization in RTAB-Map were processed separately with SIFT and BRISK descriptors. The aim of this master thesis is to analyze the behaviour ZED camera in the outdoor environment for future implementation in mobile robotics.