Visual Servoing

The goal of this book is to introduce the visional application by excellent researchers in the world currently and offer the knowledge that can also be applied to another field widely. This book collects the main studies about machine vision currently in the world, and has a powerful persuasion in the applications employed in the machine vision. The contents, which demonstrate that the machine vision theory, are realized in different field. For the beginner, it is easy to understand the development in the vision servoing. For engineer, professor and researcher, they can study and learn the chapters, and then employ another application method

Robust image watermarking scheme by discrete wavelet transform

This paper proposes a robust digital image watermarking techniques by using discrete wavelet transform. The watermark is embedded in 2-level diagonal coefficient to achieve high performance in term of imperceptibility. The watermark is embedded by selecting a seed that generating pseudorandom noise sequence. Then the Watermark is extracted back by using the same pseudorandom noise sequence seed in the embedding. The experimental results show that the proposed algorithm provides good level of imperceptibility. Moreover, the watermarked image is robust against several attacks

Intelligent watermarking of long streams of document images

Digital watermarking has numerous applications in the imaging domain, including (but not limited to) fingerprinting, authentication, tampering detection. Because of the trade-off between watermark robustness and image quality, the heuristic parameters associated with digital watermarking systems need to be optimized. A common strategy to tackle this optimization problem formulation of digital watermarking, known as intelligent watermarking (IW), is to employ evolutionary computing (EC) to optimize these parameters for each image, with a computational cost that is infeasible for practical applications. However, in industrial applications involving streams of document images, one can expect instances of problems to reappear over time. Therefore, computational cost can be saved by preserving the knowledge of previous optimization problems in a separate archive (memory) and employing that memory to speedup or even replace optimization for future similar problems. That is the basic principle behind the research presented in this thesis. Although similarity in the image space can lead to similarity in the problem space, there is no guarantee of that and for this reason, knowledge about the image space should not be employed whatsoever. Therefore, in this research, strategies to appropriately represent, compare, store and sample from problem instances are investigated. The objective behind these strategies is to allow for a comprehensive representation of a stream of optimization problems in a way to avoid re-optimization whenever a previously seen problem provides solutions as good as those that would be obtained by reoptimization, but at a fraction of its cost. Another objective is to provide IW systems with a predictive capability which allows replacing costly fitness evaluations with cheaper regression models whenever re-optimization cannot be avoided. To this end, IW of streams of document images is first formulated as the problem of optimizing a stream of recurring problems and a Dynamic Particle Swarm Optimization (DPSO) technique is proposed to tackle this problem. This technique is based on a two-tiered memory of static solutions. Memory solutions are re-evaluated for every new image and then, the re-evaluated fitness distribution is compared with stored fitness distribution as a mean of measuring the similarity between both problem instances (change detection). In simulations involving homogeneous streams of bi-tonal document images, the proposed approach resulted in a decrease of 95% in computational burden with little impact in watermarking performace. Optimization cost was severely decreased by replacing re-optimizations with recall to previously seen solutions. After that, the problem of representing the stream of optimization problems in a compact manner is addressed. With that, new optimization concepts can be incorporated into previously learned concepts in an incremental fashion. The proposed strategy to tackle this problem is based on Gaussian Mixture Models (GMM) representation, trained with parameter and fitness data of all intermediate (candidate) solutions of a given problem instance. GMM sampling replaces selection of individual memory solutions during change detection. Simulation results demonstrate that such memory of GMMs is more adaptive and can thus, better tackle the optimization of embedding parameters for heterogeneous streams of document images when compared to the approach based on memory of static solutions. Finally, the knowledge provided by the memory of GMMs is employed as a manner of decreasing the computational cost of re-optimization. To this end, GMM is employed in regression mode during re-optimization, replacing part of the costly fitness evaluations in a strategy known as surrogate-based optimization. Optimization is split in two levels, where the first one relies primarily on regression while the second one relies primarily on exact fitness values and provide a safeguard to the whole system. Simulation results demonstrate that the use of surrogates allows for better adaptation in situations involving significant variations in problem representation as when the set of attacks employed in the fitness function changes. In general lines, the intelligent watermarking system proposed in this thesis is well adapted for the optimization of streams of recurring optimization problems. The quality of the resulting solutions for both, homogeneous and heterogeneous image streams is comparable to that obtained through full optimization but for a fraction of its computational cost. More specifically, the number of fitness evaluations is 97% smaller than that of full optimization for homogeneous streams and 95% for highly heterogeneous streams of document images. The proposed method is general and can be easily adapted to other applications involving streams of recurring problems

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Watermarking based on discrete wavelet transform and q-deformed chaotic map

Hierarchy of one-dimensional ergodic chaotic maps with Tsallis type of q-deformation are studied. We find that in the chaotic region, these maps with q-deformation are ergodic as the Birkhoff ergodic theorem predicts. q-deformed maps are defined as ratios of polynomials of degree N. Hence, by using the Stieltjes transform approach (STA), invariant measure is proposed. In addition, considering Sinai-Ruelle-Bowen (SRB) measure, Kolmogorov-Sinai (KS) entropy for q-deformed maps is calculated analytically. The new q-deformed scheme have ability to keep previous significant properties such as ergodicity, sensitivity to initial condition. By adding q-parameter to the hierarchy in order increase the randomness and one-way computation, we present a new scheme for watermarking. The introduced algorithm tries to improve the problem of failure of encryption such as small key space, encryption speed and level of security. To illustrate the effectiveness of the proposed scheme, some security analyses are presented. By considering the obtained results, it can be concluded that, this scheme have a high potential to be adopted for watermarking. It can be concluded that, the proposed novel watermarking scheme for image authentication can be applied for practical applications. © 2017 Elsevier Lt

Camera model identification based on DCT coefficient statistics

International audienceThe goal of this paper is to design a statistical test for the camera model identification problem from JPEG images. The approach relies on the camera fingerprint extracted in the Discrete Cosine Transform (DCT) domain based on the state-of-the-art model of DCT coefficients. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test is presented and its performances are theoretically established. For a practical use, two Generalized Likelihood Ratio Tests are designed to deal with unknown model parameters such that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated and real JPEG images highlight the relevance of the proposed approach

Virtuaalse proovikabiini 3D kehakujude ja roboti juhtimisalgoritmide uurimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneVirtuaalne riiete proovimine on üks põhilistest teenustest, mille pakkumine võib suurendada rõivapoodide edukust, sest tänu sellele lahendusele väheneb füüsilise töö vajadus proovimise faasis ning riiete proovimine muutub kasutaja jaoks mugavamaks. Samas pole enamikel varem välja pakutud masinnägemise ja graafika meetoditel õnnestunud inimkeha realistlik modelleerimine, eriti terve keha 3D modelleerimine, mis vajab suurt kogust andmeid ja palju arvutuslikku ressurssi. Varasemad katsed on ebaõnnestunud põhiliselt seetõttu, et ei ole suudetud korralikult arvesse võtta samaaegseid muutusi keha pinnal. Lisaks pole varasemad meetodid enamasti suutnud kujutiste liikumisi realistlikult reaalajas visualiseerida. Käesolev projekt kavatseb kõrvaldada eelmainitud puudused nii, et rahuldada virtuaalse proovikabiini vajadusi. Välja pakutud meetod seisneb nii kasutaja keha kui ka riiete skaneerimises, analüüsimises, modelleerimises, mõõtmete arvutamises, orientiiride paigutamises, mannekeenidelt võetud 3D visuaalsete andmete segmenteerimises ning riiete mudeli paigutamises ja visualiseerimises kasutaja kehal. Selle projekti käigus koguti visuaalseid andmeid kasutades 3D laserskannerit ja Kinecti optilist kaamerat ning koostati nendest andmebaas. Neid andmeid kasutati välja töötatud algoritmide testimiseks, mis peamiselt tegelevad riiete realistliku visuaalse kujutamisega inimkehal ja suuruse pakkumise süsteemi täiendamisega virtuaalse proovikabiini kontekstis.Virtual fitting constitutes a fundamental element of the developments expected to rise the commercial prosperity of online garment retailers to a new level, as it is expected to reduce the load of the manual labor and physical efforts required. Nevertheless, most of the previously proposed computer vision and graphics methods have failed to accurately and realistically model the human body, especially, when it comes to the 3D modeling of the whole human body. The failure is largely related to the huge data and calculations required, which in reality is caused mainly by inability to properly account for the simultaneous variations in the body surface. In addition, most of the foregoing techniques cannot render realistic movement representations in real-time. This project intends to overcome the aforementioned shortcomings so as to satisfy the requirements of a virtual fitting room. The proposed methodology consists in scanning and performing some specific analyses of both the user's body and the prospective garment to be virtually fitted, modeling, extracting measurements and assigning reference points on them, and segmenting the 3D visual data imported from the mannequins. Finally, superimposing, adopting and depicting the resulting garment model on the user's body. The project is intended to gather sufficient amounts of visual data using a 3D laser scanner and the Kinect optical camera, to manage it in form of a usable database, in order to experimentally implement the algorithms devised. The latter will provide a realistic visual representation of the garment on the body, and enhance the size-advisor system in the context of the virtual fitting room under study