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

Medical robots with potential applications in participatory and opportunistic remote sensing: A review

Among numerous applications of medical robotics, this paper concentrates on the design, optimal use and maintenance of the related technologies in the context of healthcare, rehabilitation and assistive robotics, and provides a comprehensive review of the latest advancements in the foregoing field of science and technology, while extensively dealing with the possible applications of participatory and opportunistic mobile sensing in the aforementioned domains. The main motivation for the latter choice is the variety of such applications in the settings having partial contributions to functionalities such as artery, radiosurgery, neurosurgery and vascular intervention. From a broad perspective, the aforementioned applications can be realized via various strategies and devices benefiting from detachable drives, intelligent robots, human-centric sensing and computing, miniature and micro-robots. Throughout the paper tens of subjects, including sensor-fusion, kinematic, dynamic and 3D tissue models are discussed based on the existing literature on the state-of-the-art technologies. In addition, from a managerial perspective, topics such as safety monitoring, security, privacy and evolutionary optimization of the operational efficiency are reviewed

Resolutıon Enhancement Based Image Compression Technique using Singular Value Decomposition and Wavelet Transforms

In this chapter, we propose a new lossy image compression technique that uses singular value decomposition (SVD) and wavelet difference reduction (WDR) technique followed by resolution enhancement using discrete wavelet transform (DWT) and stationary wavelet transform (SWT). The input image is decomposed into four different frequency subbands by using DWT. The low-frequency subband is the being compressed by using DWR and in parallel the high-frequency subbands are being compressed by using SVD which reduces the rank by ignoring small singular values. The compression ratio is obtained by dividing the total number of bits required to represent the input image over the total bit numbers obtain by WDR and SVD. Reconstruction is carried out by using inverse of WDR to obtained low-frequency subband and reconstructing the high-frequency subbands by using matrix multiplications. The high-frequency subbands are being enhanced by incorporating the high-frequency subbands obtained by applying SWT on the reconstructed low-frequency subband. The reconstructed low-frequency subband and enhanced high-frequency subbands are being used to generate the reconstructed image by using inverse DWT. The visual and quantitative experimental results of the proposed image compression technique are shown and also compared with those of the WDR with arithmetic coding technique and JPEG2000. From the results of the comparison, the proposed image compression technique outperforms the WDR-AC and JPEG2000 techniques

Block based image compression technique using rank reduction and wavelet difference reduction

In this paper a new block based lossy image compression technique which is using rank reduction of the image and wavelet difference reduction (WDR) technique, is proposed. Rank reduction is obtained by applying singular value decomposition (SVD). The input image is divided into blocks of equal sizes after which quantization by SVD is carried out on each block followed by WDR technique. Reconstruction is carried out by decompressing each blocks bit streams and then merging all of them to obtain the decompressed image. The visual and quantitative experimental results of the proposed image compression technique are shown and also compared with those of the WDR technique and JPEG2000. From the results of the comparison, the proposed image compression technique outperforms the WDR and JPEG2000 techniques

Proportional Error Back-Propagation (PEB): Real-Time Automatic Loop Closure Correction for Maintaining Global Consistency in 3D Reconstruction with Minimal Computational Cost

This paper introduces a robust, real-time loop closure correction technique for achieving global consistency in 3D reconstruction, whose underlying notion is to back-propagate the cumulative transformation error appearing while merging the pairs of consecutive frames in a sequence of shots taken by an RGB-D or depth camera. The proposed algorithm assumes that the starting frame and the last frame of the sequence roughly overlap. In order to verify the robustness and reliability of the proposed method, namely, Proportional Error Back-Propagation (PEB), it has been applied to numerous case-studies, which encompass a wide range of experimental conditions, including different scanning trajectories with reversely directed motions within them, and the results are presented. The main contribution of the proposed algorithm is its considerably low computational cost which has the possibility of usage in real-time 3D reconstruction applications. Also, neither manual input nor interference is required from the user, which renders the whole process automatic

Real-time, automatic digi-tailor mannequin robot adjustment based on human body classification through supervised learning

Although mannequin robots have been in use in the context of fit advising, most of the modules involved in the process of online try-on still demand manual calculations, operations and adjustments. This article overcomes the latter deficiency, alleviates the time consumption and brings about significant enhancements to the efficiency and reliability of the foregoing service through coming up with a fully automatic solution. Notions and practices aimed at the classification of 3D scanning instances of human body using a laser scanner are explained, along with the subsequent automatic activation of the mannequin robots, upon presentation of the experimental results. The proposed methodology consists in scanning, classifying according to gender and size and performing analysis on the user's body, modelling and extracting measurements from the 3D visual data imported from the mannequins, and finally, photoshooting the garment being put on the user's body. In order to classify the data obtained by the 3D scanner, first, maximum likelihood function is used for selecting one of the digi-tailor mannequin robots, according to the presumed gender and size, to be activated, and then support vector machine is utilized so as to find out which shape template from the dictionary best matches the scanning instance being considered. The proposed automatic methodology is also compared with the currently used manual method, and the experimental results easily approve its accuracy and reliability

Statistical approach based iris recognition using local binary pattern

Among biometric features utilized for identity recognition purposes, iris has proven to be the most reliable one in terms of sufficient distinctiveness, which has direct implications and importance towards improving the performance and safety of the security verification process through which it is decided whether any instance at hand should be granted permission to access preserved locations or sources of information. This paper deals with the main challenge involved in iris recognition, which lies in its comparatively high computational complexity, having remained unresolved heretofore, at least, as far as the existing literature is concerned. The enhancement brought about by the proposed methodology originates from taking advantage of local binary patterns for processing each segment of the original image, having undergone equalization in advance, as well as applying probability distribution functions separately to every layer of the pixel values, whereas being represented with respect to mutually- independent hue-saturation-intensity color channels. Besides, the Kullback-Leibler Distance between the vectors obtained through concatenation of the feature vectors is taken into account as the classification criterion, which has led to an outstanding recognition rate of 98.44 percent when tested on the UPOL database, with 192 iris images