Literatura académica sobre el tema "Multiview2"

Multiview video consists of multiple views of the same scene. They require enormous amount of data to achieve high image quality, which makes it indispensable to compress multiview video. Therefore, data compression is a major issue for multiviews. In this paper, we explore an efficient fractal video codec to compress multiviews. The proposed scheme first compresses a view-dependent geometry of the base view using fractal video encoder with homogeneous region condition. With the extended fractional pel motion estimation algorithm and fast disparity estimation algorithm, it then generates prediction images of other views. The prediction image uses the image-based rendering techniques based on the decoded video. And the residual signals are obtained by the prediction image and the original image. Finally, it encodes residual signals by the fractal video encoder. The idea is also to exploit the statistical dependencies from both temporal and interview reference pictures for motion compensated prediction. Experimental results show that the proposed algorithm is consistently better than JMVC8.5, with 62.25% bit rate decrease and 0.37 dB PSNR increase based on the Bjontegaard metric, and the total encoding time (TET) of the proposed algorithm is reduced by 92%.

With the rapid industrialization and urbanization, pattern mining of soil contamination of heavy metals is attracting increasing attention to control soil contamination. However, the correlation over various heavy metals and the high-dimension representation of heavy metal data pose vast challenges on the accurate mining of patterns over heavy metals of soil contamination. To solve those challenges, a multiview Gaussian mixture model is proposed in this paper, to naturally capture complicated relationships over multiviews on the basis of deep fusion features of data. Specifically, a deep fusion feature architecture containing modality-specific and modality-common stacked autoencoders is designed to distill fusion representations from the information of all views. Then, the Gaussian mixture model is extended on the fusion representations to naturally recognize the accurate patterns of the intra- and inter-views. Finally, extensive experiments are conducted on the representative datasets to evaluate the performance of the multiview Gaussian mixture model. Results show the outperformance of the proposed methods.

Many real-world datasets are described by multiple views, which can provide complementary information to each other. Synthesizing multiview features for data representation can lead to more comprehensive data description for clustering task. However, it is often difficult to preserve the locally real structure in each view and reconcile the noises and outliers among views. In this paper, instead of seeking for the common representation among views, a novel robust neighboring constraint nonnegative matrix factorization (rNNMF) is proposed to learn the neighbor structure representation in each view, and L2,1-norm-based loss function is designed to improve its robustness against noises and outliers. Then, a final comprehensive representation of data was integrated with those representations of multiviews. Finally, a neighboring similarity graph was learned and the graph cut method was used to partition data into its underlying clusters. Experimental results on several real-world datasets have shown that our model achieves more accurate performance in multiview clustering compared to existing state-of-the-art methods.

Since combining features from heterogeneous data sources can significantly boost classification performance in many applications, it has attracted much research attention over the past few years. Most of the existing multiview feature analysis approaches separately learn features in each view, ignoring knowledge shared by multiple views. Different views of features may have some intrinsic correlations that might be beneficial to feature learning. Therefore, it is assumed that multiviews share subspaces from which common knowledge can be discovered. In this letter, we propose a new multiview feature learning algorithm, aiming to exploit common features shared by different views. To achieve this goal, we propose a feature learning algorithm in a batch mode, by which the correlations among different views are taken into account. Multiple transformation matrices for different views are simultaneously learned in a joint framework. In this way, our algorithm can exploit potential correlations among views as supplementary information that further improves the performance result. Since the proposed objective function is nonsmooth and difficult to solve directly, we propose an iterative algorithm for effective optimization. Extensive experiments have been conducted on a number of real-world data sets. Experimental results demonstrate superior performance in terms of classification against all the compared approaches. Also, the convergence guarantee has been validated in the experiment.

Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.

Abstract Summary Multiview datasets are the norm in bioinformatics, often under the label multi-omics. Multiview data are gathered from several experiments, measurements or feature sets available for the same subjects. Recent studies in pattern recognition have shown the advantage of using multiview methods of clustering and dimensionality reduction; however, none of these methods are readily available to the extent of our knowledge. Multiview extensions of four well-known pattern recognition methods are proposed here. Three multiview dimensionality reduction methods: multiview t-distributed stochastic neighbour embedding, multiview multidimensional scaling and multiview minimum curvilinearity embedding, as well as a multiview spectral clustering method. Often they produce better results than their single-view counterparts, tested here on four multiview datasets. Availability and implementation R package at the B2SLab site: http://b2slab.upc.edu/software-and-tutorials/ and Python package: https://pypi.python.org/pypi/multiview. Supplementary information Supplementary data are available at Bioinformatics online.

In this paper, multifractal properties of multiview 3D video are determined. Multifractal spectra are determined by using the histogram method. For the analysis of multiview video, long video traces are used, for multiview video with two views. Differences between multifractal properties of different views of multiview video and different types of frames are highlighted. Additional analysis was performed for the left view of multiview 3D videos for different quantization parameters of the frames.

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

In the era of Industry 4.0, single-view clustering algorithm is difficult to play a role in the face of complex data, i.e., multiview data. In recent years, an extension of the traditional single-view clustering is multiview clustering technology, which is becoming more and more popular. Although the multiview clustering algorithm has better effectiveness than the single-view clustering algorithm, almost all the current multiview clustering algorithms usually have two weaknesses as follows. (1) The current multiview collaborative clustering strategy lacks theoretical support. (2) The weight of each view is averaged. To solve the above-mentioned problems, we used the Havrda-Charvat entropy and fuzzy index to construct a new collaborative multiview fuzzy c-means clustering algorithm using fuzzy weighting called Co-MVFCM. The corresponding results show that the Co-MVFCM has the best clustering performance among all the comparison clustering algorithms.

L’obiettivo della tesi di ricerca è quello di analizzare le correlazioni esistenti tra il vantaggio competitivo, associato al miglioramento del processo di decision making, e la gestione dei contenuti aziendali attraverso le piattaforme di Enterprise Content Management (ECM). Con questo contributo si intende pertanto incrementare la letteratura presente all’interno del Knowledge Management (KM) ed in particolare sul rapporto esistente tra i sistemi di Knowledge Management, di Enterprise Content Management e la gestione dei processi decisionali. All’interno della letteratura del Knowledge Management, le piattaforme di Enterprise Content Management, sino ad ora, sono state analizzate solo attraverso la Transaction-Costs Theory (Reimer, 2002; McKeever, 2003; Smith e McKeen, 2003; O'Callaghan e Smits, 2005; Tyrväinen et al., 2006) e vengono generalmente descritte come dei sistemi utili per la riduzione dei costi di gestione dei contenuti aziendali presenti all’interno dell’organizzazione. Nello specifico attraverso analisi empiriche i diversi autori hanno evidenziato come gli strumenti di ECM siano in grado di aumentare l’efficienza della gestione delle informazioni aziendali, riducendone il costo di gestione e ricerca. Analizzando gli articoli presenti all’interno della letteratura manageriale, si può facilmente constatare che, a tutt’oggi, non esiste una definizione univocamente accettata del concetto di ECM. Esaminandoli congiuntamente si possono però riscontrare alcune analogie. La distinzione non dipende dal contenuto ma dal focus utilizzato dal ricercatore per descrivere, analizzare ed interpretare i sistemi ECM. Pochi ricercatori hanno però studiato gli impatti che tali strumenti di Content Management hanno sull’organizzazione e sui processi aziendali. In particolare, nessuna ricerca ha mai evidenziato il ruolo strategico delle piattaforme ECM nella gestione dei contenuti aziendali (Gupta et al., 2002; Helfat e Peteraf, 2003; Smith e McKeen, 2003; O'Callaghan e Smits, 2005). Per analizzare ed interpretare i valori rilevati all’interno del case study, verrà utilizzata la teoria della Knowledge Based View. Si considera infatti che i siano le risorse strategiche utili per raggiungere e mantenere il vantaggio competitivo (Conner e Prahalad; 1996; Choi et al.; 2008). I sistemi di ECM non verranno analizzati secondo un approccio gestionale, cioè non si valuterà l’aumento di efficienza connesso al miglioramento della gestione delle informazioni aziendali, bensì si andrà ad analizzare l’evoluzione delle performance aziendali connesso con lo sviluppo del processo decisionale. Nel corso dell’analisi, si andrà ad analizzare se la conoscenza contenuta all’interno delle organizzazioni, risulta essere fondamentale per lo sviluppo e la crescita aziendale (Wernerfelt, 1984; Grant, 1991; Penrose, 1995; Grant, 1996; Prusak, 1996; Teece et al., 1997; Piccoli et al., 2000; Piccoli et al., 2002). Le informazioni assumono però un reale valore solamente quando possono essere gestite facilmente all’interno del processo di decision making per il mantenimento di un vantaggio competitivo. Per migliorare le prestazioni aziendali, risulta fondamentale riuscire a trasformare i numerosi contenuti aziendali “passivi” in sorgenti “attive”. La potenzialità dei sistemi di Enterprise Content Management consiste nella loro capacità di elaborare elevati volumi informativi, fornendo all’utente finale o al sistema di Decision Support Systems (DSS), tutte le informazioni utili ai fini decisionali. In tal modo le migliori performance dell’attività del decision maker avviene non solo attraverso l’incremento della qualità e della quantità delle informazioni di ingresso al processo decisionale ma anche grazie ad una migliore formalizzazione della conoscenza presente all’interno della memoria organizzativa. Il metodo di ricerca utilizzato sarà il cosiddetto “interpretative case study”, il quale risulta particolarmente utile per esaminare un fenomeno nella sua naturale evoluzione (Benbasat, 1984). Il metodo del case study è stato scelto anche perché può rappresentare un veicolo ideale per giungere ad una più profonda comprensione dei processi di business espliciti ed impliciti, ma anche per comprendere meglio il ruolo degli attori all'interno dei sistemi organizzativi (Campbell, 1975; Hamel et al., 1993; Lee, 1999; Stake, 2000). Si utilizzerà l'azienda come unità di analisi (Yin, 1984) sia quando si analizzeranno le relazioni col mercato che il comportamento dei singoli partecipanti ad un processo (Zardini et al., 2010). Inizialmente si andranno ad analizzare alcune delle più significative definizioni di conoscenza presenti all’interno della letteratura e per ciascuna si evidenzieranno i punti di forza e di debolezza. Inizialmente sarà ripresa l’enunciazione proposta da Polanyi (Polanyi, 1958; Polanyi, 1967), la quale verrà poi integrata con gli studi condotti da Nonaka, Takeuchi e Konno (Nonaka, 1991; Nonaka e Takeuchi, 1995; Nonaka e Konno, 1998; Nonaka et al., 2000). Si passerà dal concetto generale di conoscenza alla nozione di knowledge assests, i quali verranno identificati anche come delle risorse intangibili generate internamente all’impresa, difficilmente acquistabili sul mercato. Dopo aver accertato che la conoscenza può essere considerata una risorsa importante per l’ottenimento di un vantaggio competitivo (Grant, 1996b; Prusak, 1996; Alavi e Leidner, 1999; Earl e Scott, 1999; Piccoli et al., 2002), il capitolo terminerà contestualizzando il concetto di knowledge assets anche all’interno della teoria della Knowledge Based View. Nel secondo capitolo verrà esplicitato il processo di creazione della conoscenza e si identificheranno le tre tipologie di Knowledge Management Systems. Il capitolo terminerà con una disamina dei principali sistemi di Knowledge Management utilizzati per la creazione, l’analisi ed il mantenimento della conoscenza presente all’interno della memoria organizzativa. Nel terzo capitolo si procederà alla disamina delle componenti principali presenti all’interno del processo di decision making e con l’analisi degli strumenti di KM specifici per il miglioramento del processo decisionale medesimo. Il capitolo si concluderà con la descrizione e la disamina dei sistemi a supporto delle decisioni. Nella quarta sezione si definirà il termine “contenuto aziendale” e lo si assocerà al concetto di dynamic capabilities (Teece et al., 1997; Eisenhardt e Martin, 2000; Helfat et al., 2007). Successivamente si analizzeranno tutte le fasi presenti all’interno del ciclo di vita dell’informazione: dalla creazione di un nuovo contenuto sino alla catalogazione, al salvataggio ed all’eventuale modifica o cancellazione dello stesso. Avendo circoscritto il concetto di content si procederà con l’analisi delle definizioni presenti all’interno della letteratura. Il capitolo terminerà con lo studio delle componenti principali presenti all’intento dei sistemi ECM ed in particolare con l’analisi degli strumenti utili a supportare i processi decisionali presenti all’interno delle organizzazioni. Nell’ultimo capitolo si procederà alla disamina della metodologia dell’Action-Research, analizzandone i punti di forza e le criticità. Successivamente si seguirà l’approccio proposto da Baskerville (Baskerville, 1999), secondo cui il termine “Ricerca-Azione” da un lato identifica un metodo di investigazione per le scienze sociali, dall’altro rappresenta una sub-categoria che la distingue dagli altri sotto-metodi presenti. Procedendo con l’analisi si giungerà al modello di Baskerville e Wood-Harper (Baskerville e Wood-Harper; 1998) secondo cui si possono individuare dieci distinte forme di Action-Research all’interno della letteratura dei Sistemi Informativi, e tra queste, la Multiview ed in particolare la Multiview2, sarà la metodologia di riferimento utilizzata per testare il framework teorico all’interno del case study.
The focus of this thesis is to analyze the correlations between the competitive advantage, associated to the improvement of the process of decision making, and the content management through the Enterprise Content Management platform (ECM). One scope of this work is to increase the Knowledge Management (KM) literature and in particular to seek the correlation between the ECM Systems and the Decision Support Systems. Enterprise Content Management platforms largely have been analyzed according to Transaction Cost Theory (Reimer, 2002; McKeever, 2003; Smith and McKeen, 2003; O'Callaghan and Smits, 2005; Tyrväinen et al., 2006) and generally are described as useful for the reduction of ECM costs inside an organization (McKeever, 2003). Through empirical analyses, various authors have stressed that ECM tools increase efficiency and reduce management and research costs. Few studies consider the impacts of these tools on the organization or company processes. In particular, no research has highlighted the strategic role of ECM platforms in Enterprise Content Management (Gupta et al., 2002; Helfat and Peteraf, 2003; Smith and McKeen, 2003; O'Callaghan and Smits, 2005) as a means to improve and speed up the decision-making process. The case study will be analyzed by the Knowledge Based View. Specifically, the knowledge-based view (KBV) constitutes a fundamental essence of the resource-based view (RBV; Conner and Prahalad, 1996), reflecting the importance of knowledge assets. The knowledge and enterprise content generated thus can be interpreted not only as strategic resources to achieve or maintain a competitive advantage but also as useful tools for developing and expanding the company’s ability to respond promptly to unexpected events in the external environment and therefore perfect decision making within the organization. According to several authors (Barney, 1991; Amit and Schoemaker, 1993; Peteraf, 1993; Winter, 1995; Grover et al., 2009), the Resource Based View (RBV) cites knowledge as a resource that can generate information asymmetries and thus a competitive advantage for the enterprises that possess it. Reconsidering the general theory on the RBV and including knowledge assets among an enterprise’s intangible resources easily results in the KBV. If the term “acquired resources” from the general RBV proposed by Lippman and Rumelt (1982) and Barney (1986) gets replaced by “knowledge,” the result is KBV theory, and knowledge represents one of the strategic factors for maintaining a competitive advantage (Grant and Baden-Fuller, 1995; Grant, 1996c; Teece et al., 1997; Sambamurthy and Subramani, 2005; Bach et al., 2008; Choi et al., 2008). The availability of content thus is necessary, but it is not a sufficient condition to improve the decision-making process and company performance. Rather, the company also needs to transform “passive” contents, such as unused information within the boundaries of organizational memory, into “active” sources that are integral to the decision-making process. To improve the decision-making process and create value, the enterprises must enrich the quality and quantity of all information that provides critical input to a decision. The goal therefore involves an ability to manage knowledge in- and outside the organization by transforming data into knowledge. In the case analyzed, decision-makers achieve the best performance not only improving the quantity and quality of input information to the decisional process but also thanks to a better formalization of the knowledge included in all phases of the process. In this view, ECM platforms are advanced KM tools that are fundamental for the development of a competitive advantage, in that they simplify and speed up the management (creation, classification, storing, change, deletion) of information, increase the productivity of each member, and improve the efficiency of the system (McKeever, 2003; Nordheim and Päivärinta, 2004; O' Callaghan and Smits, 2005). By implementing an ECM system, the company has not only an effective means for creating, tracking, managing, and archiving all company content but also can integrate business processes, develop collaborative actions through the systemic organization of work teams, and create a search engine with specialized “business logic views.” Standardized contents and layout, associated with a definition of content owners and users (i.e., management of authorizations), and document processes support the spread of updated, error-free information to various organizational actors. Similar to business intelligence systems, ECM platforms support decision making inside the organizations in terms of viewing and retrieving data and analyzing and sharing information—and thus increase organizational memory—as well as their storage and continuous maintenance along the life cycle of the enterprise. For the analysis of the case study, this study employs the action research method (Lewin, 1946; Checkland, 1985; Checkland and Scholes, 1990), and specifically Multiview2 (Avison and Wood-Harper, 2003). The original Multiview concept assumed a continuous interaction between analysts and method, including the present situation and the future scenario that originated by application of the methodology. In some respects, the original definition was limited, in that it did not describe the function of each element and the trend of possible interactions (Avison and Wood-Harper, 2003). Multiview2 fills these gaps by taking into consideration the action and reaction generated by the interactions of the elements. The three macro-categories therefore must be aligned to conduct an organizational, socio-technical, and technological analysis (Avison et al., 1998; Avison and Wood-Harper, 2003). The researcher provides a clear contribution that matches the theoretical framework used as a reference and measures and evaluates in subsequent phases the results obtained from those implemented actions.

Commonly used human motion capture systems require intrusive attachment of markers that are visually tracked with multiple cameras. In this work we present an efficient and inexpensive solution to markerless motion capture using only a few Kinect sensors. We use our system to design a smart home platform with a network of Kinects that are installed inside the house. Our first contribution is a multiview pose estimation system. Unlike the previous work on 3d pose estimation using a single depth camera, we relax constraints on the camera location and do not assume a co-operative user. We apply recent image segmentation techniques with convolutional neural networks to depth images and use curriculum learning to train our system on purely synthetic data. Our method accurately localizes body parts without requiring an explicit shape model. The body joint locations are then recovered by combining evidence from multiple views in real-time. Our second contribution is a dataset of 6 million synthetic depth frames for pose estimation from multiple cameras with varying levels of complexity to make curriculum learning possible. We show the efficacy and applicability of our data generation process through various evaluations. Our final system exceeds the state-of-the-art results on multiview pose estimation on the Berkeley MHAD dataset. Our third contribution is a scalable software platform to coordinate Kinect devices in real-time over a network. We use various compression techniques and develop software services that allow communication with multiple Kinects through TCP/IP. The flexibility of our system allows real-time orchestration of up to 10 Kinect devices over Ethernet.
Science, Faculty of
Computer Science, Department of
Graduate

3D video is a technology that has seen a tremendous attention in the recent years. Multiview Video Coding (MVC) is an extension of the popular H.264 video coding standard and is commonly used to compress 3D videos. It offers an improvement of 20% to 50% in compression efficiency over simulcast encoding of multiview videos using the conventional H.264 video coding standard. However, there are two important problems associated with it: (i) its superior compression performance comes at the cost of significantly higher computational complexity which hampers the real-world realization of MVC encoder in applications such as 3D live broadcasting and interactive Free Viewpoint Television (FTV), and (ii) compressed 3D videos can suffer from packet loss during transmission, which can degrade the viewing quality of the 3D video at the decoder. This thesis aims to solve these problems by presenting techniques to reduce the computational complexity of the MVC encoder and by proposing a consistent error concealment technique for frame losses in 3D video transmission. The thesis first analyses the complexity of the MVC encoder. It then proposes two novel techniques to reduce the complexity of motion and disparity estimation. The first method achieves complexity reduction in the disparity estimation process by exploiting the relationship between temporal levels, type of macroblocks and search ranges while the second method achieves it by exploiting the geometrical relation- ship between motion and disparity vectors in stereo frames. These two methods are then combined with other state-of-the-art methods in a unique framework where gains add up. Experimental results show that the proposed low-complexity framework can reduce the encoding time of the standard MVC encoder by over 93% while maintaining similar compression efficiency performance. The addition of new View Synthesis Prediction (VSP) modes to the MVC encoding framework improves the compression efficiency of MVC. However, testing additional modes comes at the cost of increased encoding complexity. In order to reduce the encoding complexity, the thesis, next, proposes a bayesian early mode decision technique for a VSP enhanced MVC coder. It exploits the statistical similarities between the RD costs of the VSP SKIP mode in neighbouring views to terminate the mode decision process early. Results indicate that the proposed technique can reduce the encoding time of the enhanced MVC coder by over 33% at similar compression efficiency levels. Finally, compressed 3D videos are usually required to be broadcast to a large number of users where transmission errors can lead to frame losses which can degrade the video quality at the decoder. A simple reconstruction of the lost frames can lead to inconsistent reconstruction of the 3D scene which may negatively affect the viewing experience of a user. In order to solve this problem, the thesis proposes, at the end, a consistency model for recovering frames lost during transmission. The proposed consistency model is used to evaluate inter-view and temporal consistencies while selecting candidate blocks for concealment. Experimental results show that the proposed technique is able to recover the lost frames with high consistency and better quality than two standard error concealment methods and a baseline technique based on the boundary matching algorithm.

Face recognition is a fundamental task in computer vision and has been an important field of study for many years. Its importance in activities such as face recognition and classification, 3D animation, virtual modelling or biomedicine makes it a top-demanded activity, but finding accurate solutions still represents a great challenge nowadays. This report presents a unified process for automatically extract a set of face landmarks and remove all differences related to pose, expression and environment by bringing faces to a neutral pose-centred state. Landmark detection is based on a multiple viewpoint Pictorial Structure model, which specifies first, a part for each landmark we want to extract, second a tree structure to constraint its position within the face geometry and third, multiple trees to model differences due the orientation. In this report we address both the problem of how to find a set of landmarks from a model and the problem of training such a model from a set of labelled examples. We show how such a model successfully captures a great range of deformations needing far less training examples than common commercial face detectors. The alignment process basically aims to remove differences between multiple faces so they all can be analysed under the same criteria. It is carried out with Thin-plate Splines to adjust the detected set of landmarks to the desired configuration. With this method we assure smooth interpolations while the subject identity is preserved by modifying the original extracted configuration of parts and creating a generic distribution with the help of a reference face dataset. We present results of our algorithms both in a constrained environment and in the challenging LFPW face database. Successful outcomes are shown that prove our method to be a solid process for unitedly recognise and warp faces in the wild and to be on a par with other state-of-the-art procedures.
Ansiktsigenkänning är en grundläggande uppgift inom datorseende och har varit ett viktigt område för forskning i många år. Dess betydelse i områden som ansiktsigenkänning och klassificering, 3D-animering, virtuell modellering eller biomedicin gör det till en verksamhet med hög efterfrågan. Att hitta precisa lösningar utgör fortfarande en stor utmaning idag. Denna rapport presenterar en enhetlig process för att automatiskt extrahera en uppsättning ansiktslandmärken och ta bort alla skillnader relaterade till posering, uttryck och miljö genom att ta ansiktet till ett neutralcentrerat poseringstillstånd. Landmärksdetektering baseras på en bildmässig strukturmodell med multipel synvinkel som först anger en del för varje landmärke som ska extraheras, och sen en trädstruktur där positionen sparas därefter skapas multipla trädmodeller för att modellera skillnader på grund av olika riktningar. I denna rapport behandlas både problemet med hur man hittar en uppsättning landmärken från en modell och problemet med att träna en sådan modell från en uppsättning märkta exempel. Vi visar hur en sådan modell framgångsrikt fångar ett stort utbud av formändringar där betydligt mindre träningsexempel behövs än för vanliga kommersiella ansiktsdetektorer. Inriktningsprocessen syftar huvudsakligen till att upphäva skillnaderna mellan flera ansikten så att de alla kan analyseras enligt samma kriterier. För att justera den detekterade uppsättning landmärken används en splineinterpolation till den önskade konfigurationen. Denna metod ger en dämpad interpolation medan objektets identitet bevaras. Vi presenterar resultaten av våra algoritmer både i en begränsad miljö och i utmanande LFPW face-databas. Goda resultat visar att vår metod är en bra process för enigt erkänna och förvränga ansikten i en obegränsad miljö och att vara i nivå med andra state-of-the-art förfaranden.

In this thesis, a number of novel techniques for error resilient coding and streaming for multiview video are presented. First of all, a novel coding technique for stereoscopic video is proposed where additional coding gain is achieved by downsampling one of the views spatially or temporally based on the well-known theory that the human visual system can perceive high frequencies in 3D from the higher quality view. Stereoscopic videos can be coded at a rate upto 1.2 times that of monoscopic videos with little visual quality degradation with the proposed coding technique. Next, a systematic method for design and optimization of multi-threaded multi-view video encoding/decoding algorithms using multi-core processors is proposed. The proposed multi-core decoding architectures are compliant with the current international standards, and enable multi-threaded processing with negligible loss of encoding efficiency and minimum processing overhead. End-to-end 3D Streaming system over Internet using current standards is implemented. A heuristic methodology for modeling the end-toend rate-distortion characteristic of this system is suggested and the parameters of the system is optimally selected using this model. End-to-end 3D Broadcasting system over DVB-H using current standards is also implemented. Extensive testing is employed to show the importance and characteristics of several error resilient tools. Finally we modeled end-to-end RD characteristics to optimize the encoding and protection parameters.

In multiview learning, it is essential to assign a reasonable weight to each view according to its importance. Thus, for multiview clustering task, a wise and elegant method should achieve clustering multiview data while learning the view weights. In this paper, we address this problem by exploring a Laplacian rank constrained graph, which can be approximately as the centroid of the built graph for each view with different confidences. We start our work with a natural thought that the weights can be learned by introducing a hyperparameter. By analyzing the weakness of it, we further propose a new multiview clustering method which is totally self-weighted. Furthermore, once the target graph is obtained in our models, we can directly assign the cluster label to each data point and do not need any postprocessing such as $K$-means in standard spectral clustering. Evaluations on two synthetic datasets prove the effectiveness of our methods. Compared with several representative graph-based multiview clustering approaches on four real-world datasets, experimental results demonstrate that the proposed methods achieve the better performances and our new clustering method is more practical to use.

With the advanced video streaming technology, smartphone users can share and stream real-time camera to any video subscriber with a high network transmission speed. On the other side, subscriber able to select their favorite video sources can create multiple screen (i.e., Multi-View features) display. In this paper, a video casting framework for displaying multiple video sources is proposed. This framework potentially leads to an object modelling when multiple cameras point to the same object with different angle of view. In addition, the multiview feature provides additional flexibility on a well-designed production line monitoring system in an Industrial IoT framework. With the existence of multiview content, earlier error detection and prevention can be performed to facilitate cyber-physical system, as an important element in Industrial IoT. Keywords: multi-view, Industry 4.0, Internet of Things, Real-Time Streaming Protocol, video casting

This paper explores a succinct kernel model for Group-Sparse Projections Learning (GSPL), to handle multiview feature selection task completely. Compared to previous works, our model has the following useful properties: 1) Strictness: GSPL innovatively learns group-sparse projections strictly on multiview data via ‘2;0-norm constraint, which is different with previous works that encourage group-sparse projections softly. 2) Adaptivity: In GSPL model, when the total number of selected features is given, the numbers of selected features of different views can be determined adaptively, which avoids artificial settings. Besides, GSPL can capture the differences among multiple views adaptively, which handles the inconsistent problem among different views. 3) Succinctness: Except for the intrinsic parameters of projection-based feature selection task, GSPL does not bring extra parameters, which guarantees the applicability in practice. To solve the optimization problem involved in GSPL, a novel iterative algorithm is proposed with rigorously theoretical guarantees. Experimental results demonstrate the superb performance of GSPL on synthetic and real datasets.