Optimized Image Resizing Using Seam Carving and Scaling

International audienceWe present a novel method for content-aware image resizing based on optimization of a well-defined image distance function, which preserves both the important regions and the global visual effect (the background or other decorative objects) of an image. The method operates by joint use of seam carving and image scaling. The principle behind our method is the use of a bidirectional similarity function of image Euclidean distance (IMED), while cooperating with a dominant color descriptor (DCD) similarity and seam energy variation. The function is suitable for the quantitative evaluation of the resizing result and the determination of the best seam carving number. ifferent from the previous simplex-modeapproaches, our method takes the advantages of both discrete and continuous methods. The technique is useful in image resizing for both reduction/retargeting and enlarging. We also show that this approach can be extended to indirect image resizing

Real-time content-aware video retargeting on the Android platform for tunnel vision assistance

As mobile devices continue to rise in popularity, advances in overall mobile device processing power lead to further expansion of their capabilities. This, coupled with the fact that many people suffer from low vision, leaves substantial room for advancing mobile development for low vision assistance. Computer vision is capable of assisting and accommodating individuals with blind spots or tunnel vision by extracting the necessary information and presenting it to the user in a manner they are able to visualize. Such a system would enable individuals with low vision to function with greater ease. Additionally, offering assistance on a mobile platform allows greater access. The objective of this thesis is to develop a computer vision application for low vision assistance on the Android mobile device platform. Specifically, the goal of the application is to reduce the effects tunnel vision inflicts on individuals. This is accomplished by providing an in-depth real-time video retargeting model that builds upon previous works and applications. Seam carving is a content-aware retargeting operator which defines 8-connected paths, or seams, of pixels. The optimality of these seams is based on a specific energy function. Discrete removal of these seams permits changes in the aspect ratio while simultaneously preserving important regions. The video retargeting model incorporates spatial and temporal considerations to provide effective image and video retargeting. Data reduction techniques are utilized in order to generate an efficient model. Additionally, a minimalistic multi-operator approach is constructed to diminish the disadvantages experienced by individual operators. In the event automated techniques fail, interactive options are provided that allow for user intervention. Evaluation of the application and its video retargeting model is based on its comparison to existing standard algorithms and its ability to extend itself to real-time. Performance metrics are obtained for both PC environments and mobile device platforms for comparison

Sayısal görüntülerde piksel yolu çıkarma esaslı boyut değişikliği tespiti

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Piksel yolu çıkarma (seam carving), günümüzde en çok uygulanan içeriğe duyarlı görüntü boyutlandırma yöntemlerinden biridir. Piksel yolu çıkarmanın sebep olduğu bozukluklar çok yüksek oranlarda ölçekleme yapılmadıkça insan gözü tarafından algılanamaz. Bu görsel başarının sebebi görüntüdeki piksellerin önem değerlerine göre değerlendiriliyor olmasıdır. Görüntünün optimal seam'i, görüntü genelinde toplamda en az enerji (önem) değerine sahip piksel yoludur. Tek piksel genişliğindeki önemsiz bu piksel yolları birer azaltılarak her iterasyonda görüntünün genişliği ya da yüksekliği bir azaltılır. Anlamsal olarak önemli olan ön plan nesnelerine mümkün olduğunca dokunulmaz. Görüntünün içeriğinin bu denli korunduğu bir ölçekleme yaklaşımı kötü niyetli olarak da kullanılabileceğinden, bu şekilde ölçeklenmiş görüntülerin tespiti büyük önem arz etmektedir. Piksel yolu çıkarma tabanlı ölçeklemenin tespiti diğer ölçekleme yöntemlerine göre oldukça zordur. çünkü görüntülerin geometrik açıdan ele alınması yetmez, anlamsal bir değerlendirme içeren detaylı bir analiz yapılması gerekmektedir. Bu çalışmada, piksel yolu çıkarılarak boyutları değiştirilmiş görüntülerin tespiti, görüntülerden özellik çıkarılması ve çıkarılan özelliklerle Destek Vektör Makinesi'nin eğitilmesi şeklinde gerçekleştirilmektedir. Çıkarılan özellikler piksel yolu çıkarma algoritmasının uygulanışı ile alakalı özelliklerdir. Ayrıca, yöntemin başarımını artırmak amacıyla, özellik çıkarımı öncesinde görüntülere Yerel İkili Örüntüler dönüşümü uygulanmış ve piksel yolu çıkarmanın sebep olabileceği yerel bozukluklar belirginleştirilmiştir. Tüm bunlara ek olarak, piksel yolu çıkarmanın görüntülerin farklı parçalarındaki etkileri de incelenmiştir. Bu amaçla görüntüler şeritlere ayrılarak her bir şerit seam özellikleri bakımından değerlendirilmiş ve tespit doğrulukları bu şekilde oldukça artırılmıştır. Geliştirilen yöntem ile piksel yolu çıkarma tabanlı ölçekleme %30 ölçeklenmiş görüntülerde %99,9'lara kadar tespit edilebilmiştir. Performans literatürdeki diğer yöntemlere göre ortalamada %20'den fazla artırılmıştır. Tespit performansı özellikle tespit edilmesi daha zor olan %3, %6 gibi küçük ölçekleme oranlarında %26 geliştirilmiştir.Seam carving is one of the mostly applied content-aware image resizing methods today. The deteriorations caused by seam carving are mostly unnoticeable for human eyes unless the scaling ratio is very high. The reason of this visual success comes from evaluating the pixels according to their importance values. Optimal seam of an image is a pixel path which contains the least energy (importance) throughout the image. Image width or height is decreased by one in each iteration by removing those unimportant, one-pixel width pixel paths. The semantically important foreground objects remain untouched as far as possible. Since such a scaling approach which perfectly preserves the image content can be used malevolently, the detection of the images that are scaled in this manner becomes more of an issue. The detection of seam carving is more difficult than the other scaling methods since evaluating the images geometrically is not sufficient, but a detailed analysis investigating the semantical concept is required. In this study, the detection of the images scaled by seam carving is realized by feature extraction and training a Support Vector Machine with those features. The extracted features are related to the seam carving process. In addition, Local Binary Patterns transform is applied to the images before feature extraction to reveal the local artifacts caused by seam carving. Besides, the effect of seam carving in sub parts of the images is investigated. For this purpose, the images are divided into several stripes and each and every stripe is evaluated in terms of seam features. This evaluation has been improved the detection accuracies. Seam carving based resizing has been detected up to 99,9% in 30%scaled images by the developed method. The detection performance has been improved 20% on the average when compared with other methods in the literature. The detection performance is improved 26% in low scaling ratios like 3% and 6% which are harder to detect

Task-based Adaptation of Graphical Content in Smart Visual Interfaces

To be effective visual representations must be adapted to their respective context of use, especially in so-called Smart Visual Interfaces striving to present specifically those information required for the task at hand. This thesis proposes a generic approach that facilitate the automatic generation of task-specific visual representations from suitable task descriptions. It is discussed how the approach is applied to four principal content types raster images, 2D vector and 3D graphics as well as data visualizations, and how existing display techniques can be integrated into the approach.Effektive visuelle Repräsentationen müssen an den jeweiligen Nutzungskontext angepasst sein, insbesondere in sog. Smart Visual Interfaces, welche anstreben, möglichst genau für die aktuelle Aufgabe benötigte Informationen anzubieten. Diese Arbeit entwirft einen generischen Ansatz zur automatischen Erzeugung aufgabenspezifischer Darstellungen anhand geeigneter Aufgabenbeschreibungen. Es wird gezeigt, wie dieser Ansatz auf vier grundlegende Inhaltstypen Rasterbilder, 2D-Vektor- und 3D-Grafik sowie Datenvisualisierungen anwendbar ist, und wie existierende Darstellungstechniken integrierbar sind

Artistic Content Representation and Modelling based on Visual Style Features

This thesis aims to understand visual style in the context of computer science, using traditionally intangible artistic properties to enhance existing content manipulation algorithms and develop new content creation methods. The developed algorithms can be used to apply extracted properties to other drawings automatically; transfer a selected style; categorise images based upon perceived style; build 3D models using style features from concept artwork; and other style-based actions that change our perception of an object without changing our ability to recognise it. The research in this thesis aims to provide the style manipulation abilities that are missing from modern digital art creation pipelines

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Saillance Visuelle, de la 2D à la 3D Stéréoscopique : Examen des Méthodes Psychophysique et Modélisation Computationnelle

Visual attention is one of the most important mechanisms deployed in the human visual system to reduce the amount of information that our brain needs to process. An increasing amount of efforts are being dedicated in the studies of visual attention, particularly in computational modeling of visual attention. In this thesis, we present studies focusing on several aspects of the research of visual attention. Our works can be mainly classified into two parts. The first part concerns ground truths used in the studies related to visual attention ; the second part contains studies related to the modeling of visual attention for Stereoscopic 3D (S-3D) viewing condition. In the first part, our work starts with identifying the reliability of FDM from different eye-tracking databases. Then we quantitatively identify the similarities and difference between fixation density maps and visual importance map, which have been two widely used ground truth for attention-related applications. Next, to solve the problem of lacking ground truth in the community of 3D visual attention modeling, we conduct a binocular eye-tracking experiment to create a new eye-tracking database for S-3D images. In the second part, we start with examining the impact of depth on visual attention in S-3D viewing condition. We firstly introduce a so-called "depth-bias" in the viewing of synthetic S-3D content on planar stereoscopic display. Then, we extend our study from synthetic stimuli to natural content S-3D images. We propose a depth-saliency-based model of 3D visual attention, which relies on depth contrast of the scene. Two different ways of applying depth information in S-3D visual attention model are also compared in our study. Next, we study the difference of center-bias between 2D and S-3D viewing conditions, and further integrate the center-bias with S-3D visual attention modeling. At the end, based on the assumption that visual attention can be used for improving Quality of Experience of 3D-TV when collaborating with blur, we study the influence of blur on depth perception and blur's relationship with binocular disparity.L'attention visuelle est l'un des mécanismes les plus importants mis en oeuvre par le système visuel humain (SVH) afin de réduire la quantité d'information que le cerveau a besoin de traiter pour appréhender le contenu d'une scène. Un nombre croissant de travaux est consacré à l'étude de l'attention visuelle, et en particulier à sa modélisation computationnelle. Dans cette thèse, nous présentons des études portant sur plusieurs aspects de cette recherche. Nos travaux peuvent être classés globalement en deux parties. La première concerne les questions liées à la vérité de terrain utilisée, la seconde est relative à la modélisation de l'attention visuelle dans des conditions de visualisation 3D. Dans la première partie, nous analysons la fiabilité de cartes de densité de fixation issues de différentes bases de données occulométriques. Ensuite, nous identifions quantitativement les similitudes et les différences entre carte de densité de fixation et carte d'importance visuelle, ces deux types de carte étant les vérités de terrain communément utilisées par les applications relatives à l'attention. Puis, pour faire face au manque de vérité de terrain exploitable pour la modélisation de l'attention visuelle 3D, nous procédons à une expérimentation oculométrique binoculaire qui aboutit à la création d'une nouvelle base de données avec des images stéréoscopiques 3D. Dans la seconde partie, nous commençons par examiner l'impact de la profondeur sur l'attention visuelle dans des conditions de visualisation 3D. Nous quantifions d'abord le " biais de profondeur " lié à la visualisation de contenus synthétiques 3D sur écran plat stéréoscopique. Ensuite, nous étendons notre étude avec l'usage d'images 3D au contenu naturel. Nous proposons un modèle de l'attention visuelle 3D basé saillance de profondeur, modèle qui repose sur le contraste de profondeur de la scène. Deux façons différentes d'exploiter l'information de profondeur par notre modèle sont comparées. Ensuite, nous étudions le biais central et les différences qui existent selon que les conditions de visualisation soient 2D ou 3D. Nous intégrons aussi le biais central à notre modèle de l'attention visuelle 3D. Enfin, considérant que l'attention visuelle combinée à une technique de floutage peut améliorer la qualité d'expérience de la TV-3D, nous étudions l'influence de flou sur la perception de la profondeur, et la relation du flou avec la disparité binoculaire

Remote Visual Observation of Real Places Through Virtual Reality Headsets

Virtual Reality has always represented a fascinating yet powerful opportunity that has attracted studies and technology developments, especially since the latest release on the market of powerful high-resolution and wide field-of-view VR headsets. While the great potential of such VR systems is common and accepted knowledge, issues remain related to how to design systems and setups capable of fully exploiting the latest hardware advances. The aim of the proposed research is to study and understand how to increase the perceived level of realism and sense of presence when remotely observing real places through VR headset displays. Hence, to produce a set of guidelines that give directions to system designers about how to optimize the display-camera setup to enhance performance, focusing on remote visual observation of real places. The outcome of this investigation represents unique knowledge that is believed to be very beneficial for better VR headset designs towards improved remote observation systems. To achieve the proposed goal, this thesis presents a thorough investigation of existing literature and previous researches, which is carried out systematically to identify the most important factors ruling realism, depth perception, comfort, and sense of presence in VR headset observation. Once identified, these factors are further discussed and assessed through a series of experiments and usability studies, based on a predefined set of research questions. More specifically, the role of familiarity with the observed place, the role of the environment characteristics shown to the viewer, and the role of the display used for the remote observation of the virtual environment are further investigated. To gain more insights, two usability studies are proposed with the aim of defining guidelines and best practices. The main outcomes from the two studies demonstrate that test users can experience an enhanced realistic observation when natural features, higher resolution displays, natural illumination, and high image contrast are used in Mobile VR. In terms of comfort, simple scene layouts and relaxing environments are considered ideal to reduce visual fatigue and eye strain. Furthermore, sense of presence increases when observed environments induce strong emotions, and depth perception improves in VR when several monocular cues such as lights and shadows are combined with binocular depth cues. Based on these results, this investigation then presents a focused evaluation on the outcomes and introduces an innovative eye-adapted High Dynamic Range (HDR) approach, which the author believes to be of great improvement in the context of remote observation when combined with eye-tracked VR headsets. Within this purpose, a third user study is proposed to compare static HDR and eye-adapted HDR observation in VR, to assess that the latter can improve realism, depth perception, sense of presence, and in certain cases even comfort. Results from this last study confirmed the author expectations, proving that eye-adapted HDR and eye tracking should be used to achieve best visual performances for remote observation in modern VR systems