15 research outputs found

    Manipulating Attributes of Natural Scenes via Hallucination

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    In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network which can hallucinate images of a scene as if they were taken at a different season (e.g. during winter), weather condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic details intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches. Moreover, it allows to simultaneously manipulate a given scene according to a diverse set of transient attributes within a single model, eliminating the need of training multiple networks per each translation task. Our comprehensive set of qualitative and quantitative results demonstrate the effectiveness of our approach against the competing methods.Comment: Accepted for publication in ACM Transactions on Graphic

    Disentangling Content and Motion for Text-Based Neural Video Manipulation

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    Giving machines the ability to imagine possible new objects or scenes from linguistic descriptions and produce their realistic renderings is arguably one of the most challenging problems in computer vision. Recent advances in deep generative models have led to new approaches that give promising results towards this goal. In this paper, we introduce a new method called DiCoMoGAN for manipulating videos with natural language, aiming to perform local and semantic edits on a video clip to alter the appearances of an object of interest. Our GAN architecture allows for better utilization of multiple observations by disentangling content and motion to enable controllable semantic edits. To this end, we introduce two tightly coupled networks: (i) a representation network for constructing a concise understanding of motion dynamics and temporally invariant content, and (ii) a translation network that exploits the extracted latent content representation to actuate the manipulation according to the target description. Our qualitative and quantitative evaluations demonstrate that DiCoMoGAN significantly outperforms existing frame-based methods, producing temporally coherent and semantically more meaningful results

    Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020

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    We show the distribution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three genomic nomenclature systems to all sequence data from the World Health Organization European Region available until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation, compare the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2

    Image Smoothing by Using First and Second Order Region Statistics

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    Recent years have witnessed the emergence of new image smoothing techniques which have provided new insights and raised new questions about the nature of this well-studied prob- lem. Specifically, these models separate a given image into its structure and texture layers by utilizing non-gradient based definitions for edges or special measures that distinguish edges from oscillations. In this thesis, we propose an alternative yet simple image smoothing approach which depends on 1st and 2nd order feature statistics of image regions. The use of these region statistics as a patch descriptor allows us to implicitly capture local structure and texture information and makes our approach particularly effective for structure extraction from texture. Our experimental results have shown that the proposed approach leads to better image decomposition as compared to the state-of-the-art methods and preserves prominent edges and shading well.Son yıllar yeni anlayışların ve fikirlerin sağlandığı yeni görüntü düzleştirme tekniklerinin ortaya çıkmasına tanıklık etti ve bu iyi çalışılmış ̧ problemin doğasıyla ilgili yeni sorular sorulmaya başlandı. Son yıllardaki çalışmalar özellikle kenarlar için ve kenarları salınımlı yapılardan ayıran özel ölçüler için parlaklık değeri degişikliklerine bağlı olmayan(gradyan olmayan) tanımlamalardan faydalanarak verilen bir resmi yapı ve doku katmanlarına ayırırlar. Bu tezde görüntü bölgelerinin birinci ve ikinci dereceden istatistiklerine bağlı alternatif ve basit bir görüntü düzleştirme yaklaşımı öneriyoruz. Bölge istatistiklerinin bir resim parçası tanımlayıcısı olarak kullanımı yerel yapıyı ve doku bilgisini dolaylı olarak elde edebilmemizi sağlar ve yapı bilgisinin doku bilgisinden çıkarılması için yaklaşımımızı oldukça etkili yapar. Deney sonuçlarımız, önerilen yaklaşmın en son yapılan çalısmalarla karşılaştırıldığında daha iyi görüntü ayrıştırımı yaptığını gösterdi

    Learning Based Image and Video Editing

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    Image and video editing encompasses the wide range of image operations to give desired visual effects to a given image or video either for improving various visual properties such as color, contrast, luminance or better emphasizing some aspects of scenes such as an objects, background, activity, attribute, emotion etc. Popular graphical tools(e.g. Adobe Photoshop, GIMP) that provide rich image operations can be utilized to achieve the desired visual effects, however users need to be familiar with image processing methods and have skills to overcome challenging low-level operations on images and videos. Therefore, easy and efficient image and video editing methods are needed for casual users to manipulate visual contents with high-level interactions such as natural languages. On the other hand, it is expected that the processes will be imperceptibly flawless on the image, in other words, photorealism should not be degraded. Recently, data-driven or learning based new works which try to meet those expectations have been proposed for various image and video editing problems. In this thesis, we propose learning-based methods for a number of image and video editing problems which are alpha matting, visual attribute manipulation and language-based video manipulation following recent trends and developments. Our methods produce competitive or better results against state-of-the-art methods on benchmark datasets quantitatively and qualitatively while providing simple high-level interactions such as natural language and visual attributes. Besides, our visual attribute manipulation method is the first high-level photo editing approach to enable continuous control on transient attributes of natural landscapes in the literature. For alpha matting, we present a new sampling-based alpha matting approach for the accurate estimation of foreground and background layers of an image. Previous sampling-based methods typically rely on certain heuristics in collecting representative samples from known regions, and thus their performance deteriorates if the underlying assumptions are not satisfied. To alleviate this, we take an entirely new approach and formulate sampling as a sparse subset selection problem where we propose to pick a small set of candidate samples that best explains the unknown pixels. Moreover, we describe a new dissimilarity measure for comparing two samples which is based on KL-divergence between the distributions of features extracted in the vicinity of the samples. The proposed framework is general and could be easily extended to video matting by additionally taking temporal information into account in the sampling process. Evaluation on standard benchmark datasets for image and video matting demonstrates that our approach provides more competitive results compared to the state-of-the-art methods. For visual attribute manipulation, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network which can hallucinate images of a scene as if they were taken at a different season (e.g. during winter), weather condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic details intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches. Moreover, it allows to simultaneously manipulate a given scene according to a diverse set of transient attributes within a single model, eliminating the need of training multiple networks per each translation task. Our comprehensive set of qualitative and quantitative results demonstrate the effectiveness of our approach against the competing methods. In our last work, we introduce a new task of manipulating person videos with natural language, which aims to perform local and semantic edits on a video clip of an individual to automatically change their outfit based on a description of target look. To this end, we first collect a new video dataset containing full-body images of different persons wearing different types of clothes and their textual descriptions. The nature of our problem allows for better utilization of multi-view information and we exploit this property and design a new language-guided video editing model. Our architecture is composed of two subnetworks trained simultaneously: a network for constructing a concise representation of the person from multiple observations (representation network), and another network that benefits from the extracted internal representation for performing the manipulation according to the target description (translation network). Our qualitative and quantitative evaluations demonstrate that our proposed approach significantly outperforms existing frame-wise methods, producing temporally coherent and semantically more meaningful results.Görüntü ve video düzenleme renk, kontrast, parlaklık gibi çeşitli görsel özellikleri iyileştirmek için veya bazı sahneleri, nesneleri, nitelikleri, duyguları vb. daha iyi vurgulamak için görsel içeriği değiştirmemize yarayan çok çeşitli işlemleri kapsar. Verilen bir görüntüye istenilen görsel etkiyi vermek için zengin görüntü işleme olanağı sunan Fotoşop gibi grafik araçlarından faydalanılabilir, ancak bunun için kullanıcıların görüntü işleme yöntemlerini bilmesi ve görüntüler üzerindeki zorlu düşük seviyeli işlemlerin üstesinden gelebilmesi için bu alanda yetenekli olmaları gerekir. Bu yüzden, sıradan kullanıcılar için görsel içerikleri doğal bir yol olan doğal dil gibi yüksek düzeyli etkileşimlerle değiştirebilen basit ve etkili görüntü ve video düzenleme yöntemlerine ihtiyaç duyulmaktadır. Öte yandan yapılan işlemlerin görüntü üzerinde farkedilmeyecek derece kusursuz olması başka bir ifadeyle fotoğraf gerçekçiliğinin sağlanması beklenmektedir. Son yıllarda çok çeşitli görüntü ve video düzenleme problemleri için bu beklentileri karşılamaya çalışan veriye dayalı veya öğrenme temelli yeni çalışmalar önerilmektedir. Biz de bu tezde, alfa matlama, görsel nitelik düzenleme, dil tabanlı video düzenleme gibi bir dizi görüntü ve video düzenleme problemi için öğrenme temelli yeni yöntemler öneriyoruz. Yöntemlerimiz doğal dil ve nitelik tanımı gibi kolay yüksek seviyeli etkileşim sağlarken, en son yöntemlerle yarışır veya daha iyi sonuçlar üretmektedir. Ayrıca görsel nitelik düzenleme yöntemimiz, doğal manzaraların geçici nitelikleri üzerinde sürekli kontrol sağlamaya imkan veren literatürdeki ilk yüksek düzeyli fotoğraf düzenleme yaklaşımıdır. Alfa matlama için, bir görüntünün önalan ve artalan katmanlarının yanlışsız tahmini için yeni bir örnekleme tabanlı alfa matlama yaklaşımı sunuyoruz. Önceki örnekleme tabanlı yöntemler bilinen bölgeden temsili örnekler toplamak için genellikle belli sezgisellere dayanmaktadır, ve bu nedenle eğer dayanılan varsayımlar gerçekleşmezse başarım kötü etkilenmektedir. Bunun üstesinden gelebilmek için, tümüyle yeni bir yaklaşım benimsiyoruz ve aday kümesinden bilinmeyen pikselleri en iyi ifade eden küçük aday örnekler kümesini seçmek için önerdiğimiz seyrek altküme seçim problemi olarak olarak tanımlıyoruz. Ayrıca, örneklerin etrafından çıkarılan öznitelik dağılımları arasındaki KL-diverjans'a dayalı iki örneği karşılaştırmak için yeni bir benzemezlik ölçüsü tanımlıyoruz. Önerilen çatı yöntem genel bir yaklaşımdır ve örnekleme aşamasında zamansal bilgi de dikkate alınarak video matlamaya kolayca genişletilebilmektedir. Standart karşılaştırma veri kümeleri üzerinde yapılan değerlendirmeler yaklaşımımızın en son yöntemlerle kıyaslandığında daha yarışır sonuçlar sağladığını göstermektedir. Görsel nitelik düzenlemesi için, kullanıcılara doğal bir sahnenin yüksek düzeyli niteliklerini doğrudan düzenlemeye imkan veren iki aşamalı bir yöntem çatısı araştırıyoruz. Yaklaşımımızın anahtarı görüntüler sanki başka bir mevsimde(örneğin kış sırasında), hava durumunda(örneğin bulutlu bir günde) veya günün zamanında(örneğin gün batımında) çekilmiş gibi bir sahnenin görüntülerini sanrılayabilen derin üretici ağdır. Sahne verilen niteliğe göre sanrılandıktan sonra, ilgili görünüş sonrasında anlamsal ayrıntıları koruyarak ve foto-gerçekçi düzenleme sonucu vererek girdi görüntüsüne taşınır. Önerilen yöntem çatısı sahnenin farklı nitelikteki görünümünü sanrılayabildiği için, birçok görünüş ve stil taşıma yaklaşımlarında kullanıldığı gibi referans bir görüntü gerektirmez. Üstelik, yöntemimiz her çivirim görevi için ayrı bir ağ eğitme ihtiyacını ortadan kaldırarak, tek bir model içinde farklı geçici niteliklere göre eş zamanlı olarak verilen sahneyi düzenlemeye olanak sağlar. Kapsamlı nitel ve nicel sonuçlarımız, rakip yöntemlere karşı yaklaşımımızın etkililiğini göstermektedir. Son çalışmamızda, hedef görünüş tanımına dayalı otomatik kıyafet değişimi yapmak için birisinin video klibi üzerinde otomatik olarak yerel ve anlamsal düzenleme yapmayı amaçlayan yeni bir doğal dille insan videoları düzenleme görevi tanıtıyoruz. Bunun için, öncelikle farklı türde kıyafetler giyen farklı insanların tüm vücut görüntülerini ve bunların metinsen tanımlamalarını içeren yeni bir video veri kümesi topluyoruz.Problemin doğası çoklu-görüntülü(multi-view) bilgiden daha iyi yararlanmak için olanak sağladığından, bu özelliği kullanıyoruz ve yeni bir doğal dille yönlendirilen video düzenleme modeli tasarlıyoruz. Mimarimiz eş zamanlı eğitilen iki alt ağdan oluşmaktadır: çoklu gözlemden insanın özet temsilini oluşturmak için bir ağ(temsil ağı) ve hedef tanıma göre düzenleme gerçekleştirmek için çıkarılan iç temsilden başka bir ağ(çevirim ağı). Nitel ve nicel değerlendirmelerimiz önerdiğimiz yaklaşımın daha anlamsal ve zamansal olarak uyumlu sonuçlar üreterek tek görüntü tabanlı yöntemleri önemli derecede geçtiğini göstermektedir

    Formation of Non-graphitizing Carbon Fibers Prepared from Poly(p-phenylene terephthalamide) Precursor Fibers

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    The effect of carbonization temperature on the structure and properties of poly(p-phenylene terephthalamide)-based carbon fibers from commercially available Twaror (R) (PPTA) presursor is reported. Turbostratic PPTA-based carbon fibers were produced using a single step procedure in an inert atmosphere at temperatures ranging from 600 to 1100 degrees C. In the present study, fiber diameter, mass yield, density, elemental analysis, X-ray diffraction, Raman spectroscopy, tensile testing and electrical conductivity measurements were performed and evaluated to follow and monitor the properties and structural transformations of carbon fibers with rising temperature. The increase of heat-treatment temperature to 1100 degrees C decreased the interlayer d-spacing (d(002)) and increased the in-plane size (L-a) and thickness (L-c) of the graphene layers. The intensity ratios of D to G bands in the Raman spectra increased with rising temperature, suggesting, in agreement with the X-ray diffraction measurements, that the in-plane size (L-a) of the graphene planes increased with temperature. The density, carbon content, C/H ratio, apparent crystallite size (L-a and L-c), electrical conductivity and tensile properties of the resultant carbon fibers were enhanced with rising temperature. It has been shown that the gage length of the carbon fibers tested has a significant effect on the tensile strength obtained. After taking into account the effects of gage length and porosity dependence, the carbonization of PPTA precursor fibers prepared at 1100 degrees C gave a tensile strength of 191 MPa and a tensile modulus of 83 GPa, respectively.Carbon fibers with diameters ranging from 8.1 and 12.7 mm were produced using a commercially available meta-aramid precursor after oxidation and carbonization steps. The carbonization process was performed using a single-step procedure between 500 and 1100 &deg;C. The effect of&nbsp; temperature on the structure and properties of&nbsp; carbon fibers was investigated in detail. The process of&nbsp; carbonization was examined using several characterization techniques including fiber diameter, mass yield, density, elemental analysis, mechanical property testing and electrical property measurements. Structural transformations were followed and monitored using X-ray diffraction, IR and Raman spectroscopy techniques. The results suggested that the mass yield&nbsp; reached a value of 40.4% at a heating temperature of 1100 &deg;C. Density, carbon content, mechanical properties and electrical conductivity values increased and hydrogen and nitrogen contents decreased with an increase in temperature. Analysis of the X-ray diffraction traces of carbon fibers suggested broadening of the (002) and (100) diffraction planes which was attributed to the formation of an amorphous carbon structure. The IR spectra showed, at temperatures of 500 &deg;C and above, initial weakening and eventual disappearance of&nbsp; the major amide bands (amide I, II, III and IV) due to the loss of hydrogen bonds between the polymer chains indicating the partial removal of nitrogen, hydrogen and oxygen atoms during the carbonization reactions.&nbsp; The analysis of Raman spectra demonstrated that the positions and the peak widths of the G- and D-bands showed great dependence on treatment temperature. The mechanical properties of the carbon fibers showed strong dependence on heat-treatment temperature, porosity and gage length. Temperature and gage length showed a significant effect on the tensile strength values obtained after each treatment temperature. A marked increase was observed in tensile strength values after extrapolation to 1 mm, which increased from 186 to 589 MPa with carbonization up to 1100 &deg;C.&nbsp; Tensile modulus values were affected by both temperature and porosity and reached a value of&nbsp; 81 GPa at 1100 &deg;C.&nbsp; Porosity correction caused an enhancement in tensile modulus values between 21 and 33.5%.&nbsp; SEM images of carbon fibers demonstrated the presence of structural imperfections confirming the results obtained from the porosity measurements.</p

    The effect of carbonization temperature on the structure and properties of carbon fibers prepared from poly(m-phenylene isophthalamide) precursor

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    Carbon fibers with diameters ranging from 8.1 and 12.7 A mu m were produced using a commercially available meta-aramid precursor after oxidation and carbonization steps. The carbonization process was performed using a single-step procedure between 500 and 1100 A degrees C. The effect of temperature on the structure and properties of carbon fibers was investigated in detail. The process of carbonization was examined using several characterization techniques including fiber diameter, mass yield, density, elemental analysis, mechanical property testing and electrical property measurements. Structural transformations were followed and monitored using X-ray diffraction, IR and Raman spectroscopy techniques. The results suggested that the mass yield reached a value of 40.4 % at a heating temperature of 1100 A degrees C. Density, carbon content, mechanical properties and electrical conductivity values increased and hydrogen and nitrogen contents decreased with an increase in temperature. Analysis of the X-ray diffraction traces of carbon fibers suggested broadening of the (002) and (100) diffraction planes which was attributed to the formation of an amorphous carbon structure. The IR spectra showed, at temperatures of 500 A degrees C and above, initial weakening and eventual disappearance of the major amide bands (amide I, II, III and IV) due to the loss of hydrogen bonds between the polymer chains indicating the partial removal of nitrogen, hydrogen and oxygen atoms during the carbonization reactions. The analysis of Raman spectra demonstrated that the positions and the peak widths of the G- and D-bands showed great dependence on treatment temperature. The mechanical properties of the carbon fibers showed strong dependence on heat-treatment temperature, porosity and gage length. Temperature and gage length showed a significant effect on the tensile strength values obtained after each treatment temperature. A marked increase was observed in tensile strength values after extrapolation to 1 mm, which increased from 186 to 589 MPa with carbonization up to 1100 A degrees C. Tensile modulus values were affected by both temperature and porosity and reached a value of 81 GPa at 1100 A degrees C. Porosity correction caused an enhancement in tensile modulus values between 21 and 33.5 %. SEM images of carbon fibers demonstrated the presence of structural imperfections confirming the results obtained from the porosity measurements.Carbon fibers with diameters ranging from 8.1 and 12.7 &mu;m were produced using a commercially available metaaramid&nbsp;precursor after oxidation and carbonization steps. The carbonization process was performed using a single-step&nbsp;procedure between 500 and 1100C. The effect of temperature on the structure and properties of carbon fibers was&nbsp;investigated in detail. The process of carbonization was examined using several characterization techniques including fiber&nbsp;diameter, mass yield, density, elemental analysis, mechanical property testing and electrical property measurements.Structural transformations were followed and monitored using X-ray diffraction, IR and Raman spectroscopy techniques. The&nbsp;results suggested that the mass yield reached a value of 40.4 % at a heating temperature of 1100C. Density, carbon content,mechanical properties and electrical conductivity values increased and hydrogen and nitrogen contents decreased with an increase in temperature. Analysis of the X-ray diffraction traces of carbon fibers suggested broadening of the (002) and (100)diffraction planes which was attributed to the formation of an amorphous carbon structure. The IR spectra showed, at&nbsp;temperatures of 500C and above, initial weakening and eventual disappearance of the major amide bands (amide I, II, IIIand IV) due to the loss of hydrogen bonds between the polymer chains indicating the partial removal of nitrogen, hydrogen&nbsp;and oxygen atoms during the carbonization reactions. The analysis of Raman spectra demonstrated that the positions and thepeak widths of the G- and D-bands showed great dependence on treatment temperature. The mechanical properties of the&nbsp;carbon fibers showed strong dependence on heat-treatment temperature, porosity and gage length. Temperature and gage&nbsp;length showed a significant effect on the tensile strength values obtained after each treatment temperature. A marked increase&nbsp;was observed in tensile strength values after extrapolation to 1 mm, which increased from 186 to 589 MPa with carbonizationup to 1100C. Tensile modulus values were affected by both temperature and porosity and reached a value of 81 GPa at&nbsp;1100C. Porosity correction caused an enhancement in tensile modulus values between 21 and 33.5 %. SEM images of&nbsp;carbon fibers demonstrated the presence of structural imperfections confirming the results obtained from the porosity&nbsp;measurements.</p

    Formulas for the Exponential of a Semi Skew-Symmetric Matrix of Order 4

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    In this paper the formula of the exponential matrix e A when A is a semi skew-symmetric real matrix of order 4 is derived. The formula is a generalization of the Rodrigues formula for skew-symmetric matrices of order 3 in Minkowski 3-space
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