Distribusi Tipe Iklim Oldeman dan Proyeksinya Berdasarkan RCP 4.5 di Kabupaten Aceh Utara

Pergeseran Pergeseran dan perubahan pola curah hujan akibat perubahan iklim juga berpengaruh pada pergerseran tipe iklim Oldeman di Kabupaten Aceh Utara. Gambaran tipe iklim Oldeman saat ini dan di masa depan sangat diperlukan sebagai langkah adaptasi masyarakat di bidang pertanian dalam menghadapi tantangan perubahan iklim di masa depan. Penelitian ini betujuan untuk mengetahui distribusi tipe iklim Oldeman saat ini dan proyeksinya untuk 20 tahun ke depan. Penelitian ini menggunakan data curah hujan terbaru (2008-2017) dari 16 titik pengamatan dan data model MIROC5 dari skenario RCP 4.5 di Kabupaten Aceh Utara. Metode yang digunakan adalah overlay dari interpolasi bulan basah dan bulan kering dengan aplikasi Sistem Informasi Geografi (SIG). Hasil penelitian menunjukkan bahwa hampir seluruh kecamatan di wilayah Kabupaten Aceh Utara di dominasi tipe iklim Oldeman kering untuk saat ini (99.19%) dan kondisi ini diprediksi juga bertahan hingga 20 tahun ke depan (95.26 – 97.11%). Perubahan dan meluasnya iklim ke tipe lebih basah terjadi di daerah ketinggian seperti bagian selatan Kecamatan Sawang (2.89 – 4.74%)

Non-photorealistic rendering: a critical examination and proposed system.

In the first part of the program the emergent field of Non-Photorealistic Rendering is explored from a cultural perspective. This is to establish a clear understanding of what Non-Photorealistic Rendering (NPR) ought to be in its mature form in order to provide goals and an overall infrastructure for future development. This thesis claims that unless we understand and clarify NPR's relationship with other media (photography, photorealistic computer graphics and traditional media) we will continue to manufacture "new solutions" to computer based imaging which are confused and naive in their goals. Such solutions will be rejected by the art and design community, generally condemned as novelties of little cultural worth ( i.e. they will not sell). This is achieved by critically reviewing published systems that are naively described as Non-photorealistic or "painterly" systems. Current practices and techniques are criticised in terms of their low ability to articulate meaning in images; solutions to this problem are given. A further argument claims that NPR, while being similar to traditional "natural media" techniques in certain aspects, is fundamentally different in other ways. This similarity has lead NPR to be sometimes proposed as "painting simulation" — something it can never be. Methods for avoiding this position are proposed. The similarities and differences to painting and drawing are presented and NPR's relationship to its other counterpart, Photorealistic Rendering (PR), is then delineated. It is shown that NPR is paradigmatically different to other forms of representation — i.e. it is not an "effect", but rather something basically different. The benefits of NPR in its mature form are discussed in the context of Architectural Representation and Design in general. This is done in conjunction with consultations with designers and architects. From this consultation a "wish-list" of capabilities is compiled by way of a requirements capture for a proposed system. A series of computer-based experiments resulting in the systems "Expressive Marks" and 'Magic Painter" are carried out; these practical experiments add further understanding to the problems of NPR. The exploration concludes with a prototype system "Piranesi" which is submitted as a good overall solution to the problem of NPR. In support of this written thesis are : - • The Expressive Marks system • Magic Painter system • The Piranesi system (which includes the EPixel and Sketcher systems) • A large portfolio of images generated throughout the exploration

Higher level techniques for the artistic rendering of images and video

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Colour videos with depth : acquisition, processing and evaluation

The human visual system lets us perceive the world around us in three dimensions by integrating evidence from depth cues into a coherent visual model of the world. The equivalent in computer vision and computer graphics are geometric models, which provide a wealth of information about represented objects, such as depth and surface normals. Videos do not contain this information, but only provide per-pixel colour information. In this dissertation, I hence investigate a combination of videos and geometric models: videos with per-pixel depth (also known as RGBZ videos). I consider the full life cycle of these videos: from their acquisition, via filtering and processing, to stereoscopic display. I propose two approaches to capture videos with depth. The first is a spatiotemporal stereo matching approach based on the dual-cross-bilateral grid – a novel real-time technique derived by accelerating a reformulation of an existing stereo matching approach. This is the basis for an extension which incorporates temporal evidence in real time, resulting in increased temporal coherence of disparity maps – particularly in the presence of image noise. The second acquisition approach is a sensor fusion system which combines data from a noisy, low-resolution time-of-flight camera and a high-resolution colour video camera into a coherent, noise-free video with depth. The system consists of a three-step pipeline that aligns the video streams, efficiently removes and fills invalid and noisy geometry, and finally uses a spatiotemporal filter to increase the spatial resolution of the depth data and strongly reduce depth measurement noise. I show that these videos with depth empower a range of video processing effects that are not achievable using colour video alone. These effects critically rely on the geometric information, like a proposed video relighting technique which requires high-quality surface normals to produce plausible results. In addition, I demonstrate enhanced non-photorealistic rendering techniques and the ability to synthesise stereoscopic videos, which allows these effects to be applied stereoscopically. These stereoscopic renderings inspired me to study stereoscopic viewing discomfort. The result of this is a surprisingly simple computational model that predicts the visual comfort of stereoscopic images. I validated this model using a perceptual study, which showed that it correlates strongly with human comfort ratings. This makes it ideal for automatic comfort assessment, without the need for costly and lengthy perceptual studies

A Temporally Coherent Neural Algorithm for Artistic Style Transfer

Within the fields of visual effects and animation, humans have historically spent countless painstaking hours mastering the skill of drawing frame-by-frame animations. One such animation technique that has been widely used in the animation and visual effects industry is called rotoscoping and has allowed uniquely stylized animations to capture the motion of real life action sequences, however it is a very complex and time consuming process. Automating this arduous technique would free animators from performing frame by frame stylization and allow them to concentrate on their own artistic contributions. This thesis introduces a new artificial system based on an existing neural style transfer method which creates artistically stylized animations that simultaneously reproduce both the motion of the original videos that they are derived from and the unique style of a given artistic work. This system utilizes a convolutional neural network framework to extract a hierarchy of image features used for generating images that appear visually similar to a given artistic style while at the same time faithfully preserving temporal content. The use of optical flow allows the combination of style and content to be integrated directly with the apparent motion over frames of a video to produce smooth and visually appealing transitions. The implementation described in this thesis demonstrates how biologically-inspired systems such as convolutional neural networks are rapidly approaching human-level behavior in tasks that were once thought impossible for computers. Such a complex task elucidates the current and future technical and artistic capabilities of such biologically-inspired neural systems as their horizons expand exponentially. Further, this research provides unique insights into the way that humans perceive and utilize temporal information in everyday tasks. A secondary implementation that is explored in this thesis seeks to improve existing convolutional neural networks using a biological approach to the way these models adapt to their inputs. This implementation shows how these pattern recognition systems can be greatly improved by integrating recent neuroscience research into already biologically inspired systems. Such a novel hybrid activation function model replicates recent findings in the field of neuroscience and shows significant advantages over existing static activation functions

Generating Calligraphic Trajectories with Model Predictive Control

We describe a methodology for the interactive definition of curves and motion paths using a stochastic formulation of optimal control. We demonstrate how the same optimization framework can be used in different ways to generate curves and traces that are geometrically and dynamically similar to the ones that can be seen in art forms such as calligraphy or graffiti art. The method provides a probabilistic description of trajectories that can be edited similarly to the control polygon typically used in the popular spline based methods. Furthermore, it also encapsulates movement kinematics, deformations and variability. The user is then provided with a simple interactive interface that can generate multiple movements and traces at once, by visually defining a distribution of trajectories rather than a single one. The input to our method is a sparse sequence of targets defined as multivariate Gaussians. The output is a dynamical system generating curves that are natural looking and reflect the kinematics of a movement, similar to that produced by human drawing or writing

Algorithmen zur Korrespondenzschätzung und Bildinterpolation für die photorealistische Bildsynthese

Free-viewpoint video is a new form of visual medium that has received considerable attention in the last 10 years. Most systems reconstruct the geometry of the scene, thus restricting themselves to synchronized multi-view footage and Lambertian scenes. In this thesis we follow a different approach and describe contributions to a purely image-based end-to-end system operating on sparse, unsynchronized multi-view footage. In particular, we focus on dense correspondence estimation and synthesis of in-between views. In contrast to previous approaches, our correspondence estimation is specifically tailored to the needs of image interpolation; our multi-image interpolation technique advances the state-of-the-art by disposing the conventional blending step. Both algorithms are put to work in an image-based free-viewpoint video system and we demonstrate their applicability to space-time visual effects production as well as to stereoscopic content creation.3D-Video mit Blickpunktnavigation ist eine neues digitales Medium welchem die Forschung in den letzten 10 Jahren viel Aufmerksamkeit gewidmet hat. Die meisten Verfahren rekonstruieren dabei die Szenengeometrie und schränken sich somit auf Lambertsche Szenen und synchron aufgenommene Eingabedaten ein. In dieser Dissertation beschreiben wir Beiträge zu einem rein bild-basierten System welches auf unsynchronisierten Eingabevideos arbeitet. Unser Fokus liegt dabei auf der Schätzung dichter Korrespondenzkarten und auf der Synthese von Zwischenbildern. Im Gegensatz zu bisherigen Verfahren ist unser Ansatz der Korrespondenzschätzung auf die Bedürfnisse der Bilderinterpolation ausgerichtet; unsere Zwischenbildsynthese verzichtet auf das Überblenden der Eingabebilder zu Gunsten der Lösung eines Labelingproblems. Das resultierende System eignet sich sowohl zur Produktion räumlich-zeitlicher Spezialeffekte als auch zur Erzeugung stereoskopischer Videosequenzen

Toward a Perceptually-relevant Theory of Appearance

Two approaches are commonly employed in Computer Graphics to design and adjust the appearance of objects in a scene. A full 3D environment may be created, through geometrical, material and lighting modeling, then rendered using a simulation of light transport; appearance is then controlled in ways similar to photography. A radically different approach consists in providing 2D digital drawing tools to an artist, whom with enough talent and time will be able to create images of objects having the desired appearance; this is obviously strongly similar to what traditional artists do, with the computer being a mere modern drawing tool.In this document, I present research projects that have investigated a third approach, whereby pictorial elements of appearance are explicitly manipulated by an artist. On the one side, such an alternative approach offers a direct control over appearance, with novel applications in vector drawing, scientific illustration, special effects and video games. On the other side, it provides an modern method for putting our current knowledge of the perception of appearance to the test, as well as to suggest new models for human vision along the way