The report should be minimum 2 pages and should describe the following points as clearly as possible: Give a short defition of your image effects or your theme. Related work Describe the methodology and image operations you will use to obtain desired image effects. Preliminary results and improvements you will do in final results.
- Earthquake Engineering: Application to Design!
- Environmental Kuznets Curve Hypothesis and Carbon Dioxide Emissions;
- Artistic Stylization of Images and Video.
- High Performance Computing in Science and Engineering 2000: Transactions of the High Performance Computing Center Stuttgart (HLRS) 2000.
Each student is expected to submit a project report prepared using the style files provided in the course web page. The report should be maximum 6 pages and should be structured as a research paper. It will be graded based on clarity of presentation and technical content. A typical organization of a report might follow: Title, Author s. There are many tools to edit images using different image operations so that result image can be more attractive than original one.
Recently in the social media Facebook,Instagram , there is a trend toward giving photos different image effects and sharing them to others. This upward trend was noticed by social media managers and they provided their users simple image editing tools which let them to perform different operations on the images to give various types of image effect so that their photos can look like sketch, drawing, painting etc.
Immersive media such as virtual and augmented reality pose some interesting new challenges for non-photorealistic animation: we must not only balance the screen-space rules of a 2D visual style against 3D motion coherence, but also account for stereo spatialization and interactive camera movement, at a rate of 90 frames per second.
We introduce two new real-time rendering techniques: MetaTexture, an example-based multiresolution texturing method that adheres to the movement of 3D geometry while maintaining a consistent level of screen-space detail, and Edge Breakup, a method for roughening edges by warping with structured noise. This paper introduces a video stylization method that increases the apparent rigidity of motion. Existing stylization methods often retain the 3D motion of the original video, making the result look like a 3D scene covered in paint rather than a 2D painting of a scene.
In contrast, traditional hand-drawn animations often exhibit simplified in-plane motion, such as in the case of cut-out animations where the animator moves pieces of paper from frame to frame. Inspired by this technique, we propose to modify a video such that its content undergoes 2D rigid transforms. To achieve this goal, our approach applies motion segmentation and optimization to best approximate the input optical flow with piecewise-rigid transforms, and re-renders the video such that its content follows the simplified motion.
The output of our method is a new video and its optical flow, which can be fed to any existing video stylization algorithm. We propose a fast feed-forward network for arbitrary style transfer, which can generate stylized image for previously unseen content and style image pairs. Besides the traditional content and style representation based on deep features and statistics for textures, we use adversarial networks to regularize the generation of stylized images. Our adversarial network learns the intrinsic property of image styles from large-scale multi-domain artistic images.
The adversarial training is challenging because both the input and output of our generator are diverse multi-domain images. We use a conditional generator that stylized content by shifting the statistics of deep features, and a conditional discriminator based on the coarse category of styles. Moreover, we propose a mask module to spatially decide the stylization level and stabilize adversarial training by avoiding mode collapse. As a side effect, our trained discriminator can be applied to rank and select representative stylized images.
We qualitatively and quantitatively evaluate the proposed method, and compare with recent style transfer methods. We present a learning-based style transfer algorithm for human portraits which significantly outperforms current state-of- the-art in computational overhead while still maintaining comparable visual quality.
Artistic Stylization of Images and Video
Since the resulting end-to-end network can be evaluated quickly on current consumer GPUs, our solution enables first real-time high-quality style transfer to facial videos that runs at interactive frame rates. We demonstrate the practical utility of our approach on a variety of different styles and target subjects. We present a new approach to example-based style transfer which combines neural methods with patch-based synthesis to achieve compelling stylization quality even for high-resolution imagery. We take advantage of neural techniques to provide adequate stylization at the global level and use their output as a prior for subsequent patch-based synthesis at the detail level.
Thanks to this combination, our method keeps the high frequencies of the original artistic media better, thereby dramatically increases the fidelity of the resulting stylized imagery. We also show how to stylize extremely large images e. We present a variant of the skeletal strokes algorithm aimed at mimicking the appearance of hand made graffiti art.
It includes a unique fold-culling process that stylizes folds rather than eliminating them. We demonstrate how the stroke structure can be exploited to generate non-global layering and self-overlap effects like the ones that are typically seen in graffiti art and other related art forms like traditional calligraphy. The method produces vector output with no artificial artwork splits, patches or masks to render the non-global layering; each path of the vector output is part of the desired outline.
The method lets users interactively generate a wide variety of stylised outputs. This paper investigates trajectory generation alternatives for creating single-stroke light paintings with a small quadrotor robot. We propose to reduce the cost of a minimum snap piecewise polynomial quadrotor trajectory passing through a set of waypoints by displacing those waypoints towards or away from the camera while preserving their projected position.
It is in regions of high curvature, where waypoints are close together, that we make modifications to reduce snap, and we evaluate two different strategies: one that uses a full range of depths to increase the distance between close waypoints, and another that tries to keep the final set of waypoints as close to the original plane as possible. Using a variety of one-stroke animal illustrations as targets, we evaluate and compare the cost of different optimized trajectories, and discuss the qualitative and quantitative quality of flights captured in long exposure photographs.
We show that our approach outperforms recent work in terms of how accurately the image gamut is reproduced, and we present an approximation algorithm that is an order of magnitude faster with an acceptable loss in quality. DiVerdi, M. Tan, S. Lu, Y. Hertzmann, F. Saeedi, M. Hoffman, S. Ghandeharioun, M.
Jin, G. Mysore, S. Lu, A. Shugrina, J. Lu, S. Best Paper Honorable Mention. Jin, A. Finkelstein, S. Lu, G. Chen, O. Fried, Y. Liu, S.
BBM Fundamentals of Image Processing Class Projects
Fried, S. Halber, E. Sizikova, A. Benjamin, S. DiVerdi, W. Chen, C.
ordiadoipiera.ga Barnes, A. Best Paper Award. Chaudhury, S. DiVerdi, S. Lu, C. Barnes, S. Kim, W. Li, N. Mitra, S. Chaudhuri, S. DiVerdi, T. Krishnaswamy, R. Mech, D. Rosin and J. Collomosse Eds. Lu, F. Yu, A.