Creating Drawings from Digital Images

-
Creating Drawings from Digital Images
Description

 There are many algorithms out there for non-photorealistic rendering of images to look like  drawings. Jin et al and Li et al create drawings from images by creating directional lines that  match the intensity of the image. However, the resulting images don’t stylistically look like an  artist drew them. Efros et al found that image quilting can be used to transfer a specific drawing  style to an image such that it looks like it has actually been drawn by an artist. I will attempt to implement his algorithm to create drawings from digital images.

 Plan

 I believe the following steps will be necessary to creating drawings from digital images that resemble the drawings of famous artists.
1. Split the source texture (for example Enrico Donati’s drawing) into overlapping blocks of size B.
2. Split the source image into overlapping blocks of size B.
3. Choose a block from the source texture such that it has similar intensity as of the block in  the source image and such that the overlap with other blocks meets some sort of  similarity threshold.
4. Find the minimum cost boundary between the new block and the previously pasted 
blocks and then paste the new block into the image respecting the boundary.
5. Update existing algorithm until it produces desired results. On suggested update is to 

repeat the algorithm multiple times with decreasing block sizes

 Alexei A. Efros and William T. Freeman. 2001. Image quilting for texture synthesis and transfer. 
InProceedings of the 28th annual conference on Computer graphics and interactive 
techniques(SIGGRAPH '01). ACM, New York, NY, USA, 341-346.
Nan Li and Zhiong Huang. 2003. A feature-based pencil drawing method. In Proceedings of the 
1st international conference on Computer graphics and interactive techniques in Australasia and 
South East Asia (GRAPHITE '03). ACM, New York, NY, USA, 135-ff.
Wang Jin, Bao Hujun, Zhou Weihua, Peng Qunsheng, and Xu Yingqing. 2002. Automatic 
image-based pencil sketch rendering. J. Comput. Sci. Technol. 17, 3 (May 2002), 347-355.

 DOWNLOAD SOURCE CODE CLICK HERE

Comments

Post a Comment

Popular posts from this blog

Light Field Images for Background Removal

-
Image
Light Field Images for Background Removal OUTPUT OVERVIEW Standard edge detection or foreground/background separation techniques, such as Otzu’smethod, require color or intensity differences between the background and regions that need tobe separated. For example, green screens are routinely set up as the background in a scene so thatthere is a clear difference in color between the background and foreground.Light filed images, captured in 4D and passively containing depth information for thescene can be used to approximate this effect. Depth estimation in the scene alone could provide a metric to separate the foreground and background, but more sophisticated methods are available.Considering the edge detection from a single image from a single viewpoint and analyzing the depths from the light field around the edges in that image, occluded edges and intensity or color edges can be distinguished. Along with the rest of the depth information this can allow the foreground and b...
Read more

Using Image Processing to Identify and Score Darts thrown into a Dartboard

-
Image
✔️Using Image Processing to Identify and Score Darts thrown into a Dartboard ✔️Introduction The game of darts is a throwing sport in which participants toss projectiles into a circular target  attached to a vertical surface. The target is divided into many regions which correspond to different point and multiplier values. Darts is commonly played across North America and Europe and is a popular past time for many so an application that allows players to more conveniently keep score would be widely  beneficial. “Darts” is a general term for a targeting game following this basic premise and many game variations exist within this archetype; all utilizing the standard dart projectiles with a regulation dart board. Each game variant may have different objectives for which players to aim but identifying the region in  which the dart has hit in the dartboard is necessary for proper scoring. This project proposes the use of image processing to identify thrown da...
Read more

Face Detection, Extraction, and Swapping on Mobile Devices

-
Image
Face Detection, Extraction, and Swapping on Mobile Devices OUTPUT: DOCUMENTATION: We will use the Viola-Jones face detector in OpenCV as a starting point. Once we have found two faces we will apply some post processing to make sure that we have the entire face without any holes. Once we have a binary mask of the faces’ fully connected components, we plan to use cvBlobsLib (similar to regionprops function in Matlab) to do face region labelling and extraction. Choosing the faces to swap will either be random or done through a simple UI. Initially we will translate the faces to match the new face’s centroid with the centroid of the old face. Then, using the relative locations of features detected using OpenCV (such as the eyes, nose, and mouth) we will determine the orientation of the face in the plane parallel to the camera lens. Then, the relative sizes of these features will allow us to determine the degree of rotation away from the camera. However, if the faces we ar...
Read more