Create Pointillism Art with 3 Primary Colors from Natural Images

-

Create Pointillism Art with 3 Primary Colors from Natural Images



Description

Pointillism is a branch of impressionism that can be dated back to the late 19th century. It is a painting technique that only uses tiny, distinct dots to form patterns of color. It enjoys a duality in
being both discrete up close (the dots) and continuous from distance (the patterns). Combining this
artistic inspiration with the techniques of digital image processing, we want to develop a special
image filter that creates pointillism art from ordinary digital images.

Plan

he process of creating pointillism art will be as followed:
(1) We take an image.
(2) We will apply various image processing techniques to our image, such as blurring the image
with a structuring element that is larger and proportional to the size of the dots we want to
use in our pointillism art.
(3) We will then downsample the image to reduce the resolution of the image. By blurring the
image and downsampling, we want to create an image that allows us to more easily deconstruct the image into Pointillism art composed of 3 primary colors.
(4) After the preprocessing, we will determine the intensity map of the image by converting it
to grayscale. The intensity map of the image will be used to determine a density map of the dots in our subsequent pointillism art.
(5) We will apply histogram normalization to the image to make it more saturated so that the
distribution of colors in the image can be better mapped to the limited gamut of colors we can create with three primary colors. Then we can create a linear mapping between the colors in the image to the colors we want to use for the dots.
(6) Once the conversion is done, we will analyze pixels in the original image to create regions of
dots that represent each pixel of the original image. We will determine the distribution of
dots and color in the regions to closely represent the original pixel in an artistic manner. We
will overlap the resulting regions of dots, to reduce artifacts and create a more blended
distribution of dots across the entire resulting pointillism image.
(7) Once our Pointillism art is created, we will display the image on a printout. The goal is if you
stand close to the art, the art looks like a bunch of dots, but the farther you stand from it,
then it looks like an artistic representation of the image.The procedures here are tentative and subject to changes for the pursuit of aesthetics.
Equipment

We won’t be using an Android device.

References

Dongxiang Chi, “A Natural Image Pointillism with Controlled Ellipse Dots,” Advances in Multimedia, vol. 2014,
Article ID 567846, 16 pages, 2014. doi:10.1155/2014/567846
Greenberg, Ira, Dianna Xu, and Deepak Kumar. "Image SpecialFX: Pointilism." Processing Creative Coding and Generative Art in Processing 2. Dordrecht: Springer, 2013. 399-401. Print.
Lansdown, John, and Simon Schofield. "Expressive Rendering: A Review of Nonphotorealistic Techniques."
Computer Graphics and Applications, IEEE 15.3 (1995): 29-37. IEEE. Web. 3 Nov. 2015.
Luong, Tran-Quan, Ankush Seth, Allison Klein, and Jason Lawrence. "Isoluminant Color Picking for Non-Photorealistic Rendering."SpringerReference (2011): n. pag. Isoluminant Color Picking for Non-Photorealistic
Rendering. Princeton, 2005. Web. 3 Nov. 2015.
.


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