Hey so I was wondering if anyone has any reading suggestions for papers/algorithms on how to render scenes in the style that was used for the video game Okami.
I've done some searching on my own and have found some "sumie" style papers but the algorithms presented there are black and white and I'm looking for something with color.
Any help is appreciated!
You're looking for Non-photorealistic rendering. There are dozens of algorithms, I can't tell you which one is used in the game exactly, it might be more algorithms together. Anyway, you may find some algorithms and techniques on Stylized Depiction in Computer Graphics and by searching on google..
Related
I'm developing a image editor by webgl. I did some research with the existing apps which contains image dehaze effect, FotoJet and Polarr. Here are the example images exported by FotoJet and Polarr.
Original:
FotoJet:
Polarr:
So, what's the dehaze algorithm they used? I think their algorithm is different, can anyone help me figure out the algorithms?
I find the algorithm of Kaiming He at http://kaiminghe.com/cvpr09/. I think it's the solution.
The FotoJet and Polarr don't implement the perfect result as Kaiming He's Demo. I'll try it by myself.
I have searched around the internet, only seen motion detection can be done in video or two consecutive images. I wonder is that possible to detect a motion from an image(like jumping running swimming).The motion is referring any significant body movement. If it can be done, please tell me the algorithm and ways to learn it. thank you
As others have commented, for the general case, you probably can't. But, there are still avenues to explore, if you have control over some of the parameters.
One idea that comes to mind is detecting motion blur for some fast movement. You can accent that if you have control over the camera type/exposure.
You can find academic papers on the subject, and can start with:
https://www.google.com/search?q=detecting+motion+blur+in+one+image
A technique that can be helpful to you is called scene understanding. Basically you train a deep neural net with images and labels that describe that image. In that way you can know that a person is running, swimming or doing any other activity.
There is a good presentation about the subject by Prof. LeCun.
What yu are implying is an implicit comparison with an image of a person standng in a "stable/not moving directed way. So there is a two image comparison there non-withstanding.
What is the easiest open source implementation of an image comparison algorithm that can produce a similarity score for a black white picture / edges ?
It doesn't need to generic, i could teach it with a couple of images.
Background:
I am working on a cat flap, that only opens when my cat approaches without prey.
I found similar but dead project: Flo Control
Thanks for help.
This field is known in the research literature as classification, and the algorithms as an "Image classifier". Googling for one of these phrases will give you lots of hits, and adding the words "open source" and your programming language will probably give you what you want.
WND-CHARM seems popular, but there are lots.
PS, if you are using Java, you might find the following question helpful. Image Classification Algorithms Using Java
Recently I've been messing about with algorithms on images, partly for fun and partly to keep my programming skills sharp.
I've just implemented a 'nearest-neighbour' algorithm that picks n random pixels in an image, and then converts the colour of each other pixel in the image to the colour of its nearest neighbour in the set of n chosen pixels. The result is a kind of "frosted glass" effect on the image, for a reasonably large value of n (if n is too small then the image gets blocky).
I'm just wondering if anyone has any other good/fun algorithms on images that might be interesting to implement?
Tom
This book, Digital Image Processing, is one of the most commonly used books in image processing classes, and it will teach you a lot of basic techniques that will help you understand other algorithms better, like the ones Ants Aasma suggested.
Try making an Andy Warhol print. It's pretty easy in Java. For more ideas, just look at the filters available in GIMP or a similar program.
Marching Squares is a computer vision algorithm. Try using that to convert black and white raster images to object based scenes.
Turns the image into a pizza
Take N images, relate them via an MC-Escher-style painting
"Explode" an image from the inside out
Convert the image into a single-color blocks (piet-style) based on all the colours within.
How about tie-dye algorithm?
Fun to toy with and easy to code filters are:
kaleidoscope
lens
twirl
There are a lot of other filters, but especially the kaleidoscope gives much bang for the bucks. I have made my own graphics editor with lots of filters and is also looking for inspiration.
Instead of coding image filters, I personally would love to code Diffusion Curves, but unfortunately have little time for fun.
If you want to try something more challenging look for SIGGRAPH papers on the web. There are some really nifty image algorithms presented at that conference. Seam carving is one cool example that is reasonably straightforward to implement.
If you want something more challenging try to complete the symmetry of broken objects
I am looking for a recommendation for an introduction to image processing algorithms (face and shape recognition, etc.) and wondered if anyone had an good recommendations, either for books, whitepapers or websites.
I am starting from knowing very little about image recognition and did some maths at University (a long time ago).
Any help or pointers would be greatly appreciated.
I found this blog very helpful.
There are quite a lot of topics related to CV that you might want to read up on.
Some of the topics:
Kernels
Blurring
Eigenfaces (face detection)
Connected components
Canny edge detection
Gradients
haar-like features
Histogram Of Gradients
Image segmentation
The two books that are pretty good on this subject are:
Computer Vision: Shapiro
Computer Vision A Modern Approach: Forsyth et al
I used the CV: A modern approach for a CV class I took a semester or two ago. It is fairly concise and includes explanations of how the techniques work. Its not for the faint of heart. Also: Forsyth is a well known author of many CV Papers.
Be aware that Computer Vision is in general very math heavy, so if you feel that your linear algebra skills are not up to date, then update them before attempting to read anything. Knowing your way around some basic signal processing will also be of great help.
For basic shape recognition like lines and circles an edge detector coupled with a simple(?) Hough transform could be enough to do the trick.
If you want to find other stuff that is not faces or basic shapes (cars, people, ...) then you are in for some really heavy reading as this is a quite large area of research with lots of different methods for feature extraction and classification.
If you want to look at faces only, then I suggest finding literature that deals with this specifically to not drown in a sea of math heavy information.
MIT OpenCourseWare has an image recognition course. Unfortunately, there are no video lectures for this course yet, but you'll find lecture notes and other materials.
http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-801Fall-2004/CourseHome/index.htm
Here is a great book that you can download for free. http://szeliski.org/Book/