My application presents an image that can be scaled to a certain size. I'm using the Image WPF control with the scaling method of FANT.
However, there is no documentation how this scaling algorithm works.
Can anyone reference me to the relevant link for this algorithm description?
Nir
Avery Lee of VirtualDub states that it's a box filter for downscaling and linear for upscaling. If I'm not mistaken, "box filter" here means basically that each output pixel is a "flat" average of several input pixels.
In practice, it's a lot more blurry for downscaling than GDI's cubic downscaling, so the theory about averaging sounds about right.
I know what it is, but I couldn't find much on Google either :(
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4056711 is the appropriate paper I think; behind a pay-wall.
You don't need to understand the algorithm to use it. You should explicitly make the choice each time you create a bitmap control that is scaled whether you want it high-quality scaled or low quality scaled.
Related
I'm writing an astronomy application using Apple's SceneKit and want to provide a skybox of stars to surround a planet.
I have found a large JPEG (8192x4096) with suitable content but my "camera" field of view (10 degrees) magnifies the skybox image background enough to cause serious loss of resolution. In short, it doesn't look good .. here's a screenshot showing blurry stars:
https://ramsaycons.com/screenshots/SkyBoxRes.png
One solution for greater fidelity would be, I imagine, to use a resolution independent 'material' image like a PDF, but SceneKit doesn't support PDF 'materials'.
Or, I could find, or build, a better or bigger image for the skybox material. For example, a (32768x16384) would look better, at the cost of a massive image ~ the 'small' one I'm using now is nearly 8MB already.
Another option would be to move closer to the planet and widen the field of view, but I don't want to move the camera so close (specifically, because I want it 'above' geosynchronous objects in my model).
This code-less question feels not quite appropriate for StackOverflow, but my reading of related Q&A's here reveals a knowledge of clever SceneKit tricks I wouldn't have thought off .. maybe there's a trick for me out there!
I am trying to do my own blob detection who will receive a real time video, and try to detect a white paper sheet.
Even if is something written inside the paper. I need to detect the paper and is corner, because what i really want is to draw a opengl polygon over the paper in each corner of the paper will be a corner of the polygon. Then i need the coordinates of the paper to do other stuffs.
So i need to:
- detect a square white blob.
- get the coordinates of the cornes
- draw a polygon over the white sheet.
Any ideias how can i do that?
Much depends on context. For example, suppose that you:
know that the paper is always roughly centered (i.e. W/2, Y/2 is always inside the blob), and no more rotated than 45 degrees (30 would be better)
have a suitable border around the sheet so that the corners never touch the edges of the FOV
are able (through analysis of local variance, or if you're lucky, check of background color or luminance) to say whether a point is inside or outside the blob
the inside/outside function never fails (except possibly in the close vicinity of a border)
then you could walk a line from a point on the border (surely outside) and the center (surely inside), even through bisection, and find a point - an areal - on the edge.
Two edge points give a rect (two areals give a beam), two rects give an intersection (two beams give a larger areal) - and there's your corner. You should carry along the detection uncertainty (areal radius) in order to validate corners (another less elegant approach is to roughly calculate where the corner is, and pinpoint it with a spiral search or drunkard's walk).
This algorithm is amenable to parallelization and, as long as the hypotheses hold, should be really fast.
All that said, it remains a hack -- I agree with unwind, why reinvent the wheel? If you have memory or CPU constraints (embedded systems, etc.), I believe there ought to be OpenCV and e-Vision "lite" ports also for ARM and embedded platforms.
(Sorry for my terminology - I'm monkey-translating from Italian. "Areal" is likely to correspond to your "blob", a beam is the family of lines joining all couples of points in two different blobs, line intensity being the product of distance from a point from its areal's center)
I am trying to do my own blob detection who will receive a real time video, and try to detect a white paper sheet.
Your first shot could be a simple flood-fill. That is, select a good threshold to binarize the image and apply the algorithm. The threshold can be fixed if you know the paper is always brighter than X and the background is always darker than this. Or this can be an adaptive threshold, for example Otsu's method. OpenCV offers this for free.
If you'd need to speed it up you could use a union-find data structure.
Finally you'd need to come up with some heuristic how to identify the corners (e.g. the four extreme values in x/y direction).
Then i need [...] the coordinates of the cornes [...]
Then you don't need blob detection, but corner detection or contour detection in the first place. OpenCV has some nice functionality for exactly this.
If you can't use it, I would suggest to binarize the image as above and use a harris-detector to find the corners of the object.
OpenCV's TBB support could also come quite handy if you'd use it and you have problems to meet your real-time requirements.
I'm trying to write an CAD-like application in WPF(.NET 4.0) that needs to be able to display a lot of 2D points/lines. It will be used to display CAD-plans of entire cities with zoom, pan, rotate and point snapping on mouseover.
Right now I purely use WPF. I read the objects from the CAD file draw them into a StreamGeometry, use it as stroke of a new Path and add it to a Canvas, with several transforms.
My problem is that this solution doesn't scale well enough. It works fine with small CAD-files, but when I want to display like half a city(with houses and land boundaries) it is very very delayed.
I also tried to convert my CAD-file to an image, but
- a resolution a 32000x32000 is sometimes not enough
- when zooming out the lines are too thin.
In the end I need to be able to place this on a Canvas(2D/3D) as background.
What are my best options here?
Thanks,
Niklas
wpf is not good for a large 3d models. im afraid it is too slow. Your best bet is direct 3d or openGL
However, even with the speed of direct3d,openGL you will still need to work out how to cull as many polygons/vertices as possible before the rendering of the scene if you are trying to show an entire city.
there is a large amount of information on this (generally under game development)
there are a few techniques including frustrum culling, near and far plane culling.
also, since you probably have a static scene you may be able to use binary spacial partitioning.
As I understand the subject is 2D CAD system within WPF.
Great! I use it...
OpenGL and DirectX are in infinite loop OnDraw always. The CPU works all the time.
WPF/Silverlight 2D is smart model.
Yes, total amount of elements (for example, primitives inherited from Shape) must be not so much. But how many?
I tested own app (Silverlight). WPF will be a bit faster I hope...
Here my 2D CAD results. Performance is still great. Each beam consists of multiple primitives.
Use a VirtualCanvas like this one from Chris Lovett.
I am looking for some algorithms to add a convex mirror effect and concave mirror effect to an image. I want to know also how to make this efficiently: applying the algorithm to image data or overlay it by a transparent image that contains the effect. But I don't think the second choice is applicable in this case.
If you are doing it manually instead of using hardware primitives, then the bresenham interpolation algorithm (usually used for line drawing) is the way to go: error propagation is far more efficient than other, more complex, methods.
What Bresenham does is just interpolation. Don't miss the opportunity to use its efficient design elsewhere (slope calculation for line-drwaing is just one of the many applications of interpolation: you can interpolate another dimension: 2D, 3D, transparency, reflection, colors, etc.).
25 years ago, I remember having used it to resize bitmaps and even do texture mapping in a real-time 3D engine! That was at a time graphic-accelerated video boards costed a fortune...
CImg library has a fisheye sample, in examples\CImg_demo.cpp. The core algorithm seems very simple (and fast, as generally this library). I think it's an approximation of the real optical effect, but could be modified to handle the convex mirroring. I don't know if it could be extended to handle 'negative' curvature.
You can use a pre-calculated sin() table and interpolate values to match the size of your bitmap. The inverse effect is achieved by either using an offset or a larger table.
Remembers me the (great times of the) DOS demos in the 80s...
I have a sequence of images taken from a camera. The images consists of hand and surroundings. I need to remove everything except the hand.
I am new to Image processing. Would anyone help me in regard with the above Question. I am comfortable using C and Matlab.
A really simple approach if you have a stationary background and a moving hand (and quite a few images!) is simply to take the average of the set of images away from each image. If nothing else, it's a gentle introduction to Matlab.
The name of the problem you are trying to solve is "Image Segmentation". The Wikipedia page here: wiki is a good start.
If lighting consistency isn't a problem for you, I'd suggest starting with simple RGB thresholding and see how far that gets you before trying anything more complicated.
Have a look at OpenCV, a FOSS library for computer vision applications. Specifically, see the Video Surveillance module. For a walk through of background subtraction in MATLAB, see this EETimes article.
Can you specify what kind of images you have. Is the background moving or static? For a static background it is a bit straightforward. You simply need to subtract the incoming image from the background image. You can use some morphological operations to make it look better. They all depend on the quality of images that you have. If you have moving background I would suggest you go for color based segmentation. Convert the image to YCbCr then threshold appropriately. I know there are some papers available on it(However I dont have time to locate them). I suggest reading them first. Here is one link which might help you. Read the skin segmentation part.
http://www.stanford.edu/class/ee368/Project_03/Project/reports/ee368group08.pdf
background subtraction is simple to implement (estimate background as average of all frames, then subtract each frame from background and threshold resulting absolute difference) but unfortunately only works well if 1. camera has manual gain and exposure 2. lighting conditions do not change 3.background is stationary. 4. the background is visible for much longer than the foreground.
given your description i assume these are not the case - so what you can use - as already pointed out - is colour as a means of segmenting foreground from background. as it's a hand you are trying to isolate best bet is to learn the hand colour. opencv provides some means of doing this. if you want to do this yourself you just get the colour of some of the hand pixels (you would need to specify this manually for at least one frame) and convert them to HUE (which encapsulates the colour in a brightness independen way. skin colour has a very constant hue) and then make a HUE histogram. compare this to the rest of the pixels and then decided if the hue is simmilar enough.