I have an object with similar parts, I unwrap uv of one, and then transfer to the other similar ones. When I finally join all of parts of the assembly to combine to a single map, I do average islands scales (ctrl+A) and pack islands (ctrl+P) with hot keys for windows. This final hot key operation breaks my efforts to keep similar islands stacked and I found no solution for this by googling (perhaps just wrong keywords the reason).
How to let Blender to know to lock my stacks when averaging / packing?
You can pin uvs so they don't move when you unwrap.
You should be ok pinning two or four verts in an island but selecting linked L and pinning P all of them is usually easier.
There is a blender SE site which is better for questions about using blender.
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I have a project to build a 3D model of the spinal roots in order to simulate the stimulation by an electrode. For the moment I've been handled two things, the extracted positions of the spinal roots (from the CT scans) and the selected segments out of the points (see both pictures down below). The data I'm provided is in 3D and all the segments are clearly distinct although it does not look like it on the figures below as it is zoomed out.
Points and segments extracted from the spinal cord CT scans:
.
Selected segments out of the points:
I'm now trying to connect these segments so as to have the centrelines for all the spinal roots at the end. The segments are not classified, simply of different colors to differentiate them on the plot. The task is then about vertically connecting the segments that look to be part of the same root path.
I've been reviewing the literature on how I could tackle that issue. As I'm still quite new to the field I don't have much intuition on what could work and what could not. I have two subtasks to solve here, connecting the lines and classifying the roots, and while connecting the segments after classification seems no big deal, classifying them seems decently harder. So I'm not sure in which order to proceed.
Here are the few options I'm considering to deal with the task :
Use a Kalman filter to extract the vertical lines from the selected segments and the missing parts
Use of a Hough transform to detect vertical lines, by trying to express the spinal root segments in the parametric space and see how they cluster and see if anything can be inferred from there.
Apply some sort of SVM classification algorithm on the segments to classify them by roots. I could characterize each segment by its orientation and position, and classify them based on similarities in the parameters I'm selecting, and then connect the segments. Or use the endpoint position of each segment and connect it to one of the nearest neighbours if their orientation/position is matching.
I'm open to any suggestions, any piece of advice, or any other ideas on how to deal with the current problem.
Thanks for taking your time to read this.
We have fixed stereo pairs of cameras looking into a closed volume. We know the dimensions of the volume and have the intrinsic and extrinsic calibration values
for the camera pairs. The objective being to be able to identify the 3d positions of multiple duplicate objects accurately.
Which naturally leads to what is described as the correspondence problem in litrature. We need a fast technique to match ball A from image 1 with Ball A from image 2 and so on.
At the moment we use the properties of epipolar geomentry (Fundamental matrix) to match the balls from different views in a crude way and works ok when the objects are sparse,
but gives a lot of false positives if the objects are densely scattered. Since ball A in image 1 can lie anywhere on the epipolar line going across image 2, it leads to mismatches
when multiple objects lie on that line and look similar.
Is there a way to re-model this into a 3d line intersection problem or something? Since the ball A in image 1 can only take a bounded limit of 3d values, Is there a way to represent
it as a line in 3d? and do a intersection test to find the closest matching ball in image 2?
Or is there a way to generate a sparse list of 3d values which correspond to each 2d grid of pixels in image 1 and 2, and do a intersection test
of these values to find the matching objects across two cameras?
Because the objects can be identical, OpenCV feature matching algorithms like FLANN, ORB doesn't work.
Any ideas in the form of formulae or code is welcome.
Thanks!
Sak
You've set yourself quite a difficult task. Because one point can occlude another in a view, it's not generally possible even to count the number of points. If each view has two points, but those points fall on the same epipolar line on the other view, then you can count anywhere between 2 and 4 points.
Assuming you want to minimize the points, this starts to look like Minimum Vertex Cover in a dense bipartite graph, with each edge representing the association of a point from each view, and the weight of each edge taken from the registration error of associating the corresponding points (vertices) from each view. MVC is, of course, NP-hard, and if you treat the problem as a general MVC problem then you'll never do better than O(n^2) because that's how many edges there are to examine.
Your particular MVC problem might have structure that can be exploited to perform a more efficient approximation. In particular, I might suggest calculating the epipolar lines in one view, ordering them by angle from the epipole, and similarly sorting the points in that view from the epipole. You can then iterate over the two sorted lists roughly in parallel, greedily associating each point with a nearby epipolar line. Then you can do the same in the other view, but only looking at points in that view which had not yet been associated during the previous pass. I think that a more regimented and provably optimal approach might be possible with dynamic programming (particularly if you strictly bound the registration error) which wouldn't require the second pass, but I can't sketch it out offhand.
For different types of objects it's easy- to find the match using sum-of-absolute-differences. For similar objects, the idea(s) could lead to publish a good paper. Anyway here's one quick algorithm:
detect the two balls in first image (using object detection methods).
divide the image into two segments cantaining two balls.
repeat steps 1 & 2 for second image also.
the direction of segments in two images should give correspondence of the two balls.
Try this, it should work for two balls.
Are there functions within OpenCV that will 'track' a gradually changing curve without following sharply divergent crossing lines? Ex: If one were attempting to track individual outlines of two crossed boomerangs, is there an easy way to follow the curved line 'through' the intersection where the two boomerangs cross?
This would require some kind of inertial component that would continue a 'virtual' line when the curve was interrupted by the other crossed boomerang, and then find the continuation of the original line on the opposite side.
This seems simple, but it sounds so complicated when trying to explain it. :-) It does seem like a scenario that would occur often (attempting to trace an occluded object). Perhaps part of a third party library or specialized project?
I believe I have found an approach to this. OpenCV's approxPolyDP finds polygons to approximate the contour. It is relatively easy to track angles between the polygon's sides (as opposed to finding continuous tangents to curves). When an 'internal' angle is found where the two objects meet, it should be possible to match with a corresponding internal angle on the opposite side.
Ex: When two bananas/boomerangs/whatever overlap, the outline will form a sort of cross, with four points and four 'internal angles' (> 180 degrees). It should be possible to match the coordinates of the four internal angles. If their corresponding lines (last known trajectory before overlap) are close enough to parallel, then that indicates overlapping objects rather than one more complex shape.
approxPolyDP simplifies this to geometry and trig. This should be a much easier solution than what I had previously envisioned with continuous bezier curves and inertia. I should have thought of this earlier.
I have built a system to segment a binary image containing handwritten symbols and classify them (specifically for music). I'm aware there are commercial applications which do this but this is me trying to do it ground-up as a project.
For the purposes of simplicity, let's assume that I will have two elements in my whole image:
AND
I've built something which segments an image in to regions and classifies them. This works fine most of the time.
However, sometimes the elements touch, at which point my classifier breaks down. For example:
OR
What's the best way to separate the two? I've done quite a bit of research but I think my lack of domain knowledge may be letting me down here!
Things I've found:
Template matching doesn't work well as, the symbols are handwritten
Thinning/eroding doesn't really work either, especially when it's two sharps (above right) overlapping as they degrade too much.
Watershed filling doesn't really work on two complex shapes
Things which might work and I'd appreciate a "go for it" or "avoid" vote before I go down the rabbit hole.
Slide a window L->R of varying sizes and try and classify it. Pick the window and position which has the highest positive classification confidence.
Take projections (horizontal and vertical) and "cut" the image at the minimum value (which would be the thinnest place on the respective axis
This seems to me like a very hard problem, and I do not have a good general solution. Especially the case of multiple connected # will be difficult to solve.
In your particular case I would try the following, assuming that there are usually not more than two or three symbols clumped together:
When a blob is too big for a single symbol
for each possible symbol
take a region in the top left, top right, bottom left, bottom right corner with the correct size for the symbol
run your recognition for that region
if succesfull, remove the recognized symbol, repeat for the rest
This is not a very sophisticated solution, and how well it works strongly depends on your particular character recognition
Another idea:
If most of your shapes tend to have thin vertical segments, you may be able to identify these segments via probabilistic Hough transform, and use the found vertical line segments as starting points for your recognition, whenever a blob contains more than one symbol.
Yet another idea to separate shapes:
split the blob at the biggest convexity defect that has a given minimum distance from the border of the blob. Caveat: this works best for convex shapes, and probably not at all for your # signs
Alternative 4:
In sheet music, the same sort of symbols tend to appear together, like # followed by a note on the same row, or multiple # at the start of the line in a certain pattern. It might be worthwhile to have a special combined recognizer for such symbols that tend to clump together.
(On that note, how do you currently separate the symbols from the staff lines?)
I'm currently working on a vision system for a UAV I am building. The goal of the system is to find target objects, which are rather well defined (see below), in a video stream that will be a 2-D flyover view of the ground. So far I have tried training and using a Haar-like feature based cascade, a la Viola Jones, to do the detection. I am training it with 5000+ images of the targets at different angles (perspective shifts) and ranges (sizes in the frame), but only 1900 "background" images. This does not yield good results at all, as I cannot find a suitable number of stages to the cascade that balances few false positives with few false negatives.
I am looking for advice from anyone who has experience in this area, as to whether I should: 1) ditch the cascade, in favor of something more suitable to objects defined by their outline and color (which I've read the VJ cascade is not).
2) improve my training set for the cascade, either by adding positives, background frames, organizing/shooting them better, etc.
3) Some other approach I can't fathom currently.
A description of the targets:
Primary shapes: triangles, squares, circles, ellipses, etc.
Distinct, solid, primary (or close to) colors.
Smallest dimension between two and eight feet (big enough to be seen easily from a couple hundred feet AGL
Large, single alphanumeric in the center of the object, with its own distinct, solid, primary or almost primary color.
My goal is use something very fast, such as the VJ cascade, to find possible objects and their associated bounding box, and then pass these on to finer processing routines to determine the properties (color of the object and AN, value of the AN, actual shape, and GPS location). Any advice you can give me towards completing this goal would be much appreciated. The source code I currently have is a little lengthy for post here, but is freely available should you like to see it for reference. Thanks in advance!
-JB
I would recommend ditching Haar classification, since you know a lot about your objects. In these cases you should start by checking what features you can use:
1) overhead flight means, as you said, you can basically treat these as fixed shapes on a 2D plane. There will be scaling, rotations and some minor affine transformations, which depends a lot on how wide-angled your camera is. If it isn't particularly wide-angled, that part can probably be ignored. Also, you probably know your altitude, by which you can probably also make very good assumption on the target size (scaling).
2) You know the colors, which also makes it quite easy to find objects. If these are very defined as primary color, then you can just filter the image based on color and find those contours. If you want to do something a little more advanced (which to me doesn't seem necessary though...) you can do a backprojection, which in my experience is very effective and fast. Note, if you're creating the objects, it would be better to use Red Green and Blue instead of primary colors (red green and yellow). Then you can simply split the image into it's respective channels and use a very high threshold.
3) You know the geometric shapes. I've never done this myself, but as far as I know, the options are using moments or using Hough transforms (although openCV only has hough algorithms for lines and circles, so you'd have to write your own for other shapes...). You might already have sufficiently good results without this step though...
If you want more specific recommendations, it would be very useful to upload a couple sample images. :)
May be solved but I came across a paper recently with an open-source license for generic object detection using normalised gradient features : http://mmcheng.net/bing/comment-page-9/
The details of the algorithms performance against illumination, rotation and scale may require a little digging. I can't remember on the top of my head where the original paper is.