I have 100 classified datapoints, that are a 1000x1000 grid, and the name of the grid. 10 distinct points for 10 names.
It looks like this: (in excel)
Every datapoint has between 500 to 900 points to use on the graph.
I tried classifying them using an ANN, where the network gets 256 inputs and 10 outputs.
The inputs are created using first finding the center of gravity in the graph (center is a good reference point for my case, because I have rotation and translation to worry about). I then take distances from the 256 points closest to the center, and send them to the ann.
Results are terrible..
I was wondering what method should I use, because this is not going anywhere doing like this.
Here is another image of the same name:
Related
Sorry, it is all by one question but relate to many small questions. I can't split them into seperated questions.
For example, input picture size 960x640
Through VGG16 layer 13 Conv5_3, get feature_map 60x40x512
Do 3x3 convolution.
3.1 How 3x3 convolution compress the output above to 1x512 ?
3.2 I read some article said, RPN would random select 512 samples from 2000 anchors . If 1x512 matrix mean this , what is 3x3 convolution doing ?
Loop feature_map, with 16 stride and 16 scale to find the center of the original map (corresponding feature map current point), cut 9 anchors out, calculate IoU< 0.3 as neg samples and IoU > 0.7 as pos samples.
4.1 If there are several points on the feature_map, how to cover the GT? I mean, because it need IoU > 0.7 to label pos sample, here IoU refers to [ the intersection of the area(map from this point to the original image) and GT], or [all the area of GT]? I think it should be the former.
After all the loops are over, filter out the positive and negative samples by nms . Is it possible to have multiple anchors in a single point, or is nms sure to filter this out?
Pass to softmax.
6.1 My problem is , in many cases, the positions(labeled by positive and negative) of the points on the feature_map are different. Because the position of the parameter is also fixed at a specific position on the feature_map, how to find proposals from an image in the detect phase?
6.2 random selection of anchors is at here? ? ?
RoI merges feature_map and proposals to do pooling. (1. roi (roi said a group of anchors it) is located in the feature map , and get the patch zone in feature map 2. something like SPP layer(7x7 down sampling ) is applied to the feature map patch, transform to fixed size of features, to fit full connection layer)
Another softmax. (Training phase using BP to tune the parameters), my problem is that in many cases the positions of the points on the feature_map each time labeled positive and negative are different. Because the position of the parameter is also fixed at a specific position on the feature_map, how to find proposals from the image in the detect phase?
RoI compare to GT, do reggession.
After finishing the above questions, re-think again. I found my understanding of anchors, proposals a bit confusing. Does many anchors compose to a proposal?
If so, then the above 6 becomes
Select 512 anchors , pass their parameters into softmax, the output show if it is part of the target object. So this layer is the detect phase. When doing detect phase, just loop all the anchors to get the possible ones .
6.1 But in this case, how RPN output bbox size (x, y, w h) ? I think it need merge selected anchors and then scale to the size of the original image , to get the bbox size.
6.2 If operation is merger , then randomly selected 512 from 2000 is likely to miss some areas, isn't it ?
Mainly is 3 and 6, and I think all of them are highly relative can not be seperated. Some are just need yes or no confirm, thanks
I am trying to erode objects in a binary image such that they do not become smaller than some fixed size. Consider, for instance, a binary map composed of connected components (blobs), wherein one defines blob size by either the minimal or maximal antipolar (anti-perimetric) distance (i.e., the distance between two points that are as far from one another as they can be on the perimeter or contour of the blob; if the contour consists of N consecutively numbered points, then the distances evaluated would be those between points 1 and N/2+1, points 2 and N/2+2, etc.). Given such an arrangement, I seek to erode these blobs until the distance metric reaches a specified limit. If the blobs were simple circles, then the effect could be realized by ultimate erosion followed by dilation to a fixed size; however, the contour of an irregular object would be lost by such a procedure. Is there a way to achieve such an effect for connected, irregular components using built-in functions in MATLAB?
With no image and already tried code presented I can understand you wrong, but may be iterative using bwmorph with 'thin','skel' or 'shrink' will help you.
while(cond < cond_threshold)
bw=bwmorph(bw,...,1); %one of the options above
cond = calc_cond(bw);
end
Having data points in a 3D neighbourhood (see attached picture), I would like to define a measure of spatial spread to accept or reject the data set as a candidate for further analysis. One measure could be how many of the cells in the 3D subvolume (the bounding box) are occupied by the neighbourhood. Another would be how the data stretches along each of the axis. Are there other and better criteria for measuring the spatial spread ?
Thanks
I have a volume (3D matrix) that has undergone a segmentation process. Most of the volume consist of NaNs (or zeros), except regions that have passed some criteria (see picture). I need to know how large each remaining segment is in number of voxels and how is their distribution on the 2D planes (xy, xz, yz). Is there anything in matlab that can help me do this in an efficient way rather than direct search? The volume can be rather large. For ex. in the attached picture there is one segment in yellowish/brownish colour of 7 voxels and extends more vertically than in xy.
Thanks in advance.
The most convenient solution is to use REGIONPROPS. In your example:
stats = regionprops(image, 'area', 'centroid')
For every feature, there is an entry in the structure stats with the area (i.e. # of voxels) and the centroid.
I think that what you are looking for is called bwlabeln. It allows you to find blobs in 3D space, just like bwlabel does in 2D. Afterwards, you can use regionprops to find out the properties of the data.
Taken directly from help:
bwlabeln Label connected components in binary image.
L = bwlabeln(BW) returns a label matrix, L, containing labels for the
connected components in BW. BW can have any dimension; L is the same
size as BW. The elements of L are integer values greater than or equal
to 0. The pixels labeled 0 are the background. The pixels labeled 1
make up one object, the pixels labeled 2 make up a second object, and
so on. The default connectivity is 8 for two dimensions, 26 for three
dimensions, and CONNDEF(NDIMS(BW),'maximal') for higher dimensions.
i need to find the distance between the two points.I can find the distance between them manually by the pixel to cm converter in the image processing tool box. But i want a code which detects the point positions in the image and calculate the distance.
More accurately speaking the image contains only three points one mid and the other two approximately distanced equally from it...
There might be a better way then this, but I hacked something similar together last night.
Use bwboundaries to find the objects in the image (the contiguous regions in a black/white image).
The second returned matrix, L, is the same image but with the regions numbered. So for the first point, you want to isolate all the pixels related to it,
L2 = (L==1)
Now find the center of that region (for object 1).
x1 = (1:size(L2,2))*sum(L2,1)'/size(L2,2);
y1 = (1:size(L2,1))*sum(L2,2)/size(L2,1);
Repeat that for all the regions in your image. You should have the center of mass of each point. I think that should do it for you, but I haven't tested it.