i'm trying to detect some trees in a picture and I tryed different ways. At the end I chose a BOW+SVM approach, by creating a dictionary and then applying it to the .train(...) of my SVM. I found out that this isn't enough for what I have to do, since classification gives me wrong result. So I tried a treshold on my test images by applying a clustering for colors, but of course, grass is detected too, so it's not very helpful. My training images (for dictionary and SVM training) are all .png images (so tress without background) and that's why I'm trying to segment my test images (which instead have background) to isolate trees. I have tried some basic OpenCV segmentation tecniques but they don't seem to work. One method I tried is that to cluster the image by colors, keeping only the green part: then I tried to eliminate grass by deleting all the rows that have more than 70% of the pixels colored, but this strategy is ok for only some test images that have a big part of grass. Is there any other strategy I can follow? Thank you
Related
I am using Mask-RCNN to solve an object detection problem. This is an implementation of Mask R-CNN on Python 3, cv2, Keras, and TensorFlow. I am trying to identify the damaged area of a truck. The results which I got are good when I am running the model on those images which do not have any shadow or reflection from the surrounding. But the model fails on those type of images which has a shadow or some other reflection. I have used some image processing techniques which are 1. Converting images to grayscale and 2. Color processing. But both of them not given any good results.
Please suggest what i can do to minimize false-positive results.
The problem with training custom classifiers is that even if you have enough images of the object itself, there isn't enough data of that that same object in different contexts and backgrounds.
I'd suggest you to augmentate the data by applying some sorts of distortion, including artificial shadows and reflections. By doing this, you will get more data with different contexts and minimize your false-positive results.
There are several tools for doing this. One of them is albumentations
: https://github.com/albumentations-team/albumentations. It allows you to create a lot of image augmentations, including random shadows.
Given a logo image as a reference image, how to detect/recognize it in a cluttered natural image?
The logo may be quite small in the image, it can appear in clothes, hats, shoes, background wall etc. I have tried SIFT feature for matching without any other preprocessing, and the result is good for cases in which the size of the logo in images is big and the logo is clear. However, it fails for some cases where the scene is quite cluttered and the proportion of the logo size is quite small compared with the whole image. It seems that SIFT feature is sensitive to perspective distortions.
Anyone know some better features or ideas for logo detection/recognition in natural images? For example, training a classifier to locate candidate regions first, and then apply directly SIFT matching for further recognition. However, training a model needs many data, especially it needs manually annotating logo regions in images, and it needs re-training (needs to collect and annotate new image) if I want to apply it for new logos.
So, any suggestions for this? Detailed workflow/code/reference will be highly appreciated, thanks!
There are many algorithms from shape matching to haar classifiers. The best algorithm very depend on kind of logo.
If you want to continue with feature registration, i recommend:
For detection of small logos, use tiles. Split whole image to smaller (overlapping) tiles and perform usual detection. It will use "locality" of searched features.
Try ASIFT for affine invariant detection.
Use many template images for reference feature extraction, with different lightning , different background images (black, white, gray)
I have images of mosquitos similar to these ones and I would like to automatically circle around the head of each mosquito in the images. They are obviously in different orientations and there are random number of them in different images. some error is fine. Any ideas of algorithms to do this?
This problem resembles a face detection problem, so you could try a naïve approach first and refine it if necessary.
First you would need to recreate your training set. For this you would like to extract small images with examples of what is a mosquito head or what is not.
Then you can use those images to train a classification algorithm, be careful to have a balanced training set, since if your data is skewed to one class it would hit the performance of the algorithm. Since images are 2D and algorithms usually just take 1D arrays as input, you will need to arrange your images to that format as well (for instance: http://en.wikipedia.org/wiki/Row-major_order).
I normally use support vector machines, but other algorithms such as logistic regression could make the trick too. If you decide to use support vector machines I strongly recommend you to check libsvm (http://www.csie.ntu.edu.tw/~cjlin/libsvm/), since it's a very mature library with bindings to several programming languages. Also they have a very easy to follow guide targeted to beginners (http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf).
If you have enough data, you should be able to avoid tolerance to orientation. If you don't have enough data, then you could create more training rows with some samples rotated, so you would have a more representative training set.
As for the prediction what you could do is given an image, cut it using a grid where each cell has the same dimension that the ones you used on your training set. Then you pass each of this image to the classifier and mark those squares where the classifier gave you a positive output. If you really need circles then take the center of the given square and the radius would be the half of the square side size (sorry for stating the obvious).
So after you do this you might have problems with sizes (some mosquitos might appear closer to the camera than others) , since we are not trained the algorithm to be tolerant to scale. Moreover, even with all mosquitos in the same scale, we still might miss some of them just because they didn't fit in our grid perfectly. To address this, we will need to repeat this procedure (grid cut and predict) rescaling the given image to different sizes. How many sizes? well here you would have to determine that through experimentation.
This approach is sensitive to the size of the "window" that you are using, that is also something I would recommend you to experiment with.
There are some research may be useful:
A Multistep Approach for Shape Similarity Search in Image Databases
Representation and Detection of Shapes in Images
From the pictures you provided this seems to be an extremely hard image recognition problem, and I doubt you will get anywhere near acceptable recognition rates.
I would recommend a simpler approach:
First, if you have any control over the images, separate the mosquitoes before taking the picture, and use a white unmarked underground, perhaps even something illuminated from below. This will make separating the mosquitoes much easier.
Then threshold the image. For example here i did a quick try taking the red channel, then substracting the blue channel*5, then applying a threshold of 80:
Use morphological dilation and erosion to get rid of the small leg structures.
Identify blobs of the right size to be moquitoes by Connected Component Labeling. If a blob is large enough to be two mosquitoes, cut it out, and apply some more dilation/erosion to it.
Once you have a single blob like this
you can find the direction of the body using Principal Component Analysis. The head should be the part of the body where the cross-section is the thickest.
I'm trying to extract objects from scanned images. There could be a few documents on a white background, and I need to crop and rotate them automatically. This seems like a rather simple task, but I've got stuck at some point and get bad results all the time.
I've tried to:
Binarise the image and get connected components by performing morphological operations.
Perform watershed segmentation by using dilated and eroded binary images as mask components.
Apply Canny detector and fill the contours.
None of this gets me good results. If the object does't have contrast edges (i.e a piece of paper on white background), it splits into a lot of separate components. If I connect these components by applying excessive dilation, background noise also expands and everything becomes a mess.
For example, I have an image:
After applying Canny detector and filling the contours I get something like this:
As you can see, the components are not connected. They are eve too far from each other to be connected by a reasonable amount of dilation. And when I apply watershed to this mask combined with some background points, it yields very bad results.
Some images are noisy:
In this particular case I was able to obtain contour of the whole passport by Canny detector because of it's contrast edges. But threshold method doesn't work here.
If the images are always on a very light background, then you can binarize with a threshold close to the maximum possible value. After that it is a matter of correcting the binary image to get the objects, but this step will vary depending on how your other images look like.
For instance, the following image at left is what we get with a threshold at 99% of the maximum value after a gaussian filtering on the input. After removing components connected to the border and other small components, and also combining with some basic morphological tools, we get the image at right.
This may seem a bit wishy-washy but bear with me:
This looks like quite a challenging case for image processing recipes involving only edge detection, morphological operations and segmentation.
What you are not exploiting here is that you (I believe) know what your document should look like. You are currently looking at completely general solutions which do not take into account this prior knowledge. If you can get some training data then you can go all the way from simple template/patch-based matching (SSD, Normalized Cross-Correlation) to more sophisticated object detection techniques to find the position and rotation of your documents.
My guess is that if your objects are always more or less the same and at the same scale (e.g. passports scanned at a fixed resolution/similar machines) then you can get away with a fairly crude approach. There won't be any one correct method. It's also likely that the technique you end up using will not work until you have done a significant amount of parameter tweaking, so don't give up on anything too quickly.
We as human, could recognize these two images as same image :
In computer, it will be easy to recognize these two image if they are in the same size, so we have to make Preprocessing stage or step before recognize it, like scaling, but if we look deeply to scaling process, we will know that it's not an efficient way.
Now, could you help me to find some way to convert images into objects that doesn't deal with size or pixel location, to be input for recognition method ?
Thanks advance.
I have several ideas:
Let the image have several color thresholds. This way you get large
areas of the same color. The shapes of those areas can be traced with
curves which are math. If you do this for the larger and the smaller
one and see if the curves match.
Try to define key spots in the area. I don't know for sure how
this works but you can look up face detection algoritms. In such
an algoritm there is a math equation for how a face should look.
If you define enough object in such algorithms you can define
multiple objects in the images to see if the object match on the
same spots.
And you could see if the predator algorithm can accept images
of multiple size. If so your problem is solved.
It looks like you assume that human's brain recognize image in computationally effective way, which is rather not true. this algorithm is so complicated that we did not find it. It also takes a large part of your brain to deal with visual data.
When it comes to software there are some scale(or affine) invariant algorithms. One of such algorithms is LeNet 5 neural network.