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I am working on a problem of modal regression using EM algorithm. The image shown here is achieved using mean shift algorithm by Chen et . However, using EM, I am not able to obtain a multiple estimated lines. Can anyone help me with any idea?
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Hi i have bunch of images of tyres, we need to detect and recognize the text on the tyres,
here i am facing difficulties to detect the text, because the text and background of the tyre are same, i have tried with EAST text detector and yolo text detector ( without own data train ),
is there any better solutions to detect the text from these kind of background images
here i need to detect only the 10 digit serial number like "75R-0006884"
edit: pre processed image
Some preprocessing before passing the images in through a non-retrained deep network might help! Try binarizing/grayscaling the image and playing with those thresholds to see what gives maximum contrast.
But I gotta say, you might have to retrain an existing network to achieve good performance.
GOAL: I have to create a 3d model of a machine part. I have about 25 images of the same thing taken from different angles.
Progress: I am able to extract the coordinates for a label that is on the machine for most of the images.
Problem: but I have no idea how to proceed. I have read a bit about aero-triangulation, but I couldn't figure out how to implement it. I would really appreciate it, if you could guide me in the right direction.
It would be really helpful, if you could provide your solutions using python and opencv.
Edit: sorry but I cannot upload the code for this one as it is confidential. don't blame me please I am just an intern. Although I can tell that I cropped a template of the label from an image and then used Sift to match that template on all the images to get the coordinates of the label.
If you want to implement things yourself with OpenCV, I would command looking at SIFT (or SURF) features, RANSAC and the epipolar constraint. I believe the OpenCV cookbook describe those. Warning: math involved. And I don't know how to do dense mapping in OpenCV.
I know the GUI program "VisualSFM" that can automatically recreate 3D model from images. It uses SFM and other command line utilities behind the scenes. Since everything is opensource, you could create a python wrapper around the actual libraries (I found https://github.com/mapillary/OpenSfM asking Google). VisualSFM prints the command it calls, so a hacky way could be to call the same commands from python.
If it is a simple shape and you don't want to automate it, it could be faster to model it yourself (and the result could look better). In 1.5 week I managed to learn the basics of blender and to model a guitar necklace: https://youtu.be/BCGKsh51TNA . And I would now be able to do it in less than 1h. How long are you ready to invest to find a solution with OpenCV?
I am microbiology student new to computer vision, so any help will be extremely appreciated.
This question involves microscope images that I am trying to analyze. The goal I am trying to accomplish is to count bacteria in an image but I need to pre-process the image first to enhance any bacteria that are not fluorescing very brightly. I have thought about using several different techniques like enhancing the contrast or sharpening the image but it isn't exactly what I need.
I want to reduce the noise(black spaces) to 0's on the RBG scale and enhance the green spaces. I originally was writing a for loop in OpenCV with threshold limits to change each pixel but I know that there is a better way.
Here is an example that I did in photo shop of the original image vs what I want.
Original Image and enhanced Image.
I need to learn to do this in a python environment so that I can automate this process. As I said I am new but I am familiar with python's OpenCV, mahotas, numpy etc. so I am not exactly attached to a particular package. I am also very new to these techniques so I am open to even if you just point me in the right direction.
Thanks!
You can have a look at histogram equalization. This would emphasize the green and reduce the black range. There is an OpenCV tutorial here. Afterwards you can experiment with different thresholding mechanisms that best yields the bacteria.
Use TensorFlow:
create your own dataset with images of bacteria and their positions stored in accompanying text files (the bigger the dataset the better).
Create a positive and negative set of images
update default TensorFlow example with your images
make sure you have a bunch of convolution layers.
train and test.
TensorFlow is perfect for such tasks and you don't need to worry about different intensity levels.
I initially tried histogram equalization but did not get the desired results. So I used adaptive threshold using the mean filter:
th = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 3, 2)
Then I applied the median filter:
median = cv2.medianBlur(th, 5)
Finally I applied morphological closing with the ellipse kernel:
k1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
dilate = cv2.morphologyEx(median, cv2.MORPH_CLOSE, k1, 3)
THIS PAGE will help you modify this result however you want.
I am working on text classification using Decision Tree which uses information gain as the main value for categorisation of text document. I have extracted few features by TF*IDF value. But I not able to figure out how exactly information gain should be calculated? There are some articles suggesting about this but none of them are very clear how to apply it to Text files.
you can use weka for calculating information gain . in weka InfoGainAttributeEval.java
class will calculate IG for the word with respect to document.check this answer this may help you.
I'm working on a image stabilization by using optical flow.
The algorithm that I've used is like this; first of all I have found good features to track in OpenCv "cvGoodFeaturesToTrack" and then I've estimated the optical flow by using this function for OpenCv as well "cvCalcOpticalFlowPyrLK".
Now I want to stabilize the video sequence, which I think I need to take the average of the optical flow vectors.
I'm working on a real time application so I can't use either SIFT or SURF.
The problem that I don't know how take the average.
Can anyone show me what to do?
Regards
You don't need to average anything. Optical flow will return the position of the "good features to track" in the second image. Transform the second image so that these features coincide with the features on the first image (use GetPerspectiveTransform).
I'll probably write an article on this soon on my website http://aishack.in/