I am trying to use the vision.cascadeObjectDetector (MATLAB) to detect heads from a CCTV footage(and not faces). Till now, I have tried the following:
Used vision.cascadeObjectDetector to detect faces.
trained it to detect a sign (an example shown in read me file)
created a folder with positive & negative training HEAD images & same for testing
tried to create a .mat file using trainingImageLabeler (although it gave me an error when trying to pass it as param to the object detector).
Can someone try to put me in the right track, to start detecting heads using Viola-Jones Haar features please?
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I'v been trying to do quadrangle detection and localization for weeks, my goal is to have a robust way of getting the 4 points of an quadrangle(rectangle), so I can apply projective transform to an Image then attach it to the source image. I have try the classic opencv contour method, and also using hough transform to find lines then calculate intersections, those two methods is unusable when apply it to real life images.
So I turn to CNN for help, but currently i haven't find any one try to use CNN to solve this simple problem.
My first attempt is to use state-of-art object detection and localization methods to get quadrangle's bounding box so i can narrow the search of 4 points, then use image processing & computer vision methods to further the search for 4 points. but after trying YOLOv2 and Faster-RCNN, the prediction accuracy is not ideal.
So I'm wondering if there is any idea i can do this end to end, training and feedforward all using a single neural network. it also must be able to deal with occlusion reasonably well.
Currently my idea is to remove the fc-layers and make a huge activation map that has the same width and height as the first input layer(eg. 448x448) then optimize the 4 most highly activated areas, using argmax to get the position. but this method only works for one quadrangle it doesn't work well with corner occlusions as well.
I'll be appreciated if anyone can provide any suggestions. Thanks a lot!
You are absolutely right about the first methods you mentioned. Hough transform like methods are old and not useful for images in the wild. And of course, computer vision field turned its face to object detection and recognition with rise of deep learning.
However, there is a very nice discussion came up recently.
Have we forgotten about Geometry in Computer Vision?
My suggestion would be contour detection and then apply Hough transform(use state of the art) methods to detect rectangles you want, about the occlusion, you can set parameters for Hough transform to be more forgiving for missing edge pixels with parameters.
You can for example check most recent contour detection methods as in recent CVPR paper.
I want to use the trainCascadeObjectDetector in Matlab. It requires an array with the regions of interest of the images. I found two apps where you can put boxes around the rois and the array gets created automatically:
Cascade Trainer: Specify Ground Truth, Train a Detector
Training Image Labeler
Unfortunately they both require Matlab R2014 and I only got R2013.
Is there an other way to define the rois without manually creating the array?
Regards
Philip
I did not find an other solution so I wrote a custom Matlab script for the job. The imrect function in Matlab is well suitable for this. After the image is shown, the user can drag a rectangular over the region of interest. The coordinates of the region than get stored in a structure together with the path to the image file. Additionally the parts of the image that do not belong to the roi are stored in the negative sample folder.
Recently, I have to do a project of multi view 3D scanning within this 2 weeks and I searched through all the books, journals and websites for 3D reconstruction including Mathworks examples and so on. I written a coding to track matched points between two images and reconstruct them into 3D plot. However, despite of using detectSURFFeatures() and extractFeatures() functions, still some of the object points are not tracked. How can I reconstruct them also in my 3D model?
What you are looking for is called "dense reconstruction". The best way to do this is with calibrated cameras. Then you can rectify the images, compute disparity for every pixel (in theory), and then get 3D world coordinates for every pixel. Please check out this Stereo Calibration and Scene Reconstruction example.
The tracking approach you are using is fine but will only get sparse correspondences. The idea is that you would use the best of these to try to determine the difference in camera orientation between the two images. You can then use the camera orientation to get better matches and ultimately to produce a dense match which you can use to produce a depth image.
Tracking every point in an image from frame to frame is hard (its called scene flow) and you won't achieve it by identifying individual features (such as SURF, ORB, Freak, SIFT etc.) because these features are by definition 'special' in that they can be clearly identified between images.
If you have access to the Computer Vision Toolbox of Matlab you could use their matching functions.
You can start for example by checking out this article about disparity and the related matlab functions.
In addition you can read about different matching techniques such as block matching, semi-global block matching and global optimization procedures. Just to name a few keywords. But be aware that the topic of stereo matching is huge one.
my problem is how to match one image to a set of images and to display the matched images. I am using SURF feature for feature extraction.
If you have the Computer Vision System Toolbox, take a look at the following examples:
Object Detection In A Cluttered Scene Using Point Feature Matching
Image Search using Point Features
I am attempting to do some face recognition and hallucination experiments and in order to get the best results, I first need to ensure all the facial images are aligned. I am using several thousand images for experimenting.
I have been scouring the Internet for past few days and have found many different programs which claim to do so, however due to Matlabs poor backwards compatibility, many of the programs no longer work. I have tried several different programs which don't run as they are calling onto Matlab functions which have since been removed.
The closest I found was using the SIFT algorithm, code found here
http://people.csail.mit.edu/celiu/ECCV2008/
Which does help align the images, but unfortunately it also downsamples the image, so the result ends up quite blurry looking which would have a negative effect on any experiments I ran.
Does anyone have any Matlab code samples or be able to point me in the right direction to code that actually aligns faces in a database.
Any help would be much appreciated.
You can find this recent work on Face Detection, Pose Estimation and Landmark Localization in the Wild. It has a working Matlab implementation and it is quite a good method.
Once you identify keypoints on all your faces you can morph them into a single reference and work from there.
The easiest way it with PCA and the eigen vector. To found X and Y most representative data. So you'll get the direction of the face.
You can found explication in this document : PCA Aligment
Do you need to detect the faces first, or are they already cropped? If you need to detect the faces, you can use vision.CascadeObjectDetector object in the Computer Vision System Toolbox.
To align the faces you can try the imregister function in the Image Processing Toolbox. Alternatively, you can use a feature-based approach. The Computer Vision System Toolbox includes a number of interest point detectors, feature descriptors, and a matchFeatures function to match the descriptors between a pair of images. You can then use the estimateGeometricTransform function to estimate an affine or even a projective transformation between two images. See this example for details.