I want to differentiate between two classes of objects through the differences in the shape of blob(blob is in the form of binary image) using shape descriptors and machine learning .I want to ask if there is any good shape feature which I can use to detect the descriptors for the irregular contour or blob obtained ?
there is a large body of work associated with shape descriptors, these methods work on either the outer edge detected pixels (the boundary) or the full filled-in binary shape. Both approaches rely on making the shape descriptors invariant to translation, rotation and scaling, and some to skew. The classical boundary method is Fourier Descriptors and the classic filled in method is Moment Invariants, both are covered in most good image processing textbooks and are easy to implement with OpenCV.
The answer is very subjective on the kinds of shapes you are looking for. If the contours of the shapes are discriminative enough, you can try shape context. To classify shapes, feed in these features into any classifier -- SVM or random forests for instance.
If the shapes have consistently occuring corners, then you can extract the corners using FAST or SURF, and describe the regions around the corners using SIFT or SURF. In this case, shapes are best recognised by feature matching or bags of words.
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I have a program which detect a moving object and I want to classify these objects by identifying the shape of each one with a dataset of shapes.Can any one have any idea how to compare the shape of each object with the dataset using some points of the current shape and compare it with the samples?
image1
detected object1
From the theory point of view you should start off reading two papers
1) HOG detectors by Dalal and Trigs
2) Chamfer Detectors by Gavrila
If you just want to use the edge information, Chamfer is the solution. From my experience it fails miserably on cluttered scenes. HOG produces far superior results.
OpenCV already has human body detector implemented
If you are looking for a Machine Learning adventure why not train your own HOG detector using OpenCV train_cascade? It is very fast and real time
I have started my first project in the field of Image recognition using Feature Point detectors and descriptors. I have no prior knowledge on the topics of Image recognition techniques before starting of this project and then I have researched on the available detectors and descriptors and came to know about the differences between them. Finally, I have opted out to work with the ORB detectors and descriptors for Image recognition (If it didn't worked according to my requiremnets then I would like to go out with the BRISK later).
As of now am in a stage of getting the results for Image recognition using ORB. At this Point, I was thinking of to use Gaussian Filters in my code so that I can get better results even though the Input Image is a bit blur.
My questions:
1) Is it possible to use Gaussian filters with ORB to get much better results for Image recognition?
2) When I read the paper on ORB I came to know that the lines below
FAST does not produce a measure of cornerness, and we have found that it has large
responses along edges. We employ a Harris corner measure [11] to order the FAST keypoints.
For a target number N of keypoints, we first set the threshold low enough to get more than
N keypoints, then order them according to the Harris measure, and pick the top N points.
FAST does not produce multi-scale features. We employ a scale pyramid of the image, and
produce FAST Features (filtered by Harris) at each level in the pyramid.
ORB provides the Harris Corner inorder to detect the corners in an image and is it worth for me to use Gaussian Filters along with ORB?
3) ORB uses only Harris Corner to detect the corners or any other?
Please let me know about this and just enlighten me on the above mentioned questions.
I need to prepare training data which I will then use with OpenCV's cascaded classifier. I understand that for training data I'll need to provide rectangular images as samples with aspect ratios that correspond to the -w and -h parameters in OpenCV's training commands.
I was fine with this idea, but then I saw web-based annotation tool LabelMe.
People have labelled in LabelMe using complex polygons!
Can these polygons be somehow used in cascaded training?
Wouldn't using irregular polygons improve the classification results?
If not, then what is the use of the complex polygons that outline objects in LabelMe'd images?
Data sets annotated with LabelMe are used for many different purposes. Some of them, like image segmentation, require tight boundaries, rather than bounding boxes.
On the other hand, the cascade classifier in OpenCV is designed to classify rectangular image regions. It is then used as part of a sliding-window object detector, which also works with bounding boxes.
Whether tight boundaries help improve object detection is an interesting question. There is evidence that the background pixels caught by the bounding box actually help the classification.
I have some conceptual issues in understanding SURF and SIFT algorithm All about SURF. As far as my understanding goes SURF finds Laplacian of Gaussians and SIFT operates on difference of Gaussians. It then constructs a 64-variable vector around it to extract the features. I have applied this CODE.
(Q1 ) So, what forms the features?
(Q2) We initialize the algorithm using SurfFeatureDetector detector(500). So, does this means that the size of the feature space is 500?
(Q3) The output of SURF Good_Matches gives matches between Keypoint1 and Keypoint2 and by tuning the number of matches we can conclude that if the object has been found/detected or not. What is meant by KeyPoints ? Do these store the features ?
(Q4) I need to do object recognition application. In the code, it appears that the algorithm can recognize the book. So, it can be applied for object recognition. I was under the impression that SURF can be used to differentiate objects based on color and shape. But, SURF and SIFT find the corner edge detection, so there is no point in using color images as training samples since they will be converted to gray scale. There is no option of using colors or HSV in these algorithms, unless I compute the keypoints for each channel separately, which is a different area of research (Evaluating Color Descriptors for Object and Scene Recognition).
So, how can I detect and recognize objects based on their color, shape? I think I can use SURF for differentiating objects based on their shape. Say, for instance I have a 2 books and a bottle. I need to only recognize a single book out of the entire objects. But, as soon as there are other similar shaped objects in the scene, SURF gives lots of false positives. I shall appreciate suggestions on what methods to apply for my application.
The local maxima (response of the DoG which is greater (smaller) than responses of the neighbour pixels about the point, upper and lover image in pyramid -- 3x3x3 neighbourhood) forms the coordinates of the feature (circle) center. The radius of the circle is level of the pyramid.
It is Hessian threshold. It means that you would take only maximas (see 1) with values bigger than threshold. Bigger threshold lead to the less number of features, but stability of features is better and visa versa.
Keypoint == feature. In OpenCV Keypoint is the structure to store features.
No, SURF is good for comparison of the textured objects but not for shape and color. For the shape I recommend to use MSER (but not OpenCV one), Canny edge detector, not local features. This presentation might be useful
hello everyone i am pursuing mtech my project is object recognition to recognize specific objects such as weapons etc not allowed at airport so input will be scanned images of baggage/luggage in matlab for now its for static images only now i am using edge detection and histogram processing techniques.. i have gone through internet found ANN genetic algorithm and many more but can't summarize whole scenarios each paper explain in its own way plz help me out to how to proceed with object recognition using edge detection and histogram processing techniques
If you'd like to do object recognition with only the contours, use Shape Context.
Essentially, you will have a database of shapes apriori, where you know the label of each shape (gun, something_harmless_1, knife, something_harmless_2). At query time, you take the contour of your object and compute the Shape Context Distance between your query shape and all shapes in your database. The shape with the shortest Shape Context Distance is then deemed as the true class of your object.
Alternatively, if you wanted to use the histogram of the object, you could do a similar matching but with a different distance metric. Instead of using the Shape Context Distance, you could store a histogram for all objects in your database and compute the Earth Mover's Distance between your query object and all other objects in your database.
It is possible to encode both of these distances in your final result. You can come up with some weighting scheme between them that makes sense for you.