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.
Related
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 am trying to implement Canny edge detection found hereCanny edge to differentiate objects based on their shapes. I would like to know what are the features? I need to find a score/metric so that I can define a probability from information like mean of the shape. The purpose is to differentiate between objects of different shapes. So, lets assume that the mean shape(x) of Object1 and Object2 are x1,x2 and the standard deviation(s) is s1,s2 respectively. From what do I calculate these information and How do I find these information?
Canny Algorithm is an edge detector. It searches for high frequencies in the image by computing the magnitude of the derivatives in x and y direction. In the end of you have contours of objects. What you are trying to do is to classify objects and using Canny does not sound like a right way to do it, I am not saying you cannot build features out of edges, but it might perform poorly.
In order to achieve what you want, you need first to identify what features are important for you. You mentioned the shape but is the color a good feature for the class of objects you are trying to find? Your pictures show very colorful objects. Are you only trying to distinguish one object to the other (considering the images only display only the object of interest) or do you want locate them in the screen? Does the image contain only one object or multiple ones?
I will give you some direction regarding feature modeling.
If color is a strong information for your objects, you could model your features using histogram information, compute n bins for all objects and store the distribution of the bins as a feature vector. You can use HOG.
Another possible (naive) solution is to compute all colors of patches (e.g. 7x7) belonging to each object and to compute later the histogram over patches instead of single pixels.
If you are not satisfied with color information and you would like to differentiate objects by comparing information in their neighborhood, you can use local binary patterns, which might be enough for the type of information you have.
Once you decide on the features which are important and modeled them, you can go for the classification (which is gonna determine which object you are seeing given a certain feature).
A probabilistic framework tries to estimate the posterior probability P(X|C), i.e. what is the probability of being object X given that we observed C (C could be your feature) and this is very powerful. You might consider reading about Maximum Likelihood Estimation and Maximum a posteriori. Also, a Naive Bayes classifier is a simple off the shelf algorithm available on Opencv that you could use.
You could use many other algorithms, such as SVM, Boost, Decision Trees, Neural Networks and so on. Bag of visual words is also a nice alternative.
If you are interested how to separate the object of interest from the background you are talking about image segmentation, you can look at K-Means or more powerfully Graph Cuts techniques. Of course you can always segment first and then classify the segmented blobs.
Samuel
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
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.
I have a set of 60D shape context vectors. These were constructed using a sample of 400 edge points from a silhouette using 5 radial bins and 12 angular bins (thus, I have 400 shape context vectors of 60D).
I would like to analyse just how descriptive these vectors are in representing the overall shape of the underlying silhouette. To do this, I would like to project the 60D shape context vectors back into 2D space and visually inspect the result -- what I am hoping to see is a set of points that roughly resemble the original silhouette's shape.
An approach to do this is by projecting on the first two principal components (PCA). Based on my implementation, the projected points did not resemble the silhouette's shape. I can see two main reasons for this (assuming for the time being that my implementation is correct): (1) shape context is either not appropriate as a descriptor given the silhouettes, or it's parameters need to be better tuned (2) this analysis method is flawed / not valid.
My question is whether this is the right approach for analysing the descriptiveness of shape contexts in relation to my silhouette's shape? If not, can someone please explain why and propose an alternative method?
Thanks,
Josh
The good way to check whether features are descriptive or not is to try train some classifier(svm/bayes/tree/whatever) upon them and check it cross-validated precision/recall etc. You also can filter your feature vector by feature selector like Chi/infogain.
Other than PCA, you can visualize your data with SOM, or by clustering.
I think this analysis method is flawed/not valid. I think this would be a similar reasoning: I can reconstruct the view from above on a football field by doing PCA on what each football player sees. It just isn't reasonable to expect that.
I think the simplest way to analyze the descriptiveness of shape context is to download MNIST or some other databases of written digits, and compute the 10x10 matrix of the shape similarities of 5 ones and 5 twos, and then draw this graph using (say) graphviz.