KonradZuse's profile - activity

Sorry, the last sentence was something I just made up in case there was an issue with a giant vocab.

I understand that we use multiple images to create this vocabulary. I also want to make sure we can constantly update this vocabulary? I guess it shouldn't matter when we create a new cluster with new features since the old ones will still be there.

I also find it interesting there aren't m any matchers, just a few brute force methods I saw and the FLANN. I'm assuming I will still be using a matcher with the GIST-Descriptor?

Thanks again for the help.

EDIT: It's a bit difficult to find GIST-Desciptors. I found a link here to a C++ implementation http://www.cs.cornell.edu

http://stackoverflow.com/questions/10051221/lear-gist-descriptor-c-code-used-with-c

Huh, a global descriptor, crazy... So it's a giant descriptor that contains all of the information about it's parts(the individual descriptions of each)? Is this what the "Cluster" function would do?

It seems GIST is what I would be looking at then, you seem to recommend it as well.

Glad to hear that there are varying opinions on size.

I meant spectrum with histograms. I thought that when we get the descriptor we find the images with the closest resemblance, then do a histogram compare and see which ones are the closest resemblance(since there could be more than one that it closely resem

I am going to need to recognize items, but then find out what it is. Like I mentioned a bottle, I could have multiple companies, coke, sprite, fanta, but I need to identify that bottle.

I ran out of space, but I also wanted to know about the BoW vocab size. It seemed like 2000 vocabulary items seemed to be where it started to drop in accuracy(from some paper I saw, I will try ot find it) so I'm curious about how bad it's affected as it grows.

If we have a histogram compare we shoudl be able to at least give a few options, and I would assume that even if we have 100,000 items we still would get the "correct" answer, we might just have a bunch of others that also fit within the spectrum, right?

I'm not sure how many vocab items I will have, but eventually it could be 100,000, I might be able to categorize and such, but my total will definitely be a huge number.

hello all,

I am basically looking to do a bag of words type setup where I comepare a picture taken, with items in the database.

Basically am example of what I am doing is taking a picture of a bookshelf, and identifying the books on it. So 1 picture could contain 50 "vocabulary" items.

Basically I am curious about which "keypoint detectors" "feature descriptors" and "matchers" I will need.

It seems there are so many choices and I don't know which would be better for what.

I would like to use something other than SURF or SIFT because I hear you need a license and to get that requires a good bit of money.

I have heard good things about FREAK, BRISK, and ORB, but those are only descriptors right? I would still need a keypoint detector and matcher? ( I thought I also heard that some descriptors are also detectors or...?)

I think that one of the more important things would be scale invariance as the picture I have might not be the size of the picture I'm taking within the bookshelf.

I don't think that rotation is that big a deal.

I'm not sure what else I should ask about these but if anyone has any input to help me on my path I would greatly appreciate it...

As for BoW itself I hear you basically have your vocab of keypoints, then you compare them to the keypoints in the image, and then do a histogram compare?

I also believe I heard something about training classifiers? Why exactly would we need to do that? To identify the items within the whole picture? like a bottle compared to a box?

I think that's all, thanks again to anyone who can help,

~KZ

Thanks for the reply. the file is in my classpath, int he same package as the class. If I change the text it will say the file does not exist. I tried this and the haar_frontalFace_alt.xml

I just put in the actual path and it worked, I should have realized this when I had to do that for the actual picture since it idnd't like reading it from my classpath.

and I will check for the detector.isEmpty().

Thank you!

Hello all,

I just started with the tutorials and I went directly into the example with Netbeans IDE.

I tried a couple of the xml face classifers, none worked. I used a few different pictures with faces, including the lena.png, none worked.

the outputted picture is correct(without the green box) so I don't know why it's not detecting.

Output:

run: Hello, OpenCV

Running DetectFaceDemo Detected 0 faces Writing faceDetection.png

package imagerecognition;

import org.opencv.core.Core; import org.opencv.core.Mat; import org.opencv.core.MatOfRect; import org.opencv.core.Point; import org.opencv.core.Rect; import org.opencv.core.Scalar; import org.opencv.highgui.Highgui; import org.opencv.objdetect.CascadeClassifier;

// // Detects faces in an image, draws boxes around them, and writes the results // to "faceDetection.png". // class DetectFaceDemo { public void run() { System.out.println("\nRunning DetectFaceDemo");

// Create a face detector from the cascade file in the resources
// directory.
CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath());
Mat image = Highgui.imread("F:/lena.png");

// Detect faces in the image.
// MatOfRect is a special container class for Rect.
MatOfRect faceDetections = new MatOfRect();
faceDetector.detectMultiScale(image, faceDetections);

System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));

// Draw a bounding box around each face.
for (Rect rect : faceDetections.toArray()) {
    Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
}

// Save the visualized detection.
String filename = "faceDetection.png";
System.out.println(String.format("Writing %s", filename));
Highgui.imwrite(filename, image);

} }

public class ImageRecognition { public static void main(String[] args) { System.out.println("Hello, OpenCV");

// Load the native library.
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
new DetectFaceDemo().run();

} }

I figured I might have skipped over something since I didn't try SBT or the ant build, but tried what I thought would work. Am I missing anything?

Thanks,

~KZ