feature-detection

I am developing an application that will behave slightly different depending on the type of screen. Is there any way to detect it? android.content.res.Configuration contains a value called touchscreen , which could be TOUCHSCREEN_STYLUS (=resistive), TOUCHSCREEN_FINGER (=capacitive), TOUCHSCREEN_NOTOUCH (=no touch screen), TOUCHSCREEN_UNDEFINED (=uh oh). EDIT: I got Dianne'd again :) So - bottom line, it seems like there is no way to get the actual physical properties of the screen. I guess your best bet is to have a setting to allow users to switch between your two modes. I have a small trick

I am training DLIB 's shape_predictor for 194 face landmarks using helen dataset which is used to detect face landmarks through face_landmark_detection_ex.cpp of dlib library. Now it gave me an sp.dat binary file of around 45 MB which is less compared to file given ( http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2 ) for 68 face landmarks. In training Mean training error : 0.0203811 Mean testing error : 0.0204511 and when I used trained data to get face landmarks position, IN result I got.. which are very deviated from the result got from 68

For my college project I need to identify a species of a plant from plant leaf shape by detecting edges of a leaf. (I use OpenCV 2.4.9 and C++), but the source image has taken in the real environment of the plant and has more than one leaf. See the below example image. So here I need to extract the edge pattern of just one leaf to process further. Using Canny Edge Detector I can identify edges of the whole image. But I don't know how to proceed from here to extract edge pattern of just one leaf, may be more clear and complete leaf. I don't know even if this is possible also. Can anyone please

As far as I know, CNN rely on sliding window techniques and can only indicate if a certain pattern is present or not anywhere in given bounding boxes. Is that true? Can one achieve localization with CNN without any help of such techniques? Thats an open problem in image recognition. Besides sliding windows, existing approaches include predicting object location in image as CNN output, predicting borders (classifiyng pixels as belonging to image boundary or not) and so on. See for example this paper and references therein. Also note that with CNN using max-pooling, one can identify positions of

I am using the following algorithm to detect skin color, but its not working real well in different lighting conditions. Can anybody offer any advice how to improve it or suggest a better approach R > 95 AND G > 40 AND B > 20 AND max{R, G, B} – min{R, G, B} >15 AND |R – G| > 15 AND R > G AND R > B OR R > 220 AND G > 210 AND B > 170 AND |R – G| <= 15 AND R > B AND G > B http://softexpert.wordpress.com/2007/10/17/skin-color-detection/ Cheers Your given algorithm is simple colour based thresholding. This will only work for a very basic set of conditions. For a few pictures it may give really good

I am implementing a Harris corner detector for educational purposes but I'm stuck at the harris response part. Basically, what I am doing, is: Compute image intensity gradients in x- and y-direction Blur output of (1) Compute Harris response over output of (2) Suppress non-maximas in output of (3) in a 3x3-neighborhood and threshold output 1 and 2 seem to work fine; however, I get very small values as the Harris response, and no point does reach the threshold. Input is a standard outdoor photography. [...] [Ix, Iy] = intensityGradients(img); g = fspecial('gaussian'); Ix = imfilter(Ix, g); Iy =