image-segmentation

I'm able to train a U-net with labeled images that have a binary classification. But I'm having a hard time figuring out how to configure the final layers in Keras/Theano for multi-class classification (4 classes). I have 634 images and corresponding 634 masks that are unit8 and 64 x 64 pixels. My masks, instead of being black (0) and white (1), have color labeled objects in 3 categories plus background as follows: black (0), background red (1), object class 1 green (2), object class 2 yellow (3), object class 3 Before training runs, the array containing masks is one-hot encoded as follows:

I am working on dress feature identification using opencv. As a first step, I need to segment t-shirt by removing face and hands from the image. Any suggestion is appreciated. I suggest the following approach: Use Adrian Rosebrock's skin detection algorithm for detecting the skin (thank you for Rosa Gronchi for his comment). Use region growing algorithm on the variance map. The initial seed can be calculated by using stage 1(see the attached code for more information). code: %stage 1: skin detection - Adrian Rosebrock solution im = imread(<path to input image>); hsb = rgb2hsv(im)*255; skinMask

I have to do a foreground/background segmentation using maxflow algorithm in C++. ( http://wiki.icub.org/iCub/contrib/dox/html/poeticon_2src_2objSeg_2src_2maxflow-v3_802_2maxflow_8cpp_source.html ). I get an array of pixels from a png file according to their RBG but what are the next steps. How could I use this algorithm for my problem? I recognize that source very well. That's the Boykov-Kolmogorov Graph Cuts library. What I would recommend you do first is read their paper . Graph Cuts is an interactive image segmentation algorithm. You mark pixels in your image on what you believe belong to

I am using python and PIL to find the centroid and rotation of various rectangles (and squares) in a 640x480 image, similar to this one So far my code works for a single rectangle in an image. import Image, math def find_centroid(im): width, height = im.size XX, YY, count = 0, 0, 0 for x in xrange(0, width, 1): for y in xrange(0, height, 1): if im.getpixel((x, y)) == 0: XX += x YY += y count += 1 return XX/count, YY/count #Top Left Vertex def find_vertex1(im): width, height = im.size for y in xrange(0, height, 1): for x in xrange (0, width, 1): if im.getpixel((x, y)) == 0: X1=x Y1=y return X1,

I am trying to count the number of hairs transplanted in the following image. So practically, I have to count the number of spots I can find in the center of image. (I've uploaded the inverted image of a bald scalp on which new hairs have been transplanted because the original image is bloody and absolutely disgusting! To see the original non-inverted image click here . To see the larger version of the inverted image just click on it). Is there any known image processing algorithm to detect these spots? I've found out that the Circle Hough Transform algorithm can be used to find circles in an

I am reading a lot of tutorials that state two things. "[Replacing fully connected layers with convolutional layers] casts them into fully convolutional networks that take input of any size and output classification maps." Fully Convolutional Networks for Semantic Segmentation, Shelhamer et al. A traditional CNN can't do this because it has a fully connected layer and it's shape is decided by the input image size. Based on these statements, my questions are the following? Whenever I've made a FCN, I could only get it to work with a fixed dimension of input images for both training and testing.