image-segmentation

I'm trying to implement something like a fully convolutional network, where the last convolution layer uses filter size 1x1 and outputs a 'score' tensor. The score tensor has shape [Batch, height, width, num_classes]. My question is, what function in tensorflow can apply softmax operation for each pixel, independent of other pixels. The tf.nn.softmax ops seems not for such purpose. If there is no such ops available, I guess I have to write one myself. Thanks! UPDATE: if I do have to implement myself, I think I may need to reshape the input tensor to [N, num_claees] where N = Batch x width x

I used Keras biomedical image segmentation to segment brain neurons. I used model.evaluate() it gave me Dice coefficient: 0.916. However, when I used model.predict() , then loop through the predicted images by calculating the Dice coefficient, the Dice coefficient is 0.82. Why are these two values different? The problem lies in the fact that every metric in Keras is evaluated in a following manner: For each batch a metric value is evaluated. A current value of loss (after k batches is equal to a mean value of your metric across computed k batches). The final result is obtained as a mean of all

I'm working on a a way to detect the floor in an image. I'm trying to accomplish this by reducing the image to areas of color, and then assuming that the largest area is the floor. (We get to make some pretty extensive assumptions about the environment the robot will operate in) What I'm looking for is some recommendations on algorithms that would be suited to this problem. Any help would be greatly appreciated. Edit: specifically I am looking for an image segmentation algorithm that can reliably extract one area. Everything I've tried (mainly PyrSegmentation) seems to work by reducing the

I've been working on a project for some time, to detect and track (moving) vehicles in video captured from UAV's, currently I am using an SVM trained on bag-of-feature representations of local features extracted from vehicle and background images. I am then using a sliding window detection approach to try and localise vehicles in the images, which I would then like to track. The problem is that this approach is far to slow and my detector isn't as reliable as I would like so I'm getting quite a few false positives. So I have been considering attempting to segment the cars from the background

I am using Tensorflow as a backend to Keras and I am trying to understand how to bring in my labels for image segmentation training. I am using the LFW Parts Dataset which has both the ground truth image and the ground truth mask which looks like this * 1500 training images: As I understand the process, during training, I load both the (X) Image (Y) Mask Image Doing this in batches to meet my needs. Now my question is, is it sufficient to just load them both (Image and Mask Image) as NumPy arrays (N, N, 3) or do I need to process/reshape the Mask image in some way. Effectively, the mask/labels

When people try to solve the task of semantic segmentation with CNN's they usually use a softmax-crossentropy loss during training (see Fully conv. - Long ). But when it comes to comparing the performance of different approaches measures like intersection-over-union are reported. My question is why don't people train directly on the measure they want to optimize? Seems odd to me to train on some measure during training, but evaluate on another measure for benchmarks. I can see that the IOU has problems for training samples, where the class is not present (union=0 and intersection=0 => division