Questions tagged [image-segmentation]

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Why is my Keras model not learning image segmentation?

Edit: as is turns out, not even the model's initial creator could successfully fine-tune it. This is most likely a problem of implementation, or possibly related to the non-intuitive way in which the ...
3
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2answers
67 views

Splitting a pdf containing batch of scanned documents

My question is primarily: is there any ML research paper about splitting a pdf containing a batch of scanned documents (eg bank statements) into individual documents? I have searched for this but I ...
3
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1answer
116 views

Segmentation Network produces noisy output

I've implemented a SegNet and SegNet ReLU variant in PyTorch. I'm using it as a proof-of-concept for now, but what really bothers me is the noise produced by the network. With ADAM I seem to get ...
2
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1answer
41 views

Tool for annotation of images for semantic segmentation

I have been searching around for a software tool, that I can use for annotating images. More specifically I want to do annotation to be used for semantic segmentation, meaning I want to create masks. ...
2
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1answer
311 views

How does the “skip” method work for upsampling? (fully convolutional NN)

I'm studying fully convolutional neural networks for image segmentation, so far i've study and kind of understood the deconv network. Following this tutorial (Upsampling) i can't really understand how ...
2
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0answers
27 views

Video segmentation vs image segmentation

I am new to data science & working on a segmentation model, Basically I need to deploy this segmentation model in android devices using TensorFlow-Lite for real time camera frame segmentation. I ...
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1answer
40 views

What are features in computer vision?

I'm learning how U-NET network works to do semantic segmentation. I think I have understood everything but features. What are those image features? I read that convolutional layers extract features ...
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1answer
54 views

Modifying U-Net implementation for smaller image size

I'm implementing the U-Net model per the published paper here. This is my model so far: ...
1
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1answer
23 views

Implementing U-Net segmentation model without padding

I'm trying to implement the U-Net CNN as per the published paper here. I've followed the paper architecture as closely as possible but I'm hitting an error when trying to carry out the first ...
1
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1answer
83 views

What is Deep learning approach to count the number of Diamonds in an image?

I am working on a project which involves counting the number of diamonds in the provided image. I have a set of images and a VIA annotated .json file which has all the annotations. How do I proceed ...
1
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1answer
24 views

What do the parameters used in crop mean?

When we have an image to be used as an input to a CNN and we want to classify only part of the image, we usually feed the classifier with a crop of the image. Lets say my image is called frame and <...
1
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1answer
59 views

Extremely stochastic validation loss/accuracy

I am working on training DNNs on satellite data. The class distribution in the data is extremely imbalanced, so I train the neural networks using random majority undersampling to artificially balance ...
1
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1answer
64 views

Multiple output size in neural network

In the paper "A NOVEL FOCAL TVERSKY LOSS FUNCTION WITH IMPROVED ATTENTIONU-NETFOR LESION SEGMENTATION" the author use deep supervision by outputing multiple outputmask which have different ...
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2answers
462 views

neural network probability output and loss function (example: dice loss)

A commonly loss function used for semantic segmentation is the dice loss function. (see the image below. It resume how I understand it) Using it with a neural network, the output layer can yield ...
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1answer
43 views

Google Earth Pro Satellite image segmentation using clustering

I have downloaded a satellite image from Google Earth Pro software corresponding to a particular date for a selected area around a place. I want to specifically segment the road lanes from the image ...
1
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1answer
15 views

3 images as one input in CNN (U-Net)

I have been advised by my supervisor that if my U-Net segmentation network has RGB images at the input then I could use the channels for different images - median filter for R, normalization for G, ...
1
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1answer
17 views

Photorealistic synthetic data for object segmentation

Let's say that we have a very small labelled dataset for instance segmentation and there is a photorealistic physics engine available that can produce synthetic data for us. Looking on the web I haven'...
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0answers
73 views

How do you deal with variable input sizes with an encoder-decoder net with skip connections in Keras?

I am currently getting into image segmentation with Keras, and I am using an encoder-decoder type as in the image below. My problem is that applying a MaxPooling2D ...
1
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1answer
55 views

Normalization of CT scans

I trained an infection segmentation models on a large dataset of CT scans, and want to extend it to other datasets to show the ability of the model to generalize. What I found though, is that CT scans ...
1
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1answer
30 views

Ways of calculating the area of colored regions in a map

Background I am a PHD student trying to improve my data science. One of my research projects, has me tasked with determining the size of the clusters in a colored image of regions. Here is an example ...
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0answers
13 views

How to approach coarse-grained semantic segmentation?

I’m looking at doing something like semantic segmentation of images but where I only have pretty coarse-grained labels - roughly, for each 32x32 patch, I know if the answer should be “yes”, “no” or “...
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0answers
751 views

Image Segmentation Class weight using tensorflow keras

I remember definitely being able to pass a list to class_weight with keras (binary image segmentation specifically). For example: ...
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0answers
76 views

What is the difference between proposal-based approach and proposal-free approach?

From here it says that Techniques to solve instance segmentation can be roughly grouped into two categories: proposal-based methods and proposal-free methods. In proposal-based methods, a set of ...
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0answers
24 views

Paragraph extraction from text

I am trying to separate scanned pages of a 3 column book into paragraphs. On the pages there can be images located in an arbitrary location, occupying part of one, two or all three of the columns. ...
1
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1answer
281 views

How to prepare masks for multiclass semantic segmentation?

It's very straightforward for binary semantic segmentation: black color (0s) is responsible for background, whereas white color (1s) is responsible for objects of interest. But what about multiclass ...
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0answers
34 views

Image segmentation for small images with rectangles

I am doing toy project and trying to make image segmentation for rectangles generated in small images. Here the code: ...
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0answers
17 views

An CNN seems like capturing specific range of input data. (Image Segmentation)

I'm trying to build a model to segment brain tumors. I trained a model, and the validation dice coefficient is disappointing(0.6). When i saw the predicted images with the ground truths, it seems ...
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0answers
15 views

Is there a way to test out simple filters before committing to coding them?

Is there a way to test out simple filters before committing to coding them? Like if I want to estimate the feasibility of recognizing some features from images. Or to estimate the effort/...
1
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1answer
17 views

Tools suitable for semi-automatic video labeling?

So far I have been using labelme to label objects in videos I use for training, but it is quite time consuming. Are there good tools to help with that? I was thinking about a tool where I label ...
0
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1answer
51 views

For semantic sementation, why am I getting better loss values with binary cross entropy than dice coef?

I'm learning all related to data science and how to train U-Net to do semantic segmentation. I have a U-NET with this loss function: ...
0
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1answer
9 views

Which colour channel from a TIFF image do I have to use?

I'm going to use the following dataset to do semantic segmentation with U-Net network. LGG Segmentation Dataset This dataset contains brain MR images together with manual FLAIR abnormality ...
0
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1answer
130 views

Confusion matrix of UNET image sgemenation model

I have used Unet model for image segmentation. I have used RGB images and corresponding image masks and at output i got corresponding region of interest. Now i want to find confusion matrix of this ...
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0answers
10 views

How to use mean IoU for RGB mask (keras implementation)?

I am training pix2pix GAN for converting SAR satellite images to segmentation mask. But I am not aware about how to use the mean IoU to evaluate my model. My output is of ...
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0answers
15 views

Best algorithm for real-time instance segmentation in videos?

I want to do object detection in real-time (meaning localization and classification) on videos (25 FPS), but with the added constraint that my training data is labelled using binary masks, rather than ...
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0answers
11 views

How can I create .nii (nifti) file from 3D Numpy array

I have a prediction numpy array. How can I make a .nii or .nii.gz mask file from the array?
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0answers
15 views

U-Net doesn't work with images different from the dataset

I have implemented a very similar U-Net code from github, but for a different dataset, this one, to segment roads, it works fine using the test folder images, but when i for example, pick a print from ...
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0answers
6 views

Instance segmentation mask creation for adjacent objects within the same class

How are instance segmentation masks created for adjacent objects from the same class ? If for example we have different objects, we can distinguish between them using the labels, but how to ...
0
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0answers
7 views

Boundary segmentation

I have one problem regarding segmentation. I would like to put more concentration on the boundary instead of the interior of segmented part. Is that possible to do using Tensorflow 2? For example, if ...
0
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1answer
16 views

Semantic segmentation in high-resolution images with high variance - cannot avoid underfitting

I am working on a dataset of 2K images for a semantic segmentation problem. I want to detect and localize small objects, with the smallest mask to be 5x5 pixels. The images include 5 different ...
0
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1answer
26 views

Creating parallel keras layers

I am new to Keras and ML and I want to create a NN that can seperate a bitmap-like image into its visual components. My approach is to feed a two dimensional image (lets say 8 by 8 pixels) into a NN, ...
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0answers
14 views

Changing order of input dimension in Tensorflow 3-D Layers

According to the official documentation of tf.keras.layers.Conv3D 5+D tensor with shape: batch_shape + (channels, conv_dim1, conv_dim2, conv_dim3) if data_format='channels_first' or 5+D tensor with ...
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0answers
22 views

Agglomerative Hierarchical Clustering on Images

My goal is to implement the agglomerative hierarchical clustering algorithm on an RGB image to cluster every pixel until some stopping criteria is reached. In order to do so, I assumed that each pixel ...
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0answers
11 views

Semantic segmentation to instance segmentation

Having a mask from semantic segmentation I want to split it to list of non-overlapping instances masks(like an output from instance segmentation). Do you know some fast algorithm, approach, tool or ...
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0answers
9 views

I have no access to gpu due to usage limits?

I start running my code using google colab I first set the execution to GPU and then I run my code for a training task using keras !after 1 hour I got a message saying I can't use GPU due to usage ...
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0answers
55 views

Implementing Multiclass Dice Loss Function

I am doing multi class segmentation using UNet. My input to the model is HxWxC and my output is, ...
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0answers
12 views

Satellite Change Segmentation using Unet

Hi StackExchange community I am working on to train a Unet for satellite change segmentation. My dataset consists of images(before change),images(after change) and the corresponding change ...
0
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0answers
8 views

How to generate fixed number of superpixels?

A lot of work regarding Graph Neural Networks require fixed number of nodes. In the case of image processing using graph, the image representation is often super pixels (like in this work https://...
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0answers
15 views

Loss function to compare non binary segmentation

I need to compare two images corresponding to landmark locations. I was thinking of something related to Dice loss. I cannot use dice loss since the image is not binary. The background is black but ...
0
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0answers
24 views

loss function for multi-label segmentation with class inbalance

In order to use a binary segmentation loss function in a multi label problem, I would like to permute the batch axis with the channel axis in the loss computation in order to compute the loss by ...
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0answers
9 views

How to Convolve a High-Res Image by a (fully convolutional) CNN kernel?

My CNN is an extremely simple neural network. ...