Questions tagged [image-segmentation]
For questions related to image segmentation (in computer vision and related AI fields).
89
questions
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1answer
16 views
Dissection of a depth map
I am curious about how depth maps work. While searching I came across this website which contains some images and their depth maps. I took this depth map and tried to study it using python pillow.
<...
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29 views
What are the existing AI methods to approach 3D volumes of computed tomography?
I have a dataset which consists of computed tomography images (CT scans) of parts that contain pores and cracks. The sets for each part are of about 1100 * 1100 * 3000-ish resolution. Currently, I use ...
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9 views
Segmenting an instance of an object based on training with small dataset of similar objects and background
I am seeking for your advice with the topic related to segmentation.
Imagine the flying bird in the sky and a man taking a picture of that bird every second. There is very little change happening to ...
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11 views
How to change a single object detection network to a multiple object detection network?
I have trained a CNN network to detect a circle and approximate its centre and radius in an image. What I want to do now is detect the centre and radius of all the circles if there are multiple ...
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36 views
Anything similar to BERT but for pixel-wise embedding in images
In NLP there is BERT which can take a sentence and turn it into an embedding (vector representation) which in some ways encompasses the "meaning" or more precisely the context of the ...
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12 views
Why do the authors of this paper down-sample by $ds_1 / 2$ (in the context of coarse-to-fine segmentation)?
This question is a follow-up of this post and based on this paper. In section 2.2, the authors write:
In the first level, the 3D FCN is trained on images of the lowest resolution in order to capture ...
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1answer
40 views
What does 'downsampling' and 'upsampling' mean in coarse-to-fine segmentation?
The paper here in section 2.1 Coarse-to-fine prediction:
To increase the field of view presented to the CNN and reduce the
redundancy among neighboring voxels, each image is downsampled by a factor ...
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25 views
How to improve the Loss and Learning curves and smoothen them
I am fairly new to deep learning and I have been testing out several architectures for the segmentation task of clouds in satellite imagery. I am using a simple Unet as my benchmark, Unet++, Efficient ...
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16 views
Predicting the the motion of a 3D object when the motion of a set of markers is known
trying to figure out where to get started with this:
I have a few hundred CT images where certain three-dimensional features in the image (anatomy) are moving in a correlated fashion with a set of ...
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2answers
125 views
Unet Overfitting for binary segmentation of fake images
I am working on a project where I am trying to detect and localize forgeries in images. I am using the CASIA v2 dataset and using Unet model for the task. I have the binary masks of all the images in ...
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0answers
72 views
Semantic segmentation - background or ignore for non-target classes?
I am training a deep learning model for semantic segmentation. I am using the cityscapes dataset for training/evaluation.
In cityscapes, there are 34 classes, and of which, we consider only 19 classes ...
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16 views
Why doesn't U-Net work with images different from the dataset?
I have implemented a U-Net, similar to this implementation, but for a different dataset, this one, to segment roads.
It works fine using the test folder images, but, for example, when I pick a print ...
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24 views
How a Superpixel Pooling Layer can be used for image segmentation?
The concept of Superpixel Pooling Layer can be found in the paper "Weakly Supervised Semantic Segmentation Using Superpixel Pooling Network".
The general idea of superpixel pooling is very ...
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15 views
Instance Segmentation Using A Semantic Segmentation Map and Class-Wise Bounding Boxes
Is it possible to perform instance segmentation if you have the following:
Binary Segmentation Map
Bounding Boxes (with respective class)
Let's say we're doing something within cellular microscopy ...
0
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1answer
18 views
How to use mixed data for image segmentation?
I have a task for which I have to do image segmentation (cancer detection on MRIs). If possible, I would also like to include clinical data (i.e. numeric/categorical data which comes in the form of a ...
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1answer
22 views
What are the state-of-the-art Person-Detektion / Human-Segmentation?
I would like to use a deep learning approach to detect people in videos. I have found some freely accessible implementations like Human Segementation with Pytorch or BodyPix / DeepLab / Pixellib with ...
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26 views
How would one modify the Mask-RCNN head for polyline detection?
In Mask-RCNN they modify the standard mask head for human pose keypoint detection with the following tweaks:
Each keypoint is a 1-hot mask
Instead of sigmoid non-linearity on the output of the final ...
3
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1answer
74 views
How to incorporate a symmetry constraint in the loss function to train a CNN?
I have a task of extremely sparse binary segmentation, i.e. the segmentation mask contains either 0 or 1, and there are ~95% zeros and only ~5% ones. I use the focal loss to address the sparseness (...
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20 views
What does Dice Loss should receive in case of binary segmentation
I implemented Dice loss class in pytorch:
...
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0answers
15 views
feeding a NN with tensors with varying spatial dimensions
I have a huge dataset where I have a tensor with 535 channels but varying spatial dimension (but always a square) it can vary from ...
2
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0answers
37 views
How and why do state-of-the-art models in medical segmentation differ from general segmentation models?
I am just getting into medical image segmentation and have been able to understand the state-of-the-art architectures, like Double UNet, UNet++, and Multiresunet.
What I haven't understood yet: Why ...
1
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1answer
56 views
Have I understood the loss function from the original U-Net paper correctly?
In the original U-Net paper, it is written
The energy function is computed by a pixel-wise soft-max over the final
feature map combined with the cross entropy loss function.
...
$$
E=\sum_{\mathbf{x} ...
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2answers
28 views
Semantic segmentation CNN outputs all zeroes
I'm using MATLAB 2019, Linux, and UNet (a CNN specifically designed for semantic segmentation). I'm training the network to classify all pixels in an image as either cell or background to get ...
3
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1answer
78 views
What is the use of the regular convolutional layer in expansion path of U-Net?
I was going through the paper on U-Net. U-net consists of a contracting path followed by an expanding path. Both the paths use a regular convolutional layer. I understand the use of convolutional ...
1
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1answer
49 views
Training a classifier on different datasets with different image conditions for different labels causes the model to infer using the background
I have an interesting problem related to training the model on two different datasets for the target feature on images taken on different conditions, which might affect the model's ability to ...
1
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1answer
46 views
How does general image background removal AI work?
I'm well aware of the inner workings of CNN models for object detection, and although I've not worked on a semantic segmentation problem I can imagine how it works.
With these types of models, we need ...
3
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0answers
36 views
Validation accuracy higher than training accurarcy
I implemented the unet in tensorflow for the segmentation of MRI images of the thigh. I noticed I always get a higher validation accuracy by a small gap, independently of the initial split. One ...
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0answers
30 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,
...
0
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1answer
35 views
Semantic segmentation failing in small instance detection
I performed semantic segmentation with U-net. My dataset consists of grayscale images of defects. After training the dataset for I got an metric accuracy of only 0.3 - 0.4 IOU. Eventhough it is merely ...
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0answers
7 views
How to find rotation x/y/z of chessboard diagram with what network architecture?
I want to recognize pieces of chessboard diagram (not real 3d pieces but just diagram).
I split this task in some operation like rotation/cutting/segmentation.
First of all I want to detect chessboard ...
2
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0answers
29 views
Loss function decays linearly in segmentation MRI fascia
I am working on a segmentation of MRI images of the thigh. I am trying to segment the fascia, there is a slight imbalance between the background and the mask. I have about 1400 images from 30 patients ...
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0answers
27 views
How can I improve the performance on unseen data for semantic segmentation using an auto-encoder?
I am using simple autoencoders for the task of semantic segmentation on the VOC2012 dataset. I am currently using a simple autoencoder based model. It is trained on adam optimizer with cross-entropy ...
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0answers
40 views
Is Webpage Semantic Segmentation possible nowadays?
I'm trying to do some research about semantic segmentation for webpages, in particular e-commerce webpages. I found some articles which provide some solutions based on very old dataset and those ...
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0answers
25 views
Could the data augmentation lead to the model learning features which corresponds to data augmented data and not to the real data?
I am trying to train a Unet network with Synthetic data to do binary segmentation due to the fact that is is not easy to collect real data.
And there is something in the training process that I do not ...
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0answers
235 views
Getting bounding box/boundaries from segmentations in UNet Nuclei Segmentation
From my understanding, in a tissue where nuclei are present and need to be detected, we need to predict bounding boxes (either rectangular/circular or in the shape of the nucleus, i.e. as in instance ...
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33 views
Choice of loss function for semantic segmentation
I am training a U-Net for semantic segmentation of large medical images (4096x4096px). The two classes are "too" unbalanced. The white pixels are just about 0.1% (or less) of the whole image....
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46 views
Training a CNN for semantic segmentation of large 4600x4600px images
I am trying to implement a CNN (U-Net) for semantic segmentation of similar large grayscale ~4600x4600px medical images. The area I want to segment is the empty space (gap) between a round object in ...
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0answers
18 views
Model output segmentation maps which are not full
I created a VGG based U-Net in order to perform image segmentation task on yeast cells images obtained by a microscope.
There are a couple of problems with the data:
There is inhomogeneity in the ...
2
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0answers
26 views
What is human-level performance for semantic segmentation?
I see so many papers claim to have an algorithm that beats 'human-level performance' for semantic segmentation tasks, but I can't find any papers reporting on what the human-level performance actually ...
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0answers
16 views
Why it is reshaped the last layers of VGG_UNet segmentation model?
I want to do a multiclass segmentation task using deep learning (in python). Here, is a summary of vgg_unet model that is mainly collected from GitHub. So, in my dataset 8 labels are available. So, at ...
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0answers
48 views
Extending patch based image classification into image classification
I am trying to classify tampered, pristine images from set of images, in that I have built a network in which I would divide the image into multiple overlapping patches and then classify them into ...
0
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1answer
53 views
How do we make our outputs to have the same size as the true mask?
When we are doing multi-label segmentation tasks, our y_true (the mask) will be (w, h, 3), but, in our model, at the last layer, ...
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0answers
67 views
How can the FCNN reduce the dimensions of the input from $1048 \times 100$ to $523 \times 100$ with max-pooling?
I am trying to implement a paper on Image tempering detection and localization, the paper is Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks, I was ...
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1answer
53 views
How to quickly change hand-drawn shapes to symmetrical polished shapes?
Given a hand-drawn shape, I'd like to generate the corresponding symmetrical polished shapes such as circle, rectangle, triangle, trapezoid, square, parallelogram, etc.
A short video demonstration
...
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0answers
78 views
Is it necessary to label the background when generating the labelled dataset for semantic segmentation?
When I label images for semantic segmentation (using u-net, if that matters), is labeling the background (anything I am not interested in) necessary? Will it improve the network's performance?
3
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2answers
125 views
Is such a captcha AI-resistant?
Let's say we have a captcha system that consists of a greyscale picture (of a part of a street or something akin to re-captcha), divided into 9 blocks, with 2 missing pieces.
You need to choose the ...
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0answers
46 views
Suppress heatmap non-maxima in segmentation with UNet
I'm using U-Net for image segmentation.
The model was trained with images that could contain up to 4 different classes. The train classes are never overlapping.
The output of the UNet is a heatmap (...
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0answers
53 views
Is Reinforcement Learning what I need for this image to image translation problem?
I have a paired dataset of binary images A and B: A1 paired with B1, A2-B2, etc., with simple shapes (rectangles, squares).
The external software receives both images A and B and it returns a number ...
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0answers
313 views
How to compare SegNet, U-Net and EfficientNet?
SegNet and U-Net are created for segmentation problem and EfficientNet is created for classification problem. I have a task and it is saying that train these models on the same dataset and compare ...
3
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1answer
6k views
What is a fully convolution network?
I was surveying some literature related to Fully Convolutional Networks and came across the following phrase,
A fully convolutional network is achieved by replacing the parameter-rich fully ...
