Questions tagged [image-segmentation]
For questions related to image segmentation (in computer vision and related AI fields).
128 questions
0
votes
1
answer
52
views
Should I label static objects on video dataset?
I'm using nvidia Transfer Learning Toolkit to detect cars in some video frames.
I found some dataset (for example https://www.jpjodoin.com/urbantracker/dataset.html and https://www.kaggle.com/...
-1
votes
1
answer
154
views
How to re-train an AI model to have smaller input image size
I need a PyTorch Model which can do road segmentation on OAK-D camera.
The model provided requires Input Image Size: 896x512, which is too big for running on OAK-D camera. Thus I need to re-train it ...
- 181
1
vote
3
answers
673
views
Aside from dice score, what other good metrics are used to evaluate segmentation models?
I have a segmentation which outputs only one channel image (2 class segmentation). I have used dice score for most of the time, but now higher powers in my team want me to expand evaluation metrics ...
0
votes
1
answer
682
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 a python pillow.
<...
- 1,096
0
votes
0
answers
34
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 ...
- 1
0
votes
0
answers
19
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 ...
- 133
2
votes
1
answer
384
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 ...
- 21
2
votes
0
answers
20
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 ...
- 21
4
votes
2
answers
4k
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 ...
2
votes
0
answers
942
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 ...
- 33
0
votes
0
answers
27
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 ...
- 101
1
vote
1
answer
168
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 ...
- 115
2
votes
1
answer
147
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 ...
- 21
3
votes
1
answer
669
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 (...
- 188
1
vote
0
answers
203
views
What does Dice Loss should receive in case of binary segmentation
I implemented Dice loss class in pytorch:
...
- 188
2
votes
0
answers
72
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 ...
- 635
1
vote
1
answer
773
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} ...
- 635
-1
votes
2
answers
315
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
votes
1
answer
233
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 ...
- 404
1
vote
1
answer
386
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
vote
2
answers
161
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 ...
- 1,369
8
votes
1
answer
720
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 ...
- 139
0
votes
1
answer
266
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 ...
2
votes
0
answers
53
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 ...
- 139
0
votes
0
answers
150
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 ...
- 199
1
vote
0
answers
36
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 ...
- 111
2
votes
0
answers
614
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 ...
- 159
1
vote
0
answers
146
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 ...
- 11
1
vote
0
answers
25
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 ...
- 188
2
votes
0
answers
59
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 ...
1
vote
0
answers
90
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 ...
- 21
0
votes
1
answer
91
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, ...
- 57
1
vote
0
answers
97
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 ...
- 21
1
vote
1
answer
118
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
...
- 121
1
vote
0
answers
467
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?
- 111
3
votes
2
answers
294
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 ...
user38359
1
vote
0
answers
225
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 (...
- 111
1
vote
0
answers
84
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 ...
1
vote
0
answers
1k
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 ...
- 121
20
votes
1
answer
37k
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 ...
- 335
1
vote
0
answers
24
views
Super-Resolution with Convolutional Neuronal Networks, why interpolation at the beginning?
I have read several papers about super-resolution with CNNs, where a low-resolution image is reconstructed to a high-resolution image.
What I don't understand is, why it is necessary to interpolate ...
- 175
1
vote
0
answers
404
views
what will be the best loss function for unet to predict the each pixel values?
I'm predicting the used 9 pictures to predict the last picture
so (40,40,9) -> unet -> (40,40,1)
but as you see the predict picture
It's not just a mask(0or 1) its float
so which loss function ...
- 151
1
vote
1
answer
36
views
How to create vector representation of roadmap like scans
What would be the best way to create a vector representation of roadmap like scans? The goal I am trying to achieve is illustrated below. The left side represents the source image, the right side the ...
- 121
0
votes
1
answer
130
views
How to use 'Canny/Watershed' algorithm's output as an input for Image Classification Model
I have a very silly problem in hand. I have implemented 2 methods which give me the mask to separate the objects from the background. What I get from one method is the object encapsulated in the red ...
- 263
3
votes
1
answer
72
views
Can neural network help me with detecting center coordinates of particles in an image?
I have an image of some nano particles that was taken with Scanning Electron Microscope (SEM) attached here. I want to obtain center points coordinates (x,y) for each particle. Doing it by hand is ...
- 31
4
votes
1
answer
6k
views
What are some good alternatives to U-Net for biomedical image segmentation?
Soon I will be working on biomedical image segmentation (microscopy images). There will be a small amount of data (a few dozens at best).
Is there a neural network, that can compete with U-Net, in ...
- 41
1
vote
1
answer
118
views
How do I generate a feature representation of a saliency map (or mask)?
Generally, CNNs are used to extract feature representations of an image. I'm right now dealing with the class of CNN that produces saliency maps, which are generally in the format of a mask. I'm ...
- 317
2
votes
0
answers
153
views
Tversky Loss paper implementation: Recall/Precision do not improve as stated
I have been trying to implement this paper and I am very much intrigued. I am working on a medical image problem where I have to segment very small specimens on Whole Slide Images (gigapixel ...
- 121
2
votes
0
answers
30
views
What are the current tools and techniques for image segmentation in order of pragmatism?
To explain what I mean I'll depict the two extremes and something in the middle.
1) Most pragmatic: If you need to just segment a few images for a design project, forget AI. Go into Adobe Photoshop ...
- 1,369
2
votes
0
answers
298
views
What is the difference between using a backbone architecture and transfer learning?
I'm super new to deep learning and computer vision, so this question may sound dumb.
In this link (https://github.com/GeorgeSeif/Semantic-Segmentation-Suite), there are pre-trained models (e.g., ...
- 21