Questions tagged [image-segmentation]
The image-segmentation tag has no usage guidance.
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Mask R-CNN (matterport) does not generate masks or just generates them randomly
I'm working on a project detecting two different types of olive branches. I'm following this code (based on matterports Mask R-CNN) with my own dataset: https://github.com/AarohiSingla/Mask-RCNN-on-...
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Certain Image Augmentation Prevent Unet Model from Learning
I am training a Unet model for cell image segmentation from microscopy images. In order to help the model generalize better to different microscopes, I attempted to apply brightness augmentation to ...
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Can you use a trained image segmentation model to label more training data for itself?
Labeling images for semantic segmentation can be expensive. Is it viable to train a model (such as Unet) to a good accuracy and then use this model to label more images to be used as further training ...
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Which layers are doing image segmentation on AutoEncoders/U-NET?
While I was researching for transfer learning, I saw that people are replacing encoders with VGG-16 weights and only training the decoder part of the network.
But in some representations (like This) ...
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Image recognition of specific animal drawings with body parts (tiny dataset)
I have an assignment for a class where I need to visualy detect specific animal drawing models, and extract the colors from such model after identifying the different body parts. My problem is that my ...
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Is batch size of 1 a valid choice for a very deep neural network with high memory requirement?
I am training a very deep neural network (Panoptic-DeepLab) with a ResNet34 backbone on Google Colab on CityScapes dataset for Panoptic Segmentation, and noticed that, with a big crop size, the batch ...
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Most efficient use of rare real input images - in training or validation set?
I want to do image segmentation with only very few realistic example images.
Do I train on artificial data only and use the few real images as validation, thereby never directly learning from them at ...
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Are more target labels in a multi-label classification always better?
Context
We work on medical image segmentation. There are a lot of potential labels for one and the same region we segment. There can be different medically defined labels like anatomical regions, more ...
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how to apply segmentation on objects only
i have this image which is an output from my object detection model
i wanted to apply segmentation on this image so that my mask will be like that
i used grabcut algorithm but the results was too ...
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At the first epochs, what will segmentation model get?
I am working at a semantic segmentation problem now, with 5-classes task.
But when I running on validation function and output my probablities map.
I found that with the background class (the extra ...
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how to label 3d model for segmentation task
I'm working on 3d meshes dataset, i have to label it to train my deep learning model for a segmentation task like the picture shows.
I spent days looking for a tool to label my 3d data but ...
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Identify visible stones in the image - Approach in OpenCV & Deeplearning
I have samples images of stones present in the images. I need to identify the visible stones only. The approach which I tried is threshold based filtering and detecting cv2.contours. Also, I am ...
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proper solution to synthesize nailart on hand picture
I'm trying to synthesize nailart on hand picture. Next 3 steps are what I'm trying to do.
take hand pictures
select options like color, cubic .. etc
synthesize
And the way I thought to solve this is
...
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U-Net for Crack Segmentation
I used a U-Net model that was built for Oxford Pet Segmentation to a crack segmentation project. Without transfer learning, model works fine for pet segmentation but not for crack segmentation. What ...
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Post processing in medical segmentation with attemtion unet
I am doing a lesion segmentation for multiple sclerosis (MS), and at the moment I am using a attention unet for my thesis. The best validation dice score I have recieved is 0.771 and train 0.84. I am ...
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How to train a form recognizer
I'm working on a project in which I need to build a form recognizer that, given a form image, returns de key - values pairs. As I just got started, I wanted to hear some opinions about what should I ...
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Is it possible (if yes, then how) to provide the semantic segmentation results on the Original Size of the input image?
Just like this website, it does not matter what shape of image you provide them, instead of giving you a fixed size output, they'll segment your image and provide you the resulting image with original ...
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How to measure the similarity between two medical images of different imaging modalities according to similar objects in both of them?
I have two series of medical images each one from different imaging modalities.
According to that, I have been segmented the Region of interest (the object which appears in both modalities )using U-...
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What is the best strategy for labeling and training this CNN?
I want to start off by saying that I am completely new to machine learning/neural network. This is part of a research class in my high school and everything I know has been self thought. Now that my ...
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StratifiedKFold in Pixel-Wise Image Segmentation
When I use regular code for StratifiedKFold Cross-validation
...
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Why are wavelet transforms not scale-equivariant?
One can rely on continuous wavelets to build a multi-resolution analysis that is equivariant ("covariant") under the action of a discrete subgroup of translation.
When not downsampled,
the ...
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Neural network architecture to automatically crop a photo of a paper sheet
With an RGB image of a paper sheet with text, I want to obtain an output image which is cropped and deskewed. Example of input:
I have tried non-AI tools (such as ...
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How to find correlation between segmentation outline attributes and dichotomous data
Background: So I have a dataset of a pair of doctors' diagnosis on N=10,000 patients' eyes for ocular disease. The data is indexed by the 10,000 patients and the columns are:
1 column w/ PNG images ...
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Is Yoloact working on different images?
is it possible to train Yolact on one dataset containing various image sizes? I've used different cameras so resolution and size are varying.
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Floor plan analysis
Given an image of a floor plan, is there a known algorithm I can use to understand measurements of all apartments present ? (for example, that means in the attached picture understanding there are 4 ...
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226
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multiple images inside one large CSV file
I'm very new to data science, and was admiring how people had made these massive open-source datasets, on places like kaggle. I noticed that all of the datasets where all in CSV format. I have lots of ...
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Where can I find windows environment image dataset?
I'm looking for a dataset of windows environment images labeled with main windows elements like window, taskbar, close button, etc., independent of the Windows version. Is there any?
Edit:
something ...
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Understanding last convolution of U-NET for image segmentation
I was trying to understand the last layer of Image segmentation architecture (U-NET). For example what will be the logits-probability distribution of pixels in each case? I know that its there as ...
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Image segmentation with U-net
I am trying to understand if Semantic segmentation with U-NET. Are we training kernels to extract features or are we training a fully connected layer at the end? Or both? If so, how are we training ...
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31
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How to measure temporal instability of a segmentation model?
I have several segmentation models, which were trained using image data (and not video).
Some of them provide higher IoU, but when applying them to video frames (one after one) - there is a lot of ...
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How to get the expected output shape from a unet model?
I have an image segmentation task where my input image shape is (140, 85, 95, 4) and the output label shape is (140, 85, 95). Below is my model:
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ValueError: Data cardinality is ambiguous: (Jupyter Notebook)
I'm building an OCR to read text off of water meters. I'm running into the error mentioned above when I try to fit the machine learning model. I am using the segmentation_models python library.
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146
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Deep learning - edge detection
I am trying to build a model, which would be used for edge detection of the iris.
For this purpose, I have built a U-net model, which successfully works for image segmentation tasks. Also, I have a ...
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Multi-task learning for improving segmentation
I am building a multi-task model, where my main task is segmentation and my auxiliary task is either denoising or image inpainting. The goal is to try to improve the quality of the segmentation with ...
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Which model is used for document extraction (CamScanner, Microsoft Lens etc)
I want to start a small project where I'd create a model(s) that would extract document from a picture and rescale it, something like CamScanner or Microsoft Lens apps do.
I've gathered a small ...
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What is the best Panoptic Segmentation algorithm up to date?
A Cityscapes ranking would suggest that EfficientPS and Panoptic-Deeplab are the best, while the Coco ranking suggests other algorithms, more recent, like mask2former.
Which one is the best?
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Scaling the output of a segmentation model (UNet)
So, I have to solve an instance segmentation problem and I am thinking of implementing a UNet model based on Ronneberger et. al. 2015 paper. The problem I have is that the output size has to be ...
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Yolov5 image detection without segmentation?
I have read a number of papers on Yolov5 images detection techniques. But the papers don't refers to any segmentation step done by Yolov5. While I know that it is not possible to do image ...
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How to preprocess heavy MRI images?
I have a large MRI dataset for an image segmentation task that cannot directly fit in memory in Colab, you can access the data with the link I put at the end. They are brain MRI images:
484 training ...
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Image Segmentation with Time Information
Lets say that I have a image showing cell tissue and I hope to segment the image to outline the healthy and unhealthy areas. This image is called the current image. I also have images taken an hour ...
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413
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resnet50 implementation for semantic segmentation
I am new to resnet models.
I want to implement a resnet50 model for semantic segmentation
I am following the code from this video, but my numclasses is 21. I have a few questions:
If i pass in any ...
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How to extract specific region from black and white image using openCV
I have been trying to learn OpenCV as I have a deep interest in Computer Vision and one of the problems I have been trying to figure out is how to extract a particular region of an image with OpenCV. ...
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Algorithms to do a CTRL+F (find object) on an image
We all know the CTRL+F "Find text..." feature in text editors / browsers.
I'd like to study the available algorithms to do something similar on an image.
Example of UI/UX: let's say you have ...
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How to break a binary image mask into multiple masks?
I have image with binary mask. I want to break the binary mask into many individual masks of same dimension, but each mask should contain only one segmentation mask.
Is there a way to do it in python, ...
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If an FCN accept rectangular image as input or has to be square?
Some say that for FCN it doesn't matter if the input image is rectangular the only thing matters that the size must be constant ...
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Determining which deep learning model architecture is better
I am solving a specific segmentation task, using two versions of U-net architecture - the first one being classic U-net and the other Attention U-net. Currently, I am trying to determine which one ...
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What is the difference between dilated convolution and bilinear interpolation?
I started studying semantic segmentation, so I read some papers about image segmentation to clinical images using as main architecture, the u-net. Maybe because I am newer on the field, but I don't ...
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Does segmentation algorithms perform better in frequency domain than spatial domain?
I am implementing a published paper, where cellular region needs to be segmented from non-cellular region in a microscopic image of human cells.
In the paper LFT coefficients of each pixel are ...
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Why my encoder-decoder based segmentation model is producing high loss, when UNet/SegNet is working fine
I have got a UNet and SegNet model implemented on a dataset, with high mean-IoU and accuracy. Now I tried to implement SegNet(not FCN, just encoder-decoder portion) on the same dataset.
But for this ...
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Is there any way to remove background of an image fully with the help of post-processor techniques(like edge detector) after deep learning based model
I'm using a deep learning model (deep lab v3+ with xception as the backbone) for image segmentation and removing the background. The subject of the image is a person. And my target is to extract the ...
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