Questions tagged [image-segmentation]
The image-segmentation tag has no usage guidance.
114
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Train multiple models to predict subsets of classes in data
If you are aiming to predict a number of classes that may be too high for the available data, is there any merit to training multiple models to predict a subset of the classes, each, and combining the ...
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Floor segmentation by refining the results of DeepLabV3+ using something similar to the boundary detection feature of photoshop
I'm looking to detect floors using segmentation. In my previous attempt using a UNet, the segmentation was not bad, but it was imprecise. Things like the legs of the chairs were not segmented properly....
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Unsupervised vine trunk foreground segmentation
I'm currently working on a computer vision project for a vineyard robot. I trained a robust object detection for the vine trunks but now I need to apply semantic segmentation on the trunks so I can ...
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Why is my segmentation model not returning a heat map?
I have implemented two CNN architectures to perform segmentations on medical images: the classic UNet and a modified version called the Attention UNet. I have been training the models on roughly 50,...
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Obscured object segmentation in binary segmentation
I am using deep learning for building outline segmentation. I have used binary segmentation method using UNet model which runs on Tensorflow 2.0. The output is fine when there are no other object (Ex. ...
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Tensorflow Text Segmentation or Open CV Text Segmenation
I want to extract text data from the image, however not sure what approach I should take.
Steps successfully performed
Dataset collection
Image Preprocessing done
Image Augmentation done
Final Image ...
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How to combine multiple dataset efficiently to solve using meta learning?
I am solving a meta-learning problem using Reptile Algorithm as used here. I have two datasets. One contains the following classes: iris, pupil, and sclera along with their annotations. Another ...
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Difference between Fine Tuned and Fully Trained Model
Hi I've come across a study that uses a fine tuned and a fully trained model of the same Deep Learning Algorithm for which the accuracy of each model is being compared and analysed. I just wanna know ...
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Choosing the right Deep Learning Model for Image Segmentation
How do you choose the appropriate Deep Learning Algorithm if you wanted to do image segmentation with an image datset which consist of hispathology images of almost 10000? I'am new to deep learning ...
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The semantic segmentation prediction qualitatively is very good but dice shows a small number
I encountered a problem regarding the evaluation the semantic segmentation. Qualitatively the ground truth and prediction are quite similar but dice shows a small number about 0.56
How's this possible?...
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Deep Learning Segmentation without using Convolutional Neural Networks
I have continuously read that semantic segmentation using Deep Learning is performed using Convoutional Neural Networks. I have read probably more than a dozen research papers exclusively using CNN to ...
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Mark up text as instance segmentation or semantic segmentation?
I have data with a page of handwriting and I want to segment each word individually. If I mark each word for semantic segmentation, then the model will impose a segmentation mask on each word ...
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Why well trained unet with a larger kernel giving plain grey output even after showing good accuracy?
I cloned the git of original Unet and trained it in the default kernal size as given with the data provided along with it.
The model performed well(78% with binary classification of membrane data, but ...
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Is an overlap or a gap between masks preferrable for Instance Segmentation?
I have an Instance Segmentation Problem with objects of the same type and have to prepare a Labeling Task.
During the Labeling Process errors will happen, as adjacent instances can't be annotated ...
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Which deep learning models could be used to filter right path in the binary image?
I would need some help. It is something like segmentation of a binary image. At the input is the binary image with a lot of "paths" (pixel value 1s, background 0s) and at the output, there ...
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Predicted images are quite good with loss=0.20 while are black with loss=0.02
I'm trying to train a U-net with VGG16 as a backbone in order to recognize 4 classes: sky, rocks, trees and background in a dataset of about 10000 images.
I'm using categorical crossentropy as a loss ...
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Is my image too low res for semantic segmentation task?
I am trying to solve a semantic segmentation task in the field of agriculture and I have some ortophoto drone images that have been acquired at the same height above the crops and in different ...
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Which deep learning model is best in terms of instance segmentation and Object detection both?
I am trying to find most efficient and robust Object detector+Segmentation model. I came to know about Mask-rcnn, Yolov5, Yolact, yolov7.
As, YOlov7 is new and i read somewhere that yolov7 surpasses ...
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Pros and Cons of Object Detection vs Instance Segmentation?
I know what the difference is between object detection and instance segmentation (i.e. both detect individual objects and label them but one is via bounding boxes versus one is pixel-wise), however I ...
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Applyingv a 2D mask onto a 3D rgb color list
Problem
I have the following image data as a 3D numpy array containing rgb values of the image in a (n,n,3) shaped list (Image). I also have data of the corresponding black and white mask image in a (...
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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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53
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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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189
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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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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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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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417
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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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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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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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320
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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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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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321
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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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