Questions tagged [image-segmentation]
For questions related to image segmentation (in computer vision and related AI fields).
119
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Image segmentation with varying resolution
I am looking to create a model that is able to perform binary segmentation of images with varying resolutions. For model should be able to classify tree or not tree regardless of the resolution of the ...
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36
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How to add engineered features to an image segmentation model
I have built a U-net model for image segmentation of 3-channel remote sensing images. I have a total of four classes; two of these classes look very similar and are hard to distinguish in the images ...
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19
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What is the best type of input for a 3D UNet?
I want to use 3D U-Net (or similar) network to create a 3D reconstruction of my microscopy data.
The original paper for the 3D U-Net (https://arxiv.org/abs/1606.06650) describes the implementation ...
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Batch Normalization Layer is not learning the data semantics of a dataset comprised of datasets from different sources
I have built a dataset for image segmentation that is comprised of datasets from several different sources.
Almost all of my models have problems with learning the correct parameters of the ...
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60
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Use AI/Computer Vision to detect scene changes
I'm trying to use AI and computer vision techniques to identify scene changes for a camera. Something like this:
What are some approaches to do this? Any ideas?
The scene is static. Somewhere I saw a ...
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Should I apply a min-max scale (range 0 to 1) before applying the normalisation or should I apply the z-score normalisation directly?
I want to implement a neural network in Pytorch for medical image segmentation. I should normalise my data.
Should I apply a min-max scale (range 0 to 1) before applying the normalisation or should I ...
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19
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What is the best approach to remove this additional container from the cropped image?
I'm working on a computer vision application in Python to analyze images of ice cream cuttings to measure the amount of variegate(ie. fruit syrup or fudge) compared to the base ice cream. My approach ...
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26
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how to preprocess satellite imagery for semantic segmentation?
I have to train binary semantic segmentation in Python(using Tenforflow, rasterio, geopandas, sh). And I have raster .tif images and vector .shp images which is mask. I know that I should divide each ...
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Clustering bounding boxes to reduce image cutout overlap
Given an image and bounding boxes identified within this image, the objective is to group these bounding boxes in such as way that we can define a bigger bounding box of size NxN that will encompass ...
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50
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How can I tell a CNN to ignore nodata values in satellite images?
I'm trying to train an image segmentation model on satellite images. There are two main issues: first, not all of the images are the same size. My understanding is that by using a fully convolutional ...
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Should softmax be in the model or in the loss function?
Suppose I have an image segmentation model with an output of [ 128, 128, 2 ], segmenting an input image into 2 parts.
Commonly, loss functions have the sigmoid or ...
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Creating border around specific areas of images
I have some 1000+ image, containing data like this
Red area: Symbols
Grey area: Text describing the symbol
Note: I draw these red/grey boxes just for visualization only.
Each symbol is unique in this ...
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66
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How does one deal with images that are too large to fit in the GPU memory for doing ML image analysis?
How does one deal with images that are too large to fit in the GPU memory for doing ML image analysis?
I am interested in detecting small structures on images which are themselves many GB in size. ...
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35
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How to generate multi-class segmentation masks for grapevine(plant) having the image (mostly white background) and the skeleton of the plant?
I have images of plants (grapevine) on mostly white background.
I have the skeleton of the plant in graph representation, where each edge has a label - the category of the edge. The categories ...
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43
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Document Processing using AI
Deal all
I want to Build a Document Processing AI Model
1-that Identify Document Elements
Like table, Text , List , Heading , etc.
2-Sort this element in correct way if I wan to dump this data as XML
...
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What's the difference between classification and segmentation in deep learning?
What's the difference between classification and segmentation in deep learning?
In particular, can the classification loss function be used for segmentation problems?
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Is it a good idea to have a category and its subcategories in the training set of an object segmentation model?
I am currently training an object segmentation model (detectron2 : mask rcnn)
The objective is to detect materials like wood, plastic, glass etc...
wood is one of the categories in my training set.
Is ...
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29
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Latest status on stability of CNN architectures to noise and "clever hans" issues
I was working with CNN architectures for image segmentation a few years ago, and I remember there was a big concern about on the stability of the predicted masks due to the introduction of small ...
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60
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Segmentation of x-ray images to detect Covid-19
I’m currently working on covid detection project using x-rays. I applied K -means clustering algorithm (https://www.kaggle.com/code/naim99/image-classification-clustering-step-by-step?scriptVersionId=...
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114
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How does accuracy (for a single class of interest) of a segmentation network vary with increasing number of classes?
BACKGROUND: I have a real world problem of developing a U-net-like model for segmenting lung tumors in lung CT images.
On the one hand, I can make this a two class problem:
...
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33
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Medical Image Segmentation of Pulmonary Embolism
The segmentation model, unet-resnet34, gives an IoU of 76% on training data and 74% on validation data. But when I tested it on test data, the IoU that I was getting was 60%. I don't know why it is so ...
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38
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possible to combine multiple labeled objects as one object?
So I have labeled the entire skeletal muscles in detail. For example instead of just labeling shoulders I have labeled:
Rear Delt
Middle Delt
Front Delt
but now you want all of the delts to be ...
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2
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271
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Are masks needed for images that don't contain the object of interest in (binary) Image Segmentation tasks?
Total Dataset :- 100 (on case level)
Training :- 76 cases (18000 slices)
Validation :- 19 cases (4000 slices)
Test :- 5 cases (2000 slices)
I have a dataset that consists of approx. Eighteen thousand ...
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50
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Extract a document page from a photo
I am trying to extract a document as an image from another image. Let's say that we take a photo of a document on a surface. My ultimate goal is to be able to digitize this document but as an image, ...
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74
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How to incorporate domain knowledge into a semantic segmentation network?
I'm working on a semantic segmentation project, and want to add some domain knowledge to the system. I want to ensure that for segmentation, there can only be one group of pixels that are predicted as ...
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2
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488
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Using pre-trained models on image dataset that is totally different for object detection?
I have been trying out various tutorials on object detection machine learning. All the tutorials so far have been to use a pre-trained model for practical reasons when detecting objects that the pre-...
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29
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Why instance segmentation architectures using reconstruction masks but not regression?
I'm wondering why many model architectures use binary mask reconstruction for segmentational CNNs, and not regression of mask polygon coordinates? Many object detectors use regression to find ...
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65
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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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What considerations should I take to train my transformer model?
I want to train my vision transformer model on a benchmark for an image segmentation task: (LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation) (GitHub), but I don't ...
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44
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How to identify and diferentiate several edge lines of an object?
I want to create an AI to detect and identify certain edge lines on my image. The input image is a locker key, and I want to know the exact position of certain edges.
Sample input image:
Sample ...
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151
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Use of Mask in U Net for plant disease detection [closed]
I am using U-Net for plant disease detection. I am new to deep learning and computer vision.
Currently, we are feeding the masking images generated via open cv HSV format to detect colours from the ...
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72
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Is there way to segment an image without labeling/classification, as well as supervised learning?
Is there way to segment an image without labeling/classification, as well as supervised learning?
For an illustrative example, if one considers an image with a dog and a cup (we don't particularly ...
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381
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What is exactly sparse annotation?
What is exactly sparse annotation? Is it different from labeling images?
I've been reading a paper about vessel segmentation and have some issues understanding this part.
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67
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Deep RL reward design for neuron centerline extraction task
As part of a bigger scope project, I'm training a RL agent that attempts to reconstruct, pixel by pixel, the trajectory of a neuron on a segmented image. To give a better insight on the task that I'm ...
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269
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Mask R-CNN: how is the inference done?
According to the Mask R-CNN paper and the picture below (taken from the paper), the mask branch is computed in parallel with the bbox classification and regression branches.
However in the paper they ...
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Image segmentation when given masking information is incomplete
In my problem, there are about 5,000 training images and there are about 50~100 objects of identical type (or class) on average, per image. And for each training images, there is a partial mask ...
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123
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Custom Tensorflow loss function that disincentivizes all black pixels
I'm training a Tensorflow model that receives an image and segments the image into foreground and background. That is, if the input image is w x h x 3, then the ...
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121
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Why is the simplest U-Net architecture giving the best (but not good enough) results on a multi-class segmentation on microscopic data?
Currently, I'm trying to optimize a training process of a neural net to improve final results. The problem I'm dealing with is multiclass segmentation on microscopic data.
The paradox is that the best ...
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245
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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-based model (deep lab v3+ with xception as the backbone) for image segmentation and removing the background. The subject of the image will be a person. And my target is to ...
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49
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How to divide a segmented image into classes instances?
Is there a method/algorithm to generate instances of objects from image that was segmented by the use of any image segmentation models?
For example, I have an image with one class and it was segmented ...
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45
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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/...
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139
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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 ...
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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 ...
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456
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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.
<...
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33
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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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18
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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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250
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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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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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3k
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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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778
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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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