Questions tagged [image-segmentation]

For questions related to image segmentation (in computer vision and related AI fields).

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1answer
127 views

What algorithms are used for image segmentation of images where objects are not composed of pixels that are similar in value?

In the process of segmentation, pixels are assigned to regions based on features that distinguish them from the rest of the image. Value Similarity and Spatial Proximity, for example, are two ...
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1answer
13 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 ...
4
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1answer
57 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
12 views

How to Train Big Size Image and Predict Various Size of Images

I don't have deep knowledge of the neural network, but I would like to segment the road from UAV images and detect cracks on them. My first question: I am planning to do fine-tuning from pre-trained ...
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1answer
32 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. <...
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1answer
114 views

How do I segment each part of a DICOM image?

As I'm beginner in image processing, I am having difficulty in segmenting all the parts in DICOM image. Currently, I'm applying watershed algorithm, but it segments only that part that has tumour. ...
0
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1answer
33 views

How to re-training 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: 896*512, which is too big for running on OAK-D camera. Thus I need to re-training ...
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0answers
34 views

Which neural network architecture to use to detect very close and very small blobs in high resolution fluorescence images?

Context I am developing a pipeline to automate the detection of small, almost circular, bright blobs (4px) (see first image below) on high-resolution fluorescence images (2048px) and later to assign ...
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1answer
30 views

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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0answers
44 views

How to label images for image segmentation (with TensorFlow)?

I am following this tutorial on image segmentation on the TensorFlow website. The website uses its own labeled images for the tutorial, so the images have data that says which pixels are a part of the ...
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0answers
11 views

How to properly report results for a medical image segmentation task?

Let’s consider a 2-class / binary segmentation problem where c=0 for background (healthy tissues) and c=1 for foreground (...
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1answer
38 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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1answer
29 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/...
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1answer
56 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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3answers
176 views

If I trained a model to perform semantic segmentation on images with only one object, would it also work on images with multiple objects?

I'm working on semantic segmentation tasks in the medical space using the U-Net. Let's say that I train a U-Net model on medical images with the goal of segmenting out, say, ligaments, from a medical ...
1
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1answer
57 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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19 views

Any papers or implementations of Multi label segmentation in pytorch/keras

I am currently working on a project related to Multi label segmentation. I haven't been able to find any substantial papers where objects in images were segmented based on a membership function. For ...
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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 ...
2
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1answer
2k 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 ...
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0answers
30 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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1answer
49 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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0answers
11 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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0answers
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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0answers
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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0answers
13 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
56 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 ...
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0answers
28 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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2answers
270 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
17 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 ...
2
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0answers
97 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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0answers
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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0answers
34 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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0answers
18 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 ...
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1answer
21 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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2answers
34 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 ...
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0answers
29 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
85 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 (...
5
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1answer
70 views

Do models train better if the labelling information is more specific (or dense)?

I'm working on a project where there is a limited dataset of videos (about 200). We want to train a model that can detect a single class in the videos. That class can be of multiple different types of ...
0
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1answer
136 views

How to give each segment a new colour in image segmentation? [closed]

I'm currently working on a tumour detection project using Dicom images. As I'm a beginner, I don't know how to segment each part in the image and give each segment a new different colour. I'm using ...
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0answers
23 views

What does Dice Loss should receive in case of binary segmentation

I implemented Dice loss class in pytorch: ...
2
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1answer
55 views

What are some references that describe known filters (or kernels) and how we can create new ones?

I'm pursuing a master's degree in Artificial Intelligence. My final work is about Convolutional Neural Networks. I was looking for information about filters (or kernels) of the convolutional layers. I ...
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0answers
16 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
39 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 ...
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1answer
62 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} ...
3
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1answer
85 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 ...
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1answer
52 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
48 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 ...
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0answers
34 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, ...
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0answers
8 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 ...
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0answers
30 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 ...