Questions tagged [image-segmentation]

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

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11 views

Custom LRscheduler and val loss logging in detectron2

I want to add validation loss logging in the training process of detectron2 MRCNN. I also want to add a LR scheduler in the process as the inbuilt one is multiplier. I have configured the code for ...
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28 views

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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14 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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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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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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51 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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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
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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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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1answer
19 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 ...
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21 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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34 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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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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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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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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37 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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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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62 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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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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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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394 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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114 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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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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37 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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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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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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28 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 ...
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36 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 ...
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93 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 (...
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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 ...
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40 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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80 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} ...
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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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88 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
58 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 ...
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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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59 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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43 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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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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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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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 ...
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How can I improve the performance on unseen data for semantic segmentation using an auto-encoder?

I am using simple autoencoders for the task of semantic segmentation on the VOC2012 dataset. I am currently using a simple autoencoder based model. It is trained on adam optimizer with cross-entropy ...
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42 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 ...
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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 ...
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301 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 ...
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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 ...
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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 ...
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29 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 ...
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49 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 ...