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Why do we need to concatenate in a U-Net?

These types of connections between layers are called skip connections, searching for this will give you a way to find more in-depth information. Broadly speaking, there are two advantages to using ...
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What are features in computer vision?

CNNs like U-Net extract lower level features like edges on lower layers (i.e. the first convolutional layers) and higher level features on higher layers (i.e. convolutional layers closer to the final ...
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How can I create .nii (nifti) file from 3D Numpy array

You can this using nibabel: import nibabel as nb ni_img = nib.Nifti1Image(numpy_array, affine=np.eye(4)) nib.save(ni_img, "dicom_volume_image.nii")

Splitting a pdf containing batch of scanned documents

"Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction" by Esposito, Ferilli, Basile, and Mauro goes into detail about how to create a custom system for parsing ...

Modifying U-Net implementation for smaller image size

It appears that the original images are 68x68 pixels and the model expects 256x256. You can use the Keras image processing API, in particular the smart_resize function to transform the images to ...

Algorithms to do a CTRL+F (find object) on an image

Invariant object recognition(IOR), refers to rapid and accurate recognition of objects in the presence of variations such as size, rotation and position. SIFT and SURF are the most popular among them, ...
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Determining which deep learning model architecture is better

Not sure what language/framework you are using for your experiments but, you should try to deep seed your models so that performance is decoupled from random as much as possible. See an example here: ...
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Can you use a trained image segmentation model to label more training data for itself?

I assume you're thinking of only using images where you are confident the model has segmented them correctly? I don't think this would cause overfitting - at least what we normally think of as ...
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Yolov5 image detection without segmentation?

segmentation mainly uses Fully Convolutional Network(FCN) architecture. FCN is a CNN without fully connected layers(FC). segmenation can be thought as an encoder followed by a decoder. Here encoder ...
1 vote

resnet50 implementation for semantic segmentation

The output from the ResNet model is a vector containing the probability that the image belongs to each of the n classes, in your case to any of the 21 classes. If ...
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Google Earth Pro Satellite image segmentation using clustering

Whoa, are you trying to do clustering based on satellite images?! Just use the underlying long & lat coordinates to do this. It will be infinitely easier and so much more accurate too. Look at ...
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How does the "skip" method work for upsampling? (fully convolutional NN)

So as you already know, the convolutions extract some local information from the image, but for some decisions you need more global info. This is what pooling is supposed to do, it basically erases ...
• 1,495
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Hook up PyTorch U-Net model to video

The RuntimeError error you're getting is caused by an incorrect number of dimensions in the input. The model expects an input with four dimensions with the first ...
• 6,524
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Segmentation model to predict face forward and profile parts of the face

There is a library called facemesh for Python. It can detect face landmark points in Python. Connecting facemesh points to crop image to a desired polygon is ...
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Implementing U-Net segmentation model without padding

$\hspace{3cm}$ If we follow the definition of each arrow. Gray => Copy and Crop Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 convolution (“up-...
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Photorealistic synthetic data for object segmentation

I used such an approach at the Computer Vision Lab at TU Delft for my Bachelor thesis. The goal was to analyze different aspects of the same problem: identifying LEGO bricks in a photograph of an ...
• 201
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What is Deep learning approach to count the number of Diamonds in an image?

I say that go with Object Detection. Because you already have annoted images. And segmentation applied to locate objects and boundaries (lines, curves, etc.) in images. . In Recent days EfficientDet ...
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What do the parameters used in crop mean?

Lets say my image is called frame and x, y, w and h are xmin, ymin, xmax and ymax You're confusing $w$ with $xmax$ and $h$ with $ymax$: Usually $w$ is the width of the crop whereas $xmax$ is the ...
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Tool for annotation of images for semantic segmentation

Diffgram is really great for this! I used it for a construction monitoring project. It's Open Source. From their site: Semantic Segmentation Tools: Auto Bordering: Automatically detects edges to ...
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Extremely stochastic validation loss/accuracy

There can be a few reasons for this behavior as already pointed out in "Why is the validation accuracy fluctuating?": Size of train / validation stes: Fluctuations may become stronger the ...
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For semantic sementation, why am I getting better loss values with binary cross entropy than dice coef?

So the question asks about why different loss function lead to different error scores. So globally error is there to help us measure the level of discrimination between the output of the model and the ...
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Which colour channel from a TIFF image do I have to use?

From the readme of your kaggle link; All images are provided in .tif format with 3 channels per image. For 101 cases, 3 sequences are available, i.e. pre-contrast, FLAIR, post-contrast (in this order ...
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Multiple output size in neural network

As you can see in lines 286-296 in newmodels.py the model can use two different loss functions for the four different outputs. ...
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neural network probability output and loss function (example: dice loss)

I think there is a bit of confusion here. The dice coefficient is defined for binary classification. Softmax is used for multiclass classification. Softmax and sigmoid are both interpreted as ...
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Confusion matrix of UNET image sgemenation model

If your masks have only one channel and in the case of binary segmentation, you can easily compute a confusion matrix from one image thanks to: TP: ground truth and predicted pixel are of class 1 (...
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What are features in computer vision?

In addition to the previous answer, I would add that many convolutional NN architectures (and not only convolutional!), are effectively contraction mappings of data points from input space to the ...

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