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Questions tagged [image-processing]

For questions related to image processing (in the context of AI).

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Keras model can't process anything but 600 by 400 images, even though it's not trained on them [closed]

I'm trying to train an AI to correct image colors. But can anyone tell me why this model is limited to processing 600 by 400 images? It can train on any resolution images i want to give it (currently ...
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Is there a model for image pair duplicate detection?

Is there a deep learning model for duplicate image pair detection? Looks like I have to use a Siamese network for this. I have a dataset with image pairs with labelling that they are duplicates or not:...
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I am facing an error : not enough values to unpack (expected 3, got 1) [closed]

This is the function:: def DarkChannel(im,sz): b,g,r = cv2.split(im) dc = cv2.min(cv2.min(r,g),b); kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(sz,sz)) dark = cv2.erode(dc,kernel) return dark if ...
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Identifying country name with input as country flag image [closed]

Identifying country name with input given as country flag image. Identify the country name. This could be a good quiz question in General Knowledge (GK). What are the machine learning algorithms to ...
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Sketch-based segmentation attempt with Deep Learning

I'm looking for a deep-learning based segmentation capability, which should primarily consist of two steps: The image to be examined contains certain structures, which are mainly defined by geometric ...
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What are some of the commonly used image processing techniques of OpenCV for multiclass image classification?

I'm working on multiclass skin disease image classification(caused by bacteria and fungus). Some of the sample images are shown below. Images contain different background as shown in image_1 and ...
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1 answer
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How to normalize multi inputs and single output? [closed]

Input: 2 channels X1: 2D image where the intensity range is roughly [1.5, 3] X2: 2D image where the intensity range is roughly [-1, 1] Both X1 and X2 have the same dimension (e.g., 512 X 512) Output: ...
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How can I reduce the loss? Why do I have the high loss and why do I have the gradient?

I want to classify some images (there are about 200.000 images) with a CNN. But I get a very high loss, see figures: Loss over the hole training run Loss for each epoch It's confused me, that there ...
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1 answer
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Is using separate channels of a RBG image a valid data augmentation technique?

Suppose there is a ML network that takes grayscale images as the input. The images that I have are RGB images. So, instead of converting these RGB images to grayscale, I treat each individual colour ...
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1 answer
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Image Recognition Method, calculate deviation from rectangular grid

I have a set-up which creates pictures of a grid that is a bit bend towards the ends, and I need some kind of program that can calculate the deviation, resp. it just needs to be some kind of indicator,...
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1 answer
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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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2 answers
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Musical notes interpretation [closed]

Musical notes Musical notes videos Piano Can AI, Machine learning, Data science, Computer vision, image processing technologies assist in interpreting musical notes ? Input dataset : Musical notes ...
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2 answers
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Image-in image-out neural network architectures

With an RGB image of a paper sheet with text, I want to obtain an output image which is cropped and deskewed. Example of input: I have tried non-AI tools (such as ...
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What is meant by non-linearity in Convolutional Neural Networks? And why do we focus on removing it entirely? [closed]

I'm aware of the working of ReLU that it's turns every negative value to zero and doesn't effect any positive value, but what confuses me is that: what is actually meant by Non-linearity in feature ...
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What's the best model to use for CNN(deep learning) regression task for small image dataset?

What are the best Deep learning models(with how many layers) to use in a regression task for a custom dataset containing around 100 images of only one object per image which is more or less ...
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Test accuracy go down after decreasing learning rate

My project include classification of images into several classes. I'm having a strange issue related to adding mixup augmentation. The accuracy of the training set and the validation set keep rising ...
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What does it mean by "lazy mean" here?

Consider the following paragraph, taken from 3.4: Named Tensors of the textbook named Deep Learning with PyTorch by Eli Stevens et al., regarding the calculation of the mean for RGB channels of an RGB ...
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Is it effective to use deep learning method to produce a 1D signal as output from a 2D image as input?

I have a 1D signal that will produce a 2D image after some image processing algorithm. Would it be possible and effective to use deep learning method to reproduce the 1D signal if I have the 2D image ...
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How to train a FCNN with audio spectrogram images?

I'm working on an audio dereverberation deep learning model, based on the U-net architecture. The idea of my project came from image denoising with autoencoders. I feed the reverberated spectrogram to ...
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What are the "per image" annotations that are generally used for image datasets in AI?

Computer vision is highly benefited by AI algorithms. Image data is abundantly available. There are different varieties of tasks such as image classification, prediction, segmentation, generation, ...
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What is the expression for projective transformation?

The following are the two types are projections that are generally used in image processing Affine transformation Projective transformation Affine transformation is a backbone operation in neural ...
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How to improve accuracy of CNN used for facial micro-expression analysis

From the paper (1) Facial expression analysis using CNN, the results using CNN show a 65% and 62% accuracy respectively, for emotion classification and state of mind identification. Proposed Method: ...
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4 votes
1 answer
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What is the stride information of an image referring here?

In convolutional neural networks, the convolution and pooling operations have a parameter known as stride, which decides the amount of jump the kernel needs to do on the input image. You can get more ...
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What is meant by sub-region of an image?

Consider the following sentences from the research paper titled PatternNet: Visual Pattern Mining with Deep Neural Network by Hongzhi Li et al. The value of each pixel in a feature map is the ...
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Simple Image-based example for not utilising the variable-sized input handling capability of a Convolutional neural network

Convolutional neural networks are capable of handling inputs of varying sizes. It is one of the key benefits of convolutional neural networks. But I am unsure about the cases when we should not ...
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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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What is the name of the method for the smart extend of image surroundings?

I'm looking for the name of the method (or algorithms family, or research body) used for the smart extend of image surroundings. For example, the method I'm looking for would take this image: And ...
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variational autoencoder - decoder output for images

Following the standard setup/notation for a VAE, let $z$ denote the latent variables, $q$ as the encoder, $p$ as the decoder, and $x$ as the label. Let the objective be to maximize the ELBO, where a ...
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Is it okay to use publicly available Instagram videos to train an AI?

Since I haven't found any good training data for my university project, I want to use pictures and videos from public Instagram profiles. Am I allowed to do that?
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Why do some of the algorithms take some extra space around the actual bounding box?

In some of the algorithms, there is a need to crop the object in an image. So, bounding boxes need to be used in order to crop the image to contain object only. Bounding boxes provide the information ...
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How to understand the common practices followed for writing a "bounding box" for an image in datasets?

For the image datasets, there may be a bounding box for each image at the dataset. It is an annotation for an image. It is a rectangular box intended for focusing on something inside the image. I read ...
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Uniform representation of images for machine learning

I'm new to the field of ML so please bear with me while I try to explain what I'm looking for. In most machine learning pipelines that deal with images there is a requirement to "normalize" ...
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How to ensemble two different computer vision models?

I have prepared two distinct models: Representing contour of the image Representing edges of the image. I would like to create a model which can take advantage of both models in predicting data. May ...
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Preprocessing images for test and validation datasets for training a convolutional neural network (CLAHE)?

I'm training a convolutional neural network for image classification,and i want to preprocess the images, for example with the CLAHE method. I'm not sure if this preprocessing has to be used on the ...
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1 answer
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Why do some techniques use random augmentations during convolution processes

While going over PyTorch image augmentations, https://pytorch.org/vision/stable/transforms.html, I see that some augmentations can be applied with a certain probability. What is the purpose of ...
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What is the significance behind having small kernel sizes over having one large kernel size that covers the entire input in a CNN?

I have hardly ever seen anyone cover the entire input image with a filter of the same dimensions. I was wondering why that is the case, and if the performance in say, an image detection application ...
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Material(s) for understanding "image channels"

I am pretty confused about the concept of "image channels". I want material that explains the concept of channels from scratch to whatever is required to understand their role in machine ...
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2 votes
1 answer
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What do equations 1 and 3 describe in the "Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels" paper?

This paper uses image augmentation to improve RL algorithms. It contains the following paragraph - "Our approach, DrQ, is the union of the three separate regularization mechanisms introduced ...
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-1 votes
1 answer
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how produce image of face working with AI [closed]

I came across a https://generated.photos/ site that claims to produce images entirely by artificial intelligence. My question is how does this program work? What mechanism and libraries should I use ...
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1 answer
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What does 'input planes' mean in the phrase 'input signal/image composed of several input planes'?

PyTorch documentation provided the following descriptions to the Convolution layers ...
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2 votes
1 answer
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What does 'channel' mean in the case of an 1D convolution?

While reading about 1D-convolution in PyTorch, I encountered the concept of channels ...
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How to approach receipt interpretation with neural networks

I have never worked with neural networks before, but I want to learn and I have a specific use case that I would want to solve (or at least try to). I have developed software for multiple years, in a ...
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What image augmentations can help a neural network identify the smallest pixels within an image?

I am training a CNN to identify objects and I believe the network will learn much faster if it can learn to focus on the smallest pixels. One way to go about this would be to augment the images before ...
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1 answer
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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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1 vote
3 answers
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Why is AI Super Resolution Reconstruction more than just guessing?

I saw a video on Youtube about AI and Super Resolution Image Reconstruction with TecoGAN. I must say I am impressed. Now, I am wondering how reliable this is. I have learned at university that you ...
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1 vote
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Convolutional Neural Network (CNN) with Tree architecture to organize the number of classes

At the moment, I have around 1.000 classes with accuracy and loss that are acceptable. In the long term, there could be more than 100.000 classes. The main problem is that every time a new class is ...
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In SIFT, how is the coordinate system being rotated?

I need to understand how SIFT calculates the descriptors for the keypoints. Intuitively, I understand that it takes each keypoint, calculates the gradients for each pixel in a neighborhood of the ...
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1 vote
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What do "spatial" and "temporal" mean in the context of image processing?

I am new to image processing. I am trying to understand CNNs from this blog post. Here's an excerpt from that article that mentions these terms. A ConvNet is able to successfully capture the Spatial ...
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Bounding box transformation using point cloud

I am working on a project in which I have an RGB Image and the corresponding 2d bounding boxes for the objects in the image. I also have a respective point cloud and therefore, I can extract the depth ...
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1 vote
1 answer
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Feeding CNN FFT of an image, a dumb idea?

My dataset consists of about 40,000 200x200px grayscale images of centered blobs bathed in noise and occasional artifacts like stripes other blobs of different shapes and sizes, fuzzy speckles and so ...
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