Questions tagged [computer-vision]

For questions related to computer vision, which is an interdisciplinary scientific field (which can e.g. use image processing techniques) that deals with how computers can be made to gain high-level understanding from digital images or videos. For example, image recognition (that is, the identification of the type of objects in an image) is a computer vision problem.

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Is there any Fruit and Leaves (Combined) Disease Dataset

I and my team are working over a Fruit-Leaves Disease Detection problem using Neural Network as our project in Artificial Intelligence Course. But we were unable to find a reliable image dataset for ...
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How general is generalization?

I am sorry but I have to explain my question using an example, I do not know how to ask it in proper scientific terms. Let's assume, I have trained a deep learning model on classifying hand gestures, ...
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Estimate number of boxes on a pallet [closed]

Need to estimate number of boxes on the pallet. I have image from one viewpoint. Boxes can have different dimensions. They can be stacked on top of each other such that box dimensions don't exceed ...
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How to find space utilised and free area in a room from Images [closed]

I have Multiple images of the room. How could I calculate space utilised and free space in the room from those images.
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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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1answer
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What should load_mask() return if an image doesn't have any objects? (Mask RCNN) [closed]

I want to use Mask RCNN to do image segmentation. I need to override the load_mask function for the dataset class. I know this function should return mask tensors and class ids of objects in an image. ...
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1answer
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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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1answer
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How to classify two very similar images using Deep Learning?

I am a newbie in Computer Vision. I have a scenario in which I have a stationary camera in a factory. I want to detect whether the technician is working on the machine or not. Images are like the ...
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In anchor based object detection, why don't the anchors share the same weights?

After reading about YOLO V3 and Faster R-CNN, I don't understand why the weights for the regression head aren't the same across all boxes of the same size. Given that the backbone of these systems is ...
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1answer
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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. ...
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1answer
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How to normalize for perceptual loss when training neural net from scratch?

Let's say we are training a new neural network from scratch. I calculate the mean and standard deviation of my dataset (assume I am training a fully convolutional neural net and my dataset is images) ...
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What are the definitions for the content and style of an image without using deep neural network?

In deep learning, an image is said to contain two types of features. One is the content of the image and the other is the style of the image. Deep neural networks are generally used to obtain both ...
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How does bipartite matching work in DETR?

I was going through the DETR paper to understand this end-to-end detection transformer used for object detection, and I came across this bipartite matching thing. I searched a little bit, but I was ...
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Alternative to sliding window neural network (was: Object detect (or) image classification at specific locations in the frame)

Recent advances in Deeplearning and dedicated hardware has made it possible to detect images with a much better accuracy than ever. Neural networks are the gold standard for computer vision ...
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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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Mapping ground truth to downsampled embeddings

I am currently pulling embeddings out of the mid layers of PSPNet. I was wondering if anyone knows of a way to see what pixels in the ground truth map to the pixels in the intermediate layers? e.g. we ...
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276 views

Which neural networks are suitable for visual place recognition?

I am doing a project on visual place recognition in changing environments. The CNN used here is mostly AlexNet, and a feature vector is constructed from layer 3. Does anyone know of similar work ...
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is there any proof that metric learning cannot achieve better on image classification task than accepted models (resnet etc)?

Everything is in the title. Metric learning seems to be closer to our way of thinking than the best performing models (supervised learning CNNs-based models like resnet or efficientnet). I was looking ...
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1answer
27 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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1answer
37 views

Training a model to identify certain differences between images?

Newbie to CV here so sorry of this is basic. Here's the deal, I have a program that I run many times. and each run I produce a screenshot. I need to compare screenshots from N-1 and N runs and make ...
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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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Role of confidence or classification score in object detection mAP metrics

I know that mAP (mean Average Precision) is the common evaluation metric for the object detection tasks. It uses IoU (Intersection over Union) threshold such as mAP@0.5 to evaluate whether the ...
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1answer
791 views

How feasible is it to perform pose estimation on a Raspberry Pi 4 using a Pi-Cam?

I want to estimate hand poses and recognize gestures using an open-source library like OpenPose on live video. Considering the fact that such libraries are very computationally intensive. How likely ...
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How to get bounding box coordinates of all detected objects in yolov5 object detection?

While there is a similar question on stackoverflow, it pertains to yolov4 which, unlike yolov5, uses darknet. Yolov5 is far more intuitive to use than v4 and so a solution to this in v5 is desirable. ...
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60 views

What are some good papers or resources for aspect extraction and opinion modelling from video or audio?

I am quite new to deep learning. I just finished the deep learning specialization by Professor Andrew NG and Deep Learning AI. Now, my professor (instructor) has advised me to look into some classic ...
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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 ...
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1answer
315 views

Is Mask R-CNN suited to solve a multi-class classification problem where the classes are related?

I want to create a model to solve a multi-class classification problem. Here are more details about my problem. Every picture contains only one object The background is very simple All objects ...
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156 views

How to choose a suitable threshold value for shi-tomasi corner detection algorithm?

While implementing shi-tomasi corner detection algorithm i got stuck at deciding a suitable threshold for corner detection. In shi-tomasi algorithm all those points that qualify ...
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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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Size of image input of neural networks while resizing may not be appropriate

I have the following problem while using convolutional neural networks to detect forgeries: Resizing the image to fit the required input size may not be a good way because the forgery detection ...
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If you have a very distorted image, would affine transformations applied to images make object detection algorithms make more mistakes?

If you have a very distorted video/image, would affine transformations of the images make object detection algorithms make more mistakes compared to a normal camera?
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Compute IoU for each class in Mask R-cnn

I'm trying to compute the IoU, with the matterport Mask R-cnn implementation, for each class (13 in total) that i have in my dataset. For now i managed to compute the average IoU for all the classes ...
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1answer
80 views

What is the current state of the art in animal facial recognition?

As far as I can tell, most work on facial recognition has been done in relation to human faces. Has any significant work been done for dogs, and are there any special challenges that would make it ...
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Questions about a research paper on salient region detection and segmentation

I am reading this paper in an attempt to recreate the salient region detection and segmentation model employed. I have the following questions pertaining to section 3 of the paper and I would highly ...
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In Computer Vision, what is the difference between a transformer and attention?

Having been studying computer vision for a while, I still cannot understand what the difference between a transformer and attention is?
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1answer
152 views

Can a fully convolutional network always return an image of the same size as the original?

I'm trying to perform a segmentation task on images of multiple sizes using fully convolutional neural networks. Currently, I'm using EfficientNet as a feature extractor, and adding a deconvolution/...
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Video summarization similar to Summe's TextRank

We have the popular TextRank API which given a text, ranks keywords and can apply summarization given a predefined text length. I am wondering if there is a similar tool for video summarization. ...
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1answer
47 views

Is it possible to use deep learning to generate a 2D image from a few numerical values?

Is it possible to train a DL model that will generate a full resolution 2D image based on few numbers describing this image and what type of model or architecture would that be? What I want to ...
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Computing the mean attention distance for ViT

Recently I came across the paper that introduces the Vision Transformer (ViT) "AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE". The thing I don't really ...
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11 views

What is language-conditioned visual reasoning?

Can anyone explain what language-conditioned visual reasoning is? I saw this term in this paper and I searched on the internet but I couldn't find a proper explanation.
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What does the lambda parameter in the paper "Interpretable Explanations of Black Boxes by Meaningful Perturbation" do?

I do not understand the purpose of the $\lambda$ parameter in equation 3 of the paper Interpretable Explanations of Black Boxes by Meaningful Perturbation. $$m^{*}=\underset{m \in[0,1]^{\Lambda}}{\...
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222 views

Extending FaceNet’s triplet loss to object recognition

FaceNet uses a novel loss metric (triplet loss) to train a model to output embeddings (128-D from the paper), such that any two faces of the same identity will have a small Euclidean distance, and ...
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Save the Embeddings Model from a Complete Triplet Loss Network

I am working on a Siamese Neural Network with custom Triplet Loss function. As far as I learned from the documentations, we will train a complete network but we want to save only the CNN that is used ...
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How can I take continuous video input into my model?

Let's say I have designed an ML model that can take video input of a dog running around and give the breed of the dog as output. However, I do not want to wait for the video to finish before it is ...
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Comparing results of different image splicing methods on a part of the CASIA 2.0 dataset

So I am working on an image splicing detection algorithm using ResNet-50 model. I am using the CASIA 2.0 dataset which consists of 7491 Authentic images and 5123 Fake images. However out of the fake ...
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68 views

Are Markov Random Fields and Conditional Random Fields still used in computer vision?

Back before deep learning, there were a lot of different attempts at computer vision. Some involved Conditional Random Fields and Markov Random Fields, which were both computationally difficult and ...
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Does randomly adding hand-engineered features increase the CNN's sample efficiency/performance?

It is a known fact that preprocessing images using CV techniques will improve CNN performance (see this answer). But what happens when you feed in the entire image and the filtered image randomly to ...
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273 views

What is the state-of-the-art algorithm for neural style transfer?

I've read the paper A Neural Algorithm of Artistic Style by Gatys et. al. and I find the application of neural style transfer very fun. I also read that Exploring the structure of a real-time, ...
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Upscaling a low-res IR image with a high res webcam image

I have a low resolution thermal/IR image (for example 32x32 or 80x64) and a high resolution webcam image. I would like to combine the two to "fake" a high resolution thermal image (I can ...
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166 views

Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling?

Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling? If not, why do they perform as well as networks which use max-pooling?

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