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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11 views

Nonrealtime tracking for object detection (with or without deep features)

I've been doing a search for tracking systems based on bounding box for several days (like Sort, DeepSort, Motpy, ...). But neither is nonrealtime. My problem is based on the tracking of multiple ...
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How are the parameters $\alpha_i$ of hard attention trained?

I have a question about Show, Attend and Tell: Neural Image CaptionGeneration with Visual Attention paper by Xu. The basic mechanism of stochastic hard attention is that each pixel of the input image ...
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Aggregating 2D object detections into 3D object detections

I have a data set of 3D images with some bounding box annotations. The images are too large to train something like YOLO 3D (would run out of memory), so I instead created slices of the 3D images with ...
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Interpreting coordinates of the function from the image using Grid Lines

I have a very different kind of problem at hand. I have a function plotted on the grid lines as shown below Now, I need to get the x and y coordinates for this particular function at regular ...
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In the DeepView paper, do they use the same FCN for all depth slices AND all views?

I'm trying to replicate a paper from Google on view synthesis/lightfields from 2019: DeepView: View Synthesis with Learned Gradient Descent and this is the PDF. Basically the input to the neural ...
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What are the pros and cons of 3D CNN and 2D CNN combined with optical flow for action recognition?

For action recognition or similar tasks, one can either use 3D CNN or combine 2D CNN with optical flow. See this paper for details. Can someone tell the pros/cons of each, in terms of accuracy, cost ...
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27 views

How are nested bounding boxes handled in object detection (and in particular in the case of the SSD)?

The basic approach to non-maximum-suppression makes sense, but I am kind of confused about how you handle nested bounding boxes. Suppose you have two predicted boxes, with one completely enclosing ...
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1answer
43 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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40 views

How to deal with wrong labels in a classification problem? [closed]

I have a problem where I need to train a NN to classify images based on a 10k samples trainset where each sample has 2 labels - one correct and one wrong. What could be a good approach to this problem?...
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Time series analysis using computer vision principles

I'm just starting to explore topics within computer vision and curious if there are any concepts in that area that could be applied to segmenting multivariate time series with the goal of grouping ...
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37 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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How to build a commercial Image-Image search engine using LSH / Near Duplicate or some other algo on more than 20M images

TL;DR: HOW DO I APPLY LSH WITH A DEEP LEARNING MODEL TO BUILD A IMAGE-IMAGE SEARCH ENGINE ON >20M IMAGES? I want to build a system where I am helping my ...
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23 views

Reading Data from a Twilio Stream [closed]

I work for a video tutoring organisation that uses Twilio Video API to carry out the video class. I want to run the object detection and other computer vision models on the input stream. I am stuck at ...
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Is there a pre-trained network trained on RGB-D (4) channels? [closed]

The most used pre-trained networks for computer vision (e.g. ResNet50) are trained on 3 channels (RGB). At the same time, many cameras used in robotics return RGB-D outputs, that is including depth ...
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37 views

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 best metrics for Multi-Object Tracking (MOT) evaluation and why?

I want to compare multiple computer vision Multi-Object Tracking (MOT) methods on my own dataset, so first I want to choose the best metrics for this task. I have carried out some research in ...
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Deep Unsupervised clustering on big data with no prior knowledge

I have around 3M BW images and I would like to organize them in as few clusters as possible in way which is meaningful for the dataset without any prior knowledge for this data, as they come from ...
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1answer
47 views

What is the difference between Attention Gate and CNN filters?

Attention models/gates are used to focus/pay attention to the important regions. According to this paper, the authors describe that a model with Attention Gate (AG) can be trained from scratch. Then ...
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109 views

How to calculate the distance between the camera and an object using Computer Vision?

I want to create a Deep Learning model that measures the distance between the camera and certain objects in an image. Is it possible? Please, let me know some resources related to this task.
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27 views

Is it possible to use self-supervised learning on different images for the pretext and downstream tasks?

I have just come across the idea of self-supervised learning. It seems that it is possible to get higher accuracies on downstream tasks when the network is trained on pretext tasks. Suppose that I ...
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1answer
23 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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2answers
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Why is my Keras prediction always close to 100% for one image class?

I am using Keras (on top of TF 2.3) to train an image classifier. In some cases I have more than two classes, but often there are just two classes (either "good" or "bad"). I am ...
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Object Detection as a means of Anomaly Detection

Is it possible to train an Object Detector (e.g. SSD), to detect when something is not in the image. Imagine an assembly line that transports some objects. Each object needs to have 5 screws. If the ...
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Face detection and replacement in photos

I have 2 photos, and my goal is to detect the face in one and place it on the face of the person in the other photo- basically face detection and replacement. It's not deep fakes. It's more of a ...
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1answer
28 views

Why do we need to provide false labels to the discriminator on purpose to train GANs?

This is the tutorial that I used to learn about GANs. In this tutorial, it taught us to intentionally provide false labels to "fool" the discriminator, but does it make the discriminator ...
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33 views

How to compute dominant colors in an image?

I was trying Google Cloud's Vision API, and how the dominant colors part shows. I uploaded a sample image, and here is the results for the dominant colors. I realized it doesn't simply count pixel ...
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31 views

How does one measure image similarity using Radial Basis Kernel?

Here is the formula of a radial basis kernel $$ k\left(x_{i}, x_{j}\right)=\exp \left(-\frac{d\left(x_{i}, x_{j}\right)^{2}}{2 l^{2}}\right), $$ where $x'$ and $x$ are feature vectors. I have two very ...
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What is a “center loss”?

I have seen that a center loss is beneficial in computer vision, especially in face recognition. I have tried to understand this concept from the following material A Discriminative Feature Learning ...
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23 views

Generating data from a High-Res. RGB image for a CNN

Say I want to build a detection model that detects the existence of X or NO X. The only piece of information I have, though, is a high res. RGB image, say 100k (width) x ~1000 pixels (height). Let's ...
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Find object's location in an area using computer vision

I'm trying to see how to detect the location of a soccer ball in the field using the live camera. What are some ways to achieve this? 1- Assuming we have a fixed camera with a wide shot. How to find ...
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1answer
52 views

Why do we resize images before using them for object detection?

In object detection, we can resize images by keeping the ratio the same as the original image, which is often known as "letterbox" resize. My questions are Why do we need to resize images? ...
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39 views

Distinguishing between handwritten compound fraction and subtraction

I am working in a project named "Handwritten Math Evaluation". So what basically happens in this is that there are 11 classes of (0 - 9) and (+, -) each containing 50 clean handwritten ...
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48 views

Deep Learning based image restoration using multiple frames

Suppose we have a sequence of still images each of which has been contaminated by some particles(ex, dust/sand/smoke) making the images very poor in certain areas. What architecture would be best to ...
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1answer
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Are there any known models/techniques to determine whether a person in a store is a customer or a store representative?

Are there any known models/techniques to determine whether a person in a store is a customer or a store representative? For example, customer representatives can wear uniforms and then one possible ...
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1answer
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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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51 views

Computer vision - Can you put more weight on a specific part of the object?

Let's say I'm looking for any item that has a certain shape (outline) in a photo. but I can further classify it only according to particular features, that most of them are expected to be shown only ...
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2answers
187 views

What is the need for so many filters in a CNN?

Consider the following coding line related to CNNS Conv2D(64, (3,3), strides=(2, 2), padding='same') It is a convolution layer with filter size $3 \times 3$ and ...
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1answer
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How can I train a CNN to detect when a person is smoking outside of shop given images from a video camera?

My friend is working at a pizza shop. He takes cigarette breaks in an area that is covered by the public webcam of our town. I now want to train a convolutional neural network to be able to detect ...
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1answer
22 views

Is training a CNN object detector on an image containing multiple targets that are not all annotated will teach it to miss targets?

I want to train a convolutional neural network for object detection (say YOLO) to detect faces. Consider this image: In this training image, I have many people, but only 2 of them are annotated. Is ...
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51 views

Why aren't the BERT layers frozen during fine-tuning tasks?

During transfer learning in computer vision, I've seen that the layers of the base model are frozen if the images aren't too different from the model on which the base model is trained on. However, on ...
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What is a wavefront algorithm?

I am designing and researching algorithms which I call of a wavefront nature. It is image analsyis agorithms when every pixel may change many times during the processing. I have heard this name before,...
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131 views

Formal definition of the Object Detection problem

For many problems in computer science, there is a formal, mathematical problem defition. Something like: Given ..., the problem is to ... How can the Object Detection problem (i.e. detecting objects ...
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How do you make a regression model from a binary labeled dataset?

Suppose I have a dataset with hand images. Hand completely opened is labeled as 0 and hand completely closed (fist) are labeled as 1. I also have a bunch of unlabeled images of hands which, if ...
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Train 3D object detection model for custom object

I am trying to train a model that can detect a 3D object and give me a 3D bounding box around it. For this, I have a RGBD camera and a 2D LiDAR. Most of the research is done for cars/cyclists/...
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How to use SPP-net and what are its drawbacks?

I read about the spatial pyramid pooling concept, it's really cool! Now, my doubt is how to find the number of layers to use, and in each layer what should be the grid sizes when using spp. But, I ...
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Would it be possible to use AI to measure pupil dilation diameters and fluctuation, on video films on a regular webcam?

I've been researching the topic of Cognitive Load Measurement through pupil dilation measurement. All solutions to pupil dilation measurement require some kind of special hardware setup. I was ...
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Can we identify only the objects in specific parts of an image with computer vision?

I am studying computer vision for the past 3 months. I have come across the object identification problem, where given an image, CV would identify various parts in the image. If I give an image, and a ...
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41 views

How to normalise image input to backpropogation algorithm?

I am implementing a simple backpropagation neural network for classifying images. One set of images are cars another set of images are buildings (houses). So far I have used Sobel Edge detector after ...
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1answer
37 views

Is there any network/paper used to analyse music scores?

As I am curious on music theory I would like to know that If is there any such network that analyse like labeling chords, or doing a roman numeral analysis. Like an example below: Source It does not ...
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132 views

Why don't we use auto-encoders instead of GANs?

I have watched Stanford's lectures about artificial intelligence, I currently have one question: why don't we use autoencoders instead of GANs? Basically, what GAN does is it receives a random vector ...

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