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Questions tagged [computer-vision]

Questions related to image representation, segmentation, visual object categorization and image processing algorithms in general.

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Pretext Task in Computer Vision

I am new to Computer Vision. I am reading many papers and i see the term "pretext task". Can anyone explain what exactly it means. Thanks in Advance.
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How to separate training set and validation set using 80/20 rule?

I have two folders: One folder contains images of non-dogs, and the second folder contains images of dogs. I am to divide these folders into a "training set" and a "validation set" with the 80/20 rule....
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Why do we use Hough instead of RANSAC in SIFT?

In his SIFT paper, why did Lowe choose to use a Hough transform rather than RANSAC to recognize clusters of 3 consistent features? (Note that RANSAC is more efficient in comparison with Hough) Link ...
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Are there any other image classification methods besides using neural networks?

When reading about image classification, the only occurring terms are "neural networks", "deep learning" and "CNN". It seems like there are no other methods for this task. I have worked with neural ...
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Normalising predictions across datasets

I am currently training a model to predict a binary attribute. The model gives the output in range [0, 1]. The metric is TPR@FPR, e.g. I need to achieve maximum ...
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1answer
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Model to Recommend Ideal Parameter Changes for Best Performance of Industrial Machine

I'm trying to develop a machine learning model to solve this problem, and am unsure of where to start. We begin with some user-defined settings. The settings are used by a machine to create a product....
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What are the important methods that evolved in computing optical flow?

I have gone through various approaches to find optical flow. But I have a tad confusion between Horn and Shunck method and Lucas Kannede method. Where are these methods useful and where do these ...
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1answer
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How can we relate the concepts of GAN/cGAN in SRGAN? Is SRGAN a Conditional GAN?

I have been reading and looking at implementations of the SRGAN, from "Photo-realistic Single Image Super Resolution with Generative Adversarial Networks" paper. One thing that I noticed is that the ...
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What is scale-invariance and log-space translations of a bounding box?

In slow R-CNN paper, the bounding box regression's goal is to learn a transformation that maps a proposed bounding box P to a ground-truth box G and we parameterize the transformation in terms of four ...
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1answer
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Why do people use Zero-Padding in Convolutional Neural Networks?

I am wondering why people usually pad with zeros instead of e.g., using the min-value. Zero-padding, in my opinion, makes sense if you have input images with a pixel range [0, 255] or [0, 1] (after ...
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1answer
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Simple but effective face detection algorithm using neural networks

I'd like to have my undergrad machine learning students have the option of doing a face detection project using neural networks (constructed by the students using Keras). The algorithm should ideally ...
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Optical Character Recognition - digits on the screen

My task is to classify a digit based on a small image containing one digit only. The font type and size is the same across the training/test dataset, but the position of the digit in the image might ...
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1answer
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Combining custom YOLO network for face detection with another CNN

I am looking for a way to build and train an end-to-end CNN that contains two steps: 1) a CNN for finding a face and hands in the image and 2) CNN that works on the crops of the face and hands. To ...
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How can I lower the resources taken by an ANN while limiting data loss?

I designed a visual quality assurance system with a goal to determine whether a factory part during manufacturing is flawed or not. The need arose because it is rather difficult to tell if a part is ...
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1answer
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Differentiable method to convert voxel representation into pointclouds?

I'm finding a way to convert 3D voxel data into 3D point-clouds. Since I'd like to do it inside a deep-learning architecture, the conversion has to be differentiable. Is there such a method? Thanks ...
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1answer
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this question is related to activation function

I could not understand : what is activation score from the kernel in the previous stage? I know what activation function mean ,activation functions type and how its work But what about activation ...
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Learning useful semantic representations of data

Training a neural network on its final task (e.g. classification) right from the beginning is not always the best way to go. I'd like to make a short list of recognized methods of motivating a NN to ...
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Stereo image classification [closed]

I just been wondering how can I combine stereo vision and Convolutional neural networks for a classification problem
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24 views

How do convolutional predictors work in SSD Object Detection?

I'm trying to understand this paper SSD: Single Shot MultiBox Detector by Liu et al, there they mention "Convolutional predictors for detection: Each added feature layer (or optionally an ex- ...
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How to reduce false positives when novel negative images are visually similar to training images?

I am noticing that my ResNet model is showing some false positives in cases where novel negative example images are somewhat visually similar to positive examples. In these cases, it's not simply ...
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Do convnets have issues detecting small features?

I remember seeing some presentation about how convnets had issues detecting say, glasses on a person's face because they take up very little pixel space. However no one seems to be saying that anymore....
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Can haar cascades work for hand recognition with multiple hand shapes?

I am researching about using haar cascades for hand recognition. There are examples on the internet (some from OpenCV) for detecting a fist or an open hand with haar cascades (at a time). My question ...
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Image generation based on sketch

Are there any instances of image generation models, where an image (a very rough sketch) has been used as an input and was then augmented. For example: This could be a rough sketch, which is then ...
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Is it possible to give variable sized images as input to convolutioal neural network

Can we give images with variable size as input to convolutional neural network for object detection? If possible, How can we do that? But if we try to crop the image, we will be loosing some portion ...
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Using the same image across multiple classes in image classification

I have a multi-label data set that I'm trying to use for multi-class image classification. Each image potentially has more than one class and is thus being selected as a positive example of as many ...
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1answer
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When doing data augmentation, should you train with the original data as well or just the augmented data?

When doing data augmentation in computer vision problems, should you train with the original (un-augmented) data as well or just the augmented data? Are there pros and cons to the two strategies or ...
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1answer
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How does smoothing an image gives it a different scale?

In some deep learning papers i read about multiscale inputs, so i wanted to read about scale of an image. What i got to know is that fundamentally scale is related to the distance of the object being ...
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Interpreting probabilities from image classifier, which model to use?

I'm trying to interpret examples from a probability perspective and my intuition is telling me Logistic Regression should be used for such a purpose despite the score being weaker than the other ...
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What is crop size for in semantic segmentation?

In Tensorflow Deeplabv3 I saw the training and validation parameter called crop_size, but they are different values in training and validation. If my network is trained on images of size 512x512, ...
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58 views

Translation invariance of features in convolutional encoder-decoders

A big part of using convolutional layers is translation invariance, i.e., features are detected regardless of their position in the image. In a convolutional encoder-decoder that maps from an image ...
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1answer
45 views

Introduction to Conditional random fields

I came across the application of a conditional random field (CRF) to the output from a convolutional neural network (CNN) for image segmentation. The additional CRF step seems to be a common ...
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1answer
100 views

Custom Loss Function - Inducing sparsity

From the comments, I realized that my question wasn't clear enough, so I'll start with a short background. I am trying to construct an attention model that performs classification based on just a ...
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342 views

What is the difference between dice loss vs jaccard loss in semantic segmentation task?

What is the difference between dice loss vs jaccard loss in semantic segmentation task? Dice loss: ...
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what will “Faster RCNN”(or any other object detection algorithm that uses anchors) do in this situation?

Can anyone please tell me what will "Faster RCNN"(or any other object detection algorithm that uses anchors) do in this situation? If there are 2 object and both are inside 2 different anchors and ...
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clustering objects in point cloud

I am currently working on point cloud data analysis, trying to label objects which are not ground or vegetation e.t.c. So far I tried many clustering algorithms, with moderate success. In my best ...
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Metric for evaluating predicted bounding boxes from semantic segmentation on an object level outside of training

Context For simplicity let us pretend we are performing semantic segmentation on a series of one pixel high images of width w with three channels (r, g, b) with n label classes. In other words, a ...
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Is there any papers about learned image features in an unsupervised way?

I know that image features produced by pretrained models like VGG-net are the common way to initialize other networks in other tasks. But I was wondering, Is there image features that have been ...
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Different random weight initilization leading to different performances

I'm training a 3D U-Net on an EM dataset of a brain. The objective is to segment neurons in it. During the experiments, I've noticed, different random initialization of the network leads to different ...
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Scene annotation

I am looking to run my convolutional net on a tennis video and see if it can recognise the type of shot played( straight, volley or cross court). What tool can I use to annote the scenes in my video?
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CNN: Modifying VGG16 Architecture

I'm currently trying to modify the VGG16 network architecture so that it's able to accept 400x400 px images. Based on literature that I've read, the way to do it would be to covert the fully ...
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41 views

Recognizing an object in multiple images

Suppose I am doing an object detection task from images. However, unlike usual image detection tasks, there is a difference - the images comes in groups of threes and we know that only one of the ...
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Counting number of items in a box from a MP4 video using computer vision and R

I have an MP4 video in which a person keeps putting some household items into the box(container type). Items consist chips packets, colddrink bottles, mugs etc. Sometimes even person takes out some ...
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Segmentation Visualisation DNN

I was interested in what the network is exactly learning during the process of image segmentation. Take unet for example. In image classification, the hierarchy of features is more clearer to ...
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1answer
41 views

Why does distorting images improve training on a neural network?

I cannot understand why distorting an image, e.g flipping it, increasing the gamma intensity would somehow increase the accuracy on neural network. Within my situation, I am Using a CNN to detect if ...
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learning a distribution from images with a cnn

I have images which contains maybe 1000 circles of different sizes. Circles may overlap: it's not a simple picture. I'd like to do one of two things: either (1) learn the circle size probability ...
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Finding objects in an image given a single instance of the object from the same image - Deep Learning approach

I have an aerial image containing planes like this: I want to detect planes in the image (find bounding boxes around planes). For this user draws a bounding box on one of the planes and the algorithm ...
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1answer
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Difference between Mean/average accuracy and Overall accuracy

I just got confusion while reading the paper "Local Binary Pattern-Based Hyperspectral Image Classification With Superpixel Guidance". They mentioned that they repeated each experiment 10 times and ...
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1answer
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Quantitative evaluations for image classification

Hello I am working on the classification of different weed categories. I want to know what quantitative evaluation I can do other than find the accuracy?
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Developing algortihm/model to identify thin linear features in aerial imagery [closed]

I am exploring the possibility of identifying fencelines from NAIP aerial imagery (GSD = 0.6m). I have tried some basic processing in OpenCV using canny edge detection that was detailed in a question ...
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1answer
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How does Batch Normalization not lead to the model blowing up? [duplicate]

I was reading the Batch Norm paper and in this paragraph, We could consider whitening activations at every training step or at some interval, either by modifying the network directly or by ...