Questions tagged [computer-vision]

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

136 questions with no upvoted or accepted answers
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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 ...
4
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0answers
60 views

Metrics to identify the presence of more than one circle in a set of (x,y) coordinates

I have a set of $(x,y)$ coordinates that represent any number of circles in a plane. What I am trying to do is determine whether there is 1 or more circles present in the data set. This would normally ...
4
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0answers
404 views

Train Neural Network For Handwritten Chinese Characters

The article here: http://novanoid.github.io/2014/09/26/training-a-neural-network-to-recognize-handwritten-digits/ discusses and implements a way to recognize handwritten digits. For images with a ...
3
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0answers
12 views

Extreme Image Noise Removal

I've been trying to solve a noise removal (from images) problem using deep learning and I've tried a lot of the newer architectures for noise removal including FFDNet, NLRN and MWCNN. The problem is, ...
3
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1answer
142 views

Training an Object Detection Model Using with Artificial Data from Video Games

I had an interesting idea of using artificial data gathered from screen shots of a high-resolution video game as a cheap substitute for labeled real data, which can be quite expensive or difficult to ...
3
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0answers
142 views

Neural Network or Differentiable Graph Matching

Searching for: Neural Network solutions or implementations (learnable algorithm) for inexact graph matching. Graph matching: Given two graphs GM = (VM , EM) and GD = (VD, ED), with |VM| = |VD|, ...
3
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0answers
333 views

How to draw bounding boxes to make comparisons?

I have a whole bunch of prediction bounding boxes for my test images. What is the best way to draw the bounding boxes so that I can use it to compare across different models? For example, for each ...
2
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1answer
13 views

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....
2
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0answers
11 views

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 ...
2
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0answers
21 views

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 ...
2
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1answer
178 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 ...
2
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0answers
104 views

clustering objects in point cloud

I am currently working on point cloud data analysis, trying to label objects which are not ground, vegetation, etc. So far I tried many clustering algorithms, with moderate success. In my best model ...
2
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0answers
50 views

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 ...
2
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0answers
36 views

Machine Learning: mathematical verification of this text-to-image cross entropy loss function?

I'm implementing a research paper on GANs and have come across this rather convoluted text-image loss function which has these main components: $$P(D_i | Q_i) = \frac{\exp({\gamma_3 R(Q_i, D_i)})}{\...
2
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0answers
242 views

What areas/applications that still use traditional computer vision algorithms instead of deep neural networks?

What areas/applications that still use traditional computer vision algorithms instead of deep neural networks?
2
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1answer
101 views

Any way to recognize pattern(such as Char and Number) from image without labeled data?

I am trying to build a captcha recognizer. I found CNN play very well if there are enough labeled data. For example, I use this https://github.com/lepture/captcha to generate 4 size char+number ...
2
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0answers
618 views

Precise localization and characterization of rudimentary shapes with neural networks

I understand that there are flavors of (convolutional) neural networks that are useful for object localization and detection tasks of reasonable difficulty. In all of the examples I have seen so far, ...
2
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0answers
288 views

Different sized inputs for batch training in fully CNNs

The idea of transfer learning is to use already trained networks for another purposes to the one it was initially trained for. Using fully convolutional networks, the activation maps can be used to ...
2
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0answers
562 views

Using Shape Context to classify OpenCV contours with KNN

I have a set of 2D polygons represented as OpenCV contours that I would like to use to train a KNN classifier on using shape context. I am using the OpenCV python "cv2....
2
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0answers
56 views

How to build a network that detects common object in images?

I have a group of images. All contain some common object (let's assume all at the same size and no other alterations). I want to train a CNN that will learn the filter for the common object and gives ...
2
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0answers
66 views

how can deep learning model to help us explain the underlying pattern for a given data set

As it is well known, deep learning can help to learn the features. However, are there any ways for us to either understand or visualize these learned features? Moreover, how to explain the patterns ...
2
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0answers
308 views

Right procedure for Leave-One-Out Cross-Validation in multi-class classification

I am intending to implement a existent method to recognize sign language. The model uses a data-set of N (375) classes but with only 3 examples per class. The authors used Leave-One-Out Cross-...
2
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0answers
69 views

Defining the probability distribution of a Random vector given the probability over a “sub-vector”

Suppose I want the probability distribution over a random vector $X={X_1 ,X_2 ... X_n }$. What I already have with me is the distribution over a subvector $X_i , X_{i+1}...X_m$, $m<n$ which I ...
2
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0answers
36 views

What image registration metric should be employed to account for Poisson Noise?

I have a pair of adjacent frames (volumes) Xt and Xt+1 and I want to obtain a non rigid registration field relating the pixels (voxels) of Xt+1 to Xt. The intensities among the images are similar. If ...
1
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0answers
16 views

What happens when the filter size is same as that of image size in a CNN?

In Convolutional Neural Networks (CNNs), theoretically speaking, if the filter dimensions are same as that of the image (training example) dimensions, will CNNs boil down to normal Fully Connected ...
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0answers
10 views

How to visualize 3d joints of a SMPL model based on pose params

I am trying to use demo.py in https://github.com/nkolot/GraphCMR. I am interested in obtaining joints from the inferred SMPL image and visualize it similar to ...
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0answers
19 views

Pre-training a network on significant landmarks

Let's say there is a dataset consisting of photos of cars from various brands, and we're trying to train a ConvNet to identify the brand from a photo, just like these ones: One approach I was ...
1
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2answers
15 views

How can a given conv neural net layer handle filters of different size?

traditional method is to use multiple filters of same dimensions but with different weights and stack the output (basically concatenate them) that is then to be fed into the next conv layer. If I ...
1
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1answer
19 views

Looseness of the definition of domain adaption

I am a bit intrigued about “domain shift” concept. Specifically, in part 5 of the paper “Coupled Generative Adversarial Networks”, it reads We studied the problem of adapting a digit classifier ...
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0answers
46 views

Karush-Kuhn-Tucker Conditions in a Biometrics Research Paper

For one of my course works I'm supposed to find a scientific paper from my field of research that involves an optimisation problem solution based on the Karush-Kuhn-Tucker conditions. My research ...
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0answers
19 views

Reconstruction using low-light images

Let's say we have a regular photo and three low-light photos illuminated in different colors. Each pixel is a three-component vector $q=(R,G,B)$. Then $q_k^{A}$ is the $k$-th pixel of the regular ...
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0answers
15 views

Understanding the concept behind Microsoft Computer Vision- Analyze an Image. Also extract metadata of image from different search engines

My task is to upload an image and get as much information as I can get about the image or in different words its metadata (not exif) like - what is it, caption, price, what does it do, application, ...
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0answers
21 views

Shape classification by color - computer vision

I have task of classification of polygons. These shapes may differ only by color, or be transformed by linear transformation (scaled, rotated). I am using ORB for separating different polygons, but it ...
1
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1answer
29 views

Does the model architecture of a CNN depend on the dimension of your input images?

By model architecture, I'm interested in knowing the following: Number of nodes in input layer Number of nodes in subsequent layers Number of layers in the architecture Number of filters and ...
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0answers
10 views

Finding objects in an entanglement

I'd like to know if there are ways to solve entanglement problems like this one: Given a known shape (in red), can a machine locate (or at least count) the instances of this same object in an image ...
1
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1answer
14 views

Same input size for Source and Target Model?

For the transfer learning do we need to have same input image size of target model as source model? for example my source model is trained on 100x100 input images and target model is low resolution ...
1
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0answers
251 views

What is the relation between a loss function and an energy function?

A loss function is a function that measures the distance between the expected value and the actual value of a model (an example of a loss function is the cross entropy). An energy function can be ...
1
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1answer
352 views

How best to combine object detection and tracking

I am trying to make a computer vision system which will be able to detect and track objects of interest. This will require (1) detection functionality to notice the object when it appears (2) ...
1
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0answers
27 views

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 ...
1
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0answers
32 views

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 ...
1
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0answers
172 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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0answers
2k 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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0answers
10 views

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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0answers
13 views

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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0answers
20 views

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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0answers
28 views

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 ...
1
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0answers
226 views

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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0answers
134 views

Traffic density estimation using deep learning

I am working on a project implementing deep learning and computer vision to estimate the traffic density of any random given road segment/roundabout or intersection. I am given a camera mounted on the ...
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0answers
10 views

Classify a specific object amongst other diverse objects

I have a device which takes one picture per day of a slab. It contains many instances of a specific object (let's call it "Object A") and a few other objects (let's call them "Others"). I want to ...
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0answers
52 views

why the kernel size become greater as the spatial size of feature map goes down in inception network?

In the inception networks like inception-v3 and inception-v4, the kernel sizes are smaller in the lower layers,such as 3*3, but in the higher layers, the kernel sizes seem to be larger,such as 5*5,7*7,...