Questions tagged [computer-vision]

Computer Vision is a subfield of computer science which deals with analyzing and understanding images. This includes detection of objects like faces in images or segmenting images.

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Image reconstruction using low-light components

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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21 views

Help with deep learning for motorbike inspection

First of all, I am very new in machine learning and data science, so I am really sorry if my question is completely stupid. I am doing an internship in machine vision, and people of my office want me ...
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Detecting hand-written objects from images

Problem Statement: Identify the sub-region in an image with hand-written text/scribbling (Attaching an example for reference. Masked some text for privacy) Once I get the pixel values, I want to ...
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Can a convention convolution neural network train correctly with different training image size and ratio?

For example the task of transformation, the model consist convolutional layer and pooling layer only, take input of image, and output a feature map (loss MSE, trying to produce feature map that ...
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21 views

How to use Inception v3 in Tensorflow

I am trying to import Inception v3 in TensorFlow. I wish to apply it after reading this tutorial on object detection.
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if two convolution layer connected in tandem follow associative property of convolution?

Two Convolution filter follow the associative property as follows :- I want to ask whether this property will hold for two convolution layer with no operation in between them?
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How to convert photo to a vector drawing

I am using the following code to convert photo to a drawing: ...
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Detection of UI Elements using CV

I have a task to detect UI elements in the photo of the touchscreen. I did several decisions based on the research that I did, but I am no very confident with them so I would appreciate feedback on my ...
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1answer
17 views

Can the same CNN architecture be used for different data sets?

I have a CNN architecture that works well on 32x32x3 images. Can I use that same architecture for a data set made up of 28x28x1 images? (Both data sets have 10 classes). If this is possible, what ...
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Classifying objects in video without machine learning

Recently, Nick Bourdakos posted a series of videos demonstrating bottle detection in a video stream using Tensorflow.js. Specifically, he is using SSD-mobilenet. The problem could be summarised as ...
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What units are depth prediction dataset such as kitti or nyu in? km? meters?

What units are depth prediction dataset such as kitti or nyu in? km? meters? In general, when running a depth estimation network, do we pass in ground truth as km or m or some other more normalized ...
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Combining 2D Detection with Disparity Maps to Learn 3D Object Geometry

Since the disparity map above is a representation of the object's distance from the camera's origin, is it reasonable to assume that a network (perhaps a convolutional LSTM) could be trained to ...
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Deep Learning for Video Classification

Which Deep Learning architecture is best for classifying short videos of variable length? I would like to classify videos that last from 1 up to 3 seconds.
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How to get an intution for generating Keras CNN layers for a model

I am going through tutorial for handwritten text recognition. And to do hand written digit recognition the author has constructed a Keras model as follows: ...
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Fully-Configured Deep Learning Virtual Machines in Python (VirtualBox or VMware)

Fully-Configured environment setup for Deep Learning Virtual Machines in Python (VirtualBox or VMware) Often when we start working on any new technology, the most common challenge that we face is "...
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Training Algorithm for pointcloud data (3d point data)

I have a "PointNet" neural network which theoretically can work with any number of points as input. I have trained the model using an equal number of points from each object. That is fairly simple and ...
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Convolution with different dilation rates for each dimension

How can I get a dilated convolution with different dilation ratio on each axis? Tensorflow/Keras would be best. For example, the filter in the gif below would have the following properties: ...
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Is it possible to create a point clouds from just a 3D image?

I'm working with classification and segmentation of 3D images. The images I have are simple volumetric samples in .tif format of the shape [200, 512, 512]. I am ...
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Train deepfake algorithm with multiple cameras/input sources to generate 3D model?

I have seen many algorithms used for advanced video frame generation for face altering, and they all mostly work on the same paradigm: Input videos of person from front-on. These videos are converted ...
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What's the size of Google's complete Conceptual Captions image captioning dataset with all the images downloaded from the listed URLs?

The original dataset provided by Google, here, consists of 'Image URL - Caption' pairs in both the provided training and validation sets. I have to work on an image captioning project and wanted to ...
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Best framework for recognizing a specific cartoon character's face?

I have a supply of images of a specific cartoon character's face. I have hours of video. I would like to automatically find the sections of the video in which this cartoon character appears. https://...
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1answer
21 views

Synchronizing Multiple Cameras in Autonomous Driving

Please forgive the naivity of this question, it's just due to lack of experience. It goes without saying that self-driving cars have up to 8 cameras and more that do various vision related tasks: ...
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How to build a 2-d bayes classifier

I'm working on a 2-d bayes classifier and I'm a little confused on how to start exactly. My attribute space is 2-d. There are 3 classes. The data is assumed to be normally distributed. p(x | y1): P(...
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Scenario description of Roads using Deep learning

I want to do scenario description of road. If there are cars, humans infront of the blind person it should describe the scenario and output should be consumable by blind person. Question: Should it ...
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1answer
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What is the State of the art method for full body gesture recognition in images

I am working on gesture recognition in images and the best way that I am aware of, is whether using end to end approaches with deep neural networks or extracting body joint positions in an image and ...
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Bags of visual words - counter intuitive result

I'm reading the frequently-cited paper Bags of Binary words for Fast Place Recognition in Image Sequences and have found something strange in the paper. The similarity measure is presented as: $s(v_1,...
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Computer Vision model/solution deployment channels

I am very new to computer vision and am currently dealing with ways to deploy a computer vision model/solution for my current company keeping in mind the following factors :- 1)Capability to demo ...
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Fine-tuning a Pre-trained model (Resnet50) do I need to validate it or just train it?

When fine-tuning a Resnet50 model should I do the standard train validation split and train like any normal CNN or should I just do the training and not the validation if I am going to use this model ...
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43 views

why the sigmoid function will be 1 and 0 if we use a fully connected layer that produce a big enough positive(res negative )output

HI I am using a fully connected network that uses sigmoid if we feed a a big enough weights the sigmoid function will finally become 1 or 0 , is there any solution to avoid this ? and will this lead ...
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Speed Regulation of fan using Machine Learning

Can machine learning be used for the speed regulation of fan based on the environment, how many people are present in the room and routine of a particular individual and how? How can i achieve this?
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65 views

Inconsistent inference results in deeplab v3+

I trained and exported the model as suggested in :https://github.com/tensorflow/models/tree/master/research/deeplab. The validation results saved using vis.py are fine but when I apply demo code for ...
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How to grasp the full entropy of the distribution we want to model in GAN

In pix2pix GAN paper( https://arxiv.org/abs/1611.07004), authors found that the noise vector and the dropout are not efficient in grasping the full entropy of the data distribution we want to model. ...
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156 views

Convert YoloV3 output to coordinates of bounding box, label and confidence

I run YoloV3 model and get detections - dictionary of 3 entries: "detector/yolo-v3/Conv_22/BiasAdd/YoloRegion" : numpy.ndarray with shape (1,255,52,52), "detector/yolo-v3/Conv_6/BiasAdd/YoloRegion" : ...
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Random accuracy results on validation set (Binary classification)

I am configuring a CNN model for a classification problem of human action in Python using TensorFlow. My data is video frames representing human body joints. Every 3 consecutive columns represent x,y,...
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419 views

Multichannel numpy array to PIL image

I have a 4 channel Numpy image that needs to be converted to PIL image in order implement torchvision transformations on image. But when I try to do this using ...
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How to use machine learning to create combine of opposite images side by side

Inspired by: Two Worlds Pictures I just want to create a Machine Learning Model that can automatically combine the opposite images into 1 image. I am thinking about 2 possible solutions: Pose ...
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115 views

How to add a new category to a existing trained deep learning model?

i have trained my deep learning model initially with 5 classes now i want to add another class without training the whole model over again for those 5 classes. How can I do that?
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reverse engineer console game FPS sensitivity for replication

Would it be possible to reverse engineer a FPS games sensitivity/dead zone/acceleration curve/ and other data by recording the games screen and running a controller through a pc to record joystick ...
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Can Microsoft's cognitive service find similar person in a set of images without using the face service?

I need to create an application that can detect if a person X entered as an input exists in an image set and return as output all the images in which the person X exists. The problem is that the ...
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Which service could I use to train my networks?

My laptop's Intel i7 3630QM 2.4GHZ, 8Gb RAM and GXForce 670M are clearly not sufficient... By reading some papers, I've written an SRGAN with Python Keras. At runtime there is no error but training ...
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3answers
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Computer for DS curriculum

I actually need to decide on which computer I will be taking for a Data Sciences curriculum, including machine learning and further hands on Hadoop. Some computer vision applications are also planned. ...
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Hardware recommendation for FRCNN deep fashion data

I am working a research project to build a FRCNN model for attributes detection using deepfashion data(300K images with 1k attributes). I am struggled on hardware issue with my current training box ...
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1answer
38 views

Reconize a card from a video stream

I want to make an mobile app where you scan using the camera and it gives you the card you just scanned. (from a board game) I got a PNG of each and every single existing cards, but I don't really ...
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372 views

Pre trained dataset for Car damage detection

I'm making a Car Damage Detection model which would have 2 classes to detect upon. My dataset has a total of 300 images (out of which I'd be using some for testing), which are totally insufficient to ...
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Unable to understand the meaning of following lines of the research paper for image segmentation

I am implementing a paper on image segmentation. It is based on the slight modification of the u-net architecture. The paper is based on encoder and decoder steps Following are the lines of the paper ...
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Train CNN-RNN network for multi label video classification with sliding window technique

I’m implementing a model in which a CNN model is used to extract feature sequences from videos , and RNN is used to analyze the generated feature sequences, and output a multi label classification ...
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Atrous convolution allows arbitrary feature map resolution

I'm reading Deeplab paper. In this paper, the authors proposed to use atrous convolution, whose 1-D form is: $\hspace{3.0cm} y[i] = \sum_k x[i + r \cdot k] w[k]$ Given this scheme, they wrote that ...
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Saving images in a non-retraceable way, but still able to train R-CNN's on them

For a computer vision project I am working with images that the company only allows me to have on my computer for a maximum of 24 hours due to regulations. Every day a few hundred images come in via ...
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Pre-trained models

I am starting off with machine learning so could someone tell if there is some site where one can find the current best performing trained models for any specific problem like sentiment analysis or ...
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What is “spatial feature encoding”? Can anyone give a concrete example?

This book "Deep Learning and Convolutional Neural Networks for Medical Image Computing" mentioned a term spatial feature encoding On the other hand, CNN models have been proved to have much higher ...