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Questions tagged [image-recognition]

A form of signal processing where the input is an image. Usually treating the digital image as a two-dimensional signal (or multidimensional). This processing may include image restoration and enhancement (in particular, pattern recognition and projection).

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Linear Regression Approaches for Scoring Cell Images

I have 2K TIF images of cells; the images have a corresponding CSV file containing the image file name and the label of cellularity score of the image (between 0 and 1.0). My target is the "Score" ...
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R - What's the machine learning/non-machine learning way to identify points from intersecting lines in an image?

Let's say I have a image of several intersecting lines (left). Is there a way for R to detect where the lines intersect and overlay points on them (right)?: The goal is to import an image of fish ...
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Studies about computer vision with highly similar images

I'm looking for studies, academic publications, papers, blog post, or anything that relates to use cases in which image recognition have been used with highly similar inputs. When using the word "...
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Is it possible to train this image classifier?

I'm writing a mobile app that will enable a user to scan a Craft Beer Label from a bottle, tap, six pack, etc. The scan will only work for my customers who are the Brewers themselves, so I will have ...
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How to transform a folder of images in a csv file

I have a folder with a lot of images that I want to use to bild a classificator using a SVM model in python with sklearn. I've always used csv file as train/test set with sklearn, how can I make it? (...
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Counting non-overlapping objects in a semantic segmentation prediction mask

What is a good way to count the number of roofs detected by a CNN in the following output on the right (produced using keras/tensorflow): I need to count the discrete shaded areas and estimate their ...
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patched based training of fully convolutional neural network

I have a doubt regarding patch based training. I know it is suggested in supervised learning of classifiers for example, but could the same been said also for fully convolutional autoencoders? If I ...
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Generating labeled dataset for training a neural network

I am currently working on a project in which i'm supposed to classify whether an image contains a translucent watermark or not. This is hard to do with standard object classification or template ...
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When using Data augmentation is it ok to validate only with the original images?

I'm working on a multi-classification deep learning algorithm and I was getting big over-fitting: My model is supposed to classify sunglasses on 17 different brands, but I only had around 400 images ...
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Rain radar image noise reduction and cleanup

An application that I am building is plotting rain radar images on map. The images are transparent PNGs that are sourced straight from the local meteorological service. As can be seen in the example, ...
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Using a custom R generator function with fit_generator (Keras, R)

I'd like to train a convolutional network to solve a multi-class, multi-label problem on image data. Due to the nature of the data, and for reasons I'll spare you, it would be best if I could use a ...
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In handwritten digit recognition problem using logistic regression, what changes needed to add another class “Not a Digit”

In handwritten digit recognition problem using logistic regression, normal implementation would forcibly classify even a picture of dog or cat as a digit. To eliminate this, what changes are needed to ...
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Does image resizing lower the prediction accuracy of MLP?

I am implementing a vanilla neural network (MLP) to do image classification in python using tensorflow on images of honey bees to detect their health status. The images in my dataset are of different ...
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Why my model can't recognise my own hand written digit?

Currently i am working on digit recognizer[0-9]. My model train accuracy 100% and test accuracy ...
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Framework to build object detection model to predict identical or closely identical images

I am looking for an approach to build Image classification and Object detection model for a large category of items (About 200K) but with the very smaller number(4 images on each) of subject input on ...
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image recognition: fully connected network vs CNN

To recognize handwritten digits, I have a fully connected network, containing only 2 layers: input layer (all pixels of the image) and output layer (0 or 1). I use the simplest linear regression for ...
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Does CNN take care of zoom in images?

Suppose a convolution neural network is trained on small images of an object, say flower, as in following 3 training images: Will this CNN correctly classify if the same object is present in zoomed ...
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How can we use machine learning to distnguish between similarly looking images

How can I build a model which can distinguish between Milk and Phenyl? I want to predict whether a given item is edible to eat or not. If I train a model with thousands of photos of Milk and Phenyl ...
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convolution neural network:representing vector in fully connected layer

please i would like to ask about representing feature vector in the fully connected layer in cnn. i have image and i cropped it into N segments and fed each one into cnn branch and get feature maps ...
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How to properly save and load an intermediate model in Keras?

I'm working with a model that involves 3 stages of 'nesting' of models in Keras. Conceptually the first is a transfer learning CNN model, for example MobileNetV2. (Model 1) This is then wrapped by a ...
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Why is it possible to train a semantic segmentation neural network like U-net/Tiramisu from scratch using small data-set like few hundreds

Why is it possible to train a semantic segmentation neural network like U-net/Tiramisu from scratch using small dataset like few hundreds. While for the classification task, it is not possible to ...
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Why CNN and Neural network implementation not working properly

I am working on implementation of Bangla Handwriting Recognition From Scratch. The major steps involved are as follows: Reading the input image. each image shape ( 100,100,3) Number of Train ...
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Ubuntu OpenCV is not recognizing multiple faces using LBPH technique

I am working on facial recognition algorithms where I have developed the algorithm for recognizing 2 persons distinctively in OpenCV python on the windows operating system. That is working ...
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target and logits in the Mask-RCNN

I tried to implement the loss function in the mask-RCNN model using the Tensorflow tool. I used the average sigmoid cross entropy loss function: ...
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What is difference between intersection over union (IoU) and intersection over bounding box (IoBB)?

Can someone give a detailed explanation IoU and IoBB along with that the differences between them.
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Unbalanced training data for different classes

What precautions do I need to take while trying to develop a CNN for classification of images if there is much more training data for one label. For example: ...
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Magnification factor in image classification

If a CNN is trained on images focusing on an object, will it also recognize when multiple such objects are present in the image? For example can a network trained on single flower images also ...
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Detecting abnormal 'cat' behaviour via Supervised Learning

A few work colleagues and I were looking through a recently replaced 'cat', we had in the workplace. For those of you which are curious, the 'cat' in this context, refers to a specific type of pump ...
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What does the co-ordinate output in the yolo algorithm represent?

My question is similar to this topic. I was watching this lecture on bounding box prediction by Andrew Ng when I started thinking about output of yolo algorithm. Let's consider this example, We use ...
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Can we compile CoreML on Server?

I am working with CoreML and ARKit for Face recognition. But i don't want to build CoreML model with app. I have make a coreML model with python Turicate. I want this model to be put on server and it ...
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How to implement facial attendance system using less number of images of particulars

I have a project to implement facial Attendance where I have 5-6 images of particular and when individual comes, model should map the current image with person's earlier available images so if ...
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How to understand log-likelihood for generative image model?

I'm reading a paper on generative image modelling. In the paper, the authors compare various approaches by listing their "negative log-likelihoods" (see screenshot). What does this metric translate to ...
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1answer
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How to check two images(one is original image and other one captured by mobile) similarity using deep learning?

My problem statement is - In my project, original image of product is stored in database. Now whenever any person uploading that product's camera pic(for internal audit process) then I need to verify ...
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Applying a Convolutional Neural Network to Large Scale Satellite Imagery

I'm applying a Convolutional Neural Network (CNN) for semantic segmentation to map out habitats from satellite imagery. More specifically I'm using Mask-RCNN (paper and code) to a large scale ...
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1answer
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System Requirements to train a Image Recognition Neural Network

I have 6000 Images to be trained on a Neural Network. My current PC Specs :- 32GB RAM, i5 2 core Processor, Standard GPU (No work going on GPU), 1TB Hard Disk My Neural Network Specs :- 3000 ...
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Cameras for automatic customer service machine [closed]

For my university project, I am planning to build an automated customer service machine. One which recognizes when someone approaches the camera according to says hello, etc. Also, I am planning to ...
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Better to crop or compress training data?

I've been trying to make an object recogniser in Tensorflow and have used labelImg to classify large electrical transmission towers at varying distances. In order to make 10-16MP (~2-7MB) images train ...
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260 views

Converting video into frames using openCV

I am converting video into video-frames using the given code converting video into frames ...
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1answer
57 views

Why is convolution filter used instead of correlation filter in CNN?

When I saw the two results of applying convolution filter and correlation filter, the results have the same distribution and are just flipped. Why is convolution filter used instead of correlation ...
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2answers
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Should we keep all channels when doing image classification?

I am discovering the world of image recognition and now trying to build an image classifier. The set of images I have have the shape (101,101,3) which means that it has 3 channels. If I'm not ...
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OSError: cannot identify image file <_io.BytesIO object at 0x7f5b2d2d9e60>

I created a lmdb dataset of images and labels but on reading the images, it is giving me error which I can't understand. Code: ...
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Is it possible to make tensorflow print out everything it see in a given image and not just the top five results?

I'm working through the python API tutorials for Tensorflow and I'm seeing the results that are normally displayed, but it's always giving me the top five results. I'm trying to discern all ...
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What is the loss function defined by Mnih and Hinton in their paper “Learning to Label Aerial Images from Noisy Data”?

In section 3.3 of the paper, they state that they use the cross entropy. Then they define the probability for a label to be a false positive as $\theta_0$ and a false negative as $\theta_1$. They use ...
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Where can I find a dataset of images of faces and descriptions of them?

I'm doing an ML project that generates descriptions of pictures of faces. Is there a publicly-available dataset that has a set of face images along with a short description of what the face looks like?...
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Machine Learning & Image Recognition: How to start?

I've been a full stack web developer for 15 years now and would like to be involved in machine learning. There is already a specific scenario for this: We have a database with several million products ...
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How to reduce the resolution of a Image(276*276 --> 48*48) without affecting the features in it

I have an image with resolution of $276*276$. I've created a Convolution Neural Network which accepts $48*48$ images. So, I want to resize that $276*276$ image to $48*48$ without reducing any features ...
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advice on distance metric for knn w/image recognition

I'm getting my feet wet with machine learning and am implementing a knn algorithm on a dataset that i've created. I've created a set of images of circles and squares and want the knn algorithm to ...
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1answer
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How can I split an image into rectangles?

I have a labelled form to which people will add their name and a series of numbers. They will then take a picture of the form. Like so: I can get decent results by simply sending this to AWS ...
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1answer
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calculation of average ROC in IMageNet paper?

The IMageNEt paper Image Net. presents the Average ROC curve for the 16 classes in the imagenet data, visit image figure. 8 in the paper. what is the known function to compute this ROC plot. As ROC ...
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How Do Bayesian Methods in Machine Learning Help With the Problem of Limited Data? Can This Be Used for Image Classification/Recognition? [closed]

When reading about machine learning, I've often come across information stating that Bayesian methods in machine learning are effective when you only possess a limited amount of data. As someone who ...