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TURKISH BANKNOTE
RECOGNITION USING
CONVOLUTIONAL
ÖZGÜR ŞAHİN
ozgur.sahin@aol.com
SELCUK UNIVERSITY
OUTLINE
▸Image Processing
▸Machine Learning
▸Deep Neural Networks
▸Convolutional Neural Networks
▸Research
IMAGE PROCESSING
IMAGE
PROCESSING:
FEATURES
http://www.learnopencv.com/
MACHINE LEARNING
▸Data
▸Generic Algorithms
▸Supervised Learning
▸Unsupervised Learning
SUPERVISED LEARNING
20
50
100
10
20
?
TRAIN INFERENCE
UNSUPERVISED LEARNING
NEURAL NETWORKS
https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3
CONVOLUTIONAL NEURAL NETWORKS
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721
CONVOLUTIONAL NEURAL NETWORKS
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721
RESEARCH: BANKNOTE
CLASSIFICATION
▸Image Processing
▸Neural Networks
IMAGE
PROCESSING
LEAGUE▸ ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
▸ 2012 - AlexNet: Leap With Deep Learning - 85% accuracy
▸ 2013 - All winning entries were based on Deep Learning
▸ 2015 - Convolutional Neural Network algorithms (95%) > Human
RESEARCH: IMAGE
PROCESSING
▸ OpenCV
▸ Otsu Thresholding
▸ CannyEdge Detection
▸ Dilation
RESEARCH: TRAINING CONVOLUTIONAL
NEURAL NETWORK
150, 150
Conv2D
Conv2D
MaxPooling2D
Softmax
MODEL & WEIGHTS
KERAS & TENSORFLOW
RESEARCH: TESTING THE MODEL
RESEARCH: INFERENCE USING MODEL
5-> %10
10-> %5
20-> %15
50-> %5
100-> %60
200->%5
MODEL & WEIGHTS
RESEARCH: DEVELOPING IOS APP
MODEL &
WEIGHTS
59% sure that’s a 10
THANK YOU FOR YOUR
TIME

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Turkish Banknote Classification App Using Convolutional Neural Networks

Editor's Notes

  1. image processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, Most image-processing techniques involve isolating the individual color planes of an image Lena is the name given to a standard test image widely used in the field of image processing since 1973. This scan became one of the most used images in computer history. • Image sharpening and restoration, Medical field Remote sensing, Transmission and encoding, Machine/Robot vision, Color processing, Pattern recognition, Video processing, Microscopic Imaging
  2. Anatomy of an Image Classifier The following diagram illustrates the steps involved in a traditional image classifier. Interestingly, many traditional computer vision image classification algorithms follow this pipeline, while Deep Learning based algorithms bypass the feature extraction step completely. Let us look at these steps in more details.
  3. Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.
  4. In supervised learning you feed generic algorithm with lots of the data The data should be classified. You train your network with this data and trying to improve accuracy. And then it will be able to guess the new input for your classes.
  5. Data is not classified. We don't know the labels of the data. We train the network and it figure out if there is any groping or pattern. This is kind of like someone giving you a list of numbers on a sheet of paper and saying “I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
  6. a computational model used in machine learning , Similar to axons in a biological brain . Each node takes a set of inputs, apply weights to them, and calculate an output value. By chaining together lots of these nodes, we can model complex functions.
  7. we recognize the idea of a child no matter what surface the child is on. We don’t have to re-learn the idea of child for every possible surface it could appear on. But right now, our neural network can’t do this. We’ll do this using a process called Convolution. The idea of convolution is inspired partly by computer science and partly by biology Instead of feeding entire images into our neural network as one grid of numbers, we’re going to do something a lot smarter that takes advantage of the idea that an object is the same no matter where it appears in a picture. Step 1: Break the image into overlapping image tiles Step 2: Feed each image tile into a small neural network
  8. Here’s what a more realistic deep convolutional network (like you would find in a research paper) looks like: In this case, they start a 224 x 224 pixel image, apply convolution and max pooling twice, apply convolution 3 more times, apply max pooling and then have two fully-connected layers. The end result is that the image is classified into one of 1000 categories!
  9. annual Olympics of computer vision The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image classification at large scale. over ten million URLs of images have been hand-annotated by ImageNet to indicate what objects are pictured Deep Learning is that idea of this decade. Deep Learning algorithms had been around for a long time, but they became mainstream in computer vision with its resounding success at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) of 2012. In that competition, an algorithm based on Deep AlexNet shook the computer vision world with an astounding 85% accuracy — 11% better than the algorithm that won the second place! In ILSVRC 2012, this was the only Deep Learning based entry. In 2013, all winning entries were based on Deep Learning and in 2015 multiple Convolutional Neural Network (CNN) based algorithms surpassed the human recognition rate of 95%.
  10. Otsu Thresholding binarization of an image to white and black Canny detect a wide range of edges in images. Dilation operation causes bright regions within an image to “grow” (therefore the name dilation)