1. Image Recognition

2. Contents: Motivation Objective Definition Introduction Preprocessing / Edge Detection Neural Networks in Image Recognition Practical Applications Future Scope and Conclusion References

3. Motivation Makes computer vision a possibility, hence enhancing power of Artificial Intelligence. There is significant interest in creating light weight and mobile systems that can identify objects using vision Numerous practical application makes Image Recognition a motivating field of study.

4. Studying the basic principles of Image Recognition, and understanding the practical applications with state of art facilities and tremendous future possibilities. Objective

5. What is Image Recognition? Image recognition is the process of identifying and detecting an object or a feature in a Digital Image. It is also known as Computer Vision.

6. What is a digital image? A digital image is a representation of a 2D image using a finite set of digital values for each pixel. A pixel is the smallest independent block of a digital image. The digital values of these pixels are processed and used in Image Recognition and in other areas of Image Processing.

7. Introduction Basic components of a pattern recognition system Basic components of a pattern recognition system

8. Steps in Image Recognition Data acquisition and sensing Preprocessing Removal of noise Isolation of patterns of interest from the background (Segmentation) Feature Extraction Finding a new representation in terms of features (Detection)

9. Steps in Image Recognition Model Learning and Estimation -Learning a mapping between features and pattern groups. Classification - Using learned models to assign a pattern to a predefined category Post processing - Evaluation of confidence in decisions. - Exploitation of context to improve performances.

10. Edge Detection Images are preprocessed to be fed as input into the network. Preprocessing helps in better feature extraction from the image.

11. Edge detection Common methods of Edge Detection:- Canny Edge Detection: Uses calculus of variations (most widely used) – optimizes a given functional Sobel Edge Detection: It is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function

12. Classification using Neural Networks A neural network is a computer system modeled on a human brain. It is extensively used in Image Recognition / Image processing Implemented using Convolutional Neural Network to detect edges.

13. What is a neural network? An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. Advantages of using Neural Network for Image Recognition is increased accuracy up to 95% and it does not require separate training for each data set.

14. Neural Network for Image Recognition ( CNN ) Convolution Neural Networks are used for Image Recognition. Convolutions are implemented using Fast Fourier Transforms. F[f*g] = F[f]F[g]

15. Image representation in CNNs

16. Practical Applications Medical Imaging extensively used for cancer detection, retinopathy detection, improving quality of imperfect images. Industrial Application fault detection in manufacturing

17. Practical Applications Geographic Information Systems - Terrain Classification - Meteorology - Global inventory of human settlement Astronomy - Enhancement of telescopic images - Recognition of astronomical bodies - Eg: The Hubble Telescope

18. Practical Applications Security - Face and fingerprint recognition - Law enforcement Applications for creative media - Deep dream - Neural style transfer (prizma) - Human and Computer interface

19. Future Scope and Conclusion Image recognition is a futuristic and relatively unexplored field, with wide areas of practical applications, including industrial, scientific and medical applications. This field has a lot of potential for development and implementation in new areas like space exploration, processing signal images, computer vision etc. A lot of tasks can be automated using Image Recognition like processing cheques in banks etc.

20. References: Edge Detection in Digital Image Processing by Debosmit Ray (Research Paper) Pattern Recognition in Medical Imaging – Anke Mayer & Base (Book) Image Style Transfer Using Convolutional Neural Network – Leon A. Gatys, Alexander S. Ecker, Matthias Bethge (Research Paper) Image-based pattern recognition project by Dr. Jian Jiun Ding, Ph.D from National Taiwan University, Taiwan. Machine Learning is fun – Adam Geitey (Blog) Image Recognition in Industrial Application – Mobgen – A part a Accenture Digital – 22/02/2016 (Article) Wikipedia and google for images and basic definitions.