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Makine Öğrenmesi ile Görüntü Tanıma | Image Recognition using Machine Learning

Seminar by Ali Alkan - 30 November 2017
@Kadir Has University, Istanbul

Makine Öğrenmesi ile Görüntü Tanıma | Image Recognition using Machine Learning

  1. 1. Agenda Introduction to Image Processing & Recognition Image Processing & Recognition using Machine Learning Image Processing & Recognition in KNIME KNIME Image Recognition Demo • Car Counting • OCR on Xerox Copies meets Semantic Web • Celebrity Detection using AlexNet Q&A
  2. 2. Introduction to Image Processing & Recognition
  3. 3. Introduction | 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. 4. Introduction | Motivation “ On an average, over 300 million images are uploaded on Facebook daily “ Source: Zephoria
  5. 5. Introduction | Motivation “ The image recognition market is estimated to grow from US$16 Billion in 2016 to US$39 Billion by 2021 ” Source: Zephoria
  6. 6. Introduction | 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.
  7. 7. Introduction | 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.
  8. 8. Introduction | Basic Components of a Pattern Recognition System
  9. 9. Introduction | 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)
  10. 10. Introduction | 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.
  11. 11. Introduction | Preprocessing • Images are preprocessed to be fed as input into the algorithm. • Preprocessing helps in better feature extraction from the image.
  12. 12. Introduction | 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
  13. 13. Introduction | Practical Applications of Image Recognition Medical Imaging • extensively used for cancer detection, retinopathy detection, improving quality of imperfect images. Industrial Applications • fault detection in manufacturing Commercial Applications • In store shopper tracking • Inventory control
  14. 14. Introduction | Practical Applications of Image Recognition Security • Face & fingerprint & retina - iris recognition Transportation • Autonomous vehicles Applications for creative media • Deep dream • Neural style transfer (prizma) • Human and Computer interface
  15. 15. Introduction | Practical Applications of Image Recognition Geographic Information Systems • Terrain Classification • Meteorology • Global inventory of human settlement Astronomy • Enhancement of telescopic images • Recognition of astronomical bodies • Eg: The Hubble Telescope
  16. 16. Image Processing & Recognition with Machine Learning Deep Learning Case
  17. 17. What is Deep Learning? • Part of the machine learning field of learning representations of data. Exceptional effective at learning patterns. • Utilizes learning algorithms that derive meaning out of data by using a hierarchy of multiple layers that mimic the neural networks of our brain. • If you provide the system tons of information, it begins to understand it and respond in useful ways.
  18. 18. Inspired by the Brain • The first hierarchy of neurons that receives information in the visual cortex are sensitive to specific edges while brain regions further down the visual pipeline are sensitive to more complex structures such as faces. • Our brain has lots of neurons connected together and the strength of the connections between neurons represents long term knowledge.
  19. 19. Deep Learning | No more feature engineering
  20. 20. Deep Learning | Big Data
  21. 21. Deep Learning | Architecture A deep neural network consists of a hierarchy of layers, whereby each layer transforms the input data into more abstract representations e.g. edge -> nose -> face The output layer combines those features to make predictions.
  22. 22. Deep Learning | What did it learn?
  23. 23. Deep Learning | More Layers -> Better Performance The more layers the network has, the higher-level features it will learn.
  24. 24. Deep Learning | Convolutional Neural Nets (CNN) Convolutional Neural Networks learn a complex representation of visual data using vast amounts of data. • They are inspired by the human visual system and learn multiple layers of transformations, which are applied on top of each other to extract a progressively more sophisticated representation of the input. E.g. Image is a 224*224*3 (RGB) cube and will be transformed to 1*1000 vector of probabilities.
  25. 25. Image Processing & Recognition in KNIME
  26. 26. What’s KNIME? ✓ Data integration, processing, analysis and exploration platform ✓ User-friendly, open-source and easy updatable ✓ Software integration platform ✓ Highly modular, easily extendible ✓ Workflow based & Cluster execution ✓ Current Version: KNIME 3.4.2
  27. 27. Image Processing Tools
  28. 28. KNIME as Integration Platform
  29. 29. KNIME just for Integration? ✓ Data Caching (High-Throughput!!) ✓ Fast Prototyping ✓ Automation ✓ Interactive Data Exploration ✓ Machine Learning ✓ Bridging Domains ✓ …
  30. 30. KNIME Image Processing Universe
  31. 31. KNIME Image Processing Universe
  32. 32. KNIME Image Processing Universe
  33. 33. KNIME Image Processing Universe
  34. 34. KNIME Image Processing Universe
  35. 35. KNIME Image Processing Universe
  36. 36. KNIME Image Processing Universe
  37. 37. Demo
  38. 38. Deep Learning Tools Its all Open Source
  39. 39. DeepLearning4J Open-source Deep Learning framework written for Java Supports state-of-the-art network architectures GPU/CPU support Distributed computations on Apache Spark and Hadoop
  40. 40. KNIME | DeepLearning4J Extension • Visually assemble networks using KNIME nodes • Integrates with other KNIME extensions • e.g. Image Processing & Text Mining • Networks can be trained and executed on GPU and CPU
  41. 41. Deep learning benefits Robust • No need to design the features ahead of time - features are automatically learned to be optimal for the task at hand • Robustness to natural variations in the data is automatically learned Generalizable • The same neural net approach can be used for many different applications and data types Scalable • Performance improves with more data, method is massively parallelizable
  42. 42. Ali ALKAN Twitter @Ali_Alkan ali.alkan@infora.com.tr Q&A
  • AydanMuhammadi

    May. 30, 2019
  • alialkan

    Nov. 4, 2018
  • SheetalChauhan26

    Aug. 7, 2018
  • haytastan

    Mar. 16, 2018

Seminar by Ali Alkan - 30 November 2017 @Kadir Has University, Istanbul

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