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MLRF: Lecture 01

Posted May 14, 2021 Updated Jun 3, 2021 7 min read views

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Scope of this course

Apply Machine Learning (ML) techniques to solve some practical Computer Vision (CV) problems

  • About Computer Vision (CV)
  • It should be called CV-ML, ML4CV or so…

We need some definitions:

  • What is Computer Vision ? What is Pattern Recognition ? Shape Recognition ?
  • What is Machine Learning ?
  • How do those concepts relate together ?

Agenda for lecture 1

  1. Some definitions and basic notions
  2. Course outline
  3. Introduction to Twin it !
  4. Pattern Matching

Some definitions

Computer Vision

  • Input: image(s) in machine format (image acquisition of a subpart of CV)
  • Output: some pieces

Exemple

Les photos de chats sur Internet c’est important

  • Some applications are direct (like the insect recognition app):
    • a human reads and uses the output
  • Some applications are indirect (like bank checking reading)
    • The output is fed to a business system
  • Some applications extend what humans can naturally do
    • Either by extending our range

Pattern Recognition

IAPR: pattern recognition, computer vision and image processing in a broad sense

Examples

  • OCR
    • Computer vision
  • Pedestrian detection
    • Computer Vision
  • Credit fraud detection
    • Not computer vision

$\Rightarrow$ CV$\cap$PR$\neq\emptyset$

Pattern Recognition is an inverse problem

OCR example - Why Pattern Recognition is hard

“Shapes”

Sometimes used to describe “visual percepts”

Let S and S’ be 2 shapes observed in 2 different images which happen to be similar.

Machine Learning

Many forms of Machine Learning

  • Focus on inductive learning (generalize from examples)
  • We will consider both supervised (a “teacher” provides labels for examples) and unsupervised (only samples)
  • Focus on optimization-based learning techniques (examples are represented as numerical vectors)

Examples of optimization-based learning techniques

  • Linear classifiers, SVMs
  • Neural networks

(“Statistical”) Machine Learning

Learning means changing in order to be better (according to a given criterion) when a similar situation arrives Learning IS NOT learning by heart Any computer can learn by heart, the difficulty is to generalize a behavior to a novel situation Quoting S. Bengio

From an engineer’s POV

Machin Learning is about building programs with tunable parameters (typicalyy an array of floating point values) that are adjusted automatically so as to improve their behavior by adapting to previously seen data. Machine Learning can be considered a subfield of AI since those algorithms can be seen as building blocks to make computer learn Scikit Learn Documentation

Why is learning difficult ?

Given a finite amount of training data, you have to derive a relation for an infinite domain. In fact, there is an infinite number of such relations

Which relation is the most appropriate ?

… the hidden test points…

Learning bias

It is always possible to find a model complex enough to fit all the examples But how would this help us with new samples ? It should not generalize well. We need to define a family of acceptable solutions to search from. It forces to learn a “smoothed” representation

So in practice we need

  • Examples (data!)
  • A tunable algorithm (model)
  • A evalutation of the model fitness to examples (risk, loss)
  • A definition of the model search space (not too big, not too small)
  • An optimization strategy

3 kinds of problems

Regression

\[x=\underbrace{\begin{pmatrix} \vdots \end{pmatrix}}_{\in\mathbb R^T}\\ y=\underbrace{\begin{pmatrix} \vdots \end{pmatrix}}_{\in\mathbb R^5}\]

Classification

\[x=\mathbb R^5\\ y=\mathbb R^T\]

Density estimation

\[x\in\mathbb R^5\\ \mathbb P(x)\in[0,1]\]

3 types of learning

  • Supervised learning $(x,y)$
    • The training contains the desired behavior (desired class, outcome, etc.)
  • Reinforcement learning $(x,\tilde y)$
    • The training data contains partial targets (for instance, simply whether the machines did well or not)
  • Unsupervised learning
    • The training data is raw, no class or target is given
    • There is often a hidden goal in that task (compression, maximum likelihood, etc.)

Model validation

More on that later

  1. You need to test the generalization power of your approach
  2. So you need data not seen during the training: a test set
  3. For which you know the expected output (“ground-truth”, “gold standard”, “target”,…)

Benefits of ML

A duck example

How to filter the grass to keep only the duckshape, using threshold domain ?

Why using Machine Learning in computer Vision ?

To avoid knob turning. It’s complex. It’s unsafe

But beware of the Machine Learning Magic

Actual goals of this course

  • Teach you that you can (and should whenever possible) optimize the parameters of your CV/PR product
  • Show some simple tools to try to do it
  • Address practical problem
    • describe a pattern
    • look for a pattern
    • match a pattern
    • classify a pattern
    • describe a set of patterns (an object/an image)
    • retrieve an object given a query, segment objects…
    • and face the unavoidable work surrounding them

Course agenda

6 “weeks” (Friday to Friday) See the web page for complete agenda Weekly tests + assignments (practice sessions). No final exam

Weekly wokflow should be:

  • Friday, 09:30-10:00: answer the weekly quiz on Moodle (starting next Friday)
  • Friday, 10:00-12:00: attend the lecture using Teams
  • Friday, 14:00-17:00: Work on the practice session and join the discussion using Teams
  • Before next Friday: Complete the assignement and submit your results using Moodle (for sessions 4, 5 and 6 only)

No deep learning !

  • We need a course about basic techniques
  • There are cases where setting up

Pratice sessions: setup your dev. env.

Basically: Python with:

  • Jupyter
  • Numpy
  • Matplotlin
  • Scikit-image: RGB
  • Scikit-learn
  • OpenCV: BGR

Why I love Scikit-Learn

Numpy-friendly

3-way documentation: User guide, API ref, Examples

Super smart API

Decomposition, level of detail, default values, consistency, etc

Introduction to Twin it!

Overview

A poster game

  • $X$ bubbles, all different but
  • $Y$ bubbles, which have 1 (and only 1) twin

Your goals:

  • Find the pairs

Already done:

  • Scan the poster
  • Stitch the tiles
  • Normalize the contrast

Undelying problems

  1. Isolate each bubble $\Rightarrow$ Segmentation
    • We provide pre-computed results for this step
  2. Compare image pairs $\Rightarrow$ Matching
    • We will focus on this one
    • We will use Template Matching
  3. Identify pairs $\Rightarrow$ Calibration

Template matching

Why template matching ?

A simple method which will be useful to understand

  • Evaluation challenges
  • The ideas behind keypoint detection (next lecture)

It can work on the Twin it! case

  • Twice the same texture
  • Textures are the same scale, without rotation nor intensity change
  • Only need to cope with translation (and some small noise)

Step by step: Compare 2 images

  • 2 arrays of intensities
  • Take the absolute difference

\[R(x,y) = \vert I_1(x,y) - I_2(x,y)\vert\]
  • Sum the differences

\[S=\sum_{x,y}(I_1(x,y) - I_2(x,y))^2\]

(Opt.) Normalize so the results belongs to $[0,1]$

Template Matching: Sliding comparison

  • $I_1$ is a small template $T$ to match against $I_2$ (just $I$ after)
  • We rewrite the preceding formula to compute a map $R$ of the shape of $I$
  • Each pixel of $R$ will have the value of the SSD when the top-left pixel of $T$ in on the pixel $(x,y)$ of $I$

Several approaches $\Leftrightarrow$ Practice session

About the denominator

Cross correlation: 2 things to know

More robust to intensity shift

Ideal goal

For each bubble, retunr only a mathcin pair, if it exists

Image S8, MLRF
Image SCIA MLRF S8
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