Computer vision and Image processing

Requisites

Introduction

What is Computer vision?

💡

Computer vision is the science to extract the patterns in image

Computer vision or digital image processing is not Computer graphics, the latter are synthetic images, that is, we model, visualize, generate images by a computer.

https://courses.cs.washington.edu/courses/cse576/23sp/notes/1_Intro23.pdf

Why does Computer vision matter to you?

The expense associated with developing and implementing computer vision systems is justified by the immense value it offers. Its applications vary widely in terms of the sophistication of their "intelligent" observations. These include:

Low level vision

Measurement

Enhancements

Region segmentation

Features

Mid level vision

Reconstruction

Depth

Motion Estimation

High level vision

Category detection

Activity recognition

Deep understandings

Understanding why vision is challenging necessitates delving into a myriad of issues such as ill-posed problems, viewpoint variations, illumination conditions, occlusion, scale, deformation, background clutter, intra-class variation, local ambiguity, and deciphering the world behind the image.

Research

Ecosystem

Standards, jobs, industry, roles, …

OpenCV

https://roboflow.com/

YOLOv7

Distilled YOLOV7

UNET v7

https://roboflow.com/

https://pypi.org/project/face-recognition/

https://docs.ultralytics.com/

https://opencvflow.org

https://huggingface.co/docs/transformers/main/model_doc/sam

Datasets

ESA/NASA Hubble. Datasets for education and for fun. https://esahubble.org/projects/fits_liberator/datasets/

Story

FAQ

Worked examples

Image formation

stimulus

passive sensing

active sensing

feature

feature extraction

object model

rendering model

The two core problems of computer are reconstruction and recognition, however we have a lot of other changes such as view point variation, illumination, occlusion, scale, deformation, background clutter, object intra-class variation, local ambiguity, the world behind the image

Digital image processing

A gray image is a function $I:R^2 \to R$ that gives the light intensity at position $(x,y)$ and a rectangle $f:[a,b]\times [c,d] \to [min,max]$ . Similarity, a color image is a vector-valued function: $f(x,y)=[r(x,y),g(x,y),b(x,y)]$ , where we call the components as channels or planes, that is, a 3-channel color image ia mapping from a 2-dimensional space of locations to a 3-dimensional space of color intensity values $I:R^2\to R^3$ . However, in computer vision, we operate on digtal images (aka. discrete images): sample the 2D space on a regular grid (discrete pixels at locations) and quantize each sample (round to nearest integer such as 16-bit, 12-bit, 8-bit images). Thus, image is represented as matrix of integer values. Programming Languages represent those integers as uint16 and uint8, that is, unsigned integer 16 bits [0,65535] and unsigned integer 8 bits [0,255].

💡

Pixel stands for picture elements.

If you think an image as function or matrix you could apply its mathematical operators to them.

Noise in images is another function that is combined to an original to another function.

Salt and peper noise: random ocurrences of black and white pixels.

Impulse noise: random ocurrences of white pixels

Gaussian noise: normal random noise

Preserve image difference

How can you guarantee that difference between images be uint8 and be an absolute difference?

$I:[0,255]\times [0,255] \to [0,255]$

$abs(a-b)$ doesn’t work because we must consider clamp in every simple operation, that is, $abs(clamp(0,255)(a-b))$ . For example, suppose two pixels of both images in interval mentioned before:

abs(clamp(0,255)(20-56))=abs(clamp(0,255)(-36))=abs(0)=0

But we want an $f:uint8 ^2\to uint8$ such that $abs(clamp(0,255)(f(a,b)))=abs(a-b)$ , e.g.

abs(clamp(0,255)(f(20,56)))=36

Let $c(x)=clamp(0,255)$ a function that must applied in every operation (abs, -, +) , the solution is $f(a,b)=(a-b)+(b-a)$ .

Proof.

If $b\ge a$ , $c(a-b)=0$ (the argument is negative so applying clamp gives us 0) and $c(b-a)=b-a$ (the argument is a number between 0 and 255 so applying clamp gives us b-a). Similar argument for $a\ge b$ .

c = clamp(0,255)
g = (a,b) => Math.abs(c(c(a-b)+c(b-a)))
g(20,56) // 36
g(56,20) // 36
g(0,257) // 255
g(300,0) // 255
g(-10,-11) // 1
g(100,25) // 75
g(25,100) // 75

Another option is transforming values to floating point (so, you can think numbers as real numbers informally).

Clamp

// limits=[0,255] x=256, clamp is 255 min(255,256)
// limits=[0,255] x=-1, clamp is 0 max(-1,0)
// limits=[0,255] x=10, clamp is 10 max(0,10)
// 0 <= x <= 255
clamp = (a,b) => x => Math.min(Math.max(x,a), b)

Clip

Normalization

You would only normalize it in order to display it, not in order compute with it.

Signal Processing

We use 1D discrete signals, that is, an array of numbers to represent gray images. A histogram of a gray-tone image is an array of bins, one for each gray tone.

Think image as tensor

An image is a tensor rank two.

Introduction

CV Process

Process data from pixels.

Obtain information from spatial correlations.

Intrerpret images.

Common Tasks

Classification: assign a label to image.

Detection and localization: is there certain object? where? You might reduce this problem into classification.

Segmentation: partition an image into regions (an object of interest). You might reduce this problem into classification.

Generation: produce synthetic images

Applications

Style Transfer

Time Domain Description

Colorspaces

The Commission Internationale de l'Éclairage (CIE) is the organization responsible for maintaining standards related to light and color.

https://courses.cs.washington.edu/courses/cse576/23sp/notes/2_Color_Texture_FIN_20.pdf

https://www.youtube.com/watch?v=LFXN9PiOGtY

Transformations

Linear operations. Let $U$ and $V$ be vector spaces over a field $K$ An operator $A: U\to V$ is linear if

A(a\bold{x}+b\bold{y})=aA(x)+b A(y)

for all $\bold{x},\bold{y}$ in $U$ and for all $a,b$ in $K$ .

Shift invariant. Operator behaves the same everywhere.

Operations

Convolution

Convolution is a linear, shift invariant operator that is commutative and associative. Its identity is the unity impulse. Its differentation is $\dfrac{d}{dx}(f *g)=\dfrac{d}{dx}*g$ .

https://betterexplained.com/articles/intuitive-convolution/

Morphology

https://www.researchgate.net/figure/Example-of-image-processing-by-CA-a-templates-for-erosion-b-templates-for-dilation_fig6_241754173

Sensors

RGB

LIDAR

NIR

SAR

Thermal

RG-DSM

Worked example

Low-level

Homework 0: Fun with color!

High-level

We’re going to use Python, OpenCV, and Pillow.

Image processing

Kernels and filtering

Template matching

Model fitting and optimization

Deep learning

CNN

Tensorflow, Keras

Callbacks

Metrics

Pooling.

GPU

U-NET

Data augmentation

Pretrained Convolutional Neural Network models

Examples of CNN are ResNet, Inception,Xception, MobileNet, VGG16.

There are two ways to use pretrained CNN are fine-tuning and total transference.

CNN Boilerplate

Worked examples

MNIST handwritten digit database

Dogs and Cats

Preprocessing

Captcha Solver

We classify Captcha in Normal Captcha, Text Captcha, reCaptcha 2, reCaptcha3,

CAPTCHAs often use a mix of tactics to prevent automated decoding, such as:

Distorted text: The text in the CAPTCHA image is often distorted in some way to make it harder for software to recognize the characters. The letters might be skewed, rotated, or stretched, for instance.

Background noise: CAPTCHAs frequently include background patterns, colors, or images to make it harder for software to separate the text from the background.

Overlapping characters: The characters in the CAPTCHA text are often placed close together or even overlapping, which can confuse OCR software.

Varied fonts and colors: CAPTCHAs often use a variety of different fonts, sizes, and colors, which can make it more difficult for software to recognize the characters.

Line interference: Lines or shapes might be drawn over or through the text to confuse OCR software.

Object follower

https://towardsdatascience.com/automatic-vision-object-tracking-347af1cc8a3b

Representation learning

FAQ

How can I find out the best architecture?

Can I add new data during the training?

Are there available tools to label automatically?

Supervisely

Platform.ai

https://github.com/abreheret/PixelAnnotationTool

Labelme

Recognition

OCR

TrOCR

Feature detection and matching

Harris Corner Detector

OBS

SIFT

SURF

BRISK

AZAKE

Procrustes analysis and Wahba's problem

https://github.com/theochem/procrustes

Siamase neuronal network

Image aligment and stitching

Motion estimation

Computational photography

Structure from motion and SLAM

Depth estimation

3D reconstruction

Image-based rendering

Digital Image Processing

Digital images processing are everywhere in the today technician endevour. But what is a image? The images are the result of electromagnetic energy spectrum, acoustic, ultrasonic, and electronic whether we can see them or not. For that, some applications are X-Ray imaging, angiography, imaging in the ultraviolet band, imaging the visible and infrared bands, remote sensing.

Ecosystem

OpenCV is the major library for digital image processing.

Notes

Worked examples

FAQ

Further resources

Digital Image Processing, 4Th Edition by Rafael C. Gonzalez

https://www.youtube.com/watch?v=qByYk6JggQU&ab_channel=FirstPrinciplesofComputerVision

https://github.com/vinta/awesome-python

Case studies (projects)

If you wish to create a computer vision project, it normally requires a pipeline to successfully process your images or videos. Even if you don't need to use machine learning techniques, you must have a testing process.

Document Text Recognition

When your users interact with your systems through documents and lack access to a barcode format or more structured data (like computer-generated PDFs or web forms), your systems must possess some form of recognition capability over images. This project provides a pipeline to transform an image with free-text structure into a structured and meaningful data format like JSON.

The pipeline is as follows:

flowchart TB
    subgraph predict
       CaptureImage["Image"] --> ImageThresholding
       subgraph denoise
           ImageThresholding
           -->SmoothingImage
           -->MorphologicalTransformation
           -->d...["..."]
           --> feature-invariant
       end
       subgraph feature-invariant
         SIFT
         CNN
         SiameseNeuralNetwork["Siamese neural network"]
         Procustes
         ORB
       end
       feature-invariant
          --> TextDetection
          --> TextRecognition
          --> SemanticJSON
    end

Medical Image Analysis

Bankman, I. (Ed.). (2008). Handbook of medical image processing and analysis. Elsevier.

Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19, 221-248.

References

Didactic Introductions

Richard Szeliski. “Computer Vision: Algorithms and Applications”. 2nd Ed. Springer Nature. 2022.

Rafael C. Gonzalez, Richard E. Woods. “Digital Image Processing”. 4th Ed. Pearson. 2018.
Roy Davies. “Computer Vision: Principles, Algorithms, Applications, Learning”. 5th Ed. Academic Press. 2017

Computer Vision, Linda G. Shapiro and George Stockman, Prentice-Hall, 2001.

Computer Vision: Algorithms and Applications, Rick Szeliski, 2010.

Rick's Second Edition, From Spring 2020.

Theory

Ian Goodfellow, Yoshua Bengio, Aaron Courville. “Deep Learning”. MIT Press. 2016

Simon J.D. Prince. “Understanding Deep Leanring”. MIT Press. 2023.

Francois Chollet. “Deep Learning with Python”. 2nd Ed. Manning. 2021.

Application

OpenCV3-Computer-Vision-with-Python-Cookbook

Courses

https://www.kaggle.com/learn

https://app.datacamp.com/

https://www.udacity.com/course/computer-vision-nanodegree--nd891

CSCI 1430: Introduction to Computer Vision. (2022, April 19). Retrieved from https://cs.brown.edu/courses/csci1430/2022_Spring/index.html

16-385 Computer Vision, Spring 2020. (2022, November 24). Retrieved from https://www.cs.cmu.edu/~16385

https://courses.cs.washington.edu/courses/cse576/

References

Berry, R., & Burnell, J. (2000). Astronomical Image Processing. Willmann-Bell, Inc. [Clásica].

Gonzalez, R. & Woods, R. (2018). Digital Image Processing (4th ed.). Pearson.

Russ, J. & Neal, F. (2017). The Image Processing Handbook (7th ed.). CRC Press, Taylor & Francis Group. Available at: EBSCOhost.

Shih, F. (2010). Image Processing and Pattern Recognition: Fundamentals and Techniques. John Wiley & Sons. [Clásica].

Starck, J. & Murtagh, F. (2006). Astronomical Image and Data Analysis. Springer. [Clásica].

Burger, W. & Burge, M. J. (2016). Digital Image Processing: An Algorithmic Introduction Using Java (2nd ed.). Available at: EBSCOhost [Clásica].

ESA/NASA Hubble. A Short Introduction to Astronomical Image Processing. Available at: ESA Hubble.

Milone, E., & Sterken, C. (2011). Astronomical Photometry: Past, Present, and Future. Springer New York. Available at: EBSCOhost [Clásica].

OpenCV. (2021). OpenCV Tutorials. Available at: OpenCV Documentation.

https://www.youtube.com/watch?v=_NiKKCTpd2g

https://www.youtube.com/watch?v=tFSHT1dDgic

https://www.youtube.com/watch?v=l-GovgpV93w

https://www.youtube.com/watch?v=Rh6pKTNMjKY

https://www.youtube.com/watch?v=G4nFveLyzJo

https://www.youtube.com/watch?v=VsFEIBUe__I

https://www.youtube.com/watch?v=kR12pt-ipr4

https://www.youtube.com/watch?v=Pd17V_cNgz4

Ballesteros,John R. (January 17th, 2023) Remote Sensing Datasets forArtificial
Intelligence Semantic Segmentation. The Modern Scientist.

Ballesteros,John R. (January 30th, 2023) Three different applications fororthomosaic production. The Modern Scientist.

Ballesteros,John R. (January 30th, 2023) GeoAI and its four horsemen. TheModern Scientist.

Ballesteros, J.R.; Sanchez-Torres, G.; Branch-Bedoya, J.W. HAGDAVS: Height-Augmented Geo-Located Dataset for Detection and Semantic Segmentation of Vehicles in Drone Aerial Orthomosaics. Data 2022, 7, 50.

Ballesteros, J.R.; Sanchez-Torres, G.; Branch-Bedoya, J.W. A GIS Pipeline to Produce GeoAI Datasets from Drone Overhead Imagery. ISPRS Int. J. Geo-Inf.
2022, 11, 508.

https://www.kaggle.com/datasets/mateocanosolis/hagdavs?datasetId=3451891

Repo Camilo Laiton:

https://github.com/camilolaiton/SelfAttention3DBrainSegmentation

Notebook tensorflow data augmentation:

https://www.tensorflow.org/tutorials/images/data_augmentation?hl=es-419

Albumentations:

https://github.com/albumentations-team/albumentations

https://drive.google.com/drive/folders/1NsfQrFMKe3MR-3_DLtAqRZM3yaLuWce7?usp=sharing

Next steps

TODO

Top 5 Object Tracking Methods

Assigments

Homework 1: Resizing and convolutions

Homework 2: Panoramas!

Homework 3: Optical Flow

Homework 4: Neural Networks and Machine Learning

Homework 5: PyTorch