Gain insights into automated inspection with computer vision. Learn image analysis, feature detection, edge detection, transformations, and neural networks to build industrial inspection systems for quality assurance.

Automated Inspection with Computer Vision.png

AICV_jupyterOpenCV.tar.gz

load_image

annotation

color_spaces

thresholding

smoothing

masking

jup_smooth

morphology

blob_detection

jup_fiducials

edge_detection

jupyter_masking_edge_detection

corner_detection

jupyter_corner_detection

homographies

jup_homographies

topography

jup_topography

tensor_manipulation

neural_network_building_blocks

training_neural_network

jup_train_nn

cnn_building_blocks

cnn_for_classification

pretrained_classification_cnn

jup_train_classification_cnn

transfer_learning

jup_finetune_classification_CNN

cnn_for_object_detection

jup_pretrained_object_detection

cnn_for_semantic_segmentation

jup_pretrained_semantic_segmentation

annotation_formats

spa_train_mlp

prj_jup_create_train_mlp_classification

Computer vision is essential for developers who wish to learn practical skills, e.g., in industrial manufacturing, automated inspection of products is crucial for quality assurance.

In this course, you’ll apply computer vision and machine learning to analyze images for automated inspection. You’ll start by learning image I/O operations, thresholding, smoothing, and masking. You’ll learn feature detection using template matching and morphology. You’ll use the Sobel and Canny Edge Detectors, Harris Corner Detector, and Hough transform. You’ll learn about 2D transformations, including perspective and affine transformation, and 3D-to-2D projections. You’ll perform topography with a laser line to inspect the 3D shape of an object. Lastly, you’ll train convolutional neural networks for object detection and image segmentation and annotate datasets using CVAT.

By the end of this course, you will have the skills to build inspection systems for industrial applications using computer vision and machine learning.

Automated Inspection with Computer Vision

**(True or False)** The output image of the Laplacian Edge Detector can have positive and negative values.

(True or False) The output image of the Laplacian Edge Detector can have positive and negative values.

Strong positive correlations where the source image transitions from light to dark (from left to right). Strong negative correlations where the source image transitions from dark to light (from left to right). 

Strong positive correlations where the source image transitions from light to dark (from top to bottom). Strong negative correlations where the source image transitions from dark to light (from top to bottom).

Strong positive correlations where the source image transitions from dark to light (from top to bottom). Strong negative correlations where the source image transitions from light to dark (from top to bottom).

Strong positive correlations where the source image transitions from dark to light (from left to right). Strong negative correlations where the source image transitions from light to dark (from left to right).

When performing a Sobel operation detecting the $x$ first derivative, what can we expect to emphasize?
The corresponding convolution matrix is $sobel_{x} = \begin{bmatrix} -1 & 0 & 1 \\ -2 & 0 & 2 \\ -1 & 0 & 1\end{bmatrix}$  

When performing a Sobel operation detecting the <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math>x first derivative, what can we expect to emphasize?
The corresponding convolution matrix is <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>s</mi><mi>o</mi><mi>b</mi><mi>e</mi><msub><mi>l</mi><mi>x</mi></msub><mo>=</mo><mrow><mo fence="true">[</mo><mtable rowspacing="0.16em" columnalign="center center center" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mo>−</mo><mn>1</mn></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>0</mn></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>1</mn></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mo>−</mo><mn>2</mn></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>0</mn></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>2</mn></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mo>−</mo><mn>1</mn></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>0</mn></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>1</mn></mstyle></mtd></mtr></mtable><mo fence="true">]</mo></mrow></mrow><annotation encoding="application/x-tex">sobel_{x} = \begin{bmatrix} -1 &amp; 0 &amp; 1 \\ -2 &amp; 0 &amp; 2 \\ -1 &amp; 0 &amp; 1\end{bmatrix}</annotation></semantics></math>sobelx​=⎣<svg xmlns="http://www.w3.org/2000/svg" width='0.6667em' height='0.016em' style='width:0.6667em' viewBox='0 0 666.67 16' preserveAspectRatio='xMinYMin'><path d='M319 0 H403 V16 H319z M319 0 H403 V16 H319z'/></svg>⎡​−1−2−1​000​121​⎦<svg xmlns="http://www.w3.org/2000/svg" width='0.6667em' height='0.016em' style='width:0.6667em' viewBox='0 0 666.67 16' preserveAspectRatio='xMinYMin'><path d='M263 0 H347 V16 H263z M263 0 H347 V16 H263z'/></svg>⎤​

It erodes the blobs by a fixed amount, determined by the thinnest blob.

It dilates and erodes blobs using a fixed kernel.

It eliminates the thick blobs and keeps only the thin ones.

It reduces blobs to one pixel wide filaments, without eliminating any blob.

What does the thinning morphology operation do?

What does the thinning morphology operation do?

It is the angle formed by the line perpendicular and the horizontal axis.

It is the distance between the origin and the line.

It is the length of the line across the image.

It is the radius of the largest blob in the Hough accumulator.

In the $(ρ,{\space}θ)$ representation of lines, what is the physical meaning of $ρ$?

In the <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>ρ</mi><mo separator="true">,</mo><mtext> </mtext><mi>θ</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(ρ,{\space}θ)</annotation></semantics></math>(ρ, θ) representation of lines, what is the physical meaning of <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>ρ</mi></mrow><annotation encoding="application/x-tex">ρ</annotation></semantics></math>ρ?

**(True or False)** Only the strongest line in a mask can be detected by the Hough algorithm.

(True or False) Only the strongest line in a mask can be detected by the Hough algorithm.

Quiz: Edge Detection, Thinning, Hough lines

Quiz: Edge Detection, Thinning, Hough lines

Test your knowledge of edge detection, thinning, and Hough Line Detection.

Introduction

Getting Started with Images

Image I/O and Annotations

Color Spaces and Thresholding

Convert Color Spaces, Threshold

Smoothing and Masking

Detection of Features

Image Registration

3D Vision

Getting Started with Neural Networks

Convolutional Neural Networks

Project: Create and Train a CNN for Classification

Object Detection and Semantic Segmentation

Dataset Annotation

Final Remarks

Quiz: Edge Detection, Thinning, and Hough Lines