Index

A

• affine transformations
  • about / Affine transformations, What just happened?
• augmented reality
  • about / What is the premise of augmented reality?
  • geometric transformations / Geometric transformations for augmented reality
• augmented reality system
  • about / What does an augmented reality system look like?

B

• background subtraction
  • about / Background subtraction
• Bag of Words / What happened inside the code?
• Binary Robust Independent Elementary Features (BRIEF)
  • about / Binary Robust Independent Elementary Features (BRIEF)
• blurring
  • about / Blurring
• boomerang shape
  • identifying / Contour analysis and shape matching

C

• Caltech256
  • about / How do we actually implement this?
  • URL / How do we actually implement this?
• camera-to-image transformation / Geometric transformations for augmented reality
• CAMShift algorithm
  • about / Building an interactive object tracker
• Canny Edge Detector
  • URL / Edge detection
• cartoonize_image function / Deconstructing the code
• colored objects
  • tracking, colorspaces used / Colorspace based tracking
• color images
  • handling / How do we handle color images?
• color spaces
  • RGB / Image color spaces
  • YUV / Image color spaces
  • HSV / Image color spaces
  • converting between / Converting between color spaces, What just happened?
• colorspaces
  • used, for tracking colored objects / Colorspace based tracking
  • about / Colorspace based tracking
• contour
  • approximating / Approximating a contour
• contour analysis
  • about / Contour analysis and shape matching
• convex shapes
  • about / Identifying the pizza with the slice taken out
• convolution
  • about / 2D convolution
• coordinates
  • mapping, from 3D to 2D / Mapping coordinates from 3D to 2D
• corner detection
  • reference link / Detecting the corners
• corner point
  • about / Detecting the corners
• corners
  • detecting / Detecting the corners
• cross-ratio
  • reference link / Projective transformations
• cv2.rectangle() function / What's happening underneath?

D

• 2D Convolution / 2D convolution
• 3D map
  • building / Building the 3D map
• 3D objects
  • overlaying, on top of real world / How to augment our reality?
  • overlaying, on video / How to overlay 3D objects on a video?, Let's look at the code
• Deep Learning
  • about / How did we build the trainer?
  • URL / How did we build the trainer?
• dense feature detector
  • about / What is a dense feature detector?
• dilation
  • about / Erosion and dilation
  • using / Afterthought

E

• ears
  • detecting / Detecting ears
• edge detection
  • process / Edge detection
  • about / Edge detection
• embossing filter
  • about / Embossing
• epipolar geometry
  • about / What is epipolar geometry?
• epipolar lines
  • about / What is epipolar geometry?
  • versus SIFT / Why are the lines different as compared to SIFT?
• epipole
  • about / What is epipolar geometry?
• erosion
  • about / Erosion and dilation
  • using / Afterthought
• eyes
  • detecting / Detecting eyes, Afterthought
  • sunglasses, positioning / Fun with eyes, Positioning the sunglasses

F

• faces
  • detecting / Detecting and tracking faces, Understanding it better
  • tracking / Detecting and tracking faces, Understanding it better
  • funny masks, overlaying on / Fun with faces, Under the hood
• feature based tracking
  • about / Feature based tracking
• Features from Accelerated Segment Test (FAST)
  • about / Features from Accelerated Segment Test (FAST)
• feature tracker
  • building / Feature based tracking
• Flann
  • URL / Finding the overlapping regions
• frame differencing
  • using / Frame differencing
• fundamental matrix
  • about / What is epipolar geometry?
• funny masks
  • overlaying, on top of faces / Fun with faces, Under the hood

G

• Gaussian Mixture Markov Random Field (GMMRF)
  • about / How does it work?
  • URL / How does it work?
• geometric transformations, for augmented reality
  • about / Geometric transformations for augmented reality
  • object-to-scene / Geometric transformations for augmented reality
  • scene-to-camera / Geometric transformations for augmented reality
  • camera-to-image / Geometric transformations for augmented reality
• GrabCut
  • about / What is image segmentation?
  • reference link / What is image segmentation?
• graph-cuts
  • about / What is image segmentation?

H

• Haar cascades
  • used, for detecting things / Using Haar cascades to detect things
• Hamming distance
  • URL / How did we match the keypoints?
• Harris Corner Detector
  • about / Detecting the corners
• Histogram Equalization / Enhancing the contrast in an image
• Homebrew
  • about / Mac OS X
• homography
  • about / Projective transformations
• HSV color space / Image color spaces
• HSVcolorspace
  • about / Colorspace based tracking

I

• image
  • contrast, enhancing / Enhancing the contrast in an image
  • color images, handling / How do we handle color images?
  • cartoonizing / Cartoonizing an image
  • code, deconstructing / Deconstructing the code
  • expanding / Can we expand an image?
• image color spaces
  • about / Image color spaces
• image content analysis
  • about / Why do we care about keypoints?
• image filter / 2D convolution
• image rotation
  • about / Image rotation, What just happened?
• images
  • reading / Reading, displaying, and saving images
  • displaying / Reading, displaying, and saving images
  • saving / Reading, displaying, and saving images, Loading and saving an image
  • loading / Loading and saving an image
  • stitching / Stitching the images
  • stretched image, reason / Why does it look stretched?
• image scaling
  • about / Image scaling, What just happened?
• image segmentation
  • about / What is image segmentation?
  • working / How does it work?
• image translation
  • about / Image translation, What just happened?
• image warping
  • about / Image warping
• incidence
  • reference link / Projective transformations
• installing
  • OpenCV-Python / Installing OpenCV-Python
  • OpenCV-Python, on Windows / Windows
  • OpenCV-Python, on Mac OS X / Mac OS X
  • OpenCV-Python, on Linux (for Ubuntu) / Linux (for Ubuntu)
• integral images
  • about / What are integral images?
• interactive object tracker
  • building / Building an interactive object tracker

K

• K-Means clustering
  • about / How to censor a shape?
  • reference link / How to censor a shape?
• kernel / 2D convolution
• kernel size
  • versus blurriness / The size of the kernel versus the blurriness
• kernel trick
  • about / What if we cannot separate the data with simple straight lines?
• keyboard inputs
  • about / Keyboard inputs
  • application, interacting with / Interacting with the application
• keypoint descriptors
  • matching / Matching keypoint descriptors
• keypoints
  • need for / Why do we care about keypoints?
  • about / What are keypoints?
  • matching / How did we match the keypoints?

L

• Linux (for Ubuntu)
  • OpenCV-Python, installing on / Linux (for Ubuntu)
• live video stream
  • interacting with / Interacting with a live video stream
  • working with / How did we do it?
• low pass filter / Blurring
• Lucas-Kanade method
  • about / Feature based tracking
  • reference link / Feature based tracking

M

• Mac OS X
  • OpenCV-Python, installing on / Mac OS X
• matcher object
  • about / Understanding the matcher object
  • item.distance attribute / Understanding the matcher object
  • item.trainIdx attribute / Understanding the matcher object
  • item.queryIdx attribute / Understanding the matcher object
  • item.imgIdx attribute / Understanding the matcher object
• matching keypoints
  • drawing / Drawing the matching keypoints
• Meanshift algorithm
  • about / Building an interactive object tracker
• MeshLab
  • about / Building the 3D map
  • URL / Building the 3D map
• moments
  • reference link / Contour analysis and shape matching
• motion blur
  • about / Motion blur
  • using / Under the hood
• mouse inputs
  • about / Mouse inputs
  • using / What's happening underneath?
  • detect_quadrant function / What's happening underneath?
• moustache
  • overlaying / It's time for a moustache
• mouth
  • detecting / Detecting a mouth
• multiple images
  • capturing, on same plane / What if the images are at an angle to each other?

N

• nose
  • detecting / Detecting a nose
• NumPy
  • about / Windows
  • URL, for installation / Windows
  • URL / What just happened?

O

• object
  • removing / Can we remove an object completely?, How did we do it?
  • recognizing, in unknown image / What happened inside the code?
• object-to-scene transformation / Geometric transformations for augmented reality
• object detection
  • versus object recognition / Object detection versus object recognition
• object recognition
  • versus object detection / Object detection versus object recognition
• OpenCV
  • URL, for downloading / Windows
  • URL, for downloads page / Linux (for Ubuntu)
  • reference link, for modules / Mapping coordinates from 3D to 2D
• OpenCV-Python
  • installing / Installing OpenCV-Python
  • installing, on Windows / Windows
  • installing, on Mac OS X / Mac OS X
  • installing, on Linux (for Ubuntu) / Linux (for Ubuntu)
• optical flow
  • about / Feature based tracking
• optical flow based tracking
  • performing / Feature based tracking
• ord() function / Interacting with the application
• Oriented FAST and Rotated BRIEF (ORB)
  • about / Oriented FAST and Rotated BRIEF (ORB)

P

• panoramic image
  • creating / Creating the panoramic image
  • overlapping regions, finding / Finding the overlapping regions
• pip
  • about / Mac OS X
• pizza
  • identifying, with slice taken out / Identifying the pizza with the slice taken out
• planar objects
  • tracking / How to track planar objects?, What happened inside the code?
• pose estimation
  • about / What is pose estimation?
• projective transformations
  • about / Projective transformations, What just happened?
• pupils
  • detecting / Detecting pupils
  • code, deconstructing for detection / Deconstructing the code
• pyramid height
  • modifying, dynamically / Let's add some movements
• Python 2.7.x
  • URL, for installation / Windows

R

• RGB color space / Image color spaces

S

• Scale Invariant Feature Transform (SIFT)
  • using / Scale Invariant Feature Transform (SIFT)
  • reference link / Scale Invariant Feature Transform (SIFT)
• scene-to-camera transformation / Geometric transformations for augmented reality
• seam carving
  • about / Why do we care about seam carving?
  • working / How does it work?
  • reference link / How does it work?
• seams
  • computing / How do we compute the seams?
• shape
  • censoring / How to censor a shape?
• shape matching
  • about / Contour analysis and shape matching
• sharpening filter
  • about / Sharpening
  • using / Understanding the pattern
• Shi-Tomasi corner detector
  • about / Good Features To Track
• SIFT
  • versus epipolar lines / Why are the lines different as compared to SIFT?
• Sobel Filter
  • about / How do we define "interesting"?
• solidity factor
  • about / How to censor a shape?
• Speeded Up Robust Features (SURF)
  • using / Speeded Up Robust Features (SURF)
  • references / Speeded Up Robust Features (SURF)
• stereo correspondence
  • about / What is stereo correspondence?
• supervised learning
  • about / What is supervised and unsupervised learning?
• Support Vector Machines (SVM)
  • about / What are Support Vector Machines?
  • classifier, building / How do we actually implement this?
• SURF feature extractor
  • about / What is epipolar geometry?
• SVM model
  • building / How did we build the trainer?

U

• unknown image
  • object, recognizing in / What happened inside the code?
• unsupervised learning
  • about / What is supervised and unsupervised learning?

V

• vector quantization / What is a visual dictionary?
• video
  • 3D objects, overlaying on / How to overlay 3D objects on a video?, Let's look at the code
• VideoCapture function / Under the hood
• video surveillance system
  • building / Background subtraction
• vignette filter
  • creating / Creating a vignette filter
  • using / What's happening underneath?
  • effect, achieving / How do we move the focus around?
• Viola-Jones method
  • about / Using Haar cascades to detect things
  • reference link / Using Haar cascades to detect things
• visual dictionary
  • about / What is a visual dictionary?

W

• waitKey() function / Interacting with the application
• watershed algorithm
  • about / Watershed algorithm
  • reference link / Watershed algorithm
• webcam
  • accessing / Accessing the webcam, Under the hood
  • VideoCapture function, using / Under the hood
• Windows
  • OpenCV-Python, installing on / Windows

Y

• YUV color space / Image color spaces