Add review of image and feature descriptors (wip)

Author: patrick-llgc

README.md

@@ -20,6 +20,7 @@ This repository contains my paper reading notes on deep learning and machine lea
 The sections below records paper reading activity in chronological order. See notes organized according to subfields [here](organized.md) (up to 06-2019). 
 
 ## 2019-11 (15)
+- [Review of Image and Feature Descriptors](paper_notes/review_descriptors.md)
 - [Vehicle Detection With Automotive Radar Using Deep Learning on Range-Azimuth-Doppler Tensors](http://openaccess.thecvf.com/content_ICCVW_2019/papers/CVRSUAD/Major_Vehicle_Detection_With_Automotive_Radar_Using_Deep_Learning_on_Range-Azimuth-Doppler_ICCVW_2019_paper.pdf) [[Notes](paper_notes/radar_iccv.md)] <kbd>ICCV 2019</kbd>
 - [GPP: Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road](https://arxiv.org/abs/1811.06666) \[[Notes](paper_notes/gpp.md)] (UCSD, mono 3DOD)
 - [MVRA: Multi-View Reprojection Architecture for Orientation Estimation](http://openaccess.thecvf.com/content_ICCVW_2019/papers/ADW/Choi_Multi-View_Reprojection_Architecture_for_Orientation_Estimation_ICCVW_2019_paper.pdf) [[Notes](paper_notes/mvra.md)] <kbd>ICCV 2019</kbd>

paper_notes/review_descriptors.md

@@ -0,0 +1,31 @@
+# [Review of Image Descriptors](paper_notes/review_image_descriptors.md)
+
+_November 2019_
+
+This review is based on the pyimagesearch course.
+
+#### Image Descriptors
+Image descriptors and feature vectors quantify and abstractly represent an image using only a list of numbers.
+
+- Feature vector: An abstraction of an image used to characterize and numerically quantify the contents of an image. Normally real, integer, or binary valued. Simply put, a feature vector is a list of numbers used to represent an image.
+	- this feature can be passed down to image classifier or image search engine
+- Image descriptor: An image descriptor is an algorithm and methodology that governs how an input image is **globally** quantified and returns a feature vector abstractly representing the image contents.
+	- they tend to be much simpler than feature descriptors
+	- HoG, LBPs, Harlick texture
+- Feature descriptor: A feature descriptor is an algorithm and methodology that governs how an input region of an image is **locally** quantified. A feature descriptor accepts a single input image and returns **multiple feature vectors**.
+	- SIFT, SURF, ORB, BRISK, BRIEF, and FREAK
+	
+### Local features
+Keypoint detection and feature extraction: 
+- keypoints are simply the (x, y)-coordinates of the interesting, salient regions of an image.
+- feature extraction is the process of extracting multiple feature vectors, one for each keypoint
+How to use multiple features per image? Keypoint matching or bag-of-visual-words.
+	
+#### FAST (Features from Accelerated Segment Test)
+- Fast algorithm to detect corners
+- [Fusing Points and Lines for High Performance Tracking](https://gurus.pyimagesearch.com/wp-content/uploads/2015/06/rosten_2005.pdf) <kbd>ICCV 2005</kbd>
+- A test is performed for a feature at a pixel p by examining a circle of 16 pixels (a Bresenham circle of radius 3) surrounding p. A feature is detected at p if the intensities of at least 12 contiguous pixels are all above or all below the intensity of p by some threshold, t.
+![](https://docs.opencv.org/3.4/fast_speedtest.jpg)
+- [openCV example](https://docs.opencv.org/3.1.0/df/d0c/tutorial_py_fast.html#gsc.tab=0)
+
+#### SIFT