Homework| Homework: Derive the following expression for the derivative of the inverse mapping Arun Das | Waterloo Autonomous Vehicles Lab.

Slides:

Advertisements

Similar presentations

Distinctive Image Features from Scale-Invariant Keypoints

Advertisements

Distinctive Image Features from Scale-Invariant Keypoints David Lowe.

BRIEF: Binary Robust Independent Elementary Features

Group Meeting Presented by Wyman 10/14/2006

Presented by Xinyu Chang

BRISK (Presented by Josh Gleason)

Computer Vision Lecture 18: Object Recognition II

CSE 473/573 Computer Vision and Image Processing (CVIP)

Instructor: Mircea Nicolescu Lecture 15 CS 485 / 685 Computer Vision.

SURF: Speeded-Up Robust Features

Patch Descriptors CSE P 576 Larry Zitnick

Lecture 6: Feature matching CS4670: Computer Vision Noah Snavely.

Motion Analysis Slides are from RPI Registration Class.

Lecture 4: Feature matching

Automatic Image Alignment (feature-based) : Computational Photography Alexei Efros, CMU, Fall 2005 with a lot of slides stolen from Steve Seitz and.

Representation, Description and Matching

Blob detection.

Automatic Image Alignment (feature-based) : Computational Photography Alexei Efros, CMU, Fall 2006 with a lot of slides stolen from Steve Seitz and.

Special Topic on Image Retrieval

CS4670: Computer Vision Kavita Bala Lecture 8: Scale invariance.

776 Computer Vision Jan-Michael Frahm, Enrique Dunn Spring 2013.

Overview Introduction to local features

Interest Point Descriptors

Computer vision.

Interest Point Descriptors and Matching

Overview Harris interest points Comparing interest points (SSD, ZNCC, SIFT) Scale & affine invariant interest points Evaluation and comparison of different.

Local invariant features Cordelia Schmid INRIA, Grenoble.

Reporter: Fei-Fei Chen. Wide-baseline matching Object recognition Texture recognition Scene classification Robot wandering Motion tracking.

Rotation Invariant Neural-Network Based Face Detection

Axial Flip Invariance and Fast Exhaustive Searching with Wavelets Matthew Bolitho.

776 Computer Vision Jan-Michael Frahm Fall SIFT-detector Problem: want to detect features at different scales (sizes) and with different orientations!

CSE 185 Introduction to Computer Vision Local Invariant Features.

Lecture 7: Features Part 2 CS4670/5670: Computer Vision Noah Snavely.

Overview Introduction to local features Harris interest points + SSD, ZNCC, SIFT Scale & affine invariant interest point detectors Evaluation and comparison.

DIPTA 陳昱廷 Feature Detector II.

SURF: Speeded Up Robust Features

Frank Bergschneider February 21, 2014 Presented to National Instruments.

CSE 185 Introduction to Computer Vision Local Invariant Features.

Recognizing specific objects Matching with SIFT Original suggestion Lowe, 1999,2004.

Blob detection.

Another Example: Circle Detection

Differential Calculus of 3D Orientations

SIFT Scale-Invariant Feature Transform David Lowe

CS262: Computer Vision Lect 09: SIFT Descriptors

Review of Matrix Operations

Interest Points EE/CSE 576 Linda Shapiro.

Lecture 13: Feature Descriptors and Matching

Lecture 07 13/12/2011 Shai Avidan הבהרה: החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.

Distinctive Image Features from Scale-Invariant Keypoints

CS 4501: Introduction to Computer Vision Sparse Feature Detectors: Harris Corner, Difference of Gaussian Connelly Barnes Slides from Jason Lawrence, Fei.

Scale Invariant Feature Transform (SIFT)

Motion illusion, rotating snakes

SURF detectors and descriptors

SURF: Speeded-Up Robust Features

Feature description and matching

Jeremy Bolton, PhD Assistant Teaching Professor

CAP 5415 Computer Vision Fall 2012 Dr. Mubarak Shah Lecture-5

Solving Linear Systems Using Inverse Matrices

From a presentation by Jimmy Huff Modified by Josiah Yoder

The SIFT (Scale Invariant Feature Transform) Detector and Descriptor

Homework | Determine the Maximum Likelihood Estimator for the mean and variance of a Gaussian Distribution Jason Rebello | Waterloo Autonomous Vehicles.

2.1 Day 3 Linear Transformations

Edge detection f(x,y) viewed as a smooth function

ECE734 Project-Scale Invariant Feature Transform Algorithm

Fourier Transform of Boundaries

Feature descriptors and matching

Lecture 5: Feature invariance

Lecture 5: Feature invariance

Presentation transcript:

Homework| Homework: Derive the following expression for the derivative of the inverse mapping Arun Das | Waterloo Autonomous Vehicles Lab

Differential Calculus| Important Expressions (1) Box-plus and box-minus (2) Rodriguez Formula (3) Inverse Identity (4) Adjoint Related Identity (5) anticommutative Arun Das | Waterloo Autonomous Vehicles Lab

Homework| Homework: Derive the following expression for the derivative of the inverse mapping Arun Das | Waterloo Autonomous Vehicles Lab

Homework| Formulate a method to interpolate between two Transformation matrices. Arun Das | Waterloo Autonomous Vehicles Lab

Homework| Formulate a method to interpolate between two Transformation matrices. Consider linear interpolation: Can we do something similar for SO(3) or SE(3)? Arun Das | Waterloo Autonomous Vehicles Lab

Homework| Formulate a method to interpolate between two Transformation matrices. Consider linear interpolation: Can we do something similar for SO(3) or SE(3)? Arun Das | Waterloo Autonomous Vehicles Lab

Homework| Define the following interpolation scheme: Note that: Arun Das | Waterloo Autonomous Vehicles Lab

Homework| Define the following interpolation scheme: Note that: Arun Das | Waterloo Autonomous Vehicles Lab

Homework| Note that we always have closure with this scheme Essentially interpolation on the Lie Algebra Arun Das | Waterloo Autonomous Vehicles Lab

Homework | Read and Summarize: SURF Detector and Descriptor ORB Detector and Descriptor Jason Rebello | Waterloo Autonomous Vehicles Lab

SURF Detector | Integral Image Create: 16 + 12 - 7 + 3 = 24 Create: 16 + 23 - 13 + 6 = 32 Query: 64 + 5 - 14 - 16 = 39 same as 6+3+6+2+5+2+6+3+6 = 39 Jason Rebello | Waterloo Autonomous Vehicles Lab

SURF Detector | Speeded Up Robust Features Gaussian Partial Derivatives Scale space analysis Lyy Lxy Dyy Dxy Hessian Matrix and Determinant Box Filter Approximations Jason Rebello | Waterloo Autonomous Vehicles Lab

SURF Descriptor | Each patch is 4 dimensional vector 16 Patches give 64 Dimensional Vector Sign of Hessian Matrix for black vs white blobs Haar Wavelets SURF Descriptor Dominant Orientation Black vs White blobs Jason Rebello | Waterloo Autonomous Vehicles Lab

ORB Detector | Oriented fast and Robust Brief FAST corner Detector Harris Corner Measure FAST detected at multiple levels in the Pyramid for Scale Invariance BRIEF: Binary Robust Independent Elementary Features Random Selection of pairs of Intensity Values Fixed sampling Pattern of 128, 256 or 512 pairs Hamming Distance to compare descriptors (XOR) Jason Rebello | Waterloo Autonomous Vehicles Lab

ORB Descriptor | Learning the pairs Patch moments Center of Mass 300K Keypoints 205590 Possible Tests 256 dimensional descriptor Orientation Angle Calculation Jason Rebello | Waterloo Autonomous Vehicles Lab