Click to edit title style
|
|
- Lenard Ball
- 6 years ago
- Views:
Transcription
1 Class 2: Low-level Representation Liangliang Cao, Jan 31, 2013 EECS 6890 Topics in Information Processing Spring 2013, Columbia University Visual Recognition And Search 1
2 For students who have submitted HW0: Form a group (3 people) before Feb. 7 Teamwork on presentations and projects Project proposal due in Feb (date to be announced) For others Notice Please drop the class if you haven t learned Computer Vision or Learning before If you think you have strong vision background, talk to the instructor after the class You are welcome to audit the class Visual Recognition And Search 2
3 Development of Low Level Features Classical features Raw pixel Histogram feature Color Histogram Edge histogram Frequency analysis Image filters Texture features LBP Scene features GIST Shape descriptors Edge detection Corner detection 1999 SIFT HOG SURF DAISY BRIEF DoG Hessian detector Laplacian of Harris FAST ORB Local Descriptors Local Detectors Visual Recognition And Search 3
4 Outline Traditional features Raw Pixels and Histograms Image Filters Classic local feature: SIFT SIFT Descriptor SIFT Detector More recent local features SURF Binary Local Features Hands on Instruction Visual Recognition And Search 4
5 Outline Traditional features Raw Pixels and Histograms Image Filters Classic local feature: SIFT SIFT Descriptor SIFT Detector More recent local features SURF Binary Local Features Hands on Instruction Visual Recognition And Search 5
6 Traditional Low Level Features Raw Pixels and Histograms Visual Recognition And Search 6
7 Raw Pixels and Histograms Concatenating Raw Pixels As 1D Vector Credit: The Face Research Lab Visual Recognition And Search 7
8 Raw Pixels and Histograms Concatenated Raw Pixels Famous applications (widely used in ML field) Face recognition Hand written digits Pictures courtesy to Sam Roweis Visual Recognition And Search 8
9 Raw Pixels and Histograms Click to Tiny edit Images title style Antonio Torralba et al proposed to resize images to 32x32 color thumbnails, which are called tiny images Related applications Scene recognition Object recognition Fast speed with limited accuracy Torralba et al, MIT CSAIL report, 2007 Visual Recognition And Search 9
10 Raw Pixels and Histograms Problem of raw-pixel based representation Rely heavily on good alignment Assume the images are of similar scale Suffer from occlusion Recognition from different view point will be difficult We want more powerful features for real-world problems like the following Visual Recognition And Search 10
11 Raw Pixels and Histograms Click to Color edit Histogram title style Each pixel is described Distribution of color vectors by a vector of pixel values is described by a histogram r g b Note: There are different choices for color space: RGB, HSV, Lab, etc. For gray images, we usually use 256 or fewer bins for histogram. [Swain and Ballard 91] Visual Recognition And Search 11
12 Raw Pixels and Histograms Benefits of Histogram Representation No longer sensitive to alignment, scale transform, or even global rotation. Similar color histograms (after normalization). Visual Recognition And Search 12
13 Raw Pixels and Histograms Limitation of Global Histogram Global histogram has no location information at all They re equal in terms of global histogram Example courtesy to Erik Learned-Miller Visual Recognition And Search 13
14 Raw Pixels and Histograms Histogram with Spatial Layout Concatenated histogram for each region. Example courtesy to Erik Learned-Miller Visual Recognition And Search 14
15 Raw Pixels and Histograms IBM IMARS Spatial Gridding Visual Recognition And Search 15
16 Raw Pixels and Histograms Spatial Pyramid Matching Lazebnik, Schmid and Ponce, CVPR 06 Visual Recognition And Search 16
17 Raw Pixels and Histograms General Histogram Representation Color histogram patch patch patch Extract color descriptor histogram accumulation General histogram patch patch patch Extract other descriptors edge histogram shape context histogram local binary patterns histogram accumulation Visual Recognition And Search 17
18 Raw Pixels and Histograms Local Binary Pattern (LBP) For each pixel p, create an 8-bit number b1 b2 b3 b4 b5 b6 b7 b8, where bi = 0 if neighbor i has value less than or equal to p s value and 1 otherwise. Represent the texture in the image (or a region) by the histogram of these numbers Ojala et al, PR 96, PAMI 02 Visual Recognition And Search 18
19 Raw Pixels and Histograms LBP Histogram Divide the examined window to cells (e.g. 16x16 pixels for each cell). Compute the histogram, over the cell, of the frequency of each "number" occurring. Optionally normalize the histogram. Concatenate normalized histograms of all cells. Visual Recognition And Search 19
20 Image Filters Visual Recognition And Search 20
21 Image Filters Haar Wavelet Haar-like Feature Haar like features Given two adjacent rectangular regions, sums up the pixel intensities in each region and calculates the difference between the two sums Application: face detection [Viola and Jones]: Efficiently processing via integral images Visual Recognition And Search 21
22 Image Filters Integral Image In integral image, each location saves the sum of gray scale pixel value of the sub-image to the left top corner. Cost four additions operation only Visual Recognition And Search 22
23 Image Filters Gabor Filtering Gabor filters are defined by a harmonic function multiplied by a Gaussian function. They reflect edges at different scales and spatial frequencies Widely used in fingerprint, iris, OCR, texture and face recognition. Computationally expensive. J. G. Daugman discovered that simple cells in the visual cortex can be modeled by Gabor functions Visual Recognition And Search 23
24 Scale-Invariant Feature Transform (SIFT) David G. Lowe - Distinctive image features from scale-invariant keypoints, IJCV Object recognition from local scale-invariant features, ICCV 1999 Visual Recognition And Search 24
25 SIFT Why Local Features? Local Feature = Interest Point = Keypoint = Feature Point = Distinguished Region = Covariant Region ( ) local descriptor Local : robust to occlusion/clutter Invariant : to scale/view/illumination changes Local feature can be discriminant! Visual Recognition And Search 25
26 SIFT Why Local Features? (2) Visual Recognition And Search 26
27 SIFT How to Get Good Local Features Good descriptor Distinctiveness: is able to distinguish any objects Invariance: find the same object across different viewpoint/illumination change Good detector Locality: features are local, so robust to occlusion and clutter Quantity: many features can be generated for even small objects Visual Recognition And Search 27
28 SIFT Distinctive Descriptor Histogram of gradient orientation - Histogram is more robust to positioin than raw pixels - Edge gradient is more distinctive than color for local patches Concatenate histograms in spatial cells for better discriminancy Histogram of gradient orientation Concatenated histogram SIFT Descriptor [D. Lowe, 1999] Visual Recognition And Search 28
29 SIFT Distinctive Descriptor Compute image gradients at each location Histogram of the gradient orientation (8 bins) Concatenate histograms of 4 x 4 spatial grid Feature Dimension: 8 orientation x (4x4) spatial grid = 128 Visual Recognition And Search 29
30 SIFT Distinctive Descriptor David Lowe s excellent performance tuning: Good parameters: 4 ori, 4 x 4 grid Soft-assignment to spatial bins Gaussian weighting over spatial location Visual Recognition And Search 30
31 SIFT More Invariant to Illumination Changes Normalize the descriptor to norm one - A change in image contrast can change the magitude but not the orientation Reduce the influence of large gradient magnitudes -Threshold the values in the unit feature vector to be no larger than Renormalize the feature vector after thresholding. Visual Recognition And Search 31
32 SIFT Rotation Invariance Estimate the dominant orientation of each patch Rotate patch in dominant direction Each patch is associated with (X, Y, Scale, Orientation) Note: - Not every package provides such a procedure in SIFT implementation - Few existing work makes good use of the dominant orientation - With rotation invariance, SIFT may be less discriminant in general recognition Visual Recognition And Search 32
33 SIFT How to Get Good Local Features Good descriptor Distinctiveness: is able to distinguish any objects Invariance: find the same object across different viewpoint/illumination change Can we do better? Visual Recognition And Search 33
34 SIFT How to Get Good Local Features Good descriptor Distinctiveness: is able to distinguish any objects Invariance: find the same object across different viewpoint/illumination change Can we do better? More computational efficient, esp for mobile application More compact storage Combining SIFT with traditional features, e.g., LBP Improving poor color description: - SIFT is robust to illumination change, but color is not - SIFT in individual color channels (RGB): more expensive but slightly better Visual Recognition And Search 34
35 SIFT How to Get Good Local Features Good descriptor Distinctiveness: is able to distinguish any objects Invariance: find the same object across different viewpoint/illumination change Good detector Locality: features are local, so robust to occlusion and clutter Quantity: many features can be generated for even small objects Visual Recognition And Search 35
36 SIFT SIFT Detector: A First Impression SIFT employs A variant of Histogram of Gradient Orientation as descriptor DoG (Difference of Gaussians) as local region detector Hard to beat Relatively easier to beat Why? DoG has good theoretical support but may not necessarily reflect the requirements in practice Trade-off between invariance and number of detection: In many scenarios, combining multiple detectors -> good performance. Visual Recognition And Search 36
37 SIFT Detector Concerns for Detectors Detecting Corners Corners are located more robustly than edges Selecting Scales Invariant to scale changes Finding Affine Covariance Invariant to affine transform; matching ellipse with circles Only useful when there is such transform in your dataset! Visual Recognition And Search 37
38 SIFT Detector DoG Detector Detecting Corners Selecting Scales Finding Affine Covariance Detected region: (red circle) Figure courtesy to Matas and Mikolajczyk Visual Recognition And Search 38
39 SIFT Detector Difference of Gaussians Visual Recognition And Search 39
40 SIFT Detector Sampling Frequency in Scale More points are found as sampling frequency increases, but accuracy of matching decreases after 3 scales/octave Visual Recognition And Search 40
41 SIFT Detector Resample Blur Subtract DoG and Scale Space Theory Scale space representation is a family of the convolution of the given signal with Gaussian kernel = 0 = 1 = 4 = 16 Visual Recognition And Search 41
42 SIFT Detector DoG and Scale Space Theory (2) Let t =, which represents a "temperature of the intensity values of the image as "temperature distribution" in the image plane Then the scale-space family can be defined as the solution of the diffusion equation where the Gaussian kernel arises as the Green's function of this specific partial differential equation. In scale-space, the corner can be obtained by Laplacian of Gaussian where DoG is an efficient approximation of Laplacian of Gaussian. Visual Recognition And Search 42
43 SIFT Detector Resample Blur Subtract Accurate Key point localization Detect maxima and minima of difference-of-gaussian in scale space Fit a quadratic to surrounding values for sub-pixel and sub-scale interpolation (Brown & Lowe, 2002) Taylor expansion around point: Offset of extremum (use finite differences for derivatives): Visual Recognition And Search 43
44 SIFT Detector Resample Blur Subtract Eliminate Unstable Detections To get stable detection, David Lowe rejects 1. Keypoints with low contrast 2. Keypoints with strong edge but not clear corners. For 1, we filter out keypoints with small For 2, those keypoints correspond to large principle curvature along the edge but small one in the perpendicular direction. To efficient compute the curvature ratio, we need only compute the trace and determinant of local hessian. Visual Recognition And Search 44
45 SIFT Detector Example of SIFT Keypoint Detection (a) 233x189 image (b) 832 DOG extrema (c) 729 left after peak value threshold (d) 536 left after testing ratio of principle curvatures Visual Recognition And Search 45
46 SIFT Detector How to Do Better than DoG? Combine more than one detectors Harris detector Hessian detector Dense-sampling instead of sparse detector Grid based sampling + multiple resolutions More computation, more storage, but better accuracy Visual Recognition And Search 46
47 SIFT Detector Harris Detector Scale selection: Laplacian of Harris detector Visual Recognition And Search 47
48 SIFT Detector Hessian Detector Visual Recognition And Search 48
49 Modern Local Features Speeded Up Robust Features (SURF) Bay, Tuytelaars, and Van Gool, SURF: Speeded Up Robust Features, ECCV 2006 Visual Recognition And Search 49
50 SURF Motivation SIFT is slow and expensive, especially for large scale and mobile applications Recall: Integral Image, a useful technique to compute Fast box filter (in detection) Fast Haar wavelet (in description) Visual Recognition And Search 50
51 SURF Integral Image Using integral images for major speed up Integral Image (summed area tables) is an intermediate representation for fast computation Second order derivative and Haar-wavelet response Cost four additions operation only Visual Recognition And Search 51
52 SURF Fast Detection Approximated second order derivatives with box filters (mean/average filter) Both box filters and following Haar wavelets can be computed with integral images. Visual Recognition And Search 52
53 SURF Fast Description Approximate gradient orientation with Haar wavelet 1. Split the interest region into 4 x 4 cell with 5 x 5 regularly spaced sample points inside 2. Compute gradient histogram with 4 bins 3. Sum the response over each sub-region for d x and d y separately dim= In order to bring in information about the polarity of the intensity changes, extract the sum of absolute value of the responses dim= Normalize the vector Visual Recognition And Search 53
54 SURF Compared with SIFT Visual Recognition And Search 54
55 SURF Compared with SIFT (con t) SURF is an order or magnitude (3~5 times) faster than SIFT In object recognition tasks, SURF obtains a comparable or sometimes a little worse results than SIFT (esp. with illumination change or viewpoint change). But we can see how traditional techniques (integral images) can help! Visual Recognition And Search 55
56 Binary Local Feature Descriptors Visual Recognition And Search 56
57 Binary Local Descriptors DAISY Tested different configurations Steerable filters Summation Normalization Descriptor quantization PCA dimensions Better performance than SIFT with 13 bytes descriptors (vs. 128 bytes for SIFT) Slide courtesy of Michele Merler S. Winder, G. Hua, M. Brown, Picking the Best DAISY, CVPR 09 Visual Recognition And Search 57
58 Binary Local Descriptors BRIEF Identify a 31x31 neighborhood for each interest point For each interest point, randomly create 256 pairs patches (patch size 9 x 9) For each pair a, b, if a>b, set the bit to 1, else set 0 The descriptor: 256 bit vector Calonder, Lepetit, Strecha, BRIEF: binary robust independent elementary features, ECCV 2010 Visual Recognition And Search 58
59 Binary Local Descriptors ORB FAST detector Interest points are detected as >= 12 contiguous pixel brighter than P in a ring of radius 3 around P. BRIEF detector ~100x faster than SIFT, good viewpoint invariance Rublee et al, ORB: an efficient alternative to SIFT or SURF, ICCV 11 Visual Recognition And Search 59
60 Binary Local Descriptors BRISK Combination of SIFT-like scale-space detection and BRIEF-like descriptor Leutenegger, Chli, Siegwart, BRISK: Binary Robust Invariant Scalable Keypoints, ICCV 2011 Visual Recognition And Search 60
61 Hands-on Experience Most of slides in this section are courtesy to Andrea Vedaldi Visual Recognition And Search 61
62 Existing softwares VLFeat OpenCV Amsterdam Color Descriptor Oxford VGG Visual Recognition And Search 62
63 Existing softwares VLFeat OpenCV Amsterdam Color Descriptor Oxford VGG Visual Recognition And Search 63
64 VLFeat SIFT Detector and Descriptor Visual Recognition And Search 64
65 VLFeat What Does Frame Mean? Visual Recognition And Search 65
66 VLFeat Other Corner Detectors Visual Recognition And Search 66
67 VLFeat Other Corner Detectors Visual Recognition And Search 67
68 VLFeat Dominant Orientation Visual Recognition And Search 68
69 VLFeat Compute Descriptors Visual Recognition And Search 69
70 VLFeat Custom Frames Visual Recognition And Search 70
71 OpenCV List of Options - OpenCV 2.1: Harris, Tomasi&Shi, MSER, DoG, SURF, STAR and FAST, DoG + SIFT, SURF, HARRIS, GoodFeaturesToTrack - OpenCV 2.4: ORB, BRIEF, and FREAK Visual Recognition And Search 71
72 OpenCV An Example (Extracting SIFT) Courtesy to Visual Recognition And Search 72
73 Next class: How to Use SIFT for Object Classification Todo before next class - Form a group and the instructors your group information - Read the three papers on sparse coding and prepare for the discussion Any volunteer? Reminder: do not forget to drop the class if you don t plan to attend! Visual Recognition And Search 73
74 Visual Recognition And Search 74
75 A Demo Photo Tourism Snavely, Seitz and Szeliski, Photo tourism: Exploring photo collections in 3D, SIGGRAPH, 2006 Snavely, Seitz and Szeliski, Modeling the world from Internet photo collections, IJCV 2007 Visual Recognition And Search 75
76 Visual Recognition And Search 76
77 Photo Tourism Detect SIFT Visual Recognition And Search 77
78 Photo Tourism Detect SIFT Visual Recognition And Search 78
79 Photo Tourism Match SIFT Visual Recognition And Search 79
80 Photo Tourism From Correspondence To Geometry Visual Recognition And Search 80
81 Do You Want Another Video? Visual Recognition And Search 81
82 Build Rome in A Day Agarwal et al, Building Rome in a Day, ICCV, 2009 Visual Recognition And Search 82
83 Visual Recognition And Search 83
Click to edit title style
Class 3: Low-level Representation Liangliang Cao, Feb 6, 2014 EECS 6890 Topics in Information Processing Spring 2014, Columbia University http://rogerioferis.com/visualrecognitionandsearch2014 Visual Recognition
More informationLocal Image Features
Local Image Features Ali Borji UWM Many slides from James Hayes, Derek Hoiem and Grauman&Leibe 2008 AAAI Tutorial Overview of Keypoint Matching 1. Find a set of distinctive key- points A 1 A 2 A 3 B 3
More informationLearning Visual Semantics: Models, Massive Computation, and Innovative Applications
Learning Visual Semantics: Models, Massive Computation, and Innovative Applications Part II: Visual Features and Representations Liangliang Cao, IBM Watson Research Center Evolvement of Visual Features
More informationThe SIFT (Scale Invariant Feature
The SIFT (Scale Invariant Feature Transform) Detector and Descriptor developed by David Lowe University of British Columbia Initial paper ICCV 1999 Newer journal paper IJCV 2004 Review: Matt Brown s Canonical
More informationLocal features and image matching. Prof. Xin Yang HUST
Local features and image matching Prof. Xin Yang HUST Last time RANSAC for robust geometric transformation estimation Translation, Affine, Homography Image warping Given a 2D transformation T and a source
More informationOutline 7/2/201011/6/
Outline Pattern recognition in computer vision Background on the development of SIFT SIFT algorithm and some of its variations Computational considerations (SURF) Potential improvement Summary 01 2 Pattern
More informationPatch Descriptors. EE/CSE 576 Linda Shapiro
Patch Descriptors EE/CSE 576 Linda Shapiro 1 How can we find corresponding points? How can we find correspondences? How do we describe an image patch? How do we describe an image patch? Patches with similar
More informationEECS150 - Digital Design Lecture 14 FIFO 2 and SIFT. Recap and Outline
EECS150 - Digital Design Lecture 14 FIFO 2 and SIFT Oct. 15, 2013 Prof. Ronald Fearing Electrical Engineering and Computer Sciences University of California, Berkeley (slides courtesy of Prof. John Wawrzynek)
More informationLocal Patch Descriptors
Local Patch Descriptors Slides courtesy of Steve Seitz and Larry Zitnick CSE 803 1 How do we describe an image patch? How do we describe an image patch? Patches with similar content should have similar
More informationLocal Image Features
Local Image Features Computer Vision Read Szeliski 4.1 James Hays Acknowledgment: Many slides from Derek Hoiem and Grauman&Leibe 2008 AAAI Tutorial Flashed Face Distortion 2nd Place in the 8th Annual Best
More informationLocal Image Features
Local Image Features Computer Vision CS 143, Brown Read Szeliski 4.1 James Hays Acknowledgment: Many slides from Derek Hoiem and Grauman&Leibe 2008 AAAI Tutorial This section: correspondence and alignment
More informationSIFT: SCALE INVARIANT FEATURE TRANSFORM SURF: SPEEDED UP ROBUST FEATURES BASHAR ALSADIK EOS DEPT. TOPMAP M13 3D GEOINFORMATION FROM IMAGES 2014
SIFT: SCALE INVARIANT FEATURE TRANSFORM SURF: SPEEDED UP ROBUST FEATURES BASHAR ALSADIK EOS DEPT. TOPMAP M13 3D GEOINFORMATION FROM IMAGES 2014 SIFT SIFT: Scale Invariant Feature Transform; transform image
More informationMotion illusion, rotating snakes
Motion illusion, rotating snakes Local features: main components 1) Detection: Find a set of distinctive key points. 2) Description: Extract feature descriptor around each interest point as vector. x 1
More informationCS 4495 Computer Vision A. Bobick. CS 4495 Computer Vision. Features 2 SIFT descriptor. Aaron Bobick School of Interactive Computing
CS 4495 Computer Vision Features 2 SIFT descriptor Aaron Bobick School of Interactive Computing Administrivia PS 3: Out due Oct 6 th. Features recap: Goal is to find corresponding locations in two images.
More informationSCALE INVARIANT FEATURE TRANSFORM (SIFT)
1 SCALE INVARIANT FEATURE TRANSFORM (SIFT) OUTLINE SIFT Background SIFT Extraction Application in Content Based Image Search Conclusion 2 SIFT BACKGROUND Scale-invariant feature transform SIFT: to detect
More informationComputer Vision for HCI. Topics of This Lecture
Computer Vision for HCI Interest Points Topics of This Lecture Local Invariant Features Motivation Requirements, Invariances Keypoint Localization Features from Accelerated Segment Test (FAST) Harris Shi-Tomasi
More informationPatch Descriptors. CSE 455 Linda Shapiro
Patch Descriptors CSE 455 Linda Shapiro How can we find corresponding points? How can we find correspondences? How do we describe an image patch? How do we describe an image patch? Patches with similar
More informationSUMMARY: DISTINCTIVE IMAGE FEATURES FROM SCALE- INVARIANT KEYPOINTS
SUMMARY: DISTINCTIVE IMAGE FEATURES FROM SCALE- INVARIANT KEYPOINTS Cognitive Robotics Original: David G. Lowe, 004 Summary: Coen van Leeuwen, s1460919 Abstract: This article presents a method to extract
More informationScale Invariant Feature Transform
Scale Invariant Feature Transform Why do we care about matching features? Camera calibration Stereo Tracking/SFM Image moiaicing Object/activity Recognition Objection representation and recognition Image
More informationLocal Features: Detection, Description & Matching
Local Features: Detection, Description & Matching Lecture 08 Computer Vision Material Citations Dr George Stockman Professor Emeritus, Michigan State University Dr David Lowe Professor, University of British
More informationScale Invariant Feature Transform
Why do we care about matching features? Scale Invariant Feature Transform Camera calibration Stereo Tracking/SFM Image moiaicing Object/activity Recognition Objection representation and recognition Automatic
More informationKey properties of local features
Key properties of local features Locality, robust against occlusions Must be highly distinctive, a good feature should allow for correct object identification with low probability of mismatch Easy to etract
More informationSURF. Lecture6: SURF and HOG. Integral Image. Feature Evaluation with Integral Image
SURF CSED441:Introduction to Computer Vision (2015S) Lecture6: SURF and HOG Bohyung Han CSE, POSTECH bhhan@postech.ac.kr Speed Up Robust Features (SURF) Simplified version of SIFT Faster computation but
More informationEvaluation and comparison of interest points/regions
Introduction Evaluation and comparison of interest points/regions Quantitative evaluation of interest point/region detectors points / regions at the same relative location and area Repeatability rate :
More informationAK Computer Vision Feature Point Detectors and Descriptors
AK Computer Vision Feature Point Detectors and Descriptors 1 Feature Point Detectors and Descriptors: Motivation 2 Step 1: Detect local features should be invariant to scale and rotation, or perspective
More informationCAP 5415 Computer Vision Fall 2012
CAP 5415 Computer Vision Fall 01 Dr. Mubarak Shah Univ. of Central Florida Office 47-F HEC Lecture-5 SIFT: David Lowe, UBC SIFT - Key Point Extraction Stands for scale invariant feature transform Patented
More informationFeature Detection. Raul Queiroz Feitosa. 3/30/2017 Feature Detection 1
Feature Detection Raul Queiroz Feitosa 3/30/2017 Feature Detection 1 Objetive This chapter discusses the correspondence problem and presents approaches to solve it. 3/30/2017 Feature Detection 2 Outline
More informationCEE598 - Visual Sensing for Civil Infrastructure Eng. & Mgmt.
CEE598 - Visual Sensing for Civil Infrastructure Eng. & Mgmt. Section 10 - Detectors part II Descriptors Mani Golparvar-Fard Department of Civil and Environmental Engineering 3129D, Newmark Civil Engineering
More informationProf. Feng Liu. Spring /26/2017
Prof. Feng Liu Spring 2017 http://www.cs.pdx.edu/~fliu/courses/cs510/ 04/26/2017 Last Time Re-lighting HDR 2 Today Panorama Overview Feature detection Mid-term project presentation Not real mid-term 6
More informationLecture 10 Detectors and descriptors
Lecture 10 Detectors and descriptors Properties of detectors Edge detectors Harris DoG Properties of detectors SIFT Shape context Silvio Savarese Lecture 10-26-Feb-14 From the 3D to 2D & vice versa P =
More informationLecture 4.1 Feature descriptors. Trym Vegard Haavardsholm
Lecture 4.1 Feature descriptors Trym Vegard Haavardsholm Feature descriptors Histogram of Gradients (HoG) descriptors Binary descriptors 2 Histogram of Gradients (HOG) descriptors Scale Invariant Feature
More informationBSB663 Image Processing Pinar Duygulu. Slides are adapted from Selim Aksoy
BSB663 Image Processing Pinar Duygulu Slides are adapted from Selim Aksoy Image matching Image matching is a fundamental aspect of many problems in computer vision. Object or scene recognition Solving
More informationComputer Vision. Recap: Smoothing with a Gaussian. Recap: Effect of σ on derivatives. Computer Science Tripos Part II. Dr Christopher Town
Recap: Smoothing with a Gaussian Computer Vision Computer Science Tripos Part II Dr Christopher Town Recall: parameter σ is the scale / width / spread of the Gaussian kernel, and controls the amount of
More informationIntroduction. Introduction. Related Research. SIFT method. SIFT method. Distinctive Image Features from Scale-Invariant. Scale.
Distinctive Image Features from Scale-Invariant Keypoints David G. Lowe presented by, Sudheendra Invariance Intensity Scale Rotation Affine View point Introduction Introduction SIFT (Scale Invariant Feature
More informationLocal Feature Detectors
Local Feature Detectors Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Slides adapted from Cordelia Schmid and David Lowe, CVPR 2003 Tutorial, Matthew Brown,
More informationMulti-modal Registration of Visual Data. Massimiliano Corsini Visual Computing Lab, ISTI - CNR - Italy
Multi-modal Registration of Visual Data Massimiliano Corsini Visual Computing Lab, ISTI - CNR - Italy Overview Introduction and Background Features Detection and Description (2D case) Features Detection
More informationVisual Object Recognition
Visual Object Recognition Lecture 3: Descriptors Per-Erik Forssén, docent Computer Vision Laboratory Department of Electrical Engineering Linköping University 2015 2014 Per-Erik Forssén Lecture 3: Descriptors
More informationImplementation and Comparison of Feature Detection Methods in Image Mosaicing
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p-ISSN: 2278-8735 PP 07-11 www.iosrjournals.org Implementation and Comparison of Feature Detection Methods in Image
More informationObject Recognition with Invariant Features
Object Recognition with Invariant Features Definition: Identify objects or scenes and determine their pose and model parameters Applications Industrial automation and inspection Mobile robots, toys, user
More informationLocal invariant features
Local invariant features Tuesday, Oct 28 Kristen Grauman UT-Austin Today Some more Pset 2 results Pset 2 returned, pick up solutions Pset 3 is posted, due 11/11 Local invariant features Detection of interest
More informationComparison of Feature Detection and Matching Approaches: SIFT and SURF
GRD Journals- Global Research and Development Journal for Engineering Volume 2 Issue 4 March 2017 ISSN: 2455-5703 Comparison of Detection and Matching Approaches: SIFT and SURF Darshana Mistry PhD student
More informationCS 558: Computer Vision 4 th Set of Notes
1 CS 558: Computer Vision 4 th Set of Notes Instructor: Philippos Mordohai Webpage: www.cs.stevens.edu/~mordohai E-mail: Philippos.Mordohai@stevens.edu Office: Lieb 215 Overview Keypoint matching Hessian
More informationState-of-the-Art: Transformation Invariant Descriptors. Asha S, Sreeraj M
International Journal of Scientific & Engineering Research, Volume 4, Issue ş, 2013 1994 State-of-the-Art: Transformation Invariant Descriptors Asha S, Sreeraj M Abstract As the popularity of digital videos
More informationAugmented Reality VU. Computer Vision 3D Registration (2) Prof. Vincent Lepetit
Augmented Reality VU Computer Vision 3D Registration (2) Prof. Vincent Lepetit Feature Point-Based 3D Tracking Feature Points for 3D Tracking Much less ambiguous than edges; Point-to-point reprojection
More informationFeature Detection and Matching
and Matching CS4243 Computer Vision and Pattern Recognition Leow Wee Kheng Department of Computer Science School of Computing National University of Singapore Leow Wee Kheng (CS4243) Camera Models 1 /
More informationImage matching. Announcements. Harder case. Even harder case. Project 1 Out today Help session at the end of class. by Diva Sian.
Announcements Project 1 Out today Help session at the end of class Image matching by Diva Sian by swashford Harder case Even harder case How the Afghan Girl was Identified by Her Iris Patterns Read the
More informationImage Features: Local Descriptors. Sanja Fidler CSC420: Intro to Image Understanding 1/ 58
Image Features: Local Descriptors Sanja Fidler CSC420: Intro to Image Understanding 1/ 58 [Source: K. Grauman] Sanja Fidler CSC420: Intro to Image Understanding 2/ 58 Local Features Detection: Identify
More informationImage Features: Detection, Description, and Matching and their Applications
Image Features: Detection, Description, and Matching and their Applications Image Representation: Global Versus Local Features Features/ keypoints/ interset points are interesting locations in the image.
More informationFeature descriptors and matching
Feature descriptors and matching Detections at multiple scales Invariance of MOPS Intensity Scale Rotation Color and Lighting Out-of-plane rotation Out-of-plane rotation Better representation than color:
More informationSIFT - scale-invariant feature transform Konrad Schindler
SIFT - scale-invariant feature transform Konrad Schindler Institute of Geodesy and Photogrammetry Invariant interest points Goal match points between images with very different scale, orientation, projective
More informationSIFT: Scale Invariant Feature Transform
1 / 25 SIFT: Scale Invariant Feature Transform Ahmed Othman Systems Design Department University of Waterloo, Canada October, 23, 2012 2 / 25 1 SIFT Introduction Scale-space extrema detection Keypoint
More informationFeature Based Registration - Image Alignment
Feature Based Registration - Image Alignment Image Registration Image registration is the process of estimating an optimal transformation between two or more images. Many slides from Alexei Efros http://graphics.cs.cmu.edu/courses/15-463/2007_fall/463.html
More informationScale Invariant Feature Transform by David Lowe
Scale Invariant Feature Transform by David Lowe Presented by: Jerry Chen Achal Dave Vaishaal Shankar Some slides from Jason Clemons Motivation Image Matching Correspondence Problem Desirable Feature Characteristics
More informationSchool of Computing University of Utah
School of Computing University of Utah Presentation Outline 1 2 3 4 Main paper to be discussed David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, IJCV, 2004. How to find useful keypoints?
More informationLocal features: detection and description. Local invariant features
Local features: detection and description Local invariant features Detection of interest points Harris corner detection Scale invariant blob detection: LoG Description of local patches SIFT : Histograms
More informationHarder case. Image matching. Even harder case. Harder still? by Diva Sian. by swashford
Image matching Harder case by Diva Sian by Diva Sian by scgbt by swashford Even harder case Harder still? How the Afghan Girl was Identified by Her Iris Patterns Read the story NASA Mars Rover images Answer
More informationFeature Descriptors. CS 510 Lecture #21 April 29 th, 2013
Feature Descriptors CS 510 Lecture #21 April 29 th, 2013 Programming Assignment #4 Due two weeks from today Any questions? How is it going? Where are we? We have two umbrella schemes for object recognition
More informationMotion Estimation and Optical Flow Tracking
Image Matching Image Retrieval Object Recognition Motion Estimation and Optical Flow Tracking Example: Mosiacing (Panorama) M. Brown and D. G. Lowe. Recognising Panoramas. ICCV 2003 Example 3D Reconstruction
More informationHarder case. Image matching. Even harder case. Harder still? by Diva Sian. by swashford
Image matching Harder case by Diva Sian by Diva Sian by scgbt by swashford Even harder case Harder still? How the Afghan Girl was Identified by Her Iris Patterns Read the story NASA Mars Rover images Answer
More informationFeatures Points. Andrea Torsello DAIS Università Ca Foscari via Torino 155, Mestre (VE)
Features Points Andrea Torsello DAIS Università Ca Foscari via Torino 155, 30172 Mestre (VE) Finding Corners Edge detectors perform poorly at corners. Corners provide repeatable points for matching, so
More informationLocal features: detection and description May 12 th, 2015
Local features: detection and description May 12 th, 2015 Yong Jae Lee UC Davis Announcements PS1 grades up on SmartSite PS1 stats: Mean: 83.26 Standard Dev: 28.51 PS2 deadline extended to Saturday, 11:59
More informationA Comparison of SIFT, PCA-SIFT and SURF
A Comparison of SIFT, PCA-SIFT and SURF Luo Juan Computer Graphics Lab, Chonbuk National University, Jeonju 561-756, South Korea qiuhehappy@hotmail.com Oubong Gwun Computer Graphics Lab, Chonbuk National
More informationMidterm Wed. Local features: detection and description. Today. Last time. Local features: main components. Goal: interest operator repeatability
Midterm Wed. Local features: detection and description Monday March 7 Prof. UT Austin Covers material up until 3/1 Solutions to practice eam handed out today Bring a 8.5 11 sheet of notes if you want Review
More informationFast Image Matching Using Multi-level Texture Descriptor
Fast Image Matching Using Multi-level Texture Descriptor Hui-Fuang Ng *, Chih-Yang Lin #, and Tatenda Muindisi * Department of Computer Science, Universiti Tunku Abdul Rahman, Malaysia. E-mail: nghf@utar.edu.my
More informationDiscovering Visual Hierarchy through Unsupervised Learning Haider Razvi
Discovering Visual Hierarchy through Unsupervised Learning Haider Razvi hrazvi@stanford.edu 1 Introduction: We present a method for discovering visual hierarchy in a set of images. Automatically grouping
More informationFeature descriptors. Alain Pagani Prof. Didier Stricker. Computer Vision: Object and People Tracking
Feature descriptors Alain Pagani Prof. Didier Stricker Computer Vision: Object and People Tracking 1 Overview Previous lectures: Feature extraction Today: Gradiant/edge Points (Kanade-Tomasi + Harris)
More informationCS5670: Computer Vision
CS5670: Computer Vision Noah Snavely Lecture 4: Harris corner detection Szeliski: 4.1 Reading Announcements Project 1 (Hybrid Images) code due next Wednesday, Feb 14, by 11:59pm Artifacts due Friday, Feb
More informationVK Multimedia Information Systems
VK Multimedia Information Systems Mathias Lux, mlux@itec.uni-klu.ac.at Dienstags, 16.oo Uhr This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Agenda Evaluations
More informationYudistira Pictures; Universitas Brawijaya
Evaluation of Feature Detector-Descriptor for Real Object Matching under Various Conditions of Ilumination and Affine Transformation Novanto Yudistira1, Achmad Ridok2, Moch Ali Fauzi3 1) Yudistira Pictures;
More informationLocal Features Tutorial: Nov. 8, 04
Local Features Tutorial: Nov. 8, 04 Local Features Tutorial References: Matlab SIFT tutorial (from course webpage) Lowe, David G. Distinctive Image Features from Scale Invariant Features, International
More informationObject Detection Design challenges
Object Detection Design challenges How to efficiently search for likely objects Even simple models require searching hundreds of thousands of positions and scales Feature design and scoring How should
More informationObject Detection by Point Feature Matching using Matlab
Object Detection by Point Feature Matching using Matlab 1 Faishal Badsha, 2 Rafiqul Islam, 3,* Mohammad Farhad Bulbul 1 Department of Mathematics and Statistics, Bangladesh University of Business and Technology,
More informationLocal Features and Bag of Words Models
10/14/11 Local Features and Bag of Words Models Computer Vision CS 143, Brown James Hays Slides from Svetlana Lazebnik, Derek Hoiem, Antonio Torralba, David Lowe, Fei Fei Li and others Computer Engineering
More informationEE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm
EE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm Group 1: Mina A. Makar Stanford University mamakar@stanford.edu Abstract In this report, we investigate the application of the Scale-Invariant
More informationA Comparison of SIFT and SURF
A Comparison of SIFT and SURF P M Panchal 1, S R Panchal 2, S K Shah 3 PG Student, Department of Electronics & Communication Engineering, SVIT, Vasad-388306, India 1 Research Scholar, Department of Electronics
More informationImage Matching. AKA: Image registration, the correspondence problem, Tracking,
Image Matching AKA: Image registration, the correspondence problem, Tracking, What Corresponds to What? Daisy? Daisy From: www.amphian.com Relevant for Analysis of Image Pairs (or more) Also Relevant for
More informationBuilding a Panorama. Matching features. Matching with Features. How do we build a panorama? Computational Photography, 6.882
Matching features Building a Panorama Computational Photography, 6.88 Prof. Bill Freeman April 11, 006 Image and shape descriptors: Harris corner detectors and SIFT features. Suggested readings: Mikolajczyk
More informationObtaining Feature Correspondences
Obtaining Feature Correspondences Neill Campbell May 9, 2008 A state-of-the-art system for finding objects in images has recently been developed by David Lowe. The algorithm is termed the Scale-Invariant
More informationA Novel Algorithm for Color Image matching using Wavelet-SIFT
International Journal of Scientific and Research Publications, Volume 5, Issue 1, January 2015 1 A Novel Algorithm for Color Image matching using Wavelet-SIFT Mupuri Prasanth Babu *, P. Ravi Shankar **
More informationECE Digital Image Processing and Introduction to Computer Vision
ECE592-064 Digital Image Processing and Introduction to Computer Vision Depart. of ECE, NC State University Instructor: Tianfu (Matt) Wu Spring 2017 Recap, SIFT Motion Tracking Change Detection Feature
More informationEfficient Interest Point Detectors & Features
Efficient Interest Point Detectors & Features Instructor - Simon Lucey 16-423 - Designing Computer Vision Apps Today Review. Efficient Interest Point Detectors. Efficient Descriptors. Review In classical
More informationFeature Detectors and Descriptors: Corners, Lines, etc.
Feature Detectors and Descriptors: Corners, Lines, etc. Edges vs. Corners Edges = maxima in intensity gradient Edges vs. Corners Corners = lots of variation in direction of gradient in a small neighborhood
More informationA Novel Extreme Point Selection Algorithm in SIFT
A Novel Extreme Point Selection Algorithm in SIFT Ding Zuchun School of Electronic and Communication, South China University of Technolog Guangzhou, China zucding@gmail.com Abstract. This paper proposes
More informationPerformance Evaluation of Scale-Interpolated Hessian-Laplace and Haar Descriptors for Feature Matching
Performance Evaluation of Scale-Interpolated Hessian-Laplace and Haar Descriptors for Feature Matching Akshay Bhatia, Robert Laganière School of Information Technology and Engineering University of Ottawa
More information2D Image Processing Feature Descriptors
2D Image Processing Feature Descriptors Prof. Didier Stricker Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de 1 Overview
More informationImage correspondences and structure from motion
Image correspondences and structure from motion http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 20 Course announcements Homework 5 posted.
More informationA System of Image Matching and 3D Reconstruction
A System of Image Matching and 3D Reconstruction CS231A Project Report 1. Introduction Xianfeng Rui Given thousands of unordered images of photos with a variety of scenes in your gallery, you will find
More informationAutomatic Image Alignment (feature-based)
Automatic Image Alignment (feature-based) Mike Nese with a lot of slides stolen from Steve Seitz and Rick Szeliski 15-463: Computational Photography Alexei Efros, CMU, Fall 2006 Today s lecture Feature
More informationSURF: Speeded Up Robust Features. CRV Tutorial Day 2010 David Chi Chung Tam Ryerson University
SURF: Speeded Up Robust Features CRV Tutorial Day 2010 David Chi Chung Tam Ryerson University Goals of SURF A fast interest point detector and descriptor Maintaining comparable performance with other detectors
More information3D Photography. Marc Pollefeys, Torsten Sattler. Spring 2015
3D Photography Marc Pollefeys, Torsten Sattler Spring 2015 Schedule (tentative) Feb 16 Feb 23 Mar 2 Mar 9 Mar 16 Mar 23 Mar 30 Apr 6 Apr 13 Apr 20 Apr 27 May 4 May 11 May 18 May 25 Introduction Geometry,
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 FDH 204 Lecture 09 130219 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Review Feature Descriptors Feature Matching Feature
More informationCorner Detection. GV12/3072 Image Processing.
Corner Detection 1 Last Week 2 Outline Corners and point features Moravec operator Image structure tensor Harris corner detector Sub-pixel accuracy SUSAN FAST Example descriptor: SIFT 3 Point Features
More informationA NEW FEATURE BASED IMAGE REGISTRATION ALGORITHM INTRODUCTION
A NEW FEATURE BASED IMAGE REGISTRATION ALGORITHM Karthik Krish Stuart Heinrich Wesley E. Snyder Halil Cakir Siamak Khorram North Carolina State University Raleigh, 27695 kkrish@ncsu.edu sbheinri@ncsu.edu
More informationWikipedia - Mysid
Wikipedia - Mysid Erik Brynjolfsson, MIT Filtering Edges Corners Feature points Also called interest points, key points, etc. Often described as local features. Szeliski 4.1 Slides from Rick Szeliski,
More informationDesigning Applications that See Lecture 7: Object Recognition
stanford hci group / cs377s Designing Applications that See Lecture 7: Object Recognition Dan Maynes-Aminzade 29 January 2008 Designing Applications that See http://cs377s.stanford.edu Reminders Pick up
More information3D Vision. Viktor Larsson. Spring 2019
3D Vision Viktor Larsson Spring 2019 Schedule Feb 18 Feb 25 Mar 4 Mar 11 Mar 18 Mar 25 Apr 1 Apr 8 Apr 15 Apr 22 Apr 29 May 6 May 13 May 20 May 27 Introduction Geometry, Camera Model, Calibration Features,
More informationImplementing the Scale Invariant Feature Transform(SIFT) Method
Implementing the Scale Invariant Feature Transform(SIFT) Method YU MENG and Dr. Bernard Tiddeman(supervisor) Department of Computer Science University of St. Andrews yumeng@dcs.st-and.ac.uk Abstract The
More informationA Hybrid Feature Extractor using Fast Hessian Detector and SIFT
Technologies 2015, 3, 103-110; doi:10.3390/technologies3020103 OPEN ACCESS technologies ISSN 2227-7080 www.mdpi.com/journal/technologies Article A Hybrid Feature Extractor using Fast Hessian Detector and
More informationHISTOGRAMS OF ORIENTATIO N GRADIENTS
HISTOGRAMS OF ORIENTATIO N GRADIENTS Histograms of Orientation Gradients Objective: object recognition Basic idea Local shape information often well described by the distribution of intensity gradients
More informationUlas Bagci
CAP5415- Computer Vision Lecture 5 and 6- Finding Features, Affine Invariance, SIFT Ulas Bagci bagci@ucf.edu 1 Outline Concept of Scale Pyramids Scale- space approaches briefly Scale invariant region selecqon
More information