REU Week 1 Ivette Carreras UCF.

Slides:

Advertisements

Similar presentations

Content-Based Image Retrieval

Advertisements

Image Filtering. Outline Outline Concept of image filter  Focus on spatial image filter Various types of image filter  Smoothing, noise reductions 

Edges and Contours– Chapter 7

Computer vision: models, learning and inference Chapter 13 Image preprocessing and feature extraction.

Outline SIFT Background SIFT Extraction Application in Content Based Image Search Conclusion.

Activity Recognition Aneeq Zia. Agenda What is activity recognition Typical methods used for action recognition “Evaluation of local spatio-temporal features.

Bag-of-features models Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba.

Effective Image Database Search via Dimensionality Reduction Anders Bjorholm Dahl and Henrik Aanæs IEEE Computer Society Conference on Computer Vision.

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

Canny Edge Detector.

Lecture 28: Bag-of-words models

1 Image Features - I Hao Jiang Computer Science Department Sept. 22, 2009.

Announcements Mailing list: –you should have received messages Project 1 out today (due in two weeks)

Midterm review: Cameras Pinhole cameras Vanishing points, horizon line Perspective projection equation, weak perspective Lenses Human eye Sample question:

Bag-of-features models

Video Google: Text Retrieval Approach to Object Matching in Videos Authors: Josef Sivic and Andrew Zisserman University of Oxford ICCV 2003.

Blob detection.

Edge Detection Today’s readings Cipolla and Gee –supplemental: Forsyth, chapter 9Forsyth Watt, From Sandlot ScienceSandlot Science.

Multiple Object Class Detection with a Generative Model K. Mikolajczyk, B. Leibe and B. Schiele Carolina Galleguillos.

Image Filtering. Problem! Noise is a problem, even in images! Gaussian NoiseSalt and Pepper Noise.

Harris detector Convert image to greyscale Apply Gaussian convolution to blur the image and remove noise Calculate gradient of image in x and y direction.

Project 2 SIFT Matching by Hierarchical K-means Quantization

Svetlana Lazebnik, Cordelia Schmid, Jean Ponce

1 Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval Ondrej Chum, James Philbin, Josef Sivic, Michael Isard and.

Edge Detection Today’s reading Cipolla & Gee on edge detection (available online)Cipolla & Gee on edge detection Szeliski, Ch 4.1.2, From Sandlot.

0 - 1 © 2007 Texas Instruments Inc, Content developed in partnership with Tel-Aviv University From MATLAB ® and Simulink ® to Real Time with TI DSPs Edge.

UCF REU: Weeks 1 & 2. Gradient Code Gradient Direction of the Gradient: Calculating theta.

CSC508 What You Should Be Doing Code, code, code –Programming Gaussian Convolution Sobel Edge Operator.

SIFT Algorithm Scale-invariant feature transform Extracts features that are robust to changes in image scale, noise, illumination, and local geometric.

KRISTIN LAM REU WEEKS 1 & 2. MATERIAL COVERED MATLAB basics Edge Detection Harris Corner Detector Adaboost Face Detection Optical Flow Lucas-Kanade Method.

Visual Categorization With Bags of Keypoints Original Authors: G. Csurka, C.R. Dance, L. Fan, J. Willamowski, C. Bray ECCV Workshop on Statistical Learning.

Gang WangDerek HoiemDavid Forsyth. INTRODUCTION APROACH (implement detail) EXPERIMENTS CONCLUSION.

Automatic License Plate Location Using Template Matching University of Wisconsin - Madison ECE 533 Image Processing Fall 2004 Project Kerry Widder.

Templates, Image Pyramids, and Filter Banks

Kylie Gorman WEEK 1-2 REVIEW. CONVERTING AN IMAGE FROM RGB TO HSV AND DISPLAY CHANNELS.

WEEK 1-2 ALEJANDRO TORROELLA. CONVERTING AN IMAGE FROM RGB TO HSV AND DISPLAYING THE SEPARATE CHANNELS.

Sejong Univ. Edge Detection Introduction Simple Edge Detectors First Order Derivative based Edge Detectors Compass Gradient based Edge Detectors Second.

A picture is worth more than a 1000 words. It can save a life. Arjun Watane.

PRESENTATION REU IN COMPUTER VISION 2014 AMARI LEWIS CRCV UNIVERSITY OF CENTRAL FLORIDA.

CS654: Digital Image Analysis

Goggle Gist on the Google Phone A Content-based image retrieval system for the Google phone Manu Viswanathan Chin-Kai Chang Ji Hyun Moon.

Machine Vision Edge Detection Techniques ENT 273 Lecture 6 Hema C.R.

Edge Segmentation in Computer Images CSE350/ Sep 03.

Video Google: Text Retrieval Approach to Object Matching in Videos Authors: Josef Sivic and Andrew Zisserman University of Oxford ICCV 2003.

1 Edge Operators a kind of filtering that leads to useful features.

REU Week 1 Presented by Christina Peterson. Edge Detection Sobel ◦ Convolve image with derivative masks:  x:  y: ◦ Calculate gradient magnitude ◦ Apply.

Blob detection.

April 21, 2016Introduction to Artificial Intelligence Lecture 22: Computer Vision II 1 Canny Edge Detector The Canny edge detector is a good approximation.

Edge Detection slides taken and adapted from public websites:

SIFT Scale-Invariant Feature Transform David Lowe

Distinctive Image Features from Scale-Invariant Keypoints

Data Driven Attributes for Action Detection

Video Google: Text Retrieval Approach to Object Matching in Videos

Edge Detection The purpose of Edge Detection is to find jumps in the brightness function (of an image) and mark them.

Report 1: Optical Flow and Sift

EE 596 Machine Vision HW 6 Assigned: Nov 20, 2013

Levi Smith REU Week 1.

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

Dr. Chang Shu COMP 4900C Winter 2008

a kind of filtering that leads to useful features

What I learned in the first 2 weeks

a kind of filtering that leads to useful features

Canny Edge Detector.

Neil Gealy Outline What I Learned this Week Research Interests

Harris detector Convert image to greyscale

Week 1 Alan Wright - UCF.

Video Google: Text Retrieval Approach to Object Matching in Videos

SIFT SIFT is an carefully designed procedure with empirically determined parameters for the invariant and distinctive features.

Week 1 Emily Hand UNR.

Ivette Carreras Haroon Idrees

Presentation transcript:

REU Week 1 Ivette Carreras UCF

What I have learned this week Derivatives and Filters (Gaussian) Optical Flow Pyramids Convolution operation Edge detectors (Roberts, Sobel, Canny) Sift, interest points and descriptors Adaboost, face detection SVM Bag of features, Kmeans Great improved Matlab skills

Optical Flow without Pyramid

Optical Flow with Pyramids (four Level)

Edge Detectors: Robert Matlab function My function Same Threshold

Edge Detectors: Sobel Matlab function Same Threshold My function With a Gaussian Filter Sigma:1.5

Edge Detectors:Canny Using the Matlab function

SIFT Descriptor Descriptor of the point (8,8) Using vl_sift Descriptor of the a patch that contains the point (8:9,8:9) as center Discrepancy: Sigma used Point specification

Bag of Words Tested in a sample of 37 images of the Oxford 5K First the key points and descriptors are found Then the clusters are computed (words) The clusters are found using the Square Euclidean distance Used VLFeat implementation and Kmeans function in Matlab Descriptor for these images: 101246 Designated 10 words for this vocabulary

First three dimensions of the ten words

My possible project Describing Images Through Words Visual Phrases Using spatial information to relate words and obtain a better match Describing Images Through Words Tags, captions, and descriptions of images from the web can be used to produce a relevant caption of what is going on in a query image