Business Identification: Spatial Detection Alexander Darino Week 8.

Slides:

Advertisements

Similar presentations

Distinctive Image Features from Scale-Invariant Keypoints

Advertisements

Image Registration  Mapping of Evolution. Registration Goals Assume the correspondences are known Find such f() and g() such that the images are best.

WEEK 7: WEB-ASSISTED OBJECT DETECTION ALEJANDRO TORROELLA & AMIR R. ZAMIR.

CS Spring 2009 CS 414 – Multimedia Systems Design Lecture 4 – Digital Image Representation Klara Nahrstedt Spring 2009.

Interest points CSE P 576 Ali Farhadi Many slides from Steve Seitz, Larry Zitnick.

IntroductionIntroduction AbstractAbstract AUTOMATIC LICENSE PLATE LOCATION AND RECOGNITION ALGORITHM FOR COLOR IMAGES Kerem Ozkan, Mustafa C. Demir, Buket.

Facial feature localization Presented by: Harvest Jang Spring 2002.

Project by Arie Kozak.  Mark it using personal biological visual system.

Virtual Dart: An Augmented Reality Game on Mobile Device Supervisor: Professor Michael R. Lyu Prepared by: Lai Chung Sum Siu Ho Tung.

Interest points CSE P 576 Larry Zitnick Many slides courtesy of Steve Seitz.

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

Generic Object Recognition -- by Yatharth Saraf A Project on.

Lecture 3: Edge detection, continued

Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point D. Lowe, IJCV 2004 Presenting – Anat Kaspi.

Object Class Recognition Using Discriminative Local Features Gyuri Dorko and Cordelia Schmid.

Blob detection.

Smart Traveller with Visual Translator. What is Smart Traveller? Mobile Device which is convenience for a traveller to carry Mobile Device which is convenience.

1 Image Features Hao Jiang Sept Image Matching 2.

VEHICLE NUMBER PLATE RECOGNITION SYSTEM. Information and constraints Character recognition using moments. Character recognition using OCR. Signature.

Computer vision.

Spatial Business Detection and Recognition from Images Alexander Darino.

Internet-scale Imagery for Graphics and Vision James Hays cs195g Computational Photography Brown University, Spring 2010.

Spatial Business Detection and Recognition from Images Alexander Darino Weeks 10 & 11.

Business Identification: Local Neighborhood Alexander Darino.

Automated Face Detection Peter Brende David Black-Schaffer Veni Bourakov.

Gili Werner. Motivation Detecting text in a natural scene is an important part of many Computer Vision tasks.

Business Identification: Spatial Detection Alexander Darino Week 5.

Learning Object Representation Andrej Lúčny Department of Applied Informatics Faculty of Mathematics, Physics and Informatics Comenius University, Bratislava.

CAPTCHA Processing CPRE 583 Fall 2010 Project CAPTCHA Processing Responsibilities Brian Washburn – Loading Image into RAM and Preprocessing and related.

Limitations of Cotemporary Classification Algorithms Major limitations of classification algorithms like Adaboost, SVMs, or Naïve Bayes include, Requirement.

Notes on the Harris Detector

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

Autonomous Robots Vision © Manfred Huber 2014.

Visual Computing Computer Vision 2 INFO410 & INFO350 S2 2015

1 Machine Vision. 2 VISION the most powerful sense.

Dense Color Moment: A New Discriminative Color Descriptor Kylie Gorman, Mentor: Yang Zhang University of Central Florida I.Problem:  Create Robust Discriminative.

A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

Lukáš Neumann and Jiří Matas Centre for Machine Perception, Department of Cybernetics Czech Technical University, Prague 1.

Lecture 3 Template Matching Edge Detection. 2 Processes for Assignment 1  Understand Image Format  Pre Processing - Gaussian, Mean Filter to clean up.

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

Shadow Detection in Remotely Sensed Images Based on Self-Adaptive Feature Selection Jiahang Liu, Tao Fang, and Deren Li IEEE TRANSACTIONS ON GEOSCIENCE.

License Plate Recognition of A Vehicle using MATLAB

Invariant Local Features Image content is transformed into local feature coordinates that are invariant to translation, rotation, scale, and other imaging.

Recognizing objects Prof. Noah Snavely CS1114

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

CSSE463: Image Recognition Day 21

Sub title- Authors (font 50; black; bold)

Sub title- Authors (font 50; black; bold)

Sub title- Authors (font 50; black; bold)

Sub title- Authors (font 50; black; bold)

Paper Title in One or Two Lines

Sub title- Authors (font 50; black; bold)

Sub title- Authors (font 50; black; bold)

Levi Smith REU Week 1.

a kind of filtering that leads to useful features

What I learned in the first 2 weeks

REU Week 1 Ivette Carreras UCF.

a kind of filtering that leads to useful features

PowerPoint template A template for Children’s presentations

Paper Title in One or Two Lines

Sub title- Authors (font 50; black; bold)

Lecture 5: Feature invariance

COLOR WHEEL COLLAGE.

Sub title- Authors (font 50; black; bold)

Lecture 5: Feature invariance

Sub title- Authors (font 50; black; bold)

Sub title- Authors (font 50; black; bold)

Image segmentation Grey scale image Binary image

CASE/ISAM 2016 Digest Template Paper Title in One or Two Lines

Presentation transcript:

Business Identification: Spatial Detection Alexander Darino Week 8

Weaknesses to Current Approach Latitude Longitude Geocoding Reverse Geocoding Nearby Businesses Image OCR Detected Text Business Name Matching Business Identification Business Spatial Detection 2

STR Implementation STR Implementation: “Automatic Detection and Recognition of Signs From Natural Scenes” Multiresolution- based potential characters detection Character/layout geometry and color properties analysis Local affine rectification Refined Detection

Multiresolution-based potential characters detection

STR Implementation Original Next Step: Replace with readily available text detector Text detectors are not readily available (Will revisit later)

TEMPLATE-IMAGE SIFT MATCHING After many technical difficulties…

Template Name George Font Trebuchet MS # Levels 3 Peak Threshold 0 Edge Threshold 10 Scale Cutoff (95%) Image Name George … # Levels 3 Peak Threshold 0 Edge Threshold 10 Statistics Good 1 Bad 0 Total (% G) 1 (100%)

Template Name George Font Trebuchet MS # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff (95%) Image Name George … # Levels 3 Peak Threshold 0 Edge Threshold 10 Statistics Good 1 Bad 0 Total (% G) 1 (100%)

Template Name George Font Trebuchet MS # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff (95%) Image Name George … # Levels 1 Peak Threshold 0 Edge Threshold 10 Statistics Good 2 Bad 0 Total (% G) 2 (100%)

Template Name George Font Trebuchet MS # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff (95%) Image Name George … # Levels 1 Peak Threshold 20 Edge Threshold 10 Statistics Good 2 Bad 0 Total (% G) 2 (100%)

Template Name George Font Trebuchet MS # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff (95%) Image Name George … # Levels 1 Peak Threshold 20 Edge Threshold 6 Statistics Good 2 Bad 0 Total (% G) 2 (100%)

Template Name Aiken Font Trebuchet MS # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff (95%) Image Name George … # Levels 1 Peak Threshold 20 Edge Threshold 6 Statistics Good 3 Bad 0 Total (% G) 3 (100%)

Template Name Delicious Font Trebuchet MS # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff Image Name George … # Levels 1 Peak Threshold 20 Edge Threshold 6 Statistics Good 0 Bad 0 Total (% G) 0 (0%)

Template Name Prepared Font Trebuchet MS # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff Image Name George … # Levels 1 Peak Threshold 20 Edge Threshold 6 Statistics Good 0 Bad 0 Total (% G) 0 (0%)

Template Name Foods Font Trebuchet MS # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff Image Name George … # Levels 1 Peak Threshold 20 Edge Threshold 6 Statistics Good 0 Bad 0 Total (% G) 0 (0%)

Template Name Bruegger’s Font Arial Rounded MT Bold # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff Image Name Bruegger’s … # Levels 1 Peak Threshold 20 Edge Threshold 6 Statistics Good 0 Bad 0 Total (% G) 0 (0%)

Template Name Bakery Font Arial Rounded MT Bold # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff Image Name Bruegger’s … # Levels 1 Peak Threshold 20 Edge Threshold 6 Statistics Good 0 Bad 0 Total (% G) 0 (0%)

Template Name Bakery Font Arial Rounded MT Bold # Levels 5 Peak Threshold 0 Edge Threshold 10 Scale Cutoff Image Name Bruegger’s … # Levels 1 Peak Threshold 20 Edge Threshold 6 Statistics Good 0 Bad 0 Total (% G) 0 (0%)

SCENE TEXT RECOGNITION Moving away from SIFT and revisiting

Scene Text Recognition Did not hear back from individuals contacted for STR implementation Returning to STR Implementation – Further reading indicates that patches are necessary for subsequent algorithms – Text detection is not enough: need to implement specified text detector Multiresolution- based potential characters detection Character/layout geometry and color properties analysis Local affine rectification Refined Detection

Color Properties Analysis Implemented Gaussian Mixture Model (GMM) to obtain μ and σ of foreground/background for: R/G/B/H/I Calculated Confidences that component (RGBHI) can be used to recognize characters Multiresolution- based potential characters detection Character/layout geometry and color properties analysis Local affine rectification Refined Detection

Original

Red

Green

Blue

Hue

Intensity

Evaluation The highest confidence was found in Intensity even though most letters vanish, vs Hue where letters are easily distinguisible This suggests text recognition should occur individually per character The paper further suggests it needs the patches around the individual characters

Next Step

Thank You