Sliding Windows Silver Bullet or Evolutionary Deadend? - PowerPoint PPT Presentation

1 / 33
About This Presentation
Title:

Sliding Windows Silver Bullet or Evolutionary Deadend?

Description:

What is a Sliding Window Approach? Definition of 'Sliding Window Approach'? Exhaustive search ... Face detection [Schneiderman&Kanade'00],[Viola&Jones'01] ... – PowerPoint PPT presentation

Number of Views:38
Avg rating:3.0/5.0
Slides: 34
Provided by: krys53
Category:

less

Transcript and Presenter's Notes

Title: Sliding Windows Silver Bullet or Evolutionary Deadend?


1
Sliding WindowsSilver Bullet or Evolutionary
Deadend?
  • A. Efros, B. Leibe, K. Mikolajczyk

2
Sliding
A Microsoft conspiracy?
3
What is a Sliding Window Approach?
  • Definition of Sliding Window Approach?
  • Exhaustive search
  • (A kind of) segmentation
  • Localization as a classification problem

? Krystian
? Alyosha
? Bastian
4
Sliding Window
  • What is the sliding window technique?

5
Sliding Window
  • What is the sliding window technique?
  • A brute-force search over pose space with fixed
    model to find objects?

6
Sliding Window
  • What is the sliding window technique?
  • A brute-force search over pose space with fixed
    model to find objects?
  • Face detection SchneidermanKanade00,ViolaJon
    es01,Mikolajczyk et al.04,DalalTriggs05
    etc.

7
Sliding Window
  • What is the sliding window technique?
  • A brute-force search over pose space with fixed
    model to find objects?
  • Face detection SchneidermanKanade00,ViolaJon
    es01,Mikolajczyk et al.04,DalalTriggs05
    etc.
  • What isnt the sliding window technique?

8
Sliding Window
  • What is the sliding window technique?
  • A brute-force search over pose space with fixed
    model to find objects?
  • Face detection SchneidermanKanade00,ViolaJon
    es01,Mikolajczyk et al.04,DalalTriggs05
    etc.
  • What isnt the sliding window technique?
  • Intelligent search? (data driven)

9
Sliding Window
  • What is the sliding window technique?
  • A brute-force search over pose space with fixed
    model to find objects?
  • Face detection SchneidermanKanade00,ViolaJon
    es01,Mikolajczyk et al.04,DalalTriggs05
    etc.
  • What isnt the sliding window technique?
  • Intelligent search? (data driven)
  • Object recognition localization. Interest
    points and Hough transform based
    recognition?Lowe99,Leibe04,Mikolajczyk et
    al06

10
Sliding Window
11
Sliding Windows
Fixed object model
object examples
12
Sliding Windows

Fixed object model
13
Sliding Windows

Fixed object model
14
Sliding Windows

Fixed object model
15
Sliding Windows

Fixed object model
16
Sliding Windows

Fixed object model
Decision (binary, real confidence value)
17
Sliding Windows

Fixed object model
Decision (binary, real confidence value)
18
Sliding Windows

Fixed object model
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
19
Sliding Windows

Features
Fixed object model
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
20
Sliding Windows

Features
Fixed object model
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
21
Sliding Windows

Features
Fixed object model
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
22
Sliding Windows

Features
Fixed object model
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
23
Sliding Windows

Features
Fixed object model
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
24
Sliding Windows

Features
Fixed object model
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
25
Sliding Windows

Features
Fixed object model
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
26
Sliding Windows

Features
Fixed object model
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
27
Sliding Windows

Features
Fixed object model
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
28
Sliding Windows

Features
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
29
Sliding Windows
Features
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
30
Sliding Windows Interest Points and Hough
Transform
Features
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
31
Sliding Windows
Features
x
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Confidence map
32
Sliding Windows and Bayesian rules,Interest
Points and Hough Transform
Everybody uses sliding window at some stage of
the algorithm.
33
(No Transcript)
Write a Comment
User Comments (0)
About PowerShow.com