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Applications of. Shape Similarity. ASR: Applications in Computer Vision. Robotics: Shape Screening (Movie: Robot2.avi) Straightforward Training Phase Recognition of Rough Differences Recognition of Differences in Detail Recognition of Parts. ASR: Applications in Computer Vision.

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slide1

Applications of

Shape Similarity

slide2

ASR: Applications in Computer Vision

Robotics: Shape Screening

(Movie: Robot2.avi)

  • Straightforward Training Phase
  • Recognition of Rough Differences
  • Recognition of Differences in Detail
  • Recognition of Parts
slide3

ASR: Applications in Computer Vision

Application 2:

View Invariant Human Activity Recognition

(Dr. Cen Rao and Mubarak Shah, School of Electrical Engineering and Computer Science, University of Central Florida)

slide4

Application: Human Activity Recognition

Human Action Defined by Trajectory

  • Action Recognition by Comparison of Trajectories
  • (Movie: Trajectories)
  • Rao / Shah:
    • Extraction of ‘Dynamic Instants’ by Analysis of Spatiotemporal Curvature
    • Comparison of ‘Dynamic Instants’ (Sets of unconnected points !)
  • ASR:
    • Simplification of Trajectories by Curve Evolution
    • Comparison of Trajectories
slide9

Recognition of 3D Objects by Projection

Background: MPEG 7 uses fixed view angles

Improvement: Automatic Detection of Key Views

slide10

Automatic Detection of Key Views

(Pairwise) Comparison of Adjacent Views

  • Detects Appearance of Hidden Parts
slide11

Automatic Detection of Key Views

Result (work in progress):

slide12

Application: ASR

The Database Implementation

slide13

The Main Application: Back to ISS

Task:

Create Image Database

Problem:

Response Time

Comparison of 2 Shapes: 23ms on Pentium1Ghz

ISS contains 15,000 images:

Response Time about 6 min.

Clustering not possible:

ASR failed on measuring dissimilarities !

slide14

Vantage Objects

Solution:

Full search on entire database using a simpler

comparison

Vantage Objects (Vleugels / Veltkamp, 2000) provide a simple comparison of

n- dimensional vectors (n typically < 100)

slide15

Vantage Objects

The Idea:

Compare the query-shape q to a predefined subset S of the shapes in the database D

The result is an n-dimensional Vantage Vector V,

n = |S|

s1

v1

s2

v2

q

s3

v3

sn

vn

slide16

Vantage Objects

  • - Each shape can be represented by a single Vantage Vector
  • - The computation of the Vantage Vector calls the ASR – comparison only n times
  • - ISS uses 54 Vantage Objects, reducing the comparison time (needed to create the Vantage Vector) to < 1.5s
  • - How to compare the query object to the database ?
slide17

Vantage Objects

  • - Create the Vantage Vector vi for every shape di in the database D
  • - Create the Vantage Vector vq for the query-shape q
  • - compute the (euclidean) distance between vq and vi
  • - best response is minimum distance
  • Note: computing the Vantage Vectors for the database objects is an offline process !
slide18

Vantage Objects

  • How to define the set S of Vantage Objects ?
slide19

Vantage Objects

  • Algorithm 1 (Vleugels / Veltkamp 2000):
    • Predefine the number n of Vantage Objects
    • S0 = { }
    • Iteratively add shapes di  D\Si-1 to Si-1 such that
          • Si = Si-1  di
          • and
          • k=1..i-1e(di , sk)maximal. (e = eucl. dist.)
    • Stop if i = n.
slide20

Vantage Objects

  • Result:
    • Did not work for ISS.
slide21

Vantage Objects

  • Algorithm 2 (Latecki / Henning / Lakaemper):

Def.:

  • A(s1,s2): ASR distance of shapes s1,s2
  • q: query shape
  • ‘Vantage Query’ : determining the result r by minimizing e(vq , vi ) vi = Vantage Vector to si
  • ‘ASR Query’: determining the result r by minimizing A(q,di )

Vantage Query has certain loss of retrieval quality compared to ASR query.

  • Define a loss function l to model the extent of retrieval performance
slide22

Vantage Objects

Given a Database D and a set V of Vantage Vectors, the loss of retrieval performance for a single query by shape q is given by:

lV,D (q) = A(q,r),

Where r denotes the resulting shape of the vantage query to D using q.

Property:

lV,D (q) is minimal if r is the result of the ASR-Query.

slide23

Vantage Objects

Now define retrieval error function L(S) of set

S={s1 ,…, sn }  D of Vantage Vectors of Database D:

L(S) = 1/n  lS,D\{si} (si)

Task:

Find subset S  D such that L(S) is minimal.

slide24

Vantage Objects

Algorithm:

V0={ }

iteratively determine sj in D\Sj-1 such that

Sj =Sj-1 sj and L(Vj) minimal.

Stop if improvement is low

slide25

Vantage Objects

Result:

Worked fine for ISS, though handpicked objects still performed better.

Handpicked

Algorithm 2

L(S)

Number of Vantage Objects

slide26

Vantage Objects

…some of the Vantage Objects used in ISS:

slide27

Vantage Objects and ISS

The Vantage Objects are used in the ASR in the first (handdrawn) query.

The query is compared to 54 Objects, then a vector comparison is computed with the whole database.

The first result, also called ‘first guess’, is the result of the vantage vector search.

Searching for a ‘grabbed’ a shape on the user interface leads to direct comparison with the ASR, these results are precomputed, since the query is a known shape !

slide28

Vantage Objects and ISS

A: the handdrawn sketch

B: the result of the Vantage search

C: the result of the exact match