This document discusses sign recognition using principal component analysis (PCA). It begins by describing the image acquisition process using a web camera. It then explains that PCA is used to project sign images into a feature space defined by the variation among known sign images. The eigenvectors resulting from the PCA calculation represent the eigenvectors of the set of sign boards. It outlines the steps in sign recognition which include initializing the training set, calculating eigenvalues to define the eigenspace, classifying new signs based on their weights, and incorporating repeatedly recognized unknown signs into the known class.

1. Sign Recognition UsingSign Recognition Using
PCAPCA

2.  Image acquisition is done by
using the a web camera the
specifications are :
 QHMPL group.
 25 MP.
 Night vision.

3. 03/13/15 3

4.  Here we compare the intensities of RGB planes in
image and find the red color borders in image.

5.  sign Images are projected into a featuresign Images are projected into a feature
space that best encodes the variation amongspace that best encodes the variation among
known sign images.known sign images.
 Images after the Eigen vector calculation areImages after the Eigen vector calculation are
thethe eigenvectorseigenvectors of the set of sign boards .of the set of sign boards .
Eigen Space and Eigen FacesEigen Space and Eigen Faces

6.  Initialization :
 Acquire the training set and calculate eigenvalues
(using PCA projections) which define eigenspace.
 When a new sign board is encountered, calculate its
weight.
 If yes, classify the weight pattern as known or
unknown.
 (Learning) If the same unknown sign is seen several
times incorporate it into known sign boards .
Steps In Sign RecognitionSteps In Sign Recognition

7. Main assumption of PCA
approach:
 Image space forms a cluster in image space.
 PCA gives suitable representation.

8. (1) Calculate average sign : v.
(2) Collect difference between training images and
average sign in matrix A (M by N), where M is the number
of pixels and N is the number of images.
(3) The eigenvectors of covariance matrix C (M by
M) give the Eigen matrix.
◦ M is usually big, so this process would be time consuming.
What to do? T
AAC =

9. If the number of data points is smaller than the dimension
(N<M), then there will be only N-1 meaningful eigenvectors.
Instead of directly calculating the eigenvectors of C, we can
calculate the eigenvalues and the corresponding eigenvectors
of a much smaller matrix L (N by N).
if λi are the eigenvectors of L then A λi are the eigenvectors
for C.
◦ The eigenvectors are in the descent order of the corresponding
eigenvalues.
AAL T
=

10.  Representation of Sign Images using Eigenmatrix
 The training sign images and new sign images can be
represented as linear combination of the eigenmatrix.
 When we have a sign image u :
Since the eigenvectors are orthogonal :∑=
i
iiau φ
i
T
i ua φ=

11.  MATLAB 7.8.
 Keil 4.
 Proload.

12.  Image Acquisition :
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