: Chapter 10: Image Recognition 1 Montri Karnjanadecha ac.th/~montri Image Processing

: Chapter 10: Image Recognition 2 Chapter 10 Image Recognition

: Chapter 10: Image Recognition 3 Pattern Recognition and Training Pattern -- set of values (known size) that describe things The general problem Approaches to the decision-making process 1. Simple comparison 2. Common property 3.Clusters (using distance measurement |X 1 -u 1 | + |X 2 -u 2 | + … + |X n -u n | ) 4.Combination of 1, 2 and 3

: Chapter 10: Image Recognition 4 Pattern Recognition and Training Approaches to the decision-making process 1. Simple comparison 2. Common property 3.Clusters (using distance measurement |X 1 -u 1 | + |X 2 -u 2 | + … + |X n -u n | ) 4.Combination of 1, 2 and 3

: Chapter 10: Image Recognition 5 Decision Functions Decision function: w = (w 1, w 2, w 3, …, w n )

: Chapter 10: Image Recognition 6 Decision Functions If the pattern vector is x = [x 1, x 2, x 3, …, x n, 1] T, then –The unknown pattern is in group B if w T x > 0 –The unknown pattern is in group A if w T x <= 0 –Example: (8,4) is in group B because [1.5, -1.0, -3.5] [8, 4, 1] T = 8x = 4.5 and 4.5 > 0 –How about (4,4)?

: Chapter 10: Image Recognition 7 Decision Functions (Cont’d) The number of groups can be more than 2

: Chapter 10: Image Recognition 8 Decision Functions (Cont’d) Decision table Result of w1Result of w2Implication < 0 < 0 no group 0 group A > 0 < 0 group C > 0 > 0 group B Decision function need not be a linear function

: Chapter 10: Image Recognition 9 Cluster Means If the cluster consists of [3,4,8,2] [2,9,5,1][5,7,7,1], then the mean is [3.33, 6.67, 6.67, 1.33]. This represents the center of the four-dimensional cluster. The Euclidean distance from the center to a new pattern can be calculated as follows: new vector [3, 5, 7, 0], Euclidean distance = (3-3.33) 2 + (5-6.67) 2 + (7-6.67) 2 + (0-1.33) 2 = 4.78

: Chapter 10: Image Recognition 10 Automatic Clustering Technique 1: K-means clustering USE: To automatically find the best groupings and means of K clusters. OPERATION: –The pattern vectors of K different items are given to the system –Classifying them as best it can (without knowing which vector belongs to which item) –Let the pattern vectors be X 1, …, X n

: Chapter 10: Image Recognition 11 Automatic Clustering OPERATION: (cont’d) –Take the first K points as the initial estimation of the cluster means M 1 = X 1, M 2 = X 2, …, M k = X k *Allocate each pattern vector to the nearest group (minimum distance) –Calculate new cluster centers –If they are the same as the old centers, then STOP, other wise goto step *

: Chapter 10: Image Recognition 12 K-means clustering example M 1 = (2, 5.0) M 2 = (2, 5.5)

: Chapter 10: Image Recognition 13 K-means clustering example Allocating each pattern vector to the nearest center gives 1(2,5.0)group 1 2(2,5.5)group 2 3(6,2.5)group 1 4(7,2.0)group 1 5(7,3.0)group 1 6(3,4.5)group 1 The group means now become group 1: M 1 = (5, 3.4) group 2: M 2 = (2, 5.5)

: Chapter 10: Image Recognition 14 K-means clustering example This gives new groupings as follows: 1(2,5.0)group 2 2(2,5.5)group 2 3(6,2.5)group 1 4(7,2.0)group 1 5(7,3.0)group 1 6(3,4.5)group 2 And the group means become group 1: M 1 = (6.67, 2.5) group 2: M 2 = (2.33, 5.0) Groupings now stay the same and the processing stops.

: Chapter 10: Image Recognition 15 Optical Character Recognition Technique: Isolation of a character in an OCR document USE: To create a window containing only one character onto an array containing a text image

: Chapter 10: Image Recognition 16 Optical Character Recognition OPERATION: 1.Assuming that the image is correctly oriented and the text is dark on a white background 2.Calculate row sums of the pixel gray-level values. High row sums indicate a space between the rows 3.Calculate column sums of the pixel gray-level values. High column sums indicate a space between the columns

: Chapter 10: Image Recognition 17 Feature Extraction Technique: Creating the pattern vector (feature extraction) USE: To create the pattern vector for a character so that it can be compared with the library

: Chapter 10: Image Recognition 18 Feature Extraction OPERATION: 1.Assuming that the character has been isolated 2.Place a 4x4 grid over the image and count the number of “ink” pixels in each grid. 3.These number are then divided by the total number of pixels in the grid 4.Comparing resulting numbers with the library

: Chapter 10: Image Recognition 19 Feature Extraction