ng 2008 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
1087 
fold is sampled 
ling images. In 
ose angle were 
ns space of the 
for each object, 
lg views of the 
50° 
escribed in the 
finding th e 
test image and 
the training set 
tor is chosen as 
Number of eigenvectors used 
20 
25 
30 
35 
40 
45 
50 
ICA 
1000 
990 
980 
970 
960 
970 
960 
PCA 
990 
980 
960 
960 
950 
940 
930 
IPCA-ICA 
1000 
1010 
1020 
1030 
1030 
1000 
1000 
Table I Number of correct recognitions by using ICA, PCA and IPCA. The Pose angle sampling is 50° 
Total of 1280 test images 
3.2. Results with Pose Angle Sampling at Every 25° 
The recognitions are shown for a 
network to learn. The performance in both the PCA, ICA as well as the 
IPCA is increased. Number of non Gaussian vectors as follows: q= {20, 
The previous experiment is also repeated by using images sampled at 25,30,35,40, 45, 50} 
every 25°. This gave a total of 300 training objects, and the rest, 1140 
images as the test objects. In this case, there is more information for the 
. That is to say, 
g a total of 140 
•aining set were 
300 test images, 
non Gaussian 
10, 45, 50} and 
resents the test 
Number of eigenvectors used 
20 
25 
30 
35 
40 
45 
50 
ICA 
970 
1090 
900 
910 
944 
950 
930 
PCA 
980 
980 
910 
920 
900 
980 
920 
IPCA-ICA 
1090 
1000 
1020 
933 
990 
1000 
1000 
Table II Number of correct recognitions by using ICA, PCA and IPCA. The Pose angle sampling is 25°. The recognitions are shown for a 
Total of 1140 test images 
From the above figures, we can see the, present method produced 
better results, and the recognition rate has got a significant 
increasing compared with PCA. We can come to the conclusion 
that the performance of IPCA outperformed the linear PCA. But 
the Incremental PCA is recursive method and the non Gaussian 
vectors is calculated for each image and the non dominant 
vectors are not considered for the next stage so this method is best 
suitable for 3D object recognition, performance of 87.88 
recognition rate is obtained when Number of non Gaussian 
vectors equals to 7, the average success rate for the PCA, ICA 
and IPCA-ICA methods is shown in Table III and the 
performance of PCA , ICA ,IPCA-ICA methods in the 5 nearest 
match is shown in Fig 6. 
(a) 
(b) 
(c) 
(d) 
Fig 6: Object Extracting Using IPCA-ICA (25°) 
(a) Input image (b) Object is mixed with some unknown mixtures 
(c) Object Extracting Using IPCA-ICA (d) Second with Orient 25°