A Study on Object Recognition Technology Using PCA in the Variable Illumination
Object recognition technologies using PCA(principal component analysis) recognize objects by deciding representative features of objects in the model image, extracting feature vectors from objects in an image and measuring the distance between them and object representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the K-Nearest Neighbor technique(class-to-class) in which a group of object models of the same class is used as recognition unit for the images inputted on a continual input image. However, we propose the object recognition technique new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. Object recognition algorithm proposed here represents more enhanced recognition rate to change of illumination than existing methods.
KeywordsObject Recognition Recognition Rate Histogram Equalization Variable Illumination Recognition Unit
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- 1.Weng, J., Ahuja, N., Huang, T.S.: Learning recognition and segmentation of 3-D object from 2-D images. In: Proc. of Fourth Int’l Conf. on Computer Vision, Berlin, May 1993, pp. 121–128 (1993)Google Scholar
- 2.Viola, P., Jones, M.: Robust real-time object detection. In: International Conference on Computer Vision (2001)Google Scholar
- 3.Murase, H., Nayar, S.K.: Visual Learning and Recogntion 3-Dobject from appearance. International journal of Computer Vision 14 (1995)Google Scholar
- 4.Arita, D., Yonemoto, S., Taniguchi, R.-i.: Real-time Computer Vision on PC-cluster and Its Application to Real-time Motion Capture, IEEE (2000)Google Scholar
- 5.Yang, J., Zhang, D.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern analysis and Machine Intelligence 26(1) (2004)Google Scholar
- 6.Bourel, F., Chibelushi, C.C., Low, A.A.: Robust facial expression recognition using a state-based model of spatially localised facial dynamics. In: Proceedings of Fifth IEEE International Conference on Automatic Face andGesture Recognition, pp. 106–111 (2002)Google Scholar
- 8.Segen, J., Kumar, S.: Shadow Gestures: 3D Hand Pose Estimation Using a Single Camera. In: CVPR 1999, Fort Collins, Colorado, June 23-25, 1999, vol. 1, pp. 479–485 (1999)Google Scholar