Abstract
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.
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References
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, May 1993, Berlin, pp. 121–128 (1993)
Viola, P., Jones, M.: Robust real-time object detection. In: International Conference on Computer Vision (2001)
Murase, H., Nayar, S.K.: Visual Learning and Recogntion 3-Dobject from appearance. International journal of Computer Vision 14 (1995)
Arita, D., Yonemoto, S., Taniguchi, R.-i.: Real-time Computer Vision on PC-cluster and Its Application to Real-time Motion Capture. IEEE, Los Alamitos (2000)
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), 1 (2004)
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)
Yang, H.-S., Kim, J.-M., Park, S.-K.: Three Dimensional Gesture Recognition Using Modified Matching Algorithm. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 224–233. Springer, Heidelberg (2005)
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Kim, JM., Heo, JK., Yang, HS., Song, MK., Park, SK., Lee, WK. (2006). Object Recognition Using K-Nearest Neighbor in Object Space. In: Shi, ZZ., Sadananda, R. (eds) Agent Computing and Multi-Agent Systems. PRIMA 2006. Lecture Notes in Computer Science(), vol 4088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11802372_94
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DOI: https://doi.org/10.1007/11802372_94
Publisher Name: Springer, Berlin, Heidelberg
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