Image identification from still or video images is emerging as an active research area with numerous commercial and law enforcement applications. Image is represented as single or multiple arrays of pixel values. Features that uniquely characterize the object are determined. The arrays are compared with a stored pattern feature set obtained during training procedure. Number of matches of the object in the image must be obtained. As the image consists of a large amount of data, it has to be compressed using a compression technique so that data reduction is achieved. This reduced data is used for comparison process. This image identification technique can be used to recognize objects in specific areas. These identification techniques find its applications in robotics. The robot's primary sensor is the video camera. A proprietary vision algorithm lets the robot see, recognize, and avoid running into objects. The robot can also recognize where it is, analyze images, and select features such as colours and edges for comparison to a database of instances it knows about. For example, it knows it has collided with a wall when the image stops moving. The robot can be trained to recognize thousands of objects from examples it sees. The vision algorithm lets the robot recognize objects even if their orientation or lighting differs from the example. Recognition capabilities increase with a higher resolution camera.

1. M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies. Image coding using wavelet transform .IEEE Transactions on Image Processing, 1992.
2. G. Davis and A. Nosratinia. Wavelet-based image coding: An overview. Applied and Computational Control, Signals and Circuits, 1(1), 1998.
3. G. K. Wallace, The JPEG Still Picture Compression Standard, Communication of the ACM, Vol. 34, No. 4, 1991, pp. 30-44.
4. C. Christopoulos, A. Skodras, T. Ebrahimi, The JPEG2000 Still Image Coding System: An Overview, IEEE Trans. on Consumer Electronics, Vol.46, No.4, November 2000, pp. 1103-1127.
5. JPEG2000, World Wide Web: http://jj2000.epfl.ch/
6. Subramanya A, “Image Compression Technique,” Potentials IEEE, Vol. 20, Issue 1, pp 19-23, Feb-March 2001,
7. David Jeff Jackson & Sidney Joel Hannah, “Comparative Analysis of image Compression Techniques,” System Theory 1993, Proceedings SSST ’93, 25th Southeastern Symposium,pp 513-517, 7 –9 March 1993.
8. Hong Zhang, Xiaofei Zhang & Shun Cao, “ Analysis & Evaluation of Some Image Compression Techniques,” High Performance Computing in Asia- Pacific Region, 2000 Proceedings, 4th Int. Conference, vol. 2, pp 799-803,14-17 May, 2000.
9. Ming Yang & Nikolaos Bourbakis, “An Overview of Lossless Digital Image Compression Techniques,” Circuits & Systems, 2005 48th Midwest Symposium , vol. 2 IEEE, pp 1099-1102, 7 – 10 Aug, 2005.
10. Ismail Avcibas, Nasir Memon, Bulent Sankur, Khalid Sayood, “ A Progressive Lossless / Near Lossless Image Compression Algorithm,”IEEE Signal Processing Letters, vol. 9, No. 10, pp 312-314, October 2002.

Image compression Image Identification Wavelet JPEG Robot