Name
Abstract | ||
|---|---|---|
This work presents a new approach to contour representation and coding. It consists of an improved fitting of high-degree (4 th to 18 th) implicit polynomials (IPs) to the contour, which is robust to coefficient quantization. The proposed approach to solve the fitting problem is a modification of the 3L linear solution developed by Lei et al and is more robust to noise and to coefficient quantization. We use an analytic approach to limit the maximal fitting error between each data point and the zero-set generated by the quantized polynomial coefficients. We than show that consideration of the quantization error (which led to a specific sensitivity criterion) also brought about a significant improvement in fitting IPs to noisy data, as compared to the 3L algorithm. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/ICPR.2000.903542 | Pattern Recognition, 2000. Proceedings. 15th International Conference |
Keywords | Field | DocType |
image coding,object recognition,polynomials,sensitivity,2D data,3L linear solution,analytic approach,contour coding,contour representation,implicit polynomials,maximal fitting error,quantization error,quantized coefficients,robust fitting,sensitivity criterion | Computer vision,Noisy data,Polynomial,Coding (social sciences),Artificial intelligence,Quantization (physics),Polynomial coefficients,Quantization (signal processing),Coefficient quantization,Mathematics,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | ISBN |
3 | 1051-4651 | 0-7695-0750-6 |
Citations | PageRank | References |
1 | 0.37 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| amir helzer | 1 | 1 | 0.37 |
| Meir Barzohar | 2 | 94 | 11.06 |
| David Malah | 3 | 219 | 60.95 |