Titlebook: Image Analysis and Recognition; 4th International Co Mohamed Kamel,Aurélio Campilho Conference proceedings 2007 Springer-Verlag Berlin Heid

亞當心理陰影

音的強弱

A High-Accuracy Rotation Estimation Algorithm Based on 1D Phase-Only Correlationploys 1D POC instead of 2D matching for estimating a rotation angle. The use of 1D POC to estimate the rotation angle makes it possible to reduce the computational cost significantly without sacrificing the estimation accuracy.

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異端邪說2

印第安人

A New Image Scaling Algorithm Based on the Sampling Theorem of Papoulis the first and second derivatives of an image in the scaling process. The derivatives contain information about edges and discontinuities that should be preserved during resizing. The sampling theorem of Papoulis is used to combine this information. We compare our algorithm with eight of the most co

Irascible

CYN

Subband-Adaptive and Spatially-Adaptive Wavelet Thresholding for Denoising and Feature Preservation rmation or biological tissue at that spatial location. Therefore, on denoising these texture images, it is essential that the local texture details characterizing the geological formation or tissue type are not lost. When processing these images, the operator usually has prior knowledge of the type

慢慢啃

Image Denoising Based on the Ridgelet Frame Using the Generalized Cross Validation Techniquem has two advantages: first, it can select the optimal threshold automatically without the knowledge of the noise level; second, it has the ability to well recover the ‘line-type’ structures contained in noisy images. Experimentally, the high performance of the proposed algorithm is demonstrated.

averse

Parameterless Discrete Regularization on Graphs for Color Image Filteringmage filtering is considered as a variational problem which consists in minimizing an appropriate energy function. In this paper, we propose a general discrete regularization framework defined on weighted graphs which can be seen as a discrete analogue of classical regularization theory. With this f

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