with Kernel Estimators Stephan R. Sain1 De ember 8, 2000 SUMMARY: A great deal of resear h has fo used on improving the bias properties of kernel estimators. One proposal involves removing the restri tion of non-negativity on the kernel to onstru t \higher-order" kernels that eliminate additional terms in the Taylor's series expansion of the bias. This paper onsiders an alternative that uses a ...

The human visual system excels at detecting local blur of visual images, but the underlying mechanism is mysterious. Traditional views of blur such as reduction in local or global high-frequency energy and loss of local phase coherence have fundamental limitations. For example, they cannot well discriminate flat regions from blurred ones. Here we argue that high-level semantic information is cr...

A deformable mirror is used to scan the focal plane during the camera exposure, obtaining extended depth-of-field. A deconvolution kernel is approximated for a given scan depth and used to de-blur the image. OCIS codes: (180.2520) Fluorescence microscopy; (110.1080) Active or adaptive optics; (100.1830) Deconvolution

Image manipulation has become commonplace in today's social context. One of the most common types of image forgeries is image compositing. In recent years, researchers have proposed various methods for detecting such splicing. Most prior approaches to detecting blur post-processing operation suffer from their inability to identify the spliced region when the background region contained nature b...

A data-driven bandwidth choice for a kernel density estimator called critical bandwidth is investigated. This procedure allows the estimation to have as many modes as assumed for the density to estimate. Both Gaussian and uniform kernels are considered. For the Gaussian kernel, asymptotic results are given. For the uniform kernel, an argument against these properties is mentioned. These theoret...

This paper considers the problem of estimating probability density functions on the rotation group SO(3). Two distinct approaches are proposed, one based on characteristic functions and the other on wavelets using the heat kernel. Expressions are derived for their Mean Integrated Squared Errors. The performance of the estimators is studied numerically and compared with the performance of an exi...