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Non-uniform filters are frequently used in many image processing applications to describe regions or to detect specific features. However, non-uniform filtering is a computationally complex task. This paper presents a method to perform fast non-uniform filtering using a reduced number of memory accesses. The idea is based on integral images which are commonly used for box or Haar wavelet filtering...
Bilateral filter can well smooth images while preserving edges, and thus has been widely used in various applications. However, it needs significant memory cost due to a whole image storage. This paper develops three methods to significantly reduce the memory cost. The runtime updating method (RUM) discards unnecessary data in runtime. The stripe-based integral histogram method (SBM) divides image...
This paper presents a vector median filter that includes a new mechanism for the detection of impulses in color images prior to further processing operations. The proposed filter has been tested for images corrupted by two sided fixed impulse noise model. If the central vector pixel in a kernel is found to be corrupted, it is replaced by the vector median of the kernel, else it is kept unchanged....
The focus of this paper is ill-posed inverse problems. We emphasized on the Tikhonov's functional form of regularization from both numerical and Statistical methods viewpoints. We further extended the concept of regularization to the Bayesian methods framework. The Bayesian paradigm provided a general unified framework to the ill-posed inverse problem. Due to the computational complexity in estimating...
We propose a new bilateral filtering algorithm with computational complexity invariant to filter kernel size, so-called O(1) or constant time in the literature. By showing that a bilateral filter can be decomposed into a number of constant time spatial filters, our method yields a new class of constant time bilateral filters that can have arbitrary spatial and arbitrary range kernels. In contrast,...
This paper presents a method for object detection based on a cascade of scale and orientation normalized Gaussian derivative classifiers learnt with Adaboost. Normalized Gaussian derivatives provide a small but powerful feature set for rapid learning using Adaboost. Real time detection is made possible by use of a fast integer coefficient algorithm that computes a half-octave Gaussian pyramid with...
This paper presents three novel methods that enable bilateral filtering in constant time O(1) without sampling. Constant time means that the computation time of the filtering remains same even if the filter size becomes very large. Our first method takes advantage of the integral histograms to avoid the redundant operations for bilateral filters with box spatial and arbitrary range kernels. For bilateral...
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