A novel natural image noise level estimation based on flat patches and local statistics

Multimedia Tools and Applications - This paper proposes a high-precision algorithm for noise level estimation. Different from existing algorithms, we present a new noise level estimation algorithm...     

On the behavior of artificial neural network classifiers inhigh-dimensional spaces

It is widely believed in the pattern recognition field that when a fixed number of training samples is used to design a classifier, the generalization error of the classifier tends to increase as the number of features gets larger. In this paper, we discuss the generalization error of the artificial neural network (ANN) classifiers in high-dimensional spaces, under a practical condition that the ratio of the training sample size to the dimensionality is small. Experimental results show that the generalization error of ANN classifiers seems much less sensitive to the feature size than 1-NN, Parzen and quadratic classifiers     

On Wachspress quadrilateral patches

Wachspress initiated the study of rational basis functions for finite element construction over quadrilaterals and more general polygonal and curved elements. Later Apprato et al. (1979) and Gout (1979, 1985) studied the interpolatory and convergence properties of lower degree rational finite elements and their applications in solving second order boundary value problems. In the present paper we introduce higher degree Wachspress functions by an iterative technique and study their properties from the point of view of applications to surface fitting problems. It is indeed remarkable to note that these functions possess properties similar to tensor product Bernstein polynomials and hence could be effectively used to generate quadrilateral patches.     

An adaptive dynamic range compression with local contrast enhancement algorithm for real-time color image enhancement

Journal of Real-Time Image Processing - Dynamic range compression has become an important function used in modern digital video cameras to improve visual quality of color images suffered from low...     

Microscopic image restoration based on tensor factorization of rotated patches

In microscopic image processing for analyzing biological objects, structural characters of objects such as symmetry and orientation can be used as a prior knowledge to improve the results. In this study, we incorporated filamentous local structures of neurons into a statistical model of image patches and then devised an image processing method based on tensor factorization with image patch rotation. Tensor factorization enabled us to incorporate correlation structure between neighboring pixels, and patch rotation helped us obtain image bases that well reproduce filamentous structures of neurons. We applied the proposed model to a microscopic image and found significant improvement in image restoration performance over existing methods, even with smaller number of bases.     

Functional behavior in data spaces

Summary In order to verify that a nondeterministic sequential program is partially correct it is sufficient to establish the conjunction of two constituent properties: weak partial correctness and functional, that is reproducible, behavior. It is possible to continue this divide-and-conquer strategy for the concept of functional behavior. If the nondeterministic sequential program is derived from a set of interacting parallel processes then the functional behavior of the former can be expressed in terms of two weaker complementary properties of the latter: weak functional behavior and input/output liveness. The only remaining issue is input/output dependability: the absence of input/output livelock. The theoretical framework of data spaces is used to derive closure theorems for these constituent properties. For instance, it is shown that a system of weakly functional processes is again weakly functional.This research is supported in part by the Office of Naval Research under Contract No. N00014-77-C-0536 through the University of Southern CaliforniaA preliminary version of the paper was presented at the Symposium on Formal Methods and Mathematical Tools for Software Design, Mathematical Research Institute, Oberwolfach, West Germany, Nov. 21–27, 1976     

An experimental comparison of range image segmentation algorithms

A methodology for evaluating range image segmentation algorithms is proposed. This methodology involves (1) a common set of 40 laser range finder images and 40 structured light scanner images that have manually specified ground truth and (2) a set of defined performance metrics for instances of correctly segmented, missed, and noise regions, over- and under-segmentation, and accuracy of the recovered geometry. A tool is used to objectively compare a machine generated segmentation against the specified ground truth. Four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches     

CMOS图像传感器动态范围扩展技术

CMOS有源像素图像传感器动态范围的扩展可采用各种技术,如对数像素结构、横向溢出栅像素结构、多次曝光技术、局部曝光技术等,其中多次曝光技术在可获得大动态范围图像的同时,信噪比(SNR)较高。     

Shape analysis of local facial patches for 3D facial expression recognition

In this paper we address the problem of 3D facial expression recognition. We propose a local geometric shape analysis of facial surfaces coupled with machine learning techniques for expression classification. A computation of the length of the geodesic path between corresponding patches, using a Riemannian framework, in a shape space provides a quantitative information about their similarities. These measures are then used as inputs to several classification methods. The experimental results demonstrate the effectiveness of the proposed approach. Using multiboosting and support vector machines (SVM) classifiers, we achieved 98.81% and 97.75% recognition average rates, respectively, for recognition of the six prototypical facial expressions on BU-3DFE database. A comparative study using the same experimental setting shows that the suggested approach outperforms previous work.