Vehicle Extraction from aerial images is an important research topic in surveillance, traffic monitoring and military applications. In this paper, an approach based on Automatic Multilevel Thresholding has been proposed for extracting vehicles from aerial imagery. The approach combines Genetic Algorithm with DWT to make segmentation faster and geometric feature of vehicles for vehicle extractio...

Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and ye...

In the paper, a three-level thresholding method for image segmentation is presented, based on probability partition, fuzzy partition and entropy theory. A new fuzzy entropy has been defined through probability analysis. The image is divided into three parts, namely, dark, gray and white part, whose member functions of the fuzzy region are Z-function and P-function and S-function, respectively, ...

This paper presents a selective local thresholding method of a camera based document image for applying character segmentation. It is very difficult to binarize a camera based document image due to inconsistent lighting condition. It makes difficulties of character extraction and recognition. A selective local thresholding method in character region can reduce illumination effect and extract pr...

Up-down counters are commonly used in the aerospace industry for fault detection thresholding. This paper applies the up-down counter technique to detect wind turbine faults. The thresholding problem involves a tradeoff between false alarms and missed detections. Counter based thresholding can detect smaller faults with higher probability and lower false alarms than is possible using simple con...

Wavelet thresholding generally assumes independent, identically distributed normal errors when estimating functions in a nonparametric regression setting. VisuShrink and SureShrink are just two of the many common thresholding methods based on this assumption. When the errors are not normally distributed, however, few methods have been proposed. In this paper, a distribution-free method for thre...

Two main research areas in statistical text categorization are similarity-based learning algorithms and associated thresholding strategies. The combination of these techniques significantly influences the overall performance of text categorization. After investigating two similarity-based classifiers (k-NN and Rocchio) and three common thresholding techniques (RCut, PCut, and SCut), we describe...

In the context of wavelet denoising and compression, we study minimum description length (MDL) criteria for model selection criteria as exible forms of thresholding. Mixture MDL methods based on a single Laplacian, a two-piece Laplacian, and a generalized Gaussian prior are shown to be adaptive thresholding rules. While achieving mean squared error performance comparable with other popular thre...

In the context of wavelet denoising and compression, we study minimum description length (MDL) criteria for model selection criteria as exible forms of thresholding. Mixture MDL methods based on a single Laplacian, a two-piece Laplacian, and a generalized Gaussian prior are shown to be adaptive thresholding rules. While achieving mean squared error performance comparable with other popular thre...

In this paper, we propose a modified speech enhancement system using wavelet packets thresholding algorithm. First, we describe the basic algorithms of wavelet-based speech enhancement systems and we explain a previous introduced algorithm for improvement of the basic algorithm. Then we discuss on some factors affecting the performance of this system, including the thresholding function and the...