Edge detection is one of the most critical tasks in automatic image analysis. There exists no universal edge detection method which works well under all conditions. This paper shows the new approach based on the one of the most efficient techniques for edge detection, which is entropy-based thresholding. The main advantages of the proposed method are its robustness and its flexibility. We present experimental results for this method, and compare results of the algorithm against several leading edge detection methods, such as Canny, LOG, and Sobel. Experimental results demonstrate that the proposed method achieves better result than some classic methods and the quality of the edge detector of the output images is robust and decrease the computation time.

/pdf/a-new-algorithm-based-entropic-threshold-for-edge-detection-39e45knezw.pdf

A New Algorithm Based Entropic Threshold for Edge Detection in Images

In this paper we examine the behavior of Ad Hoc networks through simulations, using different routing protocols and various topologies. We examine the difference in performance, using CBR application, with packets of different size through a variety of topologies, showing the impact node placement has on networks performance. We show that the choice of routing protocol plays an important role on network’s performance. We also quantify node mobility effects, by looking into both static and fully mobile configurations. Our paper presents a systematic analysis of a variety of different ad hoc network topologies in terms of node placement, node mobility and routing protocols through several simulated scenarios.

IJCSI Publicity Board 2010

Most of the classical mathematical methods for edge detection based on the derivative of the pixels of the original image are Gradient operators, Laplacian and Laplacian of Gaussian operators. Gradient based edge detection methods, such as Roberts, Sobel and Prewitts, have used two 2-D linear filters to process vertical edges and horizontal edges separately to approximate first-order derivative of pixel values of the image. The Laplacian edge detection method has used a 2-D linear filter to approximate second-order derivative of pixel values of the image. Major drawback of second-order derivative approach is that the response at and around the isolated pixel is much stronger. In this research study, a novel approach utilizing Shannon entropy other than the evaluation of derivates of the image in detecting edges in gray level images has been proposed. The proposed approach solves this problem at some extent. In the proposed method, we have used a suitable threshold value to segment the image and achieve the binary image. After this the proposed edge detector is introduced to detect and locate the edges in the image. A standard test image is used to compare the results of the proposed edge detector with the Laplacian of Gaussian edge detector operator. In order to validate the results, seven different kinds of test images are considered to examine the versatility of the proposed edge detector. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The results of this study were quite promising.

Edge Detection in Gray Level Images based on the Shannon Entropy

Previous studies have shown that image patches can be well represented as a sparse linear combination of elements from an appropriately selected over-complete dictionary. Recently, single-image super-resolution (SISR) via sparse representation using blurred and downsampled low-resolution images has attracted increasing interest, where the aim is to obtain the coefficients for sparse representation by solving an $\ell _{0}$ or $\ell _{1}$ norm optimization problem. The $\ell _{0}$ optimization is a nonconvex and NP-hard problem, while the $\ell _{1}$ optimization usually requires many more measurements and presents new challenges even when the image is the usual size, so we propose a new approach for SISR recovery based on $\ell _{p} (0 regularization nonconvex optimization. The proposed approach is potentially a powerful method for recovering SISR via sparse representations, and it can yield a sparser solution than the $\ell _{1}$ regularization method. We also consider the best choice for $\ell _{p}$ regularization with all $p$ in (0, 1), where we propose a scheme that adaptively selects the norm value for each image patch. In addition, we provide a method for estimating the best value of the regularization parameter $\lambda $ adaptively, and we discuss an alternate iteration method for selecting $p$ and $\lambda $ . We perform experiments, which demonstrates that the proposed $\ell _{p} (0 regularization nonconvex optimization method can outperform the convex optimization method and generate higher quality images.

Image Super-Resolution via Adaptive $\ell _{p} (0 Regularization and Sparse Representation

We present a new way of constructing a fractional-based convolution mask with an application to image edge analysis. The mask was constructed based on the Riemann-Liouville fractional derivative which is a special form of the Srivastava-Owa operator. This operator is generally known to be robust in solving a range of differential equations due to its inherent property of memory effect. However, its application in constructing a convolution mask can be devastating if not carefully constructed. In this paper, we show another effective way of constructing a fractional-based convolution mask that is able to find edges in detail quite significantly. The resulting mask can trap both local discontinuities in intensity and its derivatives as well as locating Dirac edges. The experiments conducted on the mask were done using some selected well known synthetic and Medical images with realistic geometry. Using visual perception and performing both mean square error and peak signal-to-noise ratios analysis, the method demonstrated significant advantages over other known methods.

/pdf/a-new-construction-of-a-fractional-derivative-mask-for-image-4wybxi174k.pdf

A new construction of a fractional derivative mask for image edge analysis based on Riemann-Liouville fractional derivative

In this paper, a new edge detection method for SAR image using a hybrid genetic algorithm (HGA) is proposed depending on a full study about the characteristics of SAR images. According to this method, firstly some new types of edges are defined, and then the edge detection is reduced to an optimization problem. Not only original image data, but also some local information of edge, such as the continuity, thickness and regional difference of edges are included to define a cost function. Therefore, by the global searching capability of genetic algorithm, more continuous and accurate edges can be detected than other traditional methods. Moreover, a local optimization operator is employed to speed up the convergence of algorithm. So the method presents a remarkably rapider speed than classical genetic algorithm, as well as better edges. The simulations results also demonstrate its efficiency.

A hybrid genetic algorithm-based edge detection method for SAR image

Radar imaging based compressive sensing (CS) is a potential way to obtain the high-resolution radar images without the constraint of Nyquist sampling rate. In this paper, we proposed a radar remote-sensing imaging approach based on compressive sensing and adaptive Lp norm algorithm. Some experiments are taken and the results indicate that an accurate reconstruction of high-resolution radar images are obtained, with fewer measurements than its counterparts, but resulting in lower normalized MSE(NMSE).

High resolution radar imaging based on compressed sensing and adaptive Lp norm algorithm

The principle of space-time processing is discussed here after a realistic UWB pulse model introduced in the paper. The problem of realization of variable delay circuits is addressed. Delay line is not enough for accurately beam steering. A digital filter method by compensating channel dispersed delay for time domain beamforming is presented here. An ideal system with fractional sample delay hat occurs in insufficient sample of UWB pulse signal is not realizable. The two filter approaches involving FIR and allpass filter are given to approximate casual and stable system with the fractional delay. The Lagrange interpolation for FIR filter and complex cepstral computation for allpass filter are examined. A comparison of UWB pulse array directivity versus conventional phased array directivity has been done. The effect of different sampling rate and noise level is discussed. In the experiment, the FIR and allpass filter with a capability of accurate propagation delay compensation improved the beamforming performance remarkably. The results indicate the feasibility and effectiveness of the method.

A time domain beamforming method of UWB pulse array

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A hybrid genetic algorithm-based edge detection method for SAR image

High resolution radar imaging based on compressed sensing and adaptive Lp norm algorithm

A time domain beamforming method of UWB pulse array