Conventional Edge Detection Techniques Research Articles

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We demonstrate a method to extract the heart outlines from noisy echocardiograph data in this paper. In order to eliminate significant noise and obtain improved image quality, our method is based on enhancing the image before applying contour detection. To accomplish that, we combine a variety of pre-processing techniques (filtering, morphological operations, and contrast adjustment) to enhance low contrast in echocardiograph images and avoid unclear edges. After applying these techniques, we are able to detect the boundaries of the heart and the movement of the valves using conventional edge detection techniques.

Full-field strain mapping of healthy and pathological mouse aortas using stereo digital image correlation

The murine aorta is a complex, heterogeneous structure that undergoes large and sometimes asymmetrical deformations under loading. For analytical convenience, mechanical behavior is predominantly described using global quantities that fail to capture critical local information essential to elucidating aortopathic processes. Here, in our methodological study, we used stereo digital image correlation (StereoDIC) to measure the strain profiles of speckle-patterned healthy and elastase-infused, pathological mouse aortas submerged in a temperature-controlled liquid medium. Our unique device rotates two 15-degree stereo-angle cameras that gather sequential digital images while simultaneously performing conventional biaxial pressure-diameter and force-length testing. A StereoDIC Variable Ray Origin (VRO) camera system model is employed to correct for high-magnification image refraction through hydrating physiological media. The resultant Green-Lagrange surface strain tensor was quantified at different blood vessel inflation pressures, axial extension ratios, and after aneurysm-initiating elastase exposure. Quantified results capture large, heterogeneous, inflation-related, circumferential strains that are drastically reduced in elastase-infused tissues. Shear strains, however, were very small on the tissue’s surface. Spatially averaged StereoDIC-based strains were generally more detailed than those determined using conventional edge detection techniques.

Information hiding with adaptive steganography based on novel fuzzy edge identification

The evolution in communication techniques has created wide threats for crucial information transfer through a communication channel. Covert communication with steganography is a skill of concealing secret information within cover object and hence shields the data theft over rapidly growing network. Recently, diverse steganography techniques using edge identification have been proposed in literature. Numerous methods however utilize certain pixels in the cover image for inserting edge information, resulting in significant deformation. The conventional edge detection method limits the deployment of edge detection in steganography as concealing the information would introduce some variations to the cover image. Hence inserting data in pixel areas recognized by existing conventional edge detection techniques like canny cannot ensure the recognition of the exact edge locations for the cover and stego images. In this paper, an Adaptive steganography method based on novel fuzzy edge identification is proposed. The method proposed is proficient of estimating the precise edge areas of a cover image and also ensures the exact edge location after embedding the secret message. Experimental results reveal that the technique has attained good imperceptibility compared to the Hayat Al-Dmour and Ahmed Al-Ani Edge XOR method in spatial domain.

A new kernel development algorithm for edge detection using singular value ratios

The perceptual quality of an image is very sensitive to the degradation of the edge information which is usually caused by many video signal applications such as super-resolution and denoising. Hence, it is very important to detect and enhance the edge information of the image. In this research work, new sets of kernels for edge detection using ratios of singular values of an image are proposed, which results in more detailed detection of edges in the original image. The parameters, which are the elements of kernel matrices and the threshold value used for producing binary image after convolving the kernels with the image of the proposed method, are optimised to achieve more detailed edge detection of the image. The experimental results show that more detailed edges are detected by the proposed method compared to the conventional edge detection techniques.

Soft Computing Techniques for Edge Detection Problem: A state-of-the-art Review

is an abrupt change that occurs in an image. Edge detection is one of the most prevalent problems in image processing. Edge Detection is the approach used most frequently for segmenting images based on abrupt changes in intensity. It is a concept that covers a number of fields in todays environment. In this paper, a state-of- the-art review of the conventional Edge Detection Techniques is presented. The paper also presents a state- of-the-art review of Soft Computing Techniques such as Fuzzy Logic, Genetic Algorithm, Neural Networks, Evolutionary Computation, Swarm Intelligence etc. for Edge Detection Problem. Further, an analysis of the review is also presented.

EDGE DETECTION BY USING LOOKUP TABLE

Edge detection is a fundamental tool used in most iprocessing applications. We proposed a simple, fast and efficient technique to detect the edge for the identifying, locating sh arp discontinuities in an image and boundary of an image. In this paper, we found that proposed method called LookUp Table performs w ell, which requires least computational time as com pared to conventional Edge Detection techniques. And also in this paper we pre sented a comparative performance of various convent ional Edge Detection Techniques.

3-D B-spline Wavelet-Based Local Standard Deviation (BWLSD): Its Application to Edge Detection and Vascular Segmentation in Magnetic Resonance Angiography

Extracting reliable image edge information is crucial for active contour models as well as vascular segmentation in magnetic resonance angiography (MRA). However, conventional edge detection techniques, such as gradient-based methods and wavelet-based methods, are incapable of returning reliable detection responses from low contrast edges in the images. In this paper, we propose a novel edge detection method by combining B-spline wavelet magnitude with standard deviation inside local region. It is proved theoretically and demonstrated experimentally in this paper that the new edge detection method, namely BWLSD, is able to give consistent and reliable strengths for edges with different image contrasts. Moreover, the relationship between the size of local region with non-zero wavelet magnitudes and the scale of wavelet function is established. This relationship indicates that if the scale of the adopted wavelet function is s, then the size of a local region, from which the standard deviation is estimated, should be 2s?1. The proposed edge detection technique is embedded in FLUX, namely, BWLSD-FLUX, for vascular segmentation in MRA image volumes. Experimental results on clinical images show that, as compared with the conventional FLUX, BWLSD-FLUX can achieve better segmentations of vasculatures in MRA images under same initial conditions.