A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor

In this paper we present a new methodology for edge detection in digital images. The first originality of the proposed method is to consider image content as a parametric surface. Then, an original parametric local model of this surface representing image content is proposed. The few parameters involved in the proposed model are shown to be very sensitive to discontinuities in surface which correspond to edges in image content. This naturally leads to the design of an efficient edge detector. Moreover, a thorough analysis of the proposed model also allows us to explain how these parameters can be used to obtain edge descriptors such as orientations and curvatures. In practice, the proposed methodology offers two main advantages. First, it has high customization possibilities in order to be adjusted to a wide range of different problems, from coarse to fine scale edge detection. Second, it is very robust to blurring process and additive noise. Numerical results are presented to emphasis these properties and to confirm efficiency of the proposed method through a comparative study with other edge detectors.Comment: 21 pages, 13 figures and 2 table

Fuzzy Logic based Edge Detection Method for Image Processing

Edge detection is the first step in image recognition systems in a digital image processing. An effective way to resolve many information from an image such depth, curves and its surface is by analyzing its edges, because that can elucidate these characteristic when color, texture, shade or light changes slightly. Thiscan lead to misconception image or vision as it based on faulty method. This work presentsa new fuzzy logic method with an implemention. The objective of this method is to improve the edge detection task. The results are comparable to similar techniques in particular for medical images because it does not take the uncertain part into its account

Semi-automatic spline fitting of planar curvilinear profiles in digital images using the Hough transform

We develop a novel method for the recognition of curvilinear profiles in digital images. The proposed method, semi-automatic for both closed and open planar profiles, essentially consists of a preprocessing step exploiting an edge detection algorithm, and a main step involving the Hough transform technique. In the preprocessing step, a Canny edge detection algorithm is applied in order to obtain a reduced point set describing the profile curve to be reconstructed. Also, to identify in the profile possible sharp points like cusps, we additionally use an algorithm to find the approximated tangent vector of every edge point. In the subsequent main step, we then use a piecewisely defined Hough transform to locally recognize from the point set a low-degree piecewise polynomial curve. The final outcome of the algorithm is thus a spline curve approximating the underlined profile image. The output curve consists of polynomial pieces connected G^1 continuously, except in correspondence of the identified cusps, where the order of continu- ity is only C^0 , as expected. To illustrate effectiveness and efficiency of the new profile detection technique we present several numerical results dealing with detection of open and closed profiles in images of dif- ferent type, i.e., medical and photographic image

Improved Vector Median Filtering Algorithm for High Density Impulse Noise Removal in Microarray Images

The digital images are corrupted by impulse noise due to errors generated in camera sensors, analog-to-digital conversion and communication channels. Therefore it is necessary to remove impulse noise in-order to provide further processing such as edge detection, segmentation, pattern recognition etc. Filtering a noisy image, while preserving the image details is one of the most important issues in image processing. In this paper, we propose a new method for impulse noise removal in Microarray images. The proposed iterative algorithm search for the noise-free pixels within a small neighborhood. The noisy pixel is then replaced with the value estimated from the noise-free pixels. The process continues iteratively until all noisy-pixels of the noisy image are filtered. The performance of the proposed method is tested using impulse noise corrupted microarray images. The experimental results show the proposed algorithm can perform significantly better in terms of noise suppression and detail preservation in microarray images than a number of existing nonlinear techniques

A medical image steganography method based on integer wavelet transform and overlapping edge detection

© Springer International Publishing Switzerland 2015. Recently, there has been an increased interest in the transmission of digital medical images for e-health services. However, existing implementations of this service do not pay much attention to the confidentiality and protection of patients’ information. In this paper, we present a new medical image steganography technique for protecting patients’ confidential information through the embedding of this information in the image itself while maintaining high quality of the image as well as high embedding capacity. This technique divides the cover image into two areas, the Region of Interest (ROI) and the Region of Non- Interest (RONI), by performing Otsu’s method and then encloses ROI pixels in a rectangular shape according to the binary pixel intensities. In order to improve the security, the Electronic Patient Records (EPR) is embedded in the high frequency sub-bands of the wavelet transform domain of the RONI pixels. An edge detection method is proposed using overlapping blocks to identify and classify the edge regions. Then, it embeds two secret bits into three coefficient bits by performing an XOR operation to minimize the difference between the cover and stego images. The experimental results indicate that the proposed method provides a good compromise between security, embedding capacity and visual quality of the stego images

Tensiography and Liquid Drop Metrology

This research focused on drop metrology and the use of camera technology and vibration analysis influence on theoretical and practical tensiography. Drop shape and Tensiography are explained and how they relate to each other. Studies shows a relationship between vibration frequency and surface tension of liquids. However they also reveal the need for a theoretical understanding of the vibration tensiotrace of drops. Camera studies on large diameter dropheads used in tensiography are explored. Various image analysis methods were investigated for determining drop shape from camera images. Examination of the digital image reveals measurement issues. High speed camera images reveal new details of the drop separation process. An examination of drop modelling methods from camera images and the principles of such modelling were undertaken. Camera studies were developed which enabled the practical investigation of edge-detection. The theory developed links the drop shape with the tensiotrace of drops examined. The ray tracing method of the modelling of drop shape would have to be consolidated by establishing a definitive relationship between drop shape and the tensiotrace. This lead to acquiring photo images of real drops to get the profile of its edge or the drop shape. Various methods are used and assumptions are made in finding the edge of a drop from a photo image, in particular to the measurement of length, radii and angles

A Novel Method for Passive Digital Image Acquisition from a Scanning Electron Microscope

The Center for Nanotechnology at NASA Ames Research Center is developing a microcolumn scanning electron microscope with energy-dispersive x-ray spectroscopy (MSEMS). The MSEMS has the potential for space explorations because it is lightweight, low power and has superior analytical capabilities, including imaging with submicrometer resolution and elemental analysis. The image acquisition software for the MSEMS is currently in its early stages of development. The development of the image acquisition software has lead to the research problem addressed in this thesis. The objective of this thesis is to provide a detailed explanation of a novel algorithm for obtaining digital images from a scanning electron microscope (SEM). An introduction to the SEM is presented including its theory of operation and current research. Established methods for digital image acquisition from an SEM are summarized, all of which exploit electron beam position. A new method for digital image acquisition is introduced, which disregards electron beam position. The hardware requirements for the new method are discussed. The model for the new method is fully developed. Manual and automated methods for determining the model parameters are explained. The automated methods are accomplished using correlation and edge detection. Some preliminary results are shown. Some advantages and disadvantages of the method are discussed and the future work is recommended. Possible further applications are speculated

Evaluation of edge detection algorithm of frontal image of facial contour in plastic surgery

With the improvement of medical levels and the continuous improvement of people’s living standards, the demand for beauty by the general public is increasing. The plastic surgery industry has also developed by leaps and bounds. People’s dissatisfaction with their own facial appearance, facial injuries and some other reasons have prompted people to carry out facial reconstruction, and facial plastic surgery has developed rapidly. However, in the current facial plastic surgery, the edge detection effect on the contour image is general. In order to improve the edge detection effect of facial contour lines in medical images, this paper proposed a facial contour line generation algorithm. First, the detection effects of four operators were compared. After comparing the effects, the Sobel operator was used as the input data to generate an edge detection algorithm. Then, the grayscale features of the tissue in the image and the symmetry of the image were used to perform bidirectional contour tracking on the detected image to extract facial contour lines. In addition, for facial contour features, the midpoint method can be used to generate auxiliary contours. The algorithm was verified by a set of facial CT (Computed Tomography) images in the experiment. The results showed that the new generation algorithm accelerated the edge detection speed, had good denoising performance, and enhanced the edge detection effect by about 12.05% compared with the traditional edge detection algorithm. The validity and practicability of facial edge detection were verified, and it provided a theoretical basis for further realizing the design of a facial contour digital image processing system