Image Edge Extraction Research Articles

Efficient and Effective Extraction of Vocal Fold Vibratory Patterns from High-Speed Digital Imaging

High-speed digital imaging can provide valuable information on disordered voice production in voice science. However, the large amounts of high-speed image data with limited image resolutions produce significant challenges for computer analysis, and thus effective and efficient image edge extraction methods allowing for the batch analysis of high-speed images of vocal folds is clinically important. In this paper, a novel algorithm for automatic image edge detection is proposed to effectively and efficiently process high-speed images of the vocal folds. The method integrates Lagrange interpolation, differentiation, and Canny edge detection, which allow objective extraction of aperiodic vocal fold vibratory patterns from large numbers of high-speed digital images. This method and two other popular algorithms, histogram and active contour, are performed on 10 sets of high-speed video data from excised larynx experiments to compare their performances in analyzing high-speed images. The accuracy in computing glottal area and the computation time of these methods are investigated. The results show that our proposed method provides the most accurate and efficient detection, and is applicable when processing low-resolution images. In this study, we focus on developing a method to effectively and efficiently process high-speed image data from excised larynges. However, in addition we show the clinical potential of this method by use of example high-speed image data obtained from a patient with vocal nodules.The proposed automatic image-processing algorithm may provide a valuable biomedical application for the clinical assessment of vocal disorders by use of high-speed digital imaging.

Edge-based Segmentation Using Robust Evolutionary Algorithm Applied to Medical Images

Although medical image segmentation is a hard task in image processing, it is possible to reduce its complexity by considering it as an optimization problem. This paper presents a robust evolutionary algorithm based on a cost minimization function to segment and to extract image edges. Since, the goal is to outperform a high edge detection quasi independent from the input problem characteristics, an adaptive detector is considered. As a first step, the main evolutionary algorithm parameters are highlighted based on an adaptive parameterization to overcome convergence problem. In a second stage, the reached optimal setting is applied on medical images to exhibit the quality of the proposed algorithm.

Emissivity Calibration and Research of Infrared Image Processing in the Cutting Temperature Test

It is by the emissivity of the goal objects and the noise problem of the high temperature images that people are puzzled during the course of the cutting temperature tested by the application of thermal imaging device. By using thermocouples, and getting the comparison of thermocouples and the thermal imaging system temperature, we calibrate object emissivity, handle the image noise by improved median method, carry on the edge extraction of image, obtain the temperature of measured objects, and finally confirm the accuracy of the data got by the thermal imaging device with contact measurement.

Histogram Thresholding using Beam Theory and Ambiguity Measures

This paper presents a novel histogram thresholding technique based on the beam theory of solid mechanics and the minimization of ambiguity in information. First, a beam theory based histogram modification process is carried out. This beam theory based process considers a distance measure in order to modify the shape of the histogram. The ambiguity in the overall information given by the modified histogram is then minimized to obtain the threshold value. The ambiguity minimization is carried out using the theories of fuzzy and rough sets, where a new definition of rough entropy is presented. The applications of the proposed scheme in performing object and edge extraction in images are reported and compared with those of a few existing classical and ambiguity minimization based schemes for thresholding. Experimental results are given to demonstrate the effectiveness of the proposed method in terms of both qualitative and quantitative measures.

Vector Order Statistics Operators As Edge Detectors of Objects in Noised Colour Images

Vector Order Statistics Operators As Edge Detectors of Objects in Noised Colour ImagesThe paper offers review of most effective methods of detecting edges in noised colour images. Presented has been effectiveness of vector order operators and vector difference operators, as well as variants thereof enriched with filters of different types. Detectors gained in this way are complicated to different degrees from the viewpoint of computations, and resistant to impulsive and Gaussian noises. In the case of DV operators, addition of filtering elements significantly increases resistance thereof to the presence of noise. Best effects of edge extraction in noised images have been gained using the DV_α-trim operator.

Novel texture feature persistence metric for automatic-target-recognition-directed image compression

We present a novel texture feature persistence metric for automatic-target-recognition (ATR)-directed image compression based on the similarity between shapes. On the basis of spatial fuzzy representation of shapes, a similarity metric between shapes is proposed. Then the impact of lossy image compression on ATR performance is measured by the similarity between shapes, which are obtained by identical segmentation and edge extraction of the source image and degraded image after compression. Experimental results show that this metric effectively measures the extent to which target texture features are preserved after compression.

New scheme for image edge detection using the switching mechanism of nonlinear optical material

The limitations of electronics in conducting parallel arithmetic, algebraic, and logic processing are well known. Very high-speed (terahertz) performance cannot be expected in conventional electronic mechanisms. To achieve such performance we can introduce optics instead of electronics for information processing, computing, and data handling. Nonlinear optical material (NOM) is a successful candidate in this regard to play a major role in the domain of optically controlled switching systems. The character of some NOMs is such as to reflect the probe beam in the presence of two read beams (or pump beams) exciting the material from opposite directions, using the principle of four-wave mixing. In image processing, edge extraction from an image is an important and essential task. Several optical methods of digital image processing are used for properly evaluating the image edges. We propose here a new method of image edge detection, extraction, and enhancement by use of AND-based switching operations with NOM. In this process we have used the optically inverted image of a supplied image. This can be obtained by the EXOR switching operation of the NOM.

A New Algorithm for Extracting Image Edges and Its Application in Tool Condition Monitoring System

A New Algorithm for Extracting Image Edges and Its Application in Tool Condition Monitoring System

A New Efficient Edge Extraction Algorithm for Images Using Directional Tracing Techniques

Abstract The ability to recognize edges of an object is of fundamental importance in computer vision and image processing. In this paper, we present a fast, efficient method of edge extraction of images using directional tracing algorithm. Our work builds on the work of R. Nevatia and K.R. Babu [5], who use linear feature extraction to present algorithms for edge extraction. By employing an intuitive principle that an edge pixel should possess local maximum gradient, but having no more domain information or knowledge, we propose an effective, robust strategy to extract edges that integrates directional tracing and feature extraction. We discuss the computational efficiency of our approach and present a schematic for real time implementation of the algorithm. Application of our algorithm on different images shows excellent results. The main advantages of our technique include: (i) it can be used for a wide variety of images; (ii) it is simple and easy to implement; (iii) it is fast; and (iv) the algorithm ...

Partitioned Iterative Function System: A New Tool for Digital Imaging

This article deals with the fractal based representation of image to perform various image processing tasks like image compression, image magnification and image edge extraction. Representation of an image in terms of a set of affine contractive maps has been regarded as the fractal representation. The set of transformations is called iterative function system (IFS). However, the contribution of the article is based on partitioned iterative function system (PIFS). The basic difference between IFS and PIFS exist in the domain of application of their respective transformation. The cost of generating PIFS code of an image is reduced by using Genetic Algorithms (GAs) based search technique. Image compression is a direct consequence of the PIFS code. On the other hand two other operations, viz, image magnification and edge extraction can also be performed using the PIFS code obtained by the GA based method.

Multidimensional pulse image processing of chemical structure data

Recent advances in the understanding of the mammalian visual cortex have led to new approaches for image processing techniques. As a result of this, computer simulations using the proposed visual cortex model have become very useful in the field of image processing. Models of this kind have the ability to efficiently extract image segments, edges and texture. They operate by generating a set of pulse images (images with binary pixels) for a static input. These pulse images display synchronized activity of neighboring neurons, and it is these images in which the information about segments, edges and texture are displayed. Pulse image generation is dependent on autowaves that travel throughout the image. In order to extend pulse image processing to multidimensional data (i.e., data cubes), the autowaves are designed to expand in all of the cube's dimensions. In this fashion, pulse cubes can be created and the same analysis techniques that have been applied to two-dimensional pulse images can be applied to pulse image cubes. This paper examines and discusses multidimensional pulse image analysis applied to three-dimensional (3D) chemical structural data of 17β-estradiol.

Image edge detection and enhancement by an inversion operation

Edge extraction is an essential part of image processing. In digital image processing there are several optical methods for properly obtaining image edges. We propose a method of image edge extraction and enhancement by using a lens-based optical setup, the image, and its inverted form. The inverted and the noninverted images help here to obtain the image edges.

Circuit realization of Markov random fields for analog image processing

A methodological approach to the definition of nonlinear circuits for real-time image processing is presented. The image processing problem is formulated in terms of the minimization of a functional based on the Markov random fields (MRFs) theory. The terms of such a functional are related to the co-contents of proper nonlinear multiterminal resistors, thus reporting the minimization process to the achievement of an equilibrium solution in a circuit made up of these multiterminal resistors and of linear capacitors. Coupled image restoration and edge extraction in the presence of additive Gaussian noise are the specific problems addressed in this paper. Simulations shap the ability of the proposed circuits to yield real-time solutions for these problems.

Edge extraction and restoration of noisy image by innovation process in kalman filter

AbstractEdge detection and restoration methods are considered from noisy images without a priori information about original images. The chi‐square test detects edges from an innovation process when applying the Kalman filter to the first‐order AR model. Image‐extracted edges are the binary image and the thin‐line image. The success of the algorithm depends heavily on the window width for the chi‐square test and the value of the threshold in which the detector operates.Also, edge information is used for restoring images to remove noise in the background of images using a smoother and to obtain sharp edges in other regions using a sensitive filter. The effect of the approach is illustrated by the simulations.

On the extraction and alignment of image edges.

In this paper the types of image filtering, segmentation and correlational processes which are consistent with the ability of human observers to align edges in arbitrary two-dimensional images are considered. Edge-only versions of a number of images were computed by pre-filtering, segmenting and using laplacians to extract luminance boundaries. The matching of such edge-only images with the originals seems well predicted by a (derivative) matched filtering (cross-correlation) process showing some bias toward edges contained within the middle frequency range of the images used.