Local threshold and Boolean function based edge detection

This paper proposes a new approach for edge detection using a combination of artificial bee colony (ABC) algorithm and an improved derivative technique. ABC algorithm simulates the foraging behavior of a honey bee swarm. The proposed approach find the edge pixels by an improved derivative technique to compute fitness function, and then applying ABC algorithm determine the most fit pixels to be considered as the edge pixels. Qualitative and quantitative analysis of the proposed approach and its comparison with other standard edge detection methods are presented. Shannon’s entropy function and Pratt’s figure of merit are used for quantitative analysis. The effect of variation of parameters on performance of the proposed approach is discussed. Experimental results show that proposed method outperformed most of existing techniques.

This paper present a new approach for edge detection using gravitational search algorithm (GSA) and universal law of gravity. The direct application of any edge detection algorithm on an entire image requires a huge memory which leads to optimization problems. Gravitational search algorithm is an optimization algorithm inspired by Newtonian gravity. Objects in space attract each other by way of gravity force, and this force causes a global movement of all objects towards the objects with heavier masses. Hence, masses cooperate using a direct form of communication, through gravitational force. In the proposed approach the edges are detected using the law of universal gravity and movement of agents are computed using gravitational search algorithm. The proposed approach is able to detect the edge pixel in an image up to a certain extent. The technique can be further extended for finding more edge pixels by modifying the gravitational search algorithm.

Several problems are encountered in modeling and processing of many life science applications such like tissue engineering, bioacoustics, and food processing detection. Recently, various image processing techniques have been developed in an attempt to solve these problems. Due to the rapid advance and high number of researches included in such field, there is always a room for improvement. In this work, advanced image processing approaches are presented and applied to different life science applications. The first approach utilizes image processing in the generation of micro-scaffolds for tissue engineering modeling. Furthermore, a spectrogram enhancement approach is developed to improve the pattern detection of weevils existence in grain storage, based on their detected and identified sounds. Finally, a novel edge detection method applied on intramuscular fat recognition is presented based on the energy and skewness as two smoothed versions of the meat image. Results, in comparison to experimental data, offer a maximum error of 7.81% in the specified detection process. 1. Material and Method 1.1 Generation of micro-porous approach The difficulty of modeling such complex micro-structures always lies in retrieving the geometry into the solver as edges. Edge detection algorithms are based upon highlighting discontinuities in images and were first developed for satellite images. The technique has become well-known and was further developed and widely used for other image processing applications. During the conventional detection procedures, usually all the image size is processed, which leads to redundant calculations of areas containing no edge information. In this study a redundant avoiding algorithm is developed which searches for the threshold value in the image and then propagates around it in a 2-D lattice in eight directions figure 1.1, until an edge is hit. Figure 1.1 The scanning procedure for gradient changes propagates in an eight directional lattice until an edge is hit 1.2 Spectrogram enhancement in bioacoustics This approach is based on detection of the sound pattern edges in the crest factor image, as shown in figure 1.2 and 1.3, which behaves as a smoothed version of the spectrogram image and avoids the use of edge detectors prior smoothing filters and their scaling constraints. Figure 1.2 Spectrogram with 40 dB limited dynamic range Figure 1.3 Crest factor distributions (left), the scaled crest factor image of the spectrogram (right). 1.3 Edge detection for intramuscular fat recognition A novel edge detection method applied on intramuscular fat is presented based on the energy and skewness as two smoothed versions of the meat image, as shown in the flow chart given in figure 1.4. Figure 1.4 flow chart edge detection 2. Results 2.1.Generation of micro-porous Approach The conventional approach for edge detection is based upon either thresholding the first derivative (gradient) of the image or locating zero crossings of its second derivative (Laplacian).The sample used, figure 2.1, for this simulation was of size 3 mm, 350 μm and 350 μm in z-direction, x-direction and y-direction, respectively. The geometry was uploaded on the solver by collective segmenting and edge detection techniques. The method has proven time saving and as well enables the possibility of parallel processing of the image, as each threshold value can be scanned and dealt with independently. After the predictions of the edges, the 2-d binary edge images are assembled forming a 3-D binary file of the scaffolds figure 2.1. Figure 2.1 From the micro-CT images 430 hundred 2-D binary edge images are assembled forming a 3-D binary file of the scaffolds 2.2.Spectrogram enhancement in bioacoustics The spectrogram enhancement approach is compared to other enhancement approaches, including multiband spectral subtraction and wavelet packet decomposition, for different structure bioacoustics calls including bats (ultrasound range) and birds (acoustic range), with additive white Gaussian noise added across a range of signal to noise ratios. Results measured subjectively and objectively, indicate that the proposed method is promising for efficiently spectrogram enhancing while preserving its temporal and spectral accuracy, as shown in figure 2.2. Figure 2.2 (a) One of Sitta Canadensis bird calls at SNR = 20 dB, and its enhanced spectrogram by (b) BP, (c) MBSS, (d) WF, (e) WPD, and (f) the proposed method approaches. 2.3.Edge detection for intramuscular fat recognition The generation of micro-scaffolds approach is applied on a sample meat image, which is scanned line by line and every noticeable variance in the color values or the grey levels is captured and the local thresholds values are estimated according to the highest drop gradient value. The local threshold values are averaged to estimate the global threshold for the whole image, as shown in figure 2.1. Figure 2.3 an image for a meat with fat (a), the x y scan of the image which does not show all the fat details (b) and the lattice scan of the image showing more details of the fat, as circled by red (c). For intramuscular fat, the developed edge detection approach is used to predict the fat content in marbled meat images, as shown in figure 2.3. Figure 2.4 (a) original marbled meat images, (b) edges images using the proposed method (c) detected fat regions, and (d) the percentage of fat content Conclusion As observed in the different approaches there is always a room for improvement that minimize the error observed in the specified detection process. The methods have proven time saving and as well enables the possibility of parallel processing of the image that leads to a more efficient performance highly desirable for industrial applications.

In this paper, a novel algorithm for microscopic color cell image edge detection based on Pulse Coupled Neural Networks (PCNN) is presented. Microscopic color image edge detection has proven to be a difficult task due to uneven brightness, cross-color existing between cell boundary and background caused by dyeing and noise. To solve these problems, this paper originally employs an improved PCNN model called multi-dimensional PCNN to implement edge detection of microscopic color cell image. PCNN is an advanced approach which aims at processing color images parellelly rather than separately dispose signal to every channel because it is characterized by synchronous neuronal burst and multi-dimensional convolution. To test the feasibility and effectiveness of multi-dimensional PCNN on edge detection of microscopic color cell images, experiments of several microscopic cell images are carried out. Empirical results show that multi-dimensional PCNN outperforms classical methods in terms of anti-noise and the accuracy of weak edge detection.

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

Alzheimer's disease (AD) is the most common form of dementia among older people. Dementia is a brain disorder that seriously affects a person's ability to carry out daily activities. Several reasons makes the early diagnosis in Alzheimer's Disease important. One of them is that it allows treatments for Alzheimer's disease that help slow its progression. In patients with Alzheimer's disease, the CT scan shows a degree of generalized cerebral atrophy. Thus, analysis the CT scan images are very important in early diagnosis in Alzheimer's Disease. Image processing uses for detecting for objects of CT images. Edge detection; which is a method of determining the discontinuities gray level images, is a very important initial step in Image processing. Many classical edge detectors have been developed over time. Some of the well-known edge detection operators based on the first derivative of the image are Roberts, Prewitt, Sobel which are traditionally implemented by convolving the image with masks. Also Gaussian distribution has been used to build masks for the first and second derivative. However, this distribution has a limit to only symmetric shape. This paper will use to construct the masks. The Weibull distribution which was more general than Gaussian because it has symmetric and asymmetric shape. The constructed masks are applied to images and we obtained good results.

Edge detection is a fundamental image processing technique used to spot sudden shifts in color or intensity in image. It is utilized to detect and highlight boundaries between various items or regions in image, as well as to detect features such as corners, circles and lines. Edge detection approaches typically work by applying a filter to an image to detect areas where the image undergoes an immediate shift in magnitude. Applications for edge detection techniques include recognizing objects, healthcare images, and background segmentation. Many techniques have been presented based on the classical approaches (such as Sobel, Prewitt, and Roberts, Canny, Laplacian of Gaussian (LOG), etc.) and soft computing approaches (SCA), which are the two main approaches for detection of edge. This paper provides an overview of studies carried out on edge detection using various approaches. That will assist brand-new researchers in learning about these techniques and selecting one from among them to evolve or improve according to their field of study.

A novel approach of combined edge detection and segmentation for tool wear measurement in machining

Purpose – The purpose of this paper is to present a new approach to edge detection using semiconductor flash memory networks having scalable and parallel hardware architecture. Design/methodology/approach – A flash cell can store multiple states by controlling its voltage threshold. The equivalent resistance of the operation states controlled by threshold voltage of flash cell gives out different combinations of logic 0 and 1 states. The paper explores this basic feature of flash memory in designing a resistance change memory network for implementing novel edge detector hardware. This approach of detecting the edges is inspired from the spatial change detection ability of the human visual system. Findings – The proposed approach consumes less number of electronic components for its implementation, and outperforms the conventional approaches of edge detection with respect to the processing speed, scalability and ease of design. It is also demonstrated to provide edges invariant to changes in the direction of the spatial change in the images. Research limitations/implications – This research brings about a new direction in the development of edge detection, in terms of developing high-speed parallel processing edge detection and imaging circuits. Practical implications – The proposed approach reduces the implementation complexity by removing the need to have convolution operations for spatial edge filtering. Originality/value – This paper presents one of the first edge detection approaches that is purely a hardware oriented design, uses resistance of flash memory to form edge detector cells, and one that does not use computational operations such as additions or multiplications for its implementation.

This paper presents a new efficient enhanced automatic target recognition approach based on hybrid feature and intensity based methods. The proposed approach is based on comparison and matching between the acquired target image and the stored database images through edge detection, morphological operation, image transformation, image histogram matching and mutual information for both target and stored database images. Sobel operator is used to detect edges in both acquired target image and the stored database images. Hough transform is used as an image transformation which groups the extracted edge points in edged images to an appropriate set of lines which helps in features extraction and matching processes in both of target and stored database images. This gives an initial indication about realization and recognition between target image and its corresponding database image. Comparisons of image histogram and mutual information for both target and database images are finally used to emphasize the recognition of the target image and its verification with its corresponding database image. The proposed recognition approach passed through four stages which are: edge detection by Sobel edge detector, thinning as a morphological operation, Hough transformation, matching process using median deviation and finally histogram matching and measuring the mutual information between target and the available database images. The experimental results proved that the proposed hybrid target recognition approach gives a more accurate and successful recognition rate than other techniques which depend on using feature based methods or intensity based methods.

One of the fundamental steps in image processing is edge detection. The performance of any edge detection method is highly affected by the choice of parameters, e.g. thresholds. For some applications, such as identification of objects in a scene, the interesting regions do not usually cover the whole image. Hence, applying edge detection with global parameters to these images will detect edges of interest (EoI) as well as edges caused by background details, illumination or reactance. We present here a new edge fusion approach that aims at identifying edges of interest and discarding unwanted edges. For this purpose, different parameter values are used to generate edge images from a given gray-scale image. The image is then divided into small windows and for each window the local detection accuracy of the various parameter values are estimated. Finally, the edge images are fused to produce a new edge image by locally choosing parameter values that achieve maximum detection accuracy. Initial results have shown that this method has the potential to produce very promising results.

Edge detection is important step in image processing. In spite of two decade of research the need for general purpose edge detector is still felt. Threshold decision is the key uncertainty in the edge detection algorithms. Soft computing approach represents a good mathematical framework to deal with uncertainty of information. In this work, we used fuzzy logic for automatic thresholding and generated threshold is used with Sobel methods for edge detection. Empirical performance study shows the efficacy of automatic threshold method.

One of the most fundamental features of digital image and the basic steps in image processing, analysis, pattern recognition and computer vision is the Edge of image where the preciseness and reliability of its results will affect directly the comprehension machine system made for objective world. Several edge detectors have been developed in the past decades, although no single edge detectors have been developed satisfactorily enough for all application. In this paper, a new edge detection technique is proposed basis on the BP neural network. Here, it is classified the edge patterns of binary images into 16 possible types of visual patterns. In the following, After training the pre-defined edge patterns, the BP neural network is applied to correspond any type of edges with its related visual pattern. The results demonstrated that the new proposed technique provides the better results compared with traditional edge detection techniques while improved the computations complexity.

High speed image processing is becoming increasingly important in medical imaging. Using the state-of-the-art ZYNQ-7000 system on chip (SoC) has made it possible to design powerful vision systems running software on an ARM processor and accelerating it from hardware resources on a single chip. In this paper, we take the advantage of accelerating an embedded system design on a single SoC, which offers the required features for real-time processing of skin cancer images. Different edge detection approaches such as Sobel, Kirsch, Canny and LoG have been implemented on ZYNQ-7000 for border detection of skin lesions, which can be used in early diagnosis of melanoma. The results show that the extended 5 × 5 canny edge detection implemented on the proposed embedded platform has better performance in compare with other reported methods. The performance evaluation of this approach has shown good processing time of 60 fps for real time applications.

In this paper, a fast multi-scale edge detection approach is proposed based on the theories of image diffusion and curve evolution. In comparison with the previous edge detection approaches, edge detection is performed in two steps: beginning by detecting the initial contours at a courser scale that is performed by using a linear diffusion coupling with the denoising effect and gray transformation, then the obtained contour curves are mapped and further refined up to finer scales using the fast Hermes algorithm. By this way, the experimental results show that the proposed edge detection approach are more promising than the existing methods for object detection on general images, especially on medical images.