Sobel Edge Detection Research Articles

A hybrid image processing system for X-ray images of an external fixator

In the field of orthopaedics, treatment of extremity deformities can be realised by means of external fixators. However, control of such biomedical system is very difficult. Some different mathematical models have been developed to improve quality of this service. Most of the parameters, which are used in these models, have been obtained from two orthogonal X-ray images: one from anteroposterior, AP, direction and the other from a lateral, L, direction. The quality of the results of this model is dependent on the accuracy of the input parameters. Measuring these parameters is a time-consuming issue, and the accuracy of the results is also low. To increase the quality of the measurement, the reference points should be chosen from the edges of the biomedical system, and it is important to find the edges without noise. To achieve this purpose, Sobel edge detector, binary large object analysis, thresholding and inverting are applied as image processing steps. The results are compared with manual measurement values which have been obtained earlier. The results show that semi-automatic measurement of the parameters is more accurate and faster than manual measurement. It shows that the efficiency of the fixator method has been improved.

Edge Detection of Inner Crack Defects Based on Improved Sobel Operator and Clustering Algorithm

The classical Sobel edge detection operator has the shortcomings of low edge positioning accuracy and coarse edge, image edge detection based on improved Sobel operator and clustering algorithm was proposed. Four Sobel-like edge operators are used to improve the edge positioning accuracy and clustering algorithm are used to edge thinning. The experimental result demonstrates that the effect of the edge detection is greatly improved comparing with the traditional edge detection methods.

영상에서 웨이블렛 기반 로컬 히스토그램 분석을 이용한 에지검출

Edge detection in images is an important step in image segmentation and object recognition as preprocessing for image processing. This paper presents a new edge detection using local histogram analysis based on wavelet transform. In this work, the wavelet transform uses three components (horizontal, vertical and diagonal) to find the magnitude of the gradient vector, instead of the conventional approach in which tw components are used. We compare the magnitude of the gradient vector with the threshold that is obtained from a local histogram analysis to conclude that an edge is present or not. Some experimental results for our edge detector with a Sobel, Canny, Scale Multiplication, and Mallat edge detectors on sample images are given and the performances of these edge detectors are compared in terms of quantitative and qualitative measures. Our detector performs better than the other wavelet-based detectors such as Scale Multiplication and Mallat detectors. Our edge detector also preserves a good performance even if the Sobel and Canny detector are sharply low when the images are highly corrupted.

The Research of Real-Time Image Acquisition and Sobel Edge Detection with FPGA

As the real-time image acquiring and processing need to be dealt with high speed, a image acquisition and preprocessing system is discussed in this paper. It is built on FPGA( field programmable gate array ) with pipelined and parallel technology. The configurable macro function modules provided by Altera company achieve the Sobel edge detection algorithm. The real-time display the image after edge detection works properly and The new design method shorten the development cycle.

An Edge-Based Macao License Plate Recognition System

This paper presents a system to recognize Macao license plates. Sobel edge detector is employed to extract the vertical edges, and an edge composition algorithm is proposed to combine the edges into candidate plate regions. They are further examined on the existence of the character “M” by a verification algorithm. A row separation algorithm is also proposed to cater both one–row and two–row types of plates. Projection analysis and template matching methods are exploited to segment and recognize the characters. Various pre and post processing steps are proposed other than traditional implementation so as to improve the recognition accuracy. This work achieves a high recognition rate of 95%.

基于FPGA的实时边缘检测系统

This paper presents a real-time edge detection system based on FPGA,using Veilog language to implement the system of data collection,storage and display.Aiming at the problem that the image edge detection affected by noise,this paper proposes a combination of median filtering and the improved Sobel edge detection preprocessing algorithm.Experimental results are compared with the results of the classic Sobel edge detection,which demonstrate the developed system has a strong image noise suppression and precise of edge extraction.It can be used in the fields that required for real-time,such as intelligent surveillance,identification and tracking and so on.

A new approach to simulate characterization of particulate matter employing support vector machines

This paper, for the first time, applies the support vector machines (SVMs) paradigm to identify the optimal segmentation algorithm for physical characterization of particulate matter. Size of the particles is an essential component of physical characterization as larger particles get filtered through nose and throat while smaller particles have detrimental effect on human health. Typical particulate characterization processes involve image reading, preprocessing, segmentation, feature extraction, and representation. Of these various steps, knowledge based selection of optimal image segmentation algorithm (from existing segmentation algorithms) is the key for accurately analyzing the captured images of fine particulate matter. Motivated by the emerging machine-learning concepts, we present a new framework for automating the selection of optimal image segmentation algorithm employing SVMs trained and validated with image feature data. Results show that the SVM method accurately predicts the best segmentation algorithm. As well, an image processing algorithm based on Sobel edge detection is developed and illustrated.

Fast and reliable image-noise estimation using a hybrid approach

Image denoising algorithms often require their parameters to be adjusted according to the noise level. We propose a fast and reliable method for estimating image noise. The input image is assumed to be contaminated by an additive white Gaussian noise process. To exclude structures or details from contributing to the estimation of noise variance, a Sobel edge detection operator with a self-determined threshold is first applied to each image block. Then a filter operation, followed by an averaging of the convolutions over the selected blocks, provides a very accurate estimation of noise variance. We successfully combine the effectiveness of filter-based approaches with the efficiency of block-based approaches, and the simulated results demonstrate that the proposed method performs well for a variety of images over a large range of noise variances. Performance comparisons against other approaches are also provided.

Spermatozoa Segmentation and Morphological Parameter Analysis Based Detection of Teratozoospermia

parameter assessed during the semen analysis is the overall morphology, or shape of the sperm. Currently, the morphological analysis of sperm is done manually and is based on visual observation of at least 200 spermatozoa in a microscope followed by a classification stage based on strict criteria. But this method has led to incorrect results due to various factors such as different staining procedures, experience of technicians and human errors. So this paper focuses on morphological classification of spermatozoon either as normal or abnormal using Matlab. The first stage is the image preprocessing stage which involves the conversion of RGB image to a gray scale image and then image noises are removed using median filter. The second stage is the detection and extraction of individual spermatozoon which involves the extraction of sperm objects from images using sobel edge detection algorithm. The third stage segments the spermatozoon into various region of interest such as sperm head, midpiece and tail. The fourth stage involves the statistical measurement of spermatozoon which classifies Spermatozoa as normal or abnormal.

A study of the feature‐based digital image stabilization system

This article presents a digital image stabilization scheme that uses image processing techniques to compensate for undesirable image jitter due to vehicle or platform vibration to obtain a stabilized image display. The developed digital image stabilization system is based on the image combination approach combined with advanced image selection, feature detection, feature matching and pre‐rotation processing to produce crisp images. Using the Sobel edge detector, we calculate the magnitude of edge response as the judgment criteria for image transformation. To demonstrate the validity of the proposed technique, experimental results are given for some synthetic images.

Delineating low‐count defective‐contour SPECT lung scans for PE diagnosis using adaptive dual exponential thresholding and active contours

AbstractTo develop an automated lung delineation method that greatly improves the accuracy of contour extraction in SPECT lung scans with low maximum count, total counts, and/or defective contour edges, by combining deformable contour and planar image processing techniques with our existing adaptive dual exponential thresholding method. Two datasets consisting of 90 Monte Carlo simulations and 35 SPECT scans of real patients both with low maximum/total counts were used as the basis of this study. After preliminary contour extraction using dual exponential thresholding, a deformable contour algorithm was applied, combined with edge enhancement using image processing techniques such as Gaussian transformation and Sobel edge detection. Both quantitative validation via statistical measures and qualitative verification by an experienced practitioner were implemented to evaluate the method. An average of 83% congruency was achieved for 45 simulations with maximum count values below 31. This is a significant 33% improvement on the results of our previous study using dual exponential thresholding alone. Overall congruence averaged 95% for the entire simulation dataset. With the set of 35 actual SPECT scans with relatively low maximum/total counts, congruency again averaged near 95% with a few outliers ranging between 75 and 90%. We have successfully combined active contour with adaptive dual exponential thresholding to delineate lung contours accurately for SPECT scans with low maximum/total counts and/or defective contours due to various cardiopulmonary disorders. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 149–154, 2010

A NOVEL APPROACH OF EDGE DETECTION VIA A FAST AND ADAPTIVE BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION METHOD

A novel approach of edge detection is proposed that utilizes a bidimensional empirical mode decomposition (BEMD) method as the primary tool. For this purpose, a recently developed fast and adaptive BEMD (FABEMD) is used to decompose the given image into several bidimensional intrinsic mode functions (BIMFs). In FABEMD, order statistics filters (OSFs) are employed to get the upper and lower envelopes in the decomposition process, instead of surface interpolation, which enables fast decomposition and well-characterized BIMFs. Binarization and morphological operations are applied to the first BIMF obtained from FABEMD to achieve the desired edges. The proposed approach is compared with several other edge detection methodologies, which include a combination of classical BEMD and morphological processing, the Canny and Sobel edge detectors, as well as combinations of BEMD/FABEMD and Canny/Sobel edge detectors. Simulation results with real images demonstrate the efficacy and potential of the proposed edge detection algorithm employing FABEMD.

Study on Edge Detection Technique in Material Bag Image

According to the features of material bag image, the paper compares an analyzes the detection effects of different edge detection operators detecting material bag image. A new image segmentation method is proposed to combine Sobel edge detection operator and iterative threshold. The method can extract edge information of material bag image efficiently and provide a theoretical basis for the robot automatic recognition of material bags technique.

A data mining approach to face detection

In this paper, we propose a novel face detection method based on the MAFIA algorithm. Our proposed method consists of two phases, namely, training and detection. In the training phase, we first apply Sobel's edge detection operator, morphological operator, and thresholding to each training image, and transform it into an edge image. Next, we use the MAFIA algorithm to mine the maximal frequent patterns from those edge images and obtain the positive feature pattern. Similarly, we can obtain the negative feature pattern from the complements of edge images. Based on the feature patterns mined, we construct a face detector to prune non-face candidates. In the detection phase, we apply a sliding window to the testing image in different scales. For each sliding window, if the slide window passes the face detector, it is considered as a human face. The proposed method can automatically find the feature patterns that capture most of facial features. By using the feature patterns to construct a face detector, the proposed method is robust to races, illumination, and facial expressions. The experimental results show that the proposed method has outstanding performance in the MIT-CMU dataset and comparable performance in the BioID dataset in terms of false positive and detection rate.

Three Dimensional Digital Image Processing using Edge Detectors

This paper provides an introduction to three dimensional image edge detection and its relationship to partial derivatives, convolutions and wavelets. We are especially addressing the notion of edge detection because it has far reaching applications in all areas of research to include medical research. A patient can be diagnosed as having an aneurysm by studying an angiogram. An angiogram is the visual view of the blood vessels whereby the edges are highlighted through the implementation of edge detectors. This process is completed through convolution, wavelets and matrix techniques. Some illustrations included will be vertical, horizontal, Sobel and wavelet edge detectors.

TU‐FF‐A3‐06: Improving Tracking of Implanted Radio‐Opaque Markers On MV and KV Imaging with Techniques From Computer Vision and Machine Learning

Purpose: Several groups have investigated implanting gold markers into tumors and tracking these markers with kV imaging during a radiotherapy treatment session. Our group has recently published an algorithm robust enough to detect moving markers in implanted tumors on kV and MV images. Our prior work is limited to tumors moving slower than 1.6 cm/s. Here, we expand on our prior work and the work by other groups by using techniques from computer vision and machine learning to improve the performance of the tracking algorithm and remove the need for any data‐set/patient specific parameters or breathing wave form data. Method and Materials: We replace the classification function in our prior algorithm with a support vector machine (SVM) classifier. To drop the need for data‐set specific thresholds, we use techniques from computer vision to examine additional features of the fiducial. Once a location is selected as a possible marker by template matching (correlation) we run a Sobel edge detector to trace the outline of the potential marker. With this outline, we can compute a series of morphological parameters, including the area, major and minor axis, and statistical moments. These additional features provide the SVM classifier with enough information to distinguish true and false markers without the need for data‐set specific thresholds or weights. To test our new algorithm, we image a moving MV phantom with two implanted markers, moving at various speeds. Results: We can now track markers on MV images moving at speeds between 1.6 cm/s to 3.14 cm/s with 95% efficiency. Conclusion: Compared to our prior approach and the approach of other groups, we can now track faster moving fiducials and do not require manual adjustment of threshold parameters.

Surface crack detection by flash thermography on concrete surface

. The follow-up actions and the corresponding severity index on different building defects are listed in Table 1. Therefore, the range of crack width of interest in this study is from 0.15 mm to 1 mm. A cement panel with major cracks with widths of 0.5-1 mm and micro-cracks having widths of 0.1-0.5 mm are inspected and the results are presented. Moreover, this paper provides a comparison of the effectiveness of two traditional crack detection techniques, Sobel and Canny, and one proposed method, the sheared image subtraction method, and the results are also presented. Cracking may impair the durability of concrete by allowing immigration of external aggressive agents; therefore, crack monitoring is always a vital part in building pathology. This study proposes to apply short-duration pulsed thermography - flash thermography (FT) - for surface crack detection. This method allows full-field and non-contact qualitative observation of thermal radiation from an object surface and is highly accepted in the aerospace industry. It is superior to the common practice of surface crack detection - visual inspection. The overall inspection time is reduced and hence maintenance costs lowered. During inspection, the inspected surface is excited with a heat-pulse of short duration (~3 ms). Surface cracking is detected based on the difference in heat emission between cracks and intact region. The results show that FT can detect surface cracks with 0.5 mm to 1 mm crack width successfully but micro-cracks (0.1 mm-0.5 mm) can only be detected by adding water with FT. In addition, this study also compared the performances of traditional Sobel and Canny edge detectors and a proposed shear image subtraction method, for crack detection. The results show that the sheared image subtraction method is significantly more effective than the other two edge detection techniques in identifying cracks.

Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition

Crack evaluation is essential for effective classification of pavement cracks. Digital images of pavement cracks have been analyzed using techniques such as fuzzy set theory and neural networks. Bidimensional empirical mode decomposition (BEMD), a new image analysis method recently developed, can potentially be used for pavement crack evaluation. BEMD is an extension of the empirical mode decomposition (EMD), which can decompose nonlinear and nonstationary signals into basis functions called intrinsic mode functions (IMFs). IMFs are monocomponent functions that have well-defined instantaneous frequencies. EMD is a sifting process that is nonparametric and data driven; it does not depend on an a priori basis set. It is able to remove noise from signals without complicated convolution processes. BEMD decomposes an image into two-dimensional IMFs. The present paper explores pavement crack detection using BEMD together with the Sobel edge detector. A number of images are filtered with BEMD to remove noise, and the residual image analyzed with the Sobel edge detector for crack detection. The results are compared with results from the Canny edge detector, which uses a Gaussian filter for image smoothing before performing edge detection. The objective is to qualitatively explore how well BEMD is able to smooth an image for more effective edge detection with the Sobel method.

Systematic and Automated Multiprocessor System Design, Programming, and Implementation

For modern embedded systems in the realm of high-throughput multimedia, imaging, and signal processing, the complexity of embedded applications has reached a point where the performance requirements of these applications can no longer be supported by embedded system architectures based on a single processor. Thus, the emerging embedded system-on-chip platforms are increasingly becoming multiprocessor architectures. As a consequence, two major problems emerge, namely how to design and how to program such multiprocessor platforms in a systematic and automated way in order to reduce the design time and to satisfy the performance needs of applications executed on such platforms. As an efficient solution to these two problems, in this paper, we present the methodology and techniques implemented in a tool called Embedded System-level Platform synthesis and Application Mapping (ESPAM) for automated multiprocessor system design, programming, and implementation. ESPAM moves the design specification and programming from the Register Transfer Level and low-level C to a higher system level of abstraction. We explain how, starting from system-level platform, application, and mapping specifications, a multiprocessor platform is synthesized, programmed, and implemented in a systematic and automated way. The class of multiprocessor platforms we consider is introduced as well. To validate and evaluate our methodology, we used ESPAM to automatically generate and program several multiprocessor systems that execute three image processing applications, namely Sobel edge detection, Discrete Wavelet Transform, and Motion JPEG encoder. The performance of the systems that execute these applications is also presented in this paper.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein.