Showing papers on "Canny edge detector published in 2016"

Canny Text Detector: Fast and Robust Scene Text Localization Algorithm

Abstract: This paper presents a novel scene text detection algorithm, Canny Text Detector, which takes advantage of the similarity between image edge and text for effective text localization with improved recall rate. As closely related edge pixels construct the structural information of an object, we observe that cohesive characters compose a meaningful word/sentence sharing similar properties such as spatial location, size, color, and stroke width regardless of language. However, prevalent scene text detection approaches have not fully utilized such similarity, but mostly rely on the characters classified with high confidence, leading to low recall rate. By exploiting the similarity, our approach can quickly and robustly localize a variety of texts. Inspired by the original Canny edge detector, our algorithm makes use of double threshold and hysteresis tracking to detect texts of low confidence. Experimental results on public datasets demonstrate that our algorithm outperforms the state-of the-art scene text detection methods in terms of detection rate.

Road Extraction From Very High Resolution Remote Sensing Optical Images Based on Texture Analysis and Beamlet Transform

Abstract: Road extraction from very high resolution sensors is a very popular topic in panchromatic and multispectral remote sensing image analysis. Despite the vast number of methods proposed in the literature to deal with this problem, in practice, most are quite limited and do not account for geometric and radiometric variability. Our aim is to propose a novel road extraction approach able to efficiently extract roads and reduce computation time using texture analysis and multiscale reasoning based on the beamlet transform. The proposed methodology consists of two stages: 1) road edge candidate selection and 2) multiscale reasoning with the beamlet transform. In the first step, mathematical morphology is applied to distinguish rectilinear structures, and road edge candidates are identified using the Canny edge detector. In the second phase, multiscale reasoning using the beamlet transform allows local and global information to be combined. Global information is introduced to distinguish main road axes at coarser scales, and local segments in finer scales, which are aggregated to reconstruct the road network. Rules based on the spatial relationships between segments belonging to different levels of resolution are also introduced at this stage. The experiments are performed based on the images acquired from the city of Port-au-Prince in Haiti during the earthquake of January 2010. The results demonstrate the accuracy and efficiency of our algorithm.

Fractal Dimension Estimation for Developing Pathological Brain Detection System Based on Minkowski-Bouligand Method

Abstract: It is of enormous significance to detect abnormal brains automatically. This paper develops an efficient pathological brain detection system based on the artificial intelligence method. We first extract brain edges by a Canny edge detector. Next, we estimated the fractal dimension using box counting method with grid sizes of 1, 2, 4, 8, and 16, respectively. Afterward, we employed the single-hidden layer feedforward neural network. Finally, we proposed an improved particle swarm optimization based on three-segment particle representation, time-varying acceleration coefficient, and chaos theory. This three-segment particle representation encodes the weights, biases, and number of hidden neuron. The statistical analysis showed the proposed method achieves the detection accuracies of 100%, 98.19%, and 98.08% over three benchmark data sets. Our method costs merely 0.1984 s to predict one image. Our performance is superior to the 11 state-of-the-art approaches.

Edge detection in medical ultrasound images using adjusted Canny edge detection algorithm

Abstract: Ultrasound medical images are very important component of the diagnostics process. They are widely used since ultrasound is a non-invasive and non-ionizing diagnostics method. As a part of image analysis, edge detection is often used for further segmentation or more precise measurements of elements in the picture. Edges represent high frequency components of an image. Unfortunately, ultrasound images are subject to degradations, especially speckle noise which is also a high frequency component. That poses a problem for edge detection algorithms since filters for noise removal also degrade edges. Canny operator is widely used as an excellent edge detector, however it also includes Gaussian smoothing element that may significantly soften edges. In this paper we propose a modified Canny algorithm where Gaussian smoothing is replaced by modified median filter that successfully removes speckle noise with little degradation of edges followed by weak weighted smoothing filter that in a controlled way removes other noise, again with insignificant damage to the edges. Our proposed algorithm was tested on standard benchmark image and compared to other approaches from literature where it proved to be successful in precisely determining edges of internal organs.

Edge detection of license plate using Sobel operator

Abstract: Edge detection is one of the important operations in image processing and computer vision. It is the process that is used to locate the boundaries of objects or textures depicted in an image. To know the positions of these boundaries is a critical task in the process of image enhancement, recognition, restoration and compression. The edges of image are considered to be the most important attributes of image that provide valuable information for human image perception. As the data of edge detection is very large, therefore the speed of image processing becomes a difficult problem. The sobel operator is used for edge detection. In the edge function, the Sobel method uses the derivative approximation to find edges of the image. So, it returns edges at those points where the gradient of the considered image is maximum. The horizontal and vertical gradient matrices are used for the Sobel method whose dimensions are 3×3 in the edge detection operations. It has been proven by the results we have obtained, that the edge detection mathematical method by simulation using MATLAB software is very good method for the analyzing the image. After reading the pixels of an image the algorithm is applied in VERILOG. The entire simulation of the above process is done VERILOG using “XILINX-14.1”. And to display input and output image MATLAB is used. This paper focuses on software used to detect edges of image employing mainly the MATLAB program for solving this problem.

An image matching system for autonomous UAV navigation based on neural network

Abstract: This paper proposes an image matching system using aerial images, captured in flight time, and aerial geo-referenced images to estimate the Unmanned Aerial Vehicle (UAV) position in a situation of Global Navigation Satellite System (GNSS) failure. The image matching system is based on edge detection in the aerial and geo-referenced image and posterior automatic image registration of these edge-images (position estimation of UAV). The edge detection process is performed by an Artificial Neural Network (ANN), with an optimal architecture. A comparison with Sobel and Canny edge extraction filters is also provided. The automatic image registration is obtained by a cross-correlation process. The ANN optimal architecture is set by the Multiple Particle Collision Algorithm (MPCA). The image matching system was implemented in a low cost/consumption portable computer. The image matching system has been tested on real flight-test data and encouraging results have been obtained. Results using real flight-test data will be presented.

Shearlet-based detection of flame fronts

Abstract: Identifying and characterizing flame fronts is the most common task in the computer-assisted analysis of data obtained from imaging techniques such as planar laser-induced fluorescence (PLIF), laser Rayleigh scattering (LRS), or particle imaging velocimetry (PIV). We present Complex Shearlet-Based Ridge and Edge Measure (CoShREM), a novel edge and ridge (line) detection algorithm based on complex-valued wavelet-like analyzing functions—so-called complex shearlets—displaying several traits useful for the extraction of flame fronts. In addition to providing a unified approach to the detection of edges and ridges, our method inherently yields estimates of local tangent orientations and local curvatures. To examine the applicability for high-frequency recordings of combustion processes, the algorithm is applied to mock images distorted with varying degrees of noise and real-world PLIF images of both OH and CH radicals. Furthermore, we compare the performance of the newly proposed complex shearlet-based measure to well-established edge and ridge detection techniques such as the Canny edge detector, another shearlet-based edge detector, and the phase congruency measure.

Medical image fusion by combining parallel features on multi-scale local extrema scheme

Abstract: Applying color saliency feature algorithm on PET image to get functional information.Applying canny operator on MRI and CT image to get the anatomical structural information.Entropy of image is selected as weight for fusing smoothed images at different scales.Variance of luminance image is selected as weight for fusing detailed images at different scales. Two efficient image fusion algorithms are proposed for constructing a fused image through combining parallel features on multi-scale local extrema scheme. Firstly, the source image is decomposed into a series of smoothed and detailed images at different scales by local extrema scheme. Secondly, the parallel features of edge and color are extracted to get the saliency maps. The edge saliency weighted map aims to preserve the structural information using Canny edge detection operator; Meanwhile, the color saliency weighted map works for extracting the color and luminance information by context-aware operator. Thirdly, the average and weighted average schemes are used as the fusion rules for grouping the coefficients of weighted maps obtained from smoothed and detailed images. Finally, the fused image is reconstructed by the fused smoothed and the fused detailed images. Experimental results demonstrate that the proposed algorithms show the best performances among the other fusion methods in the domain of MRI-CT and MRI-PET fusion.

Comparison of Various Edge Detection Technique

Abstract: Edge is the basic feature of image. Edges form the outline of an object. The need of edge detection is to find the discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. So edge detection is one of the most commonly used operations in image analysis and there are probably more algorithm for detecting edges. In this paper various edge detectors like Canny, Sobel, Roberts and Prewitt are compared. These operators are more susceptible to noise and do not give satisfactory result for face outline. For overcoming this disadvantage morphological method is studied and the result of edge detection using morphological method is compared with Canny edge detector, Sobel edge detector, Roberts edge detector and Prewitt edge detector.Wood and Glass Images are taken up as a special conditions for wider number of applications.

Nighttime lane markings recognition based on Canny detection and Hough transform

Abstract: It is difficult to detect the nighttime lane lines which showed darker and uneven lighted. In order to overcome these problems, a lane recognition method was proposed. Firstly, edge enhancement based on Laplacian was used to enhance the pre-processing image's edges. Then, the edges were detected by Canny based on Otsu algorithm and the straight lines which within the one third at bottom of image were detected by Hough transform. Finally, an inside lane line extraction algorithm was proposed on the basis of slope constraint. Thereby, the aim of marking the inside lane was realized. By experimenting with various lane markers, the proposed algorithm can realize the detection. Moreover, the approach can overcome the influence of uneven light and be able to eliminate the interference from the side lane line, guard rail, etc. Lane line detection is conducive to the vehicle running on its road.

License Plate Automatic Recognition based on edge detection

Abstract: In this paper, we present an Automatic License Plate Recognition System (ALPRS) to identify license plates which is an application of image processing. The main process of ALPRS is divided into four steps: The noise in the image is removed by using FMH filter. A simple algorithm is used for background subtraction. Canny edge detection is used to localize the license plate location. Finally, letters and digits are extracted through template matching technique. The proposed algorithms have two advantages: First, the method has strong robustness against noise. Second, it can deal with license plates with different colors. The performance of the algorithm is tested in a real-time video stream. Based on the result, our algorithm shows the missing rate is almost 16% from 70 vehicle images.

A Hybrid Approach Using Sobel and Canny Operator for Digital Image Edge Detection

Abstract: Edge Detection is a very essential part in digital image processing. In case of applications which require object information or feature extraction in an image, edge detection can play a vital role. Edge occurs where there is discontinuity or a sharp change in the intensity function. Now there are many operators for edge detection, but the challenge is to get better results from the existing system. This paper represents a hybrid approach which combines the Sobel and Canny edge detectors. It also uses a median filter to remove the salt and pepper noise from the image which consequently smoothen the image and edges can be detected easily. A comparison has also been shown between the hybrid approach with median filter and without median filter. The comparative study shows that using median filter shows enhanced result. As it filters the noise from the image, the accuracy of edge detection improved and achieved ideal effect.

Iris recognition using SVM and ANN

Abstract: In this paper we proposed an improved novel approach to identify the person using iris recognition technique. This approach is based on Artificial Neural Network and Support Vector Machine (SVM) as an iris pattern classifier. Prior to classifier, region of interest i.e. iris region is segmented using Canny edge detector and Hough transform. Provided that the effect of eyelid and eyelashes get reduced. Daugman's rubber sheet model used to get normalized iris to improve computational efficiency and proper dimensionality. Further, discriminating feature sequence is obtained by feature extraction from segmented iris image using 1D Log Gabor wavelet. Encoding is done using phase quantization to get feature vectors. These binary sequence feature vectors are used to train SVM and ANN as iris pattern classifier. The experimental tests are performed over standard CASIAIrisV4 database.

Remote sensing image edge-detection based on improved Canny operator

Abstract: The edge details of the remote sensing image is key for the target detection with complex background. Traditional Canny detection operator requires human intervention, does not have the adaptive ability in the variance of Gaussian filtering and the threshold. Compound morphological smoothing replaces Gaussian filtering, can not only maintain the edge information, but also reduce noise impact. Then Otsu method can specify the threshold adaptively, the edge detected is more continuous, and can decrease the false edges. In order to extract the edges of the remote sensing image proposed an approach based on Canny edge detection operator. Finally, we refine the edge by using morphological structure element. The experimental results show that the improved Canny algorithm has a good anti-noise function and precision on image processing of remote sensing.

An edge-detection method based on adaptive canny algorithm and iterative segmentation threshold

Abstract: In the technology of crack detection, the traditional Canny algorithm uses fixed spatial scale coefficient of Gauss filter and empirical values of the high and low thresholds, and it has defective in self-adaptability because it is unable to correct parameters according to the actual image. This paper proposes a method based on adaptive Canny algorithm and iterative threshold segmentation algorithm to detect surface crack. Firstly, in the process of image smoothing, the adaptive Canny algorithm automatically calculates difference between the gray value of present pixel and the average gray value of image in the filter window, and the difference is set as the spatial scale coefficient for Gauss filter of the current pixel. Secondly, the Otsu method is applied to calculate image gradient histogram so as to get high and low thresholds and those new values are applied to detect the edge of the crack. Finally, based on the iterative threshold segmentation algorithm, the image with crack is binarized, and the breakpoints in the crack and the cavities inside the crack are eliminated by morphological dilation. Experimental results show that the new crack detection method can well retain crack edge and get better effect on noise cancellation. It can also reduce the false and missed detections. Better results can be achieved on crack detection.

Using Convolutional Neural Network for edge detection in musculoskeletal ultrasound images

Abstract: Fast and accurate segmentation of musculoskeletal ultrasound images is an on-going challenge. Two principal factors make this task difficult: firstly, the presence of speckle noise arising from the interference that accompanies all coherent imaging approaches; secondly, the sometimes subtle interaction between musculoskeletal components that leads to non-uniformity of the image intensity. Our work presents an investigation of the potential of Convolutional Neural Networks (CNNs) to address both of these problems. CNNs are an effective tool that has previously been used in image processing of several biomedical imaging modalities. However, there is little published material addressing the processing of musculoskeletal ultrasound images. In our work we explore the effectiveness of CNNs when trained to act as a pre-segmentation pixel classifier that determines whether a pixel is an edge or non-edge pixel. Our CNNs are trained using two different ground truth interpretations. The first one uses an automatic Canny edge detector to provide the ground truth image; the second ground truth was obtained using the same image marked-up by an expert anatomist. In this initial study the CNNs have been trained using half of the prepared data from one image, using the other half for testing; validation was also carried out using three unseen ultrasound images. CNN performance was assessed using Mathew's Correlation Coefficient, Sensitivity, Specificity and Accuracy. The results show that CNN performance when using expert ground truth image is better than using Canny ground truth image. Our technique is promising and has the potential to speed-up the image processing pipeline using appropriately trained CNNs.

Assessment of noise reduction in ultrasound images of common carotid and brachial arteries

Abstract: The present study assessed the use of filters for noise reduction in ultrasound images of the common carotid artery (CCA) and brachial artery using intima–media thickness, which is a safe and non-invasive technique for determining subclinical atherosclerosis and cardiovascular risk. A new combined speckle reducing anisotropic diffusion (SRAD) filter for noise reduction is then proposed. Ultrasonic examination of both arteries was performed on 30 men (aged 40 ± 5 years). The programme was designed using MATLAB software to extract consecutive images in bit map format from the audio video interleaves. An additional programme was designed in MATLAB to apply the region of interest (ROI) to the thickness of the intima–media of the posterior walls of the arteries. Block-matching techniques were used to estimate arterial motion from ultrasound images of the B-mode CCA and brachial artery. Different noise reduction filters and Canny edge detection were carried out separately in the ROI. The programme measured mean square error (MSE) and peak signal-to-noise ratio (PSNR). The results demonstrated that the new combined SRAD filter with Canny edge detection identified the lowest value for MSE and the highest value for PSNR in 90 consecutive frames (∼3 cardiac cycles). The results indicate that MSE and PSNR were better detected by the proposed combined SRAD filter with Canny edge detection than did several commonly used filters with Canny detection for speckle suppression and preservation detail in carotid and brachial arteries ultrasound images.

Automatic breast segmentation and cancer detection via SVM in mammograms

Abstract: Automatic detection of breast cancer in mammograms is a challenging task in Computer Aided Diagnosis (CAD) techniques. This paper presents a simple methodology for breast cancer detection in digital mammograms. Proposed methodology consists of three major steps, i.e. segmentation of breast region, removal of pectoral muscle and classification of breast muscle into normal and abnormal tissues. Segmentation of breast muscle was performed by employing Otsus segmentation technique, afterwards removal of pectoral muscle is carried out by canny edge detection and straight line approximation technique. In next step, Gray Level Co-occurrence Matrices (GLCM) was created form which several features were extracted. At the end, SVM classifier was trained to classify breast region into normal and abnormal tissues. Proposed methodology was validated on Mini-MIAS database and results were compared with previously proposed techniques, which shows that proposed technique can be reliably apply for breast cancer detection.

Active Canny: Edge Detection and Recovery with Open Active Contour Models

Abstract: We introduce an edge detection and recovery framework based on open active contour models (snakelets). This is motivated by the noisy or broken edges output by standard edge detection algorithms, like Canny. The idea is to utilize the local continuity and smoothness cues provided by strong edges and grow them to recover the missing edges. This way, the strong edges are used to recover weak or missing edges by considering the local edge structures, instead of blindly linking them if gradient magnitudes are above some threshold. We initialize short snakelets on the gradient magnitudes or binary edges automatically and then deform and grow them under the influence of gradient vector flow. The output snakelets are able to recover most of the breaks or weak edges, and they provide a smooth edge representation of the image; they can also be used for higher level analysis, like contour segmentation.

Artificial Vision Techniques to Optimize Strawberry's Industrial Classification

Abstract: This research presents novel artificial vision techniques applied to the detection of features for strawberries used in the food industry. For this purpose, a computer vision system based in artificial neural networks is used, organized as a deep architecture and trained with noise compensated learning. This combination originates a strong network - object relations which makes possible the recognition of complex strawberry features under changing conditions of lightning, size and orientation. The programming uses OpenCV libraries and fruits databases captured with a webcam. The images used to train the Artificial Neural Network are defined with canny edge detection and a moving region of interest (ROI). After training, the network recognizes important features such as shape, color and anomalies. The system has been tested in real time with real images.

Multiresolution text detection in video frames

Abstract: This paper proposes an algorithm for detecting artificial text in video frames using edge information. First, an edge map is created using the Canny edge detector. Then, morphological dilation and opening are used in order to connect the vertical edges and eliminate false alarms. Bounding boxes are determined for every non-zero valued connected component, consisting the initial candidate text areas. Finally, an edge projection analysis is applied, refining the result and splitting text areas in text lines. The whole algorithm is applied in different resolutions to ensure text detection with size variability. Experimental results prove that the method is highly effective and efficient for artificial text detection.

Study of edge detection methods based on palmprint lines

Abstract: The palm was used in fortune telling 3000 years ago. Thus, During this period, many different problems related to palmprint recognition have been addressed. In the recent years, the palm print has been used for biometric applications as human verification and identification. The palm print has many features comparing with a fingerprint, The palm print has number of lines. One group of these lines is known as the principle lines which contains three lines(head line, heart line and life line). The lines are extracted from palm print image by edge detection algorithm which is implementing on ROI of palm print. The main goal of edge detection algorithm is to produce a line and extract important features and reduce the amount of data in the image. This paper investigates the several edge detection methods such as Sobel, Prewitt, Roberts, LOG, and Canny. In addition, we used edge detection using local entropy information and local variance. The experiment is tested on samples taken from four palm print databases (CASIA, PolyU, IIT and database available online). The analysis work has been performed by using PSNR and MSE of resultant images on these popular edge detection methods which improve the palm print matching process. The Prewitt, Roberts and LOG edge detection methods ignore the small lines and identify only the main longer lines while the Sobel identifies the medium and longer lines. The canny edge detection algorithm identifies the complete set of edges of various sizes. From experiment it was seen that good result found with an online database and polyU database by classical edge detection methods.