Canny Edge Detector Research Papers (original) (raw)

Edge Detection is an important step in any image processing algorithm, and the quality of edges detected determines the performance of the successive steps performed. The objective of this rep ort is to analyze and design algorithms that when used in conjunction to the Canny Edge Detector, gives better edge detection performance and minimizes influence of external noise which leads to detection of false edges. All the algorithms have been designed using OpenCV libraries. We analyze two areas in Edge detection 1. Applying Canny at a smaller scale and 2. Post processing the results from the Canny algorithm and filtering unwanted edges. The results improve the output of Canny, though unwanted edges are still detected and shown in the final results. Hence the algorithms hold potential but are far from giving any significant improvement in their current state and future work will be done to explore further improvements.

- by
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- Computer Vision, Image Processing, Edge Detection, Canny edge detection

Currency duplication also known as counterfeit currency is a vulnerable threat on economy. It is now a common phenomenon due to advanced printing and scanning technology. Bangladesh has been facing serious problem by the increasing rate of fake notes in the market. To get rid of this problem various fake note detection methods are available around the world and most of these are hardware based and costly. In the present paper an automated image-based technique is described for the detection of fake banknotes of Bangladesh. Security features of banknotes such as watermark, micro-printing and hologram etc. are extracted from the banknote images and then detection is performed using Support Vector Machine (SVM). Experimental results confirm the effectiveness of the proposed algorithm.

- by Pronaya P Das and +1
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- Support Vector Machines, Image Registration, Digital Image Processing, Histogram of Oriented Gradient

Image based video generation paradigms have recently emerged as an interesting problem in the field of
robotics. This paper focuses on the problem of automatic video generation of both indoor and outdoor
scenes. Automatic 3D view generation of indoor scenes mainly consist of orthogonal planes and outdoor
scenes consist of vanishing point. The algorithm infers frontier information directly from the images using
a geometric context-based segmentation scheme that uses the natural scene structure. The presence of
floor is a major cue for obtaining the termination point for the video generation of the indoor scenes and
vanishing point plays an important role in case of outdoor scenes. In both the cases, we create the
navigation by cropping the image to the desired size upto the termination point. Our approach is fully
automatic, since it needs no human intervention and finds applications, mainly in assisting autonomous
cars, virtual walk through ancient time images, in architectural sites and in forensics. Qualitative and
quantitative experiments on nearly 250 images in different scenarios show that the proposed algorithms
are more efficient and accurate.

- by International Journal on Computational Science & Applications (IJCSA) and +1
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- Hough Transform, Vanishing point, Canny Edge Detector

In a 3D seismic survey, detecting seismic discontinuities is vital to robust structural and stratigraphic analysis in the subsurface. Previous methods have difficulty highlighting subtle discontinuities from seismic data in cases where the local amplitude variation is of non-zero mean. This study proposes implementing a gray-level transformation and the Canny edge detector for improved imaging of discontinuities. Specifically, the new process transforms seismic signals to be of zero mean and helps amplify subtle discontinuities, leading to an enhanced visualization for structural and stratigraphic details. Applications to various 3D seismic datasets demonstrate that the new algorithm helps better define channels, faults, and fractures than the traditional similarity, amplitude gradient, and semblance attributes.

- by Dengliang Gao
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- Engineering, Earth Sciences, Fracture, Seismic

Edge detection is a terminology in image processing and computer vision particularly in the areas of feature detection and extraction to refer to the algorithms which aims at identifying points in a digital image at which the image... more

- by beant eè to
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- Set Theory, Computer Science, Computer Vision, Image Processing

Edge detection is a significant stage in different image processing operations like pattern recognition, feature extraction, and computer vision. Although the Canny edge detection algorithm exhibits high precision is computationally more complex contrasted to other edge detection methods. Due to the traditional Canny algorithm uses the Gaussian filter, which gives the edge detail represents blurry also its effect in filtering salt-and-pepper noise is not good. In order to resolve this problem, we utilized the median filter to maintain the details of the image and eliminate the noise. This paper presents implementing and enhance the accuracy of Canny edge detection for noisy images. Results present that this proposed method can definitely overcome noise disorders, preserve the edge useful data, and likewise enhance the edge detection precision.

- by Eduard Babulak
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- Computer Science, Artificial Intelligence, Image Processing, Edge Detection

Texts that appear in the image contain useful and important information. Optical Character Recognition technology is restricted to finding text printed against clean backgrounds, and cannot handle text printed against shaded or textured backgrounds or embedded in images. It is necessary to extract the text form image which is helpful in a society for a blind and visually impaired person when voice synthesizer is attached with the system. In this paper, we present a methodology for extracting text from printed image document and then identified Devanagari Script (Hindi language) from extracted text. Firstly we used Morphological Approach for extracting the text from image documents. The resultant text image is passed to Optical Character Recognition for Identification purpose. Projection profile is used for segmentation followed by Visual Discriminating approach for feature extraction. Finally for classification purpose Heuristic search is used. The result of proposed method for text extraction is compared with edge based and connected component with projection profile approach. After comparison using precision and recall rate it is observed that proposed algorithm work well.

- by IAEME Publication
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- Heuristic Search, Surface Area, Projection Profile, Canny Edge Detector

- by Haibin Di
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- Engineering, Earth Sciences, Fracture, Seismic

There are a large number of application fields where measurements deriving from digital images can assume a great relevance. Nevertheless, to make a profitable use of such measurements, it is indispensable to achieve a complete and quantitative control on uncertainties that real systems introduce along the chain of steps going from real-world objects to the results of the measurement process. This paper deals with this nontrivial task and, in particular, with the analytical expression of uncertainty characterizing the results of image processing software. At first, a simplified model of uncertainty of digital images is derived and experimentally tested; then the Canny edge detector output uncertainty is analytically expressed and verified both in artificial and real-world images.

- by Massimo De Santo
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- Image Processing, Image Analysis, Control Systems, Software Measurement

- by Chintan Modi
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- Computer Science, Machine Vision, Edge Detection, Chip

This paper introduces a new approach in image registration, that is a multisensor registration in Hough parameter space. Visual and thermal images of building fronts were aimed to be aligned in order to inspect thermal properties of buildings. Some preprocessing of visible images was necessary to be comparable to their thermal counterparts, namely downsampling and color space conversion from RGB to grayscale intensity. For each image pair, edges were detected with Canny edge detector and, as a result, binary edge images were obtained. These images were further processed by Hough transform which extracted all linear image segments. We decided for linear segments, because they are the most frequent feature appearing in the images of buildings. In the Hough parameter space the rotation and translation of the linear segments can be recovered using the line correspondence analysis. The method was verified first on synthetic images with only translation, only rotation, and also both the rotation and translation together. Finally, a verification on real images was done. The method was able to correctly register both type of images, synthetic and the real ones. In general, our algorithm can cope with rotated and translated images if only a few linear segments are detectible.

- by Damjan Zazula
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- Computer Vision, Image segmentation, Correspondence Analysis, Image Registration

Abstract—This paper discusses an approach for detecting edges in color
images. A color image is represented by a vector field, and the color
image edges are detected as differences in the local vector statistics. These statistical differences can include local variations in color or spatial image properties. The proposed approach can easily accommodate concepts, such as multiscale edge detection, as well as the latest developments in vector order statistics for color image processing. A distinction between the proposed approach and previous approaches for color edge detection using vector order statistics is that, besides the edge magnitude, the local edge direction is also provided. Note that edge direction information is a relevant feature to a variety of image analysis tasks (e.g., texture analysis) .

- by Jacob Scharcanski
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- Color Image Processing, Edge Detection, Canny Edge Detector, color gradient

Edge detection serves as a pre-processing step for many image processing algorithms such as image enhancement, image segmentation, tracking and image/video coding. The edge detection is one of the key stages in image processing and object recognition. This paper present a Canny edge detection algorithm that results in significantly reduced memory requirements, decreased latency and increased throughput with no loss in edge detection performance. This edge detection algorithm is based on MATLAB simulation and FPGA implementation.

- by PONNEELA VIGNESH
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- Computer Science, Canny Edge Detector

Nvidia's GPGPU based Compute Unified Device Architecture (CUDA) is a software platform for massively parallel high-performance computing on GPU. It provide several key abstractions-a hierarchy of thread block, shared memory, and barrier synchronization. This model has proven quite successful at programming multithreaded many core GPUs and scale transparently to hundreds of cores: many industry and academia are already using CUDA to achieve speedups on production and research codes. This paper analyze distinct feature of CUDA GPU, summarizes general programming mode of CUDA. This paper presents image processing algorithm i.e. Canny Edge detector which is used as pre-processing steps in many computer vision application as an optimal edge detection algorithm. Detailed comparison of parallel and sequential algorithm implementations is also presented.

- by Dr. D.B.Kulkarni
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- Parallel Algorithms, Parallel Computing, Computer Vision, Image Processing

This paper describes a novel method for image corner detection based on the curvature scale-space (CSS) representation. The first step is to extract edges from the original image using a Canny detector. The corner points of an image are defined as points where image edges have their maxima of absolute curvature. The corner points are detected at a high scale of the CSS and tracked through multiple lower scales to improve localization. This method is very robust to noise, and we believe that it performs better than the existing corner detectors. An improvement to Canny edge detector's response to 45 o and 135 o edges is also proposed. Furthermore, the CSS detector can provide additional point features (curvature zerocrossings of image edge contours) in addition to the traditional corners.

- by Mirek Bober
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- Information Systems, Mathematics, Computer Science, Computer Vision

An improved version for the JSEG color image segmentation algorithm has been presented in this paper integrating the presence of edge in canny edge detector. One major inherent problem in JSEG is that it suffers from over segmentation which ultimately reduces the segmentation quality. The focus is given to reduce the over segmentation problem in JSEG by examining the presence of canny edge between every two adjacent segment created in JSEG which matches human perception. Experiments on natural images show improved result.

- by monirul islam
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- Computer Science, Image segmentation, Edge Detection, Human Perception

An artificial vision tries to capture relevant information from environment using cameras as sensors of certain characteristics (shapes, colors, textures, etc.) for a proper functioning of some mechanisms. In order to get an identical image to real environment, it is required to generate stereoscopic images that allow us to get the depth and thus a 3D representation. This paper shows an artificial stereoscopic vision system incorporated to a mobile robot to recognize and follow the center of a path. Such a system handles the capture, processing and characterization of images using offline procedures such to standardize cameras and online methods for a disparity mapping generation, application of the Canny edge detector and the Hough transform, using the OpenCV libraries in Win32 Console project in C++. It`s important to mention that algorithms developed in this work are fundamentally for structured environments. Environments can classified as follows: structured, non-structured and semi-structured.

- by Leonardo Enrique Solaque Guzmán
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- Computer Science, Art, Stereovision, Hough Transform

Quality inspection of surface mount capacitor is an offline process and usually done by inspecting some capacitors in a lot using compound microscopes. We propose to use location M estimator with any edge detection methods to inspect the basic dimensions of multi-layer ceramic chip capacitors (MLCC) like width, length, separation distance between two end terminations and the local deviations on the termination boundaries. Usually the distances are calculated by an average distance. The average operator is not robust to outliers in the data. In this paper, we propose to use the combination of location M estimator with any type of edge detection technique which will remove the need of a specific optimal edge detection technique and thus can result into easy hardware realization to inspect the basic dimensions of MLCC.

- by Chintan Modi
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- Computer Science, Machine Vision, Edge Detection, Chip

Tool wear monitoring can be achieved by analyzing the texture of machined surfaces. In this paper, we present the connectivity oriented fast Hough transform, which easily detects all line segments in binary edge images of textures of machined surfaces. The features extracted from line segments are found to be highly correlated to the level of tool wear. A multilayer perceptron neural network is applied to estimate the ank wear in various machining processes. Our experiments show that this Hough transform based approach is e ective in analyzing the quality of machined surfaces and could be used to monitor tool wear. A performance analysis of our Hough transform is also provided.

- by ashraf kassim
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- Pattern Recognition, Neural Network, Performance Analysis, Tool wear

- by Consolatina LIGUORI
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- Image Processing, Image Analysis, Control Systems, Software Measurement

The present manuscript aims at solving four problems of edge detection: the simultaneous detection of all step edges from a fine to a coarse scale; the detection of thin bars with a width of very few pixels; the detection of trihedral junctions; the development of an algorithm with image-independent parameters. The proposed solution of these problems combines an extensive spatial filtering with classical methods of computer vision and newly developed algorithms.

- by Walter Vanzella
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- Algorithms, Computer Graphics, Artificial Intelligence, Computer Vision

In this paper, a Canny edge-based image expansion method is introduced. Our proposed expansion method outperforms the pixel replication, the bilinear interpolation and the bicu- bic interpolation methods. It gives crisp and less zigzag pic- tures. Our method is applied on the image after it has been expanded using bilinear or bicubic interpolation. The edges of such an expanded image

- by Rabab Ward
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- Performance, Edge Detection, Noise reduction, Detectors

In this paper, we exploit the powerful means of neural networks with respect to function approximation. Once they are implemented on-chip, they can be reconfigured for adjusting their input-output relation in order to achieve clustering for decision making but also various image processing tasks such as filtering, edge detection, etc. As an illustration example, we propose here a neural network-based edge detection system. Edge detection reduces significantly the amount of data and filters out information that may be regarded as less irrelevant. It is becoming an important step in segmentation for many image processing applications. The proposed network achieves Canny operator edge detection based on pulse mode operations. Indeed, pulse mode neural networks are becoming an attractive solution in neural network implementation because of the advantages they provide over the continuous mode such as compactness of the pulse multiplier and flexibility of most of the blocs. Such simplicity offers the possibility of on-chip learning. In this work, the proposed edge detection network uses new extended range synapse multipliers operating in a fixed point format with a very simple architecture and adjustable activation functions. To provide the best edge detection, the back-propagation algorithm is modified to have pulse mode operations. Simulation results show the efficient learning and good generalization results. The corresponding design was implemented on a virtex II PRO FPGA platform. Synthesis results prove that the implemented neural network is more compact in terms of size than conventional implementations of a Canny edge detector.

- by Krid Mohamed and +1
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- Image Processing, Decision Making, Neural Network, FPGA implementation