Image Processing on edge Detection using canny and sobel operators Research Papers (original) (raw)

- by Dr. Edy Winarno
- •
- Computer Science, Image Processing, Face Recognition, OpenCv or Computer Vision

The purpose of this project was to create several methods for achieving non-photorealistic appearance of the objects or, in other words, the goal was to create several different implementations of the cartoonish shader, which can... more

- by Roman Fekolkin
- •
- Algorithms, Computer Graphics, Image Processing, Visualization

In recent years, edge detection technology has gradually been widely used. This paper presents one of the
classical edge detection operators, Sobel edge detector. Field Programmable Gate Array (FPGA) technology
becomes an alternative for the implementation of software algorithms. This paper presents FPGA based
architecture for Sobel operator using Virtex-5 ML506 board to find the edges for grayscale images. Firstly,
the standard Sobel operator is used to detect the edges in grayscale images. Then the Sobel operator is
modified to find the edges for the images with noise reduction. The system for edge detection is done with the
combination of EDK(Embedded Development Kit) and Matlab environments.

Near duplicate image detection needs the matching of a bit altered images to the original image. This will help in the detection of forged images. A great deal of effort has been dedicated to visual applications that need efficient image similarity metrics and signature. Digital images can be easily edited and manipulated owing to the great functionality of image processing software. This leads to the challenge of matching somewhat altered images to their originals, which is termed as near duplicate image detection. This paper discusses the literature reviewed on the development of several image matching algorithms. Image recoloring is a technique that can transfer image color or theme and result in an imperceptible change in human eyes. Although image recoloring is one of the most important image manipulation techniques, there is no special method designed for detecting this kind of forgery. In this paper, we propose a trainable end-to-end system for distinguishing recolored images from natural images. The proposed network takes the original image and two derived inputs based on illumination consistency and inter-channel correlation of the original input into consideration and outputs the probability that it is recolored. Our algorithm adopts a CNN-based deeparchitecture, which consists of three feature extraction blocks and a feature fusion module. To train the

This paper presents an efficient architecture for various image filtering algorithms and tumor characterization using Xilinx System Generator (XSG). This architecture offers an alternative through a graphical user interface that combines MATLAB, Simulink and XSG and explores important aspects concerned to hardware implementation. Performance of this architecture implemented in SPARTAN-3E Starter kit (XC3S500E-FG320) exceeds those of similar or greater resources architectures. The proposed architecture reduces the resources available on target device by 50%.

Perspective distortion occurs due to the perspective projection of 3D scene on a 2D surface. Correcting the
distortion of a single image without losing any desired information is one of the challenging task in the
field of Computer Vision. We consider the problem of estimating perspective distortion from a single still
image of an unstructured environment and to make perspective correction which is both quantitatively
accurate as well as visually pleasing. Corners are detected based on the orientation of the image. A
method based on plane homography and transformation is used to make perspective correction. The
algorithm infers frontier information directly from the images, without any reference objects or prior
knowledge of the camera parameters. The frontiers are detected using geometric context based
segmentation. The goal of this paper is to present a framework providing fully automatic and fast
perspective correction.

In this paper, a hardware system for Sobel Edge Detection Algorithm is designed and simulated for a 128 pixel, 8-bit monochrome line-scan camera. The system is designed to detect objects as they move along a conveyor belt in a manufacturing environment, the camera will observe dark objects on a light conveyor belt. The edge detector is required to detect horizontal and vertical edges using Sobel edge detection method. The Sobel operator requires 3 lines and takes 3 pixels per line, thus using a 3×3 input block to process each pixel. The centre pixel of the 3×3 pixel block can be classified as an edge point or otherwise by thresholding the value from the operator. The FPGA based Sobel edge detector is designed and simulated using Altera Quartus II 8.1 web edition by targeting Cyclone II development boards.

With the onslaught of multimedia in the near past, there has been a tremendous increase in the uses of images. A very good example of which is the web on which most of the documents contain images. Other than this the images are being used in other applications like weather forecasting, medical diagnosis, police department. In R-Tree implementation of image database, images are made available to the program which are then stores in the database. The image database is presented using R-tree and the database is stored in separate file .This R-tree implementation results in both update as well as efficient retrieval of images from hard disk [1][2][4]. We use the similarity based retrieval feature to retrieve the required number of similar images being inquired by the user [3][5][6]. Distance matrix approach is used to find similarity of images [7]. Sobel edge detection algorithm is used to form sketches. If sketch of image is entered for similarity based retrieval, then sketches of stored images are formed and these sketches are compared with input image (sketch) using distance matrix approach[8][9].

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

This issue (DOI: 10.13140/RG.2.2.22725.55521) includes the following papers; P1151653533, sameh magdy and Mohamed Ibrahim and tarek mahmoud, "Face Recognition based on Hidden Markov Model and Canny Operators" P1151711551, Arcangel R. and Capuso S. and Gerbuyos M. and Laviña C. and Mabuti G. and Tolentino R., "Robust Mouse Control based on Dynamic Template Matching of Hand Gestures” P1151712554, D. Barrinuevo and G. Jacosalem and J. N. Lagahit and M. J. Lobedica and M. R. Salar and R. Tolentino, "Precise Obstacles Avoidance System for Visually Impaired People using Xbox 360 Kinect" P1151712556, Vladimir A. Kulyukin and Sarbajit Mukherjee, "Computer Vision in Electronic Beehive Monitoring: In Situ Vision-Based Bee Counting on Langstroth Hive Landing Pads"

The detection of double edges in x-ray images of lumbar vertebrae is of prime importance in the assessment of vertebral injury or collapse that may be caused by osteoporosis and other spine pathology. In addition, if the above double-edge detection process is conducted within an automatic framework, it would not only facilitate inexpensive and fast means of obtaining objective morphometric measurements on the spine, but also remove the human subjectivity involved in the morphometric analysis. This paper proposes a novel force-formulation scheme, termed as Pressurized Open Directional Gradient Vector Flow snakes, to discriminate and detect the superior and inferior double edges present in the radiographic images of the lumbar vertebrae. As part of the validation process, this algorithm is applied to a set of 100 lumbar images and the detection results are quantified using analyst-generated ground truth. The promising nature of the detection results bears testimony to the efficacy of the proposed approach

The paper treats a problem for detection and recognition objects in computer vision sector, where researchers recommended OpenCV software and development tool, it’s several and better-remembered library resource for isolating, detecting, and recognition of particular objects, what we would find an appropriate system for detecting and recognition traffic roads signs. The robustness with optimization is a necessary element in the computer vision algorithms, we can find a very large number of technics in object detection, for example, shapes transformation, color selection, a region of interest ROI, and edge detection, combined all these technics to reach high precision in animated video or still image processing. The system we are trying to develop, is in high demand in the automotive sector such as intelligent vehicles or autonomous driving assist systems ADAS, based on intelligent recognition, applying Artificial Intelligence, by using Deep Learning, exactly Convolutional Neural Network (CNN) architecture, our system improves the high accuracy of detection and recognition of traffic road signs with lower loss.

Edges of an image are considered a type of crucial information
that can be extracted by applying detectors with different
methodology. It is a main tool in pattern recognition, image
segmentation, edge detection and scene analysis. In this paper,
we present a new technique of edge detection based on twodimensional
Tsallis entropy. The two-dimensional Tsallis
entropy was obtained from the two-dimensional histogram which
was determined by using the gray value of the pixels and the
local average gray value of the pixels, the work it was applied a
generalized entropy formalism that represents a recent
development in statistical mechanics.

Edge detection is an important assignment in image processing, as it is used as a primary tool for pattern
recognition, image segmentation and scene analysis. Simply put, an edge detector is a high-pass filter
that can be applied for extracting the edge points within an image. Edge detection in the spatial domain is
accomplished through convolution with a set of directional derivative masks in this domain. On one hand,
the popular edge detection spatial operators such as; Roberts, Sobel, Prewitt, and Laplacian are all
defined on a 3 by 3 pattern grid, which is efficient and easy to apply. On the other hand, working in the
frequency domain has many advantages, starting from introducing an alternative description to the
spatial representation and providing more efficient and faster computational schemes with less sensitivity
to noise through high filtering, de-noising and compression algorithms. Fourier transforms, wavelet and
curvelet transform are among the most widely used frequency-domain edge detection from satellite
images. However, the Fourier transform is global and poorly adapted to local singularities. Some of
these draw backs are solved by the wavelet transforms especially for singularities detection and
computation. In this paper, the relatively new multi-resolution technique, curvelet transform, is assessed
and introduced to overcome the wavelet transform limitation in directionality and scaling.
In this research paper, the assessment of second generation curvelet transforms as an edge detection tool
will be introduced and compared to traditional edge detectors such as wavelet transform and Canny Edge
detector. Second generation curvelet transform provides optimally sparse representations of objects,
which display smoothness except for discontinuity along the curve with bounded curvature. Preliminary
results show the power of curvelet transform over the wavelet transform through the detection of nonvertical
oriented edges, with detailed detection of curves and circular boundaries, such as non straight
roads and shores. Conclusions and recommendations are given with respect to the suitability; accuracy
and efficiency of the curvelet transform method compared to the other traditional methods

- by nishad PM
- •
- Computer Science, Computer Vision, Image Processing, Image segmentation

Potholes though seem inconsequential, may cause accidents resulting in loss of human life. In this
paper, we present an automated system to efficiently manage the potholes in a ward by deploying
geotagging and image processing techniques that overcomes the drawbacks associated with the existing
survey-oriented systems. Image processing is used for identification of target pothole regions in the 2D
images using edge detection and morphological image processing operations. A method is developed to
accurately estimate the dimensions of the potholes from their images, analyze their area and depth,
estimate the quantity of filling material required and therefore enabling pothole attendance on a priority
basis. This will further enable the government official to have a fully automated system for e f f e c t i v e l y
m a n a g i ng pothole related disasters.

This paper presents two proposed approaches for enhancing the visibility of the infrared (IR) night vision images. The first approach is based on merging gamma correction with histogram matching (HM). On the other hand, the second approach depends on merging gamma correction with contrast limited adaptive histogram equalization (CLAHE). The HM depends on a reference visual image for converting IR night vision images into images with better visual quality. Quality metrics such as entropy, average gradient, and Sobel edge magnitude are used for performance evaluation of the proposed approaches.

- by دكتور عادل الفيشاوي
- •
- Near Infrared, Entropy, K-Spatial Entropy, Night Vision

Many persons can easily access their information anytime and
anywhere through its network society which spread in the world.
On the other side, there is a very risk on this information,
because of legitimate users and quacks, who are trying to seize
the information. The passwords and numbers can be guessed or
forgotten. Also, Personal Identification Numbers (4-digit PIN
numbers) cards can be stolen. Biometric authentication
technology used to solve these problems, it identifies people by
their unique biological features. In this paper, the proposed
approach consists of two main phases. First phase, we will
construct a biometric authentication technique. To increase the
accuracy factor of security recognition in this system, the features
of palm veins is used. We use in the proposed approach, the
properties of the types of entropy such as Shannon and Renyi
entropies in second phase, to achieve the desired goals of the
research.

- by Javier Lezama
- •
- Computer Vision, Image Processing, Medical Image Processing, Face Recognition

Potholes though seem inconsequential, may cause accidents resulting in loss of human life. In this paper, we present an automated system to efficiently manage the potholes in a ward by deploying geotagging and image processing techniques that overcomes the drawbacks associated with the existing survey-oriented systems. Image processing is used for identification of target pothole regions in the 2D images using edge detection and morphological image processing operations. A method is developed to accurately estimate the dimensions of the potholes from their images, analyze their area and depth, estimate the quantity of filling material required and therefore enabling pothole attendance on a priority basis. This will further enable the government official to have a fully automated system for effectively managing pothole related disasters.

Realtime video and image processing is used in a wide variety of applications from video surveillance and traffic management to medical imaging applications. This paper presents the implementation of an canny edgedetection using in realtime video from camera. The Canny algorithm uses an optimal edge detector based on a set of criteria which include finding the most edges by minimizing the error rate, marking edges as closely as possible to the actual edges to maximize localization, and marking edges only once when a single edge exists for minimal response.

- by Dr. Edy Winarno
- •
- Computer Science, Image Processing, Face Recognition, OpenCv or Computer Vision

- by ghassan issa
- •
- Computer Science, Distributed Computing, Image Processing, Languages and Linguistics

In recent years, oblique aerial images are involved in various photogrammetric processes and are used not only for interpretation or visualization purposes but also in metric applications. This paper describes an approach for the computation of vertical and horizontal distances from a single low oblique aerial image. The proposed methodology can be applied in the case where the camera exterior orientation is unknown. It relies on the automatic determination of the nadir point using edge detection and line extraction algorithms, combined with robust model fitting and least-squares techniques, taking into account the underlying geometry of oblique imagery. The workflow and the mathematical model used are presented in detail and the effect of different variables (flying height, camera constant, tilt of the camera axis, length of the line segment, error in the determination of the flying height and error due to lack of ground elevation information) on the errors of both measured vertical and horizontal distances is evaluated. Finally, the desktop application that was developed based on the proposed methodology and tested using a data set of low oblique imagery is presented and ideas for future research are discussed.

- by Styliani Verykokou
- •
- Computer Vision, Photogrammetry, Aerial Photogrammetry, Edge Detection

- by Vignesh Mohan
- •
- F Mri, FPGA, Functional MRI, Digital FPGA implementation

This research presents new three proposed approaches to enhancement the visibility of the Infrared (IR) night vision images. The first proposed approach depends on Hybrid Adaptive Gamma Correction (AGC) with Histogram Matching (HGCHM). The second proposed approach stands up Merging Gamma Correction with Contrast Limited Adaptive Histogram Equalization (MGCCLAHE). The HM uses a reference visual image for converting of night vision images into daytime images. The third approach mixes the benefits of the CLAHE with the undecimated Additive Wavelet Transform (AWT) Using Homomorphic processing (CSAWUH). The quality assessments for the suggested approaches are entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy, lightness order error and the similarity of edges. Simulation results clear that the third proposed approach gives superior results to the two proposed approaches from entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy and the computation time perspectives. On the other hand, the second proposed approach takes long computation time in the implementation with respect to the two proposed approaches. The second proposed approach gives better results to the first proposed approach entropy, average gradient, contrast improvement factor, Sobel edge magnitude, and spectral entropy perspectives. The first proposed approach gives better results to the two proposed approaches from lightness order error and the similarity of edges perspectives.

- by دكتور عادل الفيشاوي
- •
- Entropy, Thermal Imaging, Histogram, Night Vision

- by Adel El-Fishawy
- •
- Information Systems, Computer Science, Near Infrared, Entropy

Image recoloring is a method that can move picture shading or topic and result in an impalpable change in human eyes. In spite of the fact that picture recoloring is one of the most significant picture control procedures, there is no extraordinary technique intended for identifying this sort of imitation. Right now, propose a trainable start to finish framework for recognizing recolored pictures from normal pictures. The proposed arrange takes the first picture and two inferred inputs dependent on light consistency and between channel relationship of the first contribution to thought and yields the likelihood that it is recolored. Our calculation embraces a CNN-based profound design, which comprises of three component extraction squares and an element combination module. To prepare the profound neural system, we blend a dataset contained recolored pictures and relating ground truth utilizing diverse recoloring

- by Eng Mohammed Ashiba
- •
- Information Systems, Computer Science, Near Infrared, Entropy

The measurement of a plant growth without interrupting its natural growth is essential since its diameter and height are related to the development of a tree as well as the water contents. Therefore main objective of this project is to find the diameter and height of a plant. Hence in this proposed work, an interface between a C3008 smart camera and a computer has been developed. Main advantage of this camera module is that it has digital output and digital video port that supplies a continuous 8-16 bit range of data stream. It is used to capture the image of a plant at different intervals of time. A MATLAB GUI has been developed for performing Sobel Edge Detection on a plant image to determine its height, maximum and
minimum width. Such programs enable us to automatically measure and record the various parameters of a plant. The results attained prove that the system is capable of measuring the changes (diameter and height changes) of plant’s growth accurately.

Finding an efficient approach for color image segmentation is always sought by the researchers in the color image processing research. We have different clustering based and region based methods for the same. But still there arises the requirement of an optimal method. In this paper, a new approach for color image segmentation is proposed. Here the segmentation is carried out on the L channel of LAB color space. The input color image is first converted from RGB to LAB. Then L channel is extracted from the LAB converted image and sent as input to FCM algorithm. After this initial segmentation, the segmented image is filtered by sobel filter. The filtered image is then segmented by Meyer’s Watershed algorithm to produce the final segmented image of the original image. The results of the proposed approach are found efficient when the same are analyzed in terms of MSE and PSNR. Also the segmented images are found free from over segmentation.

- by huda ashiba
- •
- Information Systems, Entropy, Computer Software, Thermal Imaging

Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. In this paper, the comparative analysis of various Image Edge Detection techniques has been presented. It has been shown that the canny edge detection algorithm performs better than all these algorithms under almost all scenarios. However, it has been observed that under noisy conditions Sobel algorithm detect edges more clearly when compared to Canny. It has been also observed that Canny edge detection algorithm is computationally more expensive compared to Sobel, Prewitt and Robert's algorithms. Cancer is a disease characterized by uncontrolled growth of abnormal cells. Hence, it is necessary to detect the edges of cancer cells so that they can be easily subjected to radiation therapy without affecting the other blood cells. So, in this paper Sobel & Canny algorithms have been used to detect the boundaries of cancer cells. Sobel algorithm has detected the edges of cancer cells more clearly compared to Canny algorithm.

This paper presents a description and performance evaluation of an efficient and reliable edge-detection tool that utilize the growing computational power of local area networks (LANs). It is therefore referred to as LAN-based edge detection (LANED) tool. The processor-farm methodology is used in porting the sequential edge-detection calculations to run efficiently on the LAN. In this methodology, each computer on the LAN executes the same program independently from other computers, each operating on different part of the total data. It requires no data communication other than that involves in forwarding input data/results between the LAN computers. LANED uses the Java parallel virtual machine (JPVM) data communication library to exchange data between computers. For equivalent calculations, the computation times on a single computer and a LAN of various number of computers, are estimated, and the resulting speedup and parallelization efficiency, are computed. The estimated results demonstrated that parallelization efficiencies achieved vary between 87% to 60% when the number of computers on the LAN varies between 2 to 5 computers connected through 10/100 Mbps Ethernet switch.

In this article, a method has been proposed which can be utilized for the extraction of random required data from .jpeg/.png/.tif images. Firstly, the concepts of edge detection in image processing and how it can be used for various applications are being introduced. Then, various steps that are involved in the process of edge detection are discussed in the paper. An algorithm has been developed for the extraction of required data from .jpeg/.png/.tif images. MATLAB® has been for carrying out numerical simulations. It has been found in the study that for the efficient extraction of data from .jpeg/.png/.gif/.tif images, the font size should be > 36 and the considered image should be a high contrast with threshold 0.1 to 0.34.

Edges detection of digital images is used in a various fields of applications ranging from real-time video surveillance
and traffic management to medical imaging applications. Most of the classical methods for edge detection are based on the first and
second order derivatives of gray levels of the pixels of the original image. These processes give rise to the exponential increment
of computational time. This paper shows the new algorithm based on both the Tsallis entropy and the Shannon entropy together for
edge detection using split and merge technique. The objective is to find the best edge representation and minimize the computation
time. A set of experiments in the domain of edge detection are presented. An edge detection performance compared to the previous
classic methods, such as Canny, LOG, and Sobel. Analysis show that the effect of the proposed method is better than those methods in
execution time and also is considered as easy implementation

- by Mohamed A. El-Sayed
- •
- Entropy, Edge Detection, Thresholding, Image Thresholding