Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
Study and Comparison of Various Image Edge Detection TechniquesCSCJournals
Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. 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 is presented. The software is developed using MATLAB 7.0. It has been shown that the Canny’s edge detection algorithm performs better than all these operators under almost all scenarios. Evaluation of the images showed that under noisy conditions Canny, LoG( Laplacian of Gaussian), Robert, Prewitt, Sobel exhibit better performance, respectively. . It has been observed that Canny’s edge detection algorithm is computationally more expensive compared to LoG( Laplacian of Gaussian), Sobel, Prewitt and Robert’s operator
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
Study and Comparison of Various Image Edge Detection TechniquesCSCJournals
Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. 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 is presented. The software is developed using MATLAB 7.0. It has been shown that the Canny’s edge detection algorithm performs better than all these operators under almost all scenarios. Evaluation of the images showed that under noisy conditions Canny, LoG( Laplacian of Gaussian), Robert, Prewitt, Sobel exhibit better performance, respectively. . It has been observed that Canny’s edge detection algorithm is computationally more expensive compared to LoG( Laplacian of Gaussian), Sobel, Prewitt and Robert’s operator
Denoising and Edge Detection Using SobelmethodIJMER
The main aim of our study is to detect edges in the image without any noise , In many of the images edges carry important information of the image, this paper presents a method which consists of sobel operator and discrete wavelet de-noising to do edge detection on images which include white Gaussian noises. There were so many methods for the edge detection, sobel is the one of the method, by using this sobel operator or median filtering, salt and pepper noise cannot be removed properly, so firstly we use complex wavelet to remove noise and sobel operator is used to do edge detection on the image. Through the pictures obtained by the experiment, we can observe that compared to other methods, the method has more obvious effect on edge detection.
Comparative Analysis of Common Edge Detection Algorithms using Pre-processing...IJECEIAES
Edge detection is the process of segmenting an image by detecting discontinuities in brightness. Several standard segmentation methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image pre-processing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard edge detection methods. The proposed preprocessing approach involves median filtering to reduce the noise in image and then edge detection technique is carried out. Finally, Standard edge detection methods can be applied to the resultant pre-processing image and its Simulation results are show that our pre-processed approach when used with a standard edge detection method enhances its performance.
EDGE DETECTION IN RADAR IMAGES USING WEIBULL DISTRIBUTIONcsitconf
Radar images can reveal information about the shape of the surface terrain as well as its
physical and biophysical properties. Radar images have long been used in geological studies to
map structural features that are revealed by the shape of the landscape. Radar imagery also has
applications in vegetation and crop type mapping, landscape ecology, hydrology, and
volcanology. Image processing is using for detecting for objects in radar images. Edge
detection; which is a method of determining the discontinuities in gray level images; is a very
important initial step in Image processing. Many classical edge detectors have been developed
over time. Some of the well-known edge detection operators based on the first derivative of the
image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image
with masks. Also Gaussian distribution has been used to build masks for the first and second
derivative. However, this distribution has limit to only symmetric shape. This paper will use to
construct the masks, the Weibull distribution which was more general than Gaussian because it
has symmetric and asymmetric shape. The constructed masks are applied to images and we
obtained good results.
EDGE DETECTION IN RADAR IMAGES USING WEIBULL DISTRIBUTIONcscpconf
Radar images can reveal information about the shape of the surface terrain as well as its physical and biophysical properties. Radar images have long been used in geological studies to
map structural features that are revealed by the shape of the landscape. Radar imagery also has applications in vegetation and crop type mapping, landscape ecology, hydrology, and
volcanology. Image processing is using for detecting for objects in radar images. Edge detection; which is a method of determining the discontinuities in gray level images; is a very
important initial step in Image processing. Many classical edge detectors have been developed over time. Some of the well-known edge detection operators based on the first derivative of the image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image with masks. Also Gaussian distribution has been used to build masks for the first and second derivative. However, this distribution has limit to only symmetric shape. This paper will use to construct the masks, the Weibull distribution which was more general than Gaussian because it has symmetric and asymmetric shape. The constructed masks are applied to images and we obtained good results.
ALGORITHM AND TECHNIQUE ON VARIOUS EDGE DETECTION: A SURVEYsipij
An edge may be defined as a set of connected pixels that forms a boundary between two disjoints regions.
Edge detection is basically, a method of segmenting an image into regions of discontinuity. Edge detection
plays an important role in digital image processing and practical aspects of our life. .In this paper we
studied various edge detection techniques as Prewitt, Robert, Sobel, Marr Hildrith and Canny operators.
On comparing them we can see that canny edge detector performs better than all other edge detectors on
various aspects such as it is adaptive in nature, performs better for noisy image, gives sharp edges , low
probability of detecting false edges etc
The objective of this paper is to present the hybrid approach for edge detection. Under this technique, edge
detection is performed in two phase. In first phase, Canny Algorithm is applied for image smoothing and in
second phase neural network is to detecting actual edges. Neural network is a wonderful tool for edge
detection. As it is a non-linear network with built-in thresholding capability. Neural Network can be trained
with back propagation technique using few training patterns but the most important and difficult part is to
identify the correct and proper training set.
2-Dimensional Wavelet pre-processing to extract IC-Pin information for disarr...IOSR Journals
Abstract: Due to higher processing power to cost ratio, it is now possible to replace the manual detection methods used in the IC (Integrated Circuit) industry by Image-processing based automated methods, to detect a broken pin of an IC connected on a PCB during manufacturing, which will make the process faster, easier and cheaper. In this paper an accurate and fast automatic detection method is used where the top view camera shots of PCBs are processed using advanced methods of 2-dimensional discrete wavelet pre-processing before applying edge-detection. Comparison with conventional edge detection methods such as Sobel, Prewitt and Canny edge detection without 2-D DWT is also performed. Keywords :2-dimensional wavelets, Edge detection, Machine vision, Image processing, Canny.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Modified adaptive bilateral filter for image contrast enhancementeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
SINGLE‐PHASE TO THREE‐PHASE DRIVE SYSTEM USING TWO PARALLEL SINGLE‐PHASE RECT...ijiert bestjournal
Now a days digital image processing is rapid emerging field with fast growing
applications in sciences and engineering technologies. Digital image processing has broad
spectrum of applications such as remote sensing, medical processing, radar, sonar,
robotics, sport field and automated processes [1-2]. Edge detection techniques are
employed for the detecting the edges of the primitive picture. Earlier some primitive
methods were used for the image processing. H. C. Andrew et.al. gave the method of
digital image restoration [3-5], A. K. Jain and et.al put forwarded the partial difference
equations and finite differences in image processing [6]. Image process, image models and
estimation regarding the edge detection has been flourished during last decade [7-9]. Most
modules in practical vision system depend, directly or indirectly, on the performance of an
edge detector and digital image processing.
NMS and Thresholding Architecture used for FPGA based Canny Edge Detector for...idescitation
In this paper, an architecture designed for Non-
Maximal Suppression used in Canny edge detection algorithm
is presented in order to reduce memory requirements
significantly. The architecture also achieves decreased latency
and increased throughput with no loss in edge detection. The
new algorithm used has a low-complexity 8-bin non-uniform
gradient magnitude histogram to compute block-based
hysteresis thresholds that are used by the Canny edge detector.
Furthermore, the hardware architecture of the proposed
algorithm is presented in this paper and the architecture is
synthesized on the Xilinx Virtex 5 FPGA. The design
development is done in VHDL and simulated results are
obtained using modelsim 6.3 with Xilinx 12.2.
Hardware Unit for Edge Detection with Comparative Analysis of Different Edge ...paperpublications3
Abstract: An edge in an image is a contour across which the brightness of the image changes abruptly. In image processing, an edge is often interpreted as one class of singularities. Edge detection is an important task in image processing. It is a main tool in pattern recognition, image segmentation, and scene analysis. An edge detector is basically a high pass filter that can be applied to extract the edge points in an image. This topic has attracted many researchers and many achievements have been made. Many researchers provided different approaches based on mathematical calculations which some of them are either robust or cost effective. A new algorithm will be proposed to detect the edges of image with increased robustness and throughput. Using this algorithm we will reduce the time complexity problem which is faced by previous algorithm. We will also propose hardware unit for proposed algorithm which will reduce the area, power and speed problem. We will compare our proposed algorithm with previous approach. For image quality measurement we will use some scientific parameters those are PSNR, SSIM, FSIM. Implementation of proposed algorithm will be done by Matlab and hardware implementation will be done by using of Verilog on Xilinx 14.1 simulator. Verification will be done on Model sim.
Abstract Edge detection is a fundamental tool used in most image processing applications. We proposed a simple, fast and efficient technique to detect the edge for the identifying, locating sharp discontinuities in an image and boundary of an image. In this paper, we found that proposed method called LookUp Table performs well, which requires least computational time as compared to conventional Edge Detection techniques. And also in this paper we presented a comparative performance of various conventional Edge Detection Techniques. Keywords: Edge detectors, Lookup table.
In computing, virtualization refers to the act of creating a virtual (rather than actual) version of something, including (but not limited to) a virtual computer hardware platform, operating system (OS), storage device, or computer network resources.
Cracking of wireless networks is the defeating of security devices in Wireless local-area networks. Wireless local-area networks(WLANs) – also called Wi-Fi networks are inherently vulnerable to security lapses that wired networks Cracking is a kind of information network attack that is akin to a direct intrusion. There are two basic types of vulnerabilities associated with WLANs: those caused by poor configuration and those caused by weak encryption.
TCP/IP (Transmission Control Protocol/Internet Protocol) is the basic communication language or protocol of the Internet. It can also be used as a communications protocol in a private network (either an intranet or an extranet).
Network security consists of the provisions and policies adopted by a network administrator to prevent and monitor unauthorized access, misuse, modification, or denial of a computer network and network-accessible resources.
Infrared spectroscopy (IR spectroscopy) is the spectroscopy that deals with the infrared region of the electromagnetic spectrum, that is light with a longer wavelength and lower frequency than visible light. It covers a range of techniques, mostly based on absorption spectroscopy.
In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is "rational", as defined in economics).
Interactive voice response (IVR) is a technology that allows a computer to interact with humans through the use of voice and DTMF tones input via keypad.
In computer science and information theory, data compression, source coding,[1] or bit-rate reduction involves encoding information using fewer bits than the original representation.[2] Compression can be either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression.
A brain–computer interface (BCI), sometimes called a mind-machine interface (MMI), direct neural interface (DNI), synthetic telepathy interface (STI) or brain–machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.
In computer science, a tree is a widely used abstract data type (ADT) or data structure implementing this ADT that simulates a hierarchical tree structure, with a root value and subtrees of children, represented as a set of linked nodes.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water Industry Process Automation and Control Monthly - May 2024.pdf
EDGE DETECTION
1. 4.1 EDGE DETECTION
Edge detection refers to the process of identifying and locating sharp discontinuities in an
image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of
objects in a scene. Classical methods of edge detection involve convolving the image with an
operator (a 2-D filter), which is constructed to be sensitive to large gradients in the image while
returning values of zero in uniform regions [22]. There is an extremely large number of edge
detection operators available, each designed to be sensitive to certain types of edges. Variables
involved in the selection of an edge detection operator include:
Edge orientation: The geometry of the operator determines a characteristic direction in
which it is most sensitive to edges. Operators can be optimized to look for horizontal,
vertical, or diagonal edges.
Noise environment: Edge detection is difficult in noisy images, since both the noise and the
edges contain high-frequency content. Attempts to reduce the noise result in blurred and
distorted edges. Operators used on noisy images are typically larger in scope, so they can
average enough data to discount localized noisy pixels. This results in less accurate
localization of the detected edges.
Edge structure: Not all edges involve a step change in intensity. Effects such as refraction
or poor focus can result in objects with boundaries defined by a gradual change in intensity.
The operator needs to be chosen to be responsive to such a gradual change in those cases.
Newer wavelet-based techniques actually characterize the nature of the transition for each
edge in order to distinguish, for example, edges associated with hair from edges associated
with a face.
There are many ways to perform edge detection. However, the majority of different methods
may be grouped into two categories[23]:
Gradient: The gradient method detects the edges by looking for the maximum and
minimum in the first derivative of the image.
Laplacian: The Laplacian method searches for zero crossings in the second derivative of
the image to find edges. An edge has the one-dimensional shape of a ramp and calculating
the derivative of the image can highlight its location.
2. Suppose we have the following signal, with an edge shown by the jump in intensity below:
Figure 4.1 Example signal
If we take the gradient of this signal (which, in one dimension, is just the first derivative with
respect to t) we get the following[24]:
Figure 4.2 Gradient of this signal
Clearly, the derivative shows a maximum located at the center of the edge in the original signal.
This method of locating an edge is characteristic of the “gradient filter” family of edge detection
filters and includes the Sobel method. A pixel location is declared an edge location if the value of
the gradient exceeds some threshold[25]. As mentioned before, edges will have higher pixel
intensity values than those surrounding it. So once a threshold is set, you can compare the
gradient value to the threshold value and detect an edge whenever the threshold is exceeded.
3. Furthermore, when the first derivative is at a maximum, the second derivative is zero. As a
result, another alternative to finding the location of an edge is to locate the zeros in the second
derivative. This method is known as the Laplacian and the second derivative of the signal is
shown below:
Figure 4.3 Second derivative of the signal
4.2 EDGE DETECTION TECHNIQUES
Edge detection is one of the most commonly used operations in image analysis, and there are
probably more algorithms in the literature for enhancing and detecting edges than any other
single subject. The reason for this is that edges form the outline of an object. An edge is the
boundary between an object and the background, and indicates the boundary between
overlapping objects [26]. Some of the edge detection technique define below.
4.7.1 Sobel Operator
The operator consists of a pair of 3×3 convolution kernels as shown in Figure 1. One kernel is
simply the other rotated by 90°.
Gx Gy
-1 0 +1
-2 0 +2
-1 0 +1
+1 +2 +1
0 0 0
4. These kernels are designed to respond maximally to edges running vertically and horizontally
relative to the pixel grid, one kernel for each of the two perpendicular orientations.[21,22] The
kernels can be applied separately to the input image, to produce separate measurements of the
gradient component in each orientation (call these Gx and Gy). These can then be combined
together to find the absolute magnitude of the gradient at each point and the orientation of that
gradient. The gradient magnitude is given by:
| 𝐺| = √ 𝐺𝑥2 + 𝐺𝑦2
Typically, an approximate magnitude is computed using:
| 𝐺| = | 𝐺𝑥| + | 𝐺𝑦|
which is much faster to compute.
The angle of orientation of the edge (relative to the pixel grid) giving rise to the spatial gradient
is given by:
𝜃 = arctan
𝐺𝑦
𝐺𝑥
4.7.2 Robert’s cross operator:
The Roberts Cross operator performs a simple, quick to compute, 2-D spatial gradient
measurement on an image[20]. Pixel values at each point in the output represent the estimated
absolute magnitude of the spatial gradient of the input image at that point.
The operator consists of a pair of 2×2 convolution kernels as shown in Figure. One kernel is
simply the other rotated by 90°. This is very similar to the Sobel operator.
Gx Gy
-1 -2 -1
5. These kernels are designed to respond maximally to edges running at 45° to the pixel
grid, one kernel for each of the two perpendicular orientations [16,25]. The kernels can be
applied separately to the input image, to produce separate measurements of the gradient
component in each orientation (call these Gx and Gy). These can then be combined together to
find the absolute magnitude of the gradient at each point and the orientation of that gradient. The
gradient magnitude is given by:
| 𝐺| = √ 𝐺𝑥2 + 𝐺𝑦2
although typically, an approximate magnitude is computed using:
| 𝐺| = | 𝐺𝑥| + | 𝐺𝑦|
which is much faster to compute.
The angle of orientation of the edge giving rise to the spatial gradient (relative to the pixel grid
orientation) is given by:
𝜃 = arctan
𝐺𝑦
𝐺𝑥
−
3𝜋
4
4.7.3 Prewitt’s operator:
Prewitt operator is similar to the Sobel operator and is used for detecting vertical and horizontal
edges in images [26].
ℎ1 = [
1 1 1
0 0 0
−1 −1 −1
] ℎ1 = [
−1 0 1
−1 0 1
−1 0 1
]
+1 0
0 -1
0 +1
-1 0
6. 4.7.4 Laplacian of Gaussian:
The Laplacian is a 2-D isotropic measure of the 2nd spatial derivative of an image. The
Laplacian of an image highlights regions of rapid intensity change and is therefore often used for
edge detection. The Laplacian is often applied to an image that has first been smoothed with
something approximating a Gaussian Smoothing filter in order to reduce its sensitivity to noise.
The operator normally takes a single graylevel image as input and produces another graylevel
image as output [27].
The Laplacian L(x,y) of an image with pixel intensity values I(x,y) is given by:
𝐿( 𝑥, 𝑦) =
𝜕 2
𝐼
𝜕𝑥2
+
𝜕 2
𝐼
𝜕𝑦2
Since the input image is represented as a set of discrete pixels, we have to find a discrete
convolution kernel that can approximate the second derivatives in the definition of the Laplacian.
Three commonly used small kernels are shown below.
0 1 0 1 1 1 -1 2 -1
1 -4 1 1 -8 1 2 -4 2
0 1 0 1 1 1 -1 2 -1
Three commonly used discrete approximations to the Laplacian filter.
Because these kernels are approximating a second derivative measurement on the image, they are
very sensitive to noise. To counter this, the image is often Gaussian Smoothed before applying
the Laplacian filter. This pre-processing step reduces the high frequency noise components prior
to the differentiation step.
In fact, since the convolution operation is associative, we can convolve the Gaussian smoothing
filter with the Laplacian filter first of all, and then convolve this hybrid filter with the image to
achieve the required result. Doing things this way has two advantages:
7. Since both the Gaussian and the Laplacian kernels are usually much smaller than the
image, this method usually requires far fewer arithmetic operations.
The LoG (`Laplacian of Gaussian') kernel can be precalculated in advance so only one
convolution needs to be performed at run-time on the image.
The 2-D LOG function centered on zero and with Gaussian standard deviation has the form:
𝐿𝑂𝐺( 𝑥, 𝑦) = −
1
𝜋𝜎 4
[1 −
𝑋2
+ 𝑌2
2𝜎2
] 𝑒
−
𝑥2
+𝑦2
2𝜎 2
4.3 CANNY’S EDGE DETECTIONALGORITHM
Canny edge detector is the optimal and most widely used algorithm for edge detection.
Compared to other edge detection methods like Sobel, etc canny edge detector provides robust
edge detection, localization and linking [29]. It is a multi-stage algorithm and the stages involved
are illustrated in Figure 4.4.
Figure 4.4 Flow graph of Canny’s Algorithm
Gaussian Smoothing
Gradient Filtering
Hysteresis Thresholding
Non-Maximum Suppression
8. Thus, instead of providing the whole algorithm as a single API, kernels are provided for each
stage. This way, the user can have more flexibility and better buffer management. For more
details on the theory and formulation please refer to the Canny edge detection paper [1].
Optimized kernels and wrappers required for the implementation of canny algorithm are
provided to reduce the efforts of the integrator. Basic image processing and C64x programming
knowledge are assumed.
4.8.1 Algorithm
The algorithm runs in 5 separate steps[30]:
1. Smoothing: Blurring of the image to remove noise.
2. Finding gradients: The edges should be marked where the gradients of the image has large
magnitudes.
3. Non-maximum suppression: Only local maxima should be marked as edges.
4. Double thresholding: Potential edges are determined by thresholding.
5. Edge tracking by hysteresis: Final edges are determined by suppressing all edges that are not
connected to a very certain (strong) edge.
4.8.1.1 Smoothing
It is inevitable that all images taken from a camera will contain some amount of noise [4,30]. To
prevent that noise is mistaken for edges, noise must be reduced. Therefore the image is first
smoothed by applying a Gaussian filter. The kernel of a Gaussian filter with a standard deviation
of σ = 1.4 is shown in Equation (1). The effect of smoothing the test image with this filter is
shown in Figure.
𝑩 =
𝟏
𝟏𝟓𝟗
[
𝟐 𝟒 𝟓 𝟒 𝟐
𝟒 𝟗 𝟏𝟐 𝟗 𝟒
𝟓 𝟏𝟐 𝟏𝟓 𝟏𝟐 𝟓
𝟒 𝟗 𝟏𝟐 𝟗 𝟒
𝟐 𝟒 𝟓 𝟒 𝟐]
……………..1
4.8.1.2 Finding Gradients
9. The Canny algorithm basically finds edges where the grayscale intensity of the image changes
the most [8,32]. These areas are found by determining gradients of the image. Gradients at each
pixel in the smoothed image are determined by applying what is known as the Sobel-operator.
First step is to approximate the gradient in the x- and y-direction respectively by applying the
kernels shown in Equation (2).
𝐾 𝐺𝑋 = [
−1 0 1
−2 0 2
−1 0 1
]
𝐾 𝐺𝑋 = [
−1 2 1
0 0 0
−1 −2 −1
]
…………………………….2
A) Original B)Smoothed
Figure4.5 The Original Grayscale Image Is Smoothed With A Gaussian filters To Suppress
Noise.
The gradient magnitudes can then be determined as an Euclidean distance measure by applying
the law of Pythagoras as shown in Equation (3). It is sometimes simplified by applying
Manhattan distance measure as shown in Equation (4) to reduce the computational complexity.
The Euclidean distance measure has been applied to the test image. The computed edge strengths
are compared to the smoothed image in Figure 4.6
| 𝐺| = √𝐺𝑥2 + 𝐺𝑦2……………….3
| 𝐺| = | 𝐺𝑥| + | 𝐺𝑦|…………………4
10. where: Gx and Gy are the gradients in the x- and y-directions respectively.
It is obvious from Figure 3, that an image of the gradient magnitudes often indicate the edges
quite clearly. However, the edges are typically broad and thus do not indicate exactly where the
edges are. To make it possible to determine this, the direction of the edges must be determined
and stored as shown in Equation (5).
𝜃 = arctan (
| 𝐺𝑦|
| 𝐺𝑥|
) …………5
Figure 4.6: The gradient magnitudes of smoothed image
4.8.1.3 Non-maximum suppression
The purpose of this step is to convert the “blurred” edges in the image of the gradient magnitudes
to “sharp” edges. Basically this is done by preserving all local maxima in the gradient image, and
deleting everything else. The algorithm is for each pixel in the gradient image[31]:
1. Round the gradient direction θ to nearest 45◦, corresponding to the use of an 8-connected
neighborhoods.
2. Compare the edge strength of the current pixel with the edge strength of the pixel in the
positive and negative gradient direction. I.e. if the gradient direction is north (theta =90◦),
compare with the pixels to the north and south.
3. If the edge strength of the current pixel is largest; preserve the value of the edge strength.
11. If not, suppress (i.e. remove) the value. A simple example of non-maximum suppression is
shown in Figure 4. Almost all pixels have gradient directions pointing north. They are therefore
compared with the pixels above and below[33]. The pixels that turn out to be maximal in this
comparison are marked with white borders. All other pixels will be suppressed. Figure 4.7 shows
the effect on the test image.
Figure 4.7 Illustration of non-maximum suppression.
The edge strengths are indicated both as colors and numbers, while the gradient directions are
shown as arrows. The resulting edge pixels are marked with white borders.
12. (a) Gradient values (b) Edges after non-maximum suppression
Figure 4.8 Non-maximum suppression. Edge-pixels are only preserved where the gradient has
local maxima.
4.8.1.4 Double Thresholding
The edge-pixels remaining after the non-maximum suppression step are (still) marked with their
strength pixel-by-pixel. Many of these will probably be true edges in the image, but some may be
caused by noise or color variations for instance due to rough surfaces[34]. The simplest way to
discern between these would be to use a threshold, so that only edges stronger that a certain
value would be preserved. The Canny edge detection algorithm uses double thresholding. Edge
pixels stronger than the high threshold are marked as strong; edge pixels weaker than the low
threshold are suppressed and edge pixels between the two thresholds are marked as weak. The
effect on the test image with thresholds of 20 and 80 is shown in Figure 4.9
13. (a) Edges after non-maximum suppression (b) Double thresholding
Figure 4.9 Thresholding of edges. In the second image strong edges are white, while weak edges
are grey.
Edges with a strength below both thresholds are suppressed.
4.8.1.5 Edge Tracking By Hysteresis
Strong edges are interpreted as “certain edges”, and can immediately be included in the final
edge image. Weak edges are included if and only if they are connected to strong edges. The logic
is of course that noise and other small variations are unlikely to result in a strong edge (with
proper adjustment of the threshold levels). Thus strong edges will (almost) only be due to true
edges in the original image[33]. The weak edges can either be due to true edges or noise/color
variations. The latter type will probably be distributed independently of edges on the entire
image, and thus only a small amount will be located adjacent to strong edges. Weak edges due to
true edges are much more likely to be connected directly to strong edges.
14. (a) Double thresholding (b) Edge tracking by hysteresis (c) Final output
Figure 4.10 Edge tracking and final output. The middle image shows strong edges in white, weak
edges
connected to strong edges in blue, and other weak edges in red.
Edge tracking can be implemented by BLOB-analysis (Binary Large object). The edge pixels are
divided into connected BLOB’s using 8-connected neighborhoods. BLOB’s containing at least
one strong edge pixel are then preserved, while other BLOB’s are suppressed. The effect of edge
tracking on the test image is shown in Figure 4..10
4.4 COMPARISON OF VARIOUS EDGE DETECTIONALGORITHMS
Edge detection of all four types was performed on Figure 4.11 Canny yielded the best results.
This was expected as Canny edge detection accounts for regions in an image[35]. Canny yields
thin lines for its edges by using non-maximal suppression. Canny also utilizes hysteresis when
thresholding.
15. Figure 4.11 Image used for edge detection analysis (wheel.gif)
Figure 4.12: Results of edge detection on Figure 4.11. Notice Canny had the best results.
16. Motion blur was applied to Figure 4.13. Then, the edge detection methods previously used were
utilized again on this new image to study their affects in blurry image environments [34]. No
method appeared to be useful for real world applications. However, Canny produced the best the
results out of the set.
Figure 4.13 Result of edge detection
18. Laplacian Laplacian of Gaussian
Performance of Edge Detection Algorithms
Gradient-based algorithms such as the Prewitt filter have a major drawback of being very
sensitive to noise. The size of the kernel filter and coefficients are fixed and cannot be
adapted to a given image. An adaptive edge-detection algorithm is necessary to provide a
robust solution that is adaptable to the varying noise levels. Gradient-based algorithms such
as the Prewitt filter have a major drawback of being very sensitive to noise. The size of the
kernel filter and coefficients are fixed and cannot be adapted to a given image. An adaptive
edge-detection algorithm is necessary to provide a robust solution that is adaptable to the
varying noise levels of these images to help distinguish valid image contents from visual
artifacts introduced by noise[34,35].
The performance of the Canny algorithm depends heavily on the adjustable parameters, σ,
which is the standard deviation for the Gaussian filter, and the threshold values, ‘T1’ and
19. ‘T2’. σ also controls the size of the Gaussian filter. The bigger the value for σ, the larger the
size of the Gaussian filter becomes. This implies more blurring, necessary for noisy images,
as well as detecting larger edges. As expected, however, the larger the scale of the Gaussian,
the less accurate is the localization of the edge. Smaller values of σ imply a smaller
Gaussian filter which limits the amount of blurring, maintaining finer edges in the image.
The user can tailor the algorithm by adjusting these parameters to adapt to different
environments.
Canny’s edge detection algorithm is computationally more expensive compared to Sobel,
Prewitt and Robert’s operator. However, the Canny’s edge detection algorithm performs
better than all these operators under almost all scenarios[27,35].
4.5 ELLIPTICAL BOUNDARYMODEL
There is a new statistical color model for skin detection, called an elliptical boundary model.
This model is trained from a set of training data in two steps, preprocessing and parameter
estimation. In preprocessing step we remove outliers so that the trained model reflects the main
density of the underlying data set.
By examining skin and non-skin distributions in several color spaces Lee and Yoo [36] have
concluded that skin color cluster, being approximately elliptic in shape is not well enough
approximated by the single Gaussian model. Due to asymmetry of the skin cluster with respect to
its density peak, usage of the symmetric Gaussian model leads to high false positives rate. They
propose an alternative they call an ”elliptical boundary model” which is equally fast and simple
in training and evaluation as the single Gaussian model and gives superior detection results on
the Compaq database [37] compared both to single and mixture of Gaussians . The elliptical
boundary model is defined as:
Φ( 𝑐) = (𝑐 − ∅) 𝑇
Λ−1
(𝑐 − ∅)
This model is trained from a set of training data in two steps, preprocessing and parameter
estimation. In preprocessing step we remove outliers so that the trained model reflects the main
density of the underlying data set. In parameter estimation step we estimate model parameters
from the preprocessed data set.
1) Training Data Set: Initially training data set consists of skin chrominance samples.
20. 2) Preprocessing: Outliers are removed by eliminating k % sample data from the training set
which have low frequency. The value of k, where 0= k =5, is determined by the amount of noise
and negligible data in the training set.
3) Parameter Estimation: The parameter estimation is the third step given below. The model
training procedure has two steps - first, up to 5% of the training color samples with low
frequency are eliminated to remove noise and negligible data. Then, model parameters (f and Λ)
are estimated by
𝜙 =
1
𝑛
∑ 𝑐𝑖
𝑛
𝑖=1
Λ =
1
𝑁
∑ 𝑓𝑖
𝑛
𝑖=1
(𝑐𝑖 − μ)(𝑐𝑖 − μ) 𝑇
μ =
1
𝑁
∑ 𝑓𝑖 𝑐𝑖
𝑛
𝑖=1
𝑁 = ∑ 𝑓𝑖
𝑛
𝑖=1
Where n is the total number of distinctive training color vectors c of the training skin pixel set
(not the total samples number!), and f is the number of skin samples of color vector c . Pixel with
color c is classified as skin in case when F(c) < θ, where θ is a threshold value. The authors claim
that their model approximates the skin cluster better, because the data skew does not affect the
model centroid f calculation.