This document discusses image processing and detection based on an ARM+Linux platform. It introduces a video monitoring system that uses a camera and image recognition technology to capture scenes and detect abnormal situations. When abnormalities are detected, the system can sound an alarm, record the scene, and notify users. The key parts discussed are image acquisition, processing, and detection. Median filtering is used for noise removal. A three frame difference algorithm and thresholds are used to detect moving objects in images. Experimental results show the platform provides clear images and the modified three frame algorithm extracts moving target information well.
Moving object detection using background subtraction algorithm using simulinkeSAT 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.
Currently, in both market and the academic communities have required applications based on image and video processing with several real-time constraints. On the other hand, detection of moving objects is a very important task in mobile robotics and surveillance applications. In order to achieve this, we are using a alternative means for real time motion detection systems. This paper proposes hardware architecture for motion detection based on the background subtraction algorithm, which is implemented on FPGAs (Field Programmable Gate Arrays). For achieving this, the following steps are executed: (a) a background image (in gray-level format) is stored in an external SRAM memory, (b) a low-pass filter is applied to both the stored and current images, (c) a subtraction operation between both images is obtained, and (d) a morphological filter is applied over the resulting image. Afterward, the gravity center of the object is calculated and sent to a PC (via RS-232 interface).
SENSITIVITY OF A VIDEO SURVEILLANCE SYSTEM BASED ON MOTION DETECTIONsipij
The implementation of a stand-alone system developed in JAVA language for motion detection has been discussed. The open-source OpenCV library has been adopted for video surveillance image processing thus implementing Background Subtraction algorithm also known as foreground detection algorithm. Generally the region of interest of a body or object to detect is related to a precise objects (people, cars, etc.) emphasized on a background. This technique is widely used for tracking a moving objects. In particular, the BackgroundSubtractorMOG2 algorithm of OpenCV has been applied. This algorithm is based on Gaussian distributions and offers better adaptability to different scenes due to changes in lighting and the detection of shadows as well. The implemented webcam system relies on saving frames and creating GIF and JPGs files with previously saved frames. In particular the Background Subtraction function, find Contours, has been adopted to detect the contours. The numerical quantity of these contours has been compared with the tracking points of sensitivity obtained by setting an user-modifiable slider able to save the frames as GIFs composed by different merged JPEGs. After a full design of the image processing prototype different motion test have been performed. The results showed the importance to consider few sensitivity points in order to obtain more frequent image storages also concerning minor movements.Sensitivity points can be modified through a slider function and are inversely proportional to the number of saved images. For small object in motion will be detected a low percentage of sensitivity points.Experimental results proves that the setting condition are mainly function of the typology of moving object rather than the light conditions. The proposed prototype system is suitable for video surveillance smart
camera in industrial systems.
Emblematical image based pattern recognition paradigm using Multi-Layer Perce...iosrjce
The abstract Likewise human brain machine can be signifying diverse pattern sculpt that is
proficiently identify an image based object like optical character, hand character image, fingerprint and
something like this. To present the model of image based pattern recognition perspective by a machine, different
stages are associated like image acquiring from the digitizing image sources, preprocessing image to remove
unwanted data by the normalizing and filtering, extract the feature to represent the data as lower dimension
space and at last return the decision using Multi-Layer Perceptron neural network that is feed feature vector
from got the feature extraction process of a given input image. Performance observation complexity is discussed
rest of the description of pattern recognition model. Our goal of this paper is to introduced symbolical image
based pattern recognition model using Multi-Layer Perceptron learning algorithm in the field of artificial
neural network (like as human-like-brain) with best possible way of utilizing available processes and learning
knowledge in a way that performance can be same as human.
Recognition and tracking moving objects using moving camera in complex scenesIJCSEA Journal
In this paper, we propose a method for effectively tracking moving objects in videos captured using a
moving camera in complex scenes. The video sequences may contain highly dynamic backgrounds and
illumination changes. Four main steps are involved in the proposed method. First, the video is stabilized
using affine transformation. Second, intelligent selection of frames is performed in order to extract only
those frames that have a considerable change in content. This step reduces complexity and computational
time. Third, the moving object is tracked using Kalman filter and Gaussian mixture model. Finally object
recognition using Bag of features is performed in order to recognize the moving objects.
Moving object detection using background subtraction algorithm using simulinkeSAT 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.
Currently, in both market and the academic communities have required applications based on image and video processing with several real-time constraints. On the other hand, detection of moving objects is a very important task in mobile robotics and surveillance applications. In order to achieve this, we are using a alternative means for real time motion detection systems. This paper proposes hardware architecture for motion detection based on the background subtraction algorithm, which is implemented on FPGAs (Field Programmable Gate Arrays). For achieving this, the following steps are executed: (a) a background image (in gray-level format) is stored in an external SRAM memory, (b) a low-pass filter is applied to both the stored and current images, (c) a subtraction operation between both images is obtained, and (d) a morphological filter is applied over the resulting image. Afterward, the gravity center of the object is calculated and sent to a PC (via RS-232 interface).
SENSITIVITY OF A VIDEO SURVEILLANCE SYSTEM BASED ON MOTION DETECTIONsipij
The implementation of a stand-alone system developed in JAVA language for motion detection has been discussed. The open-source OpenCV library has been adopted for video surveillance image processing thus implementing Background Subtraction algorithm also known as foreground detection algorithm. Generally the region of interest of a body or object to detect is related to a precise objects (people, cars, etc.) emphasized on a background. This technique is widely used for tracking a moving objects. In particular, the BackgroundSubtractorMOG2 algorithm of OpenCV has been applied. This algorithm is based on Gaussian distributions and offers better adaptability to different scenes due to changes in lighting and the detection of shadows as well. The implemented webcam system relies on saving frames and creating GIF and JPGs files with previously saved frames. In particular the Background Subtraction function, find Contours, has been adopted to detect the contours. The numerical quantity of these contours has been compared with the tracking points of sensitivity obtained by setting an user-modifiable slider able to save the frames as GIFs composed by different merged JPEGs. After a full design of the image processing prototype different motion test have been performed. The results showed the importance to consider few sensitivity points in order to obtain more frequent image storages also concerning minor movements.Sensitivity points can be modified through a slider function and are inversely proportional to the number of saved images. For small object in motion will be detected a low percentage of sensitivity points.Experimental results proves that the setting condition are mainly function of the typology of moving object rather than the light conditions. The proposed prototype system is suitable for video surveillance smart
camera in industrial systems.
Emblematical image based pattern recognition paradigm using Multi-Layer Perce...iosrjce
The abstract Likewise human brain machine can be signifying diverse pattern sculpt that is
proficiently identify an image based object like optical character, hand character image, fingerprint and
something like this. To present the model of image based pattern recognition perspective by a machine, different
stages are associated like image acquiring from the digitizing image sources, preprocessing image to remove
unwanted data by the normalizing and filtering, extract the feature to represent the data as lower dimension
space and at last return the decision using Multi-Layer Perceptron neural network that is feed feature vector
from got the feature extraction process of a given input image. Performance observation complexity is discussed
rest of the description of pattern recognition model. Our goal of this paper is to introduced symbolical image
based pattern recognition model using Multi-Layer Perceptron learning algorithm in the field of artificial
neural network (like as human-like-brain) with best possible way of utilizing available processes and learning
knowledge in a way that performance can be same as human.
Recognition and tracking moving objects using moving camera in complex scenesIJCSEA Journal
In this paper, we propose a method for effectively tracking moving objects in videos captured using a
moving camera in complex scenes. The video sequences may contain highly dynamic backgrounds and
illumination changes. Four main steps are involved in the proposed method. First, the video is stabilized
using affine transformation. Second, intelligent selection of frames is performed in order to extract only
those frames that have a considerable change in content. This step reduces complexity and computational
time. Third, the moving object is tracked using Kalman filter and Gaussian mixture model. Finally object
recognition using Bag of features is performed in order to recognize the moving objects.
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.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
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.
Background differencing algorithm for moving object detection using system ge...eSAT 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
In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal ...
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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.
SECURE OMP BASED PATTERN RECOGNITION THAT SUPPORTS IMAGE COMPRESSIONsipij
In this paper, we propose a secure Orthogonal Matching Pursuit (OMP) based pattern recognition scheme that well supports image compression. The secure OMP is a sparse coding algorithm that chooses atoms sequentially and calculates sparse coefficients from encrypted images. The encryption is carried out by using a random unitary transform. The proposed scheme offers two prominent features. 1) It is capable of
pattern recognition that works in the encrypted image domain. Even if data leaks, privacy can be maintained because data remains encrypted. 2) It realizes Encryption-then-Compression (EtC) systems, where image encryption is conducted prior to compression. The pattern recognition can be carried out using a
few sparse coefficients. On the basis of the pattern recognition results, the scheme can compress selected images with high quality by estimating a sufficient number of sparse coefficients. We use the INRIA dataset to demonstrate its performance in detecting humans in images. The proposal is shown to realize human detection with encrypted images and efficiently compress the images selected in the image recognition stage.
De-Mystifying Twitter for Small Business: Tweeting Towards SalesCharlie Conard
Cut through the common confusion about Twitter & discover how this social media platform can help grow your small business.
Charlie Conard answers common questions about Twitter for business owners, including:
- Getting Your Business started on Twitter
- How do Hashtags work, & Why they are Important?
- Learning to Write an effective Tweet –in under 140 Characters!
- Case Studies: See how Twitter has helped other Small Business Owners Transform
You’ll leave this lecture with a clear understanding of how Twitter works & the ways it can help your small business build sales.
About the Speaker
Charlie Conard, co-founder and principal of Social Go To, specializes in creating social media strategies that grow audiences, increase engagement, and transform prospects into customers. Charlie has worked with brands including L’Oreal, Mount Sinai Medical Center, Continental Airlines, United Way, Publishers Weekly, and KitchenAid.
Select slides with annotation from a seminar presented at the New York Science, Industry, and Business Library Small Business Resource Center (New York Public Library) on 10/08/15.
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.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
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.
Background differencing algorithm for moving object detection using system ge...eSAT 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
In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal ...
digital image processing pdf
digital image processing books
digital image processing textbook pdf
digital image processing textbook
digital image processing pdf book
digital image processing gonzalez pdf
digital image processing 4th pdf
digital image processing 3rd pdf
digital image processing slides
history of image processing
digital image processing third edition
digital image processing pdf
digital image processing gonzalez ppt
digital image processing 3rd edition pdf
digital image processing third edition pdf
image processing basics
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.
SECURE OMP BASED PATTERN RECOGNITION THAT SUPPORTS IMAGE COMPRESSIONsipij
In this paper, we propose a secure Orthogonal Matching Pursuit (OMP) based pattern recognition scheme that well supports image compression. The secure OMP is a sparse coding algorithm that chooses atoms sequentially and calculates sparse coefficients from encrypted images. The encryption is carried out by using a random unitary transform. The proposed scheme offers two prominent features. 1) It is capable of
pattern recognition that works in the encrypted image domain. Even if data leaks, privacy can be maintained because data remains encrypted. 2) It realizes Encryption-then-Compression (EtC) systems, where image encryption is conducted prior to compression. The pattern recognition can be carried out using a
few sparse coefficients. On the basis of the pattern recognition results, the scheme can compress selected images with high quality by estimating a sufficient number of sparse coefficients. We use the INRIA dataset to demonstrate its performance in detecting humans in images. The proposal is shown to realize human detection with encrypted images and efficiently compress the images selected in the image recognition stage.
De-Mystifying Twitter for Small Business: Tweeting Towards SalesCharlie Conard
Cut through the common confusion about Twitter & discover how this social media platform can help grow your small business.
Charlie Conard answers common questions about Twitter for business owners, including:
- Getting Your Business started on Twitter
- How do Hashtags work, & Why they are Important?
- Learning to Write an effective Tweet –in under 140 Characters!
- Case Studies: See how Twitter has helped other Small Business Owners Transform
You’ll leave this lecture with a clear understanding of how Twitter works & the ways it can help your small business build sales.
About the Speaker
Charlie Conard, co-founder and principal of Social Go To, specializes in creating social media strategies that grow audiences, increase engagement, and transform prospects into customers. Charlie has worked with brands including L’Oreal, Mount Sinai Medical Center, Continental Airlines, United Way, Publishers Weekly, and KitchenAid.
Select slides with annotation from a seminar presented at the New York Science, Industry, and Business Library Small Business Resource Center (New York Public Library) on 10/08/15.
The Kiss! Innocent Happiness, my Budgie Love Birds, pets since many years, we lost three, ...these tow are Awesum! Honey and Birdie love birds, and as they love feeding by my hand, they get really happy, captured these precious moments of innocent kiss! It was a really blessed experience to see these innocent clicks in sequence, so sharing my happiness! God Bless , Love and Blessings!
Facebook for Business Seminar - 12 Secrets to Market Your Business BetterCharlie Conard
Slides from a seminar giving to the New York State Small Business Resource Center on 06/07/16, developed with support from the United States Small Business Administration (SBA).
Real Estate - Open the Door to Your Next Sale Using an Effective Social Media...Charlie Conard
97% of businesses use social media; only 20% are seeing measurable results. How are you doing?
If you’re frustrated, you’re not alone. Today’s business owner not only has be an expert at what they do, but they also are expected to have a presence and reach to current and potential customers on social media.
This seminar will help you develop a Social Media Strategy and Action Plan that can lead towards positive results for your business.
Some of what you will learn includes:
- How social media works and why you need to be using it
An action plan that saves you 75% of the time you would spend without one
- A survey of five popular social media platforms, how they differ from one another, and what kind of businesses find the most success with each one.
- Facebook, Twitter, Instagram, Pinterest and LinkedIn: What’s Next?
About the Speaker
Charlie Conard, co-founder and principal of Social Go To, specializes in creating social media strategies that grow audiences, increase engagement, and transform prospects into customers. Charlie has worked with brands including L’Oreal, Mount Sinai Medical Center, Continental Airlines, United Way, Publishers Weekly, and KitchenAid.
Slides with annotation from a seminar presented to a prominent real estate brokerage on 01/13/16.
Uncover the mystery from your social media of what you should measure and why. Learn to develop a social media strategy using a Relationship Funnel and planning multiple conversions that turn prospects into customers. Slides from a Lunch & Learn seminar.
De-Mystifying Twitter for Small Business - 2016Charlie Conard
Cut through the common confusion about Twitter & discover how this social media platform can help grow your small business. Charlie Conard answers common questions about Twitter for business owners, including:
- Getting Your Business started on Twitter
- How do Hashtags work, & Why they are Important?
- Learning to Write an effective Tweet –in under 140 Characters!
- Case Studies: See how Twitter has helped other Small Business Owners Transform
You’ll leave this lecture with a clear understanding of how Twitter works & the ways it can help your small business build sales.
About the Speaker
Charlie Conard, co-founder and principal of Social Go To, specializes in creating social media strategies that grow audiences, increase engagement, and transform prospects into customers. Charlie has worked with brands including L’Oreal, Mount Sinai Medical Center, Continental Airlines, United Way, Publishers Weekly, and KitchenAid.
Select slides with annotation from a seminar presented at the New York Science, Industry, and Business Library Small Business Resource Center (New York Public Library) on 02/09/16.
A Smart Camera Processing Pipeline for Image Applications Utilizing Marching ...sipij
Image processing in machine vision is a challenging task because often real-time requirements have to be met in these systems. To accelerate the processing tasks in machine vision and to reduce data transfer latencies, new architectures for embedded systems in intelligent cameras are required. Furthermore, innovative processing approaches are necessary to realize these architectures efficiently. Marching Pixels are such a processing scheme, based on Organic Computing principles, and can be applied for example to determine object centroids in binary or gray-scale images. In this paper, we present a processing pipeline for smart camera systems utilizing such Marching Pixel algorithms. It consists of a buffering template for image pre-processing tasks in a FPGA to enhance captured images and an ASIC for the efficient realization of Marching Pixel approaches. The ASIC achieves a speedup of eight for the realization of Marching Pixel algorithms, compared to a common medium performance DSP platform.
An optimized discrete wavelet transform compression technique for image trans...IJECEIAES
Transferring images in a wireless multimedia sensor network (WMSN) knows a fast development in both research and fields of application. Nevertheless, this area of research faces many problems such as the low quality of the received images after their decompression, the limited number of reconstructed images at the base station, and the high-energy consumption used in the process of compression and decompression. In order to fix these problems, we proposed a compression method based on the classic discrete wavelet transform (DWT). Our method applies the wavelet compression technique multiple times on the same image. As a result, we found that the number of received images is higher than using the classic DWT. In addition, the quality of the received images is much higher compared to the standard DWT. Finally, the energy consumption is lower when we use our technique. Therefore, we can say that our proposed compression technique is more adapted to the WMSN environment.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. 2
whether or not movement object by the difference of threshold
calculation is O ( N
*m
) ,and it’s easy to realize parallel of image.
processing on hardware processor. Its fast algorithm and
implementation is as follows: According to the experimental observation, the system gets
the way of judging movement objects by way of threshold: for
1) For illustration, each of the 3 x 3 pixels within the
window is respectively defined as follows and the pixels are three successive images
p i −1
(x, y) p i
(x, y)
arranged as shown in Table . p i +1
(x, y)
.
1. That whether d i − 1 , i or d i , i + 1
TABLE I. PIXEL ROWS LIST
( x, y) ( x, y)
is more than
zero column first column second column or equal to 20 shows existence of movement objects.
(x, y) −
2. When d i − 1 , i d i ,i + 1
zeroth line P00 P01 P02 | (x, y) |
is more than or
first line P10 P11 P12 equal to 10, it shows existence of movement objects.
second line P20 P21 P22 In the process of detecting of movement object, there may
2) First of all, each column (or row) within the window will be too fast or too slow movement objects which can’t be
be calculated to get the maximum value, median value and the detected only by three continuous frames. So the system
minimum value. By this way there will be three set of data, doesn’t just use three continuous image frames to carry out
which can be respectively marked for the maximum value operation but to detect movement objects by using “double
group, the median group and the minimum group. The frame three” method according to the characteristics of camera
calculation process is represented as follows: [7]. That is when the first" three frame" could not detect the
movement information, then the second" frame three" can be
MAX0=MAX(P00,P10,P20);MAX1=MAX(P01,P11,P21), used to detect it. If the second" frame three" can’t detect the
MAX2=MAX(P02,P12,P22) movement objects, we can use the value of the difference
MED0=MED(P00,P10,P20);MED1=MED(P01,P11,P21), image which come from the subtraction of continuous " frame
MED2=MED(P02,P12,P22) three". Then we can judge there is a movement object whether
or not and operate just like this again and again. Using this
MIN0=MIN(P00,P10,P20);MIN1=MIN(P01,P11,P21),MI method we can not only detect movement objects too fast but
N2=MIN(P02,P12,P22) also movement objects too slow. And the detection effect is
3) It can be seen that the maximum value of maximum very good. Movement object extraction processes such as
value group and the minimum value of minimum value group Figure 2 movement object extraction process chart.
must be the maximum and minimum value of the two columns Capture three frames of image(P1,P2,P3)
within the nine pixels. Minimum value of median group is less take the difference of image P2-P1 and
than five pixels at least. Similarly, the median value of P3-P2 and threshold T1 T2
maximum group is more than five pixels and the median value
in the minimum group is less than 5 pixels at least. We can note
the minimum value in maximum group of MAX_MIN, the T1>=20 || T2>=20 ||
Y
alarm
median value in median group of MED_MED and the T1-T2 >=10
maximum value in minimum group of MIN_MAX. then
WINMED which is output pixel value from filter results should N
be the median value of MAX_MIN, MED_MED and Capture three frames of image(P1ƍ,P2ƍ,P3ƍ)
MIN_MAX. The computational process is represented as take the difference of image P2ƍ-P1ƍ and
follows: P3ƍ-P2ƍ and threshold T1ƍ T2ƍ
WINMED = MED(MAX_MIN MED_MED MIN_MAX).
Using this method, the median is calculated for seventeen T1ƍ>=20 || T2ƍ>=20 ||
Y
times. Compared with the traditional algorithm, it can reduce T1ƍ-T2ƍ >=10
for in the number of nearly two times, and the algorithm is very
applicable to the real-time processor for parallel processing. N
T11ƍ= P1-P1ƍ T22ƍ= P2-P2ƍ
IV. MOVEMENT INFORMATION DETECTION T33ƍ= P3-P3ƍ
Movement information detection is mainly responsible for
difference value and threshold calculation coming from the
Y
acquired image frames. And then it can judge that there is T11ƍ>=15||T22>=15 ||
T33ƍ>=15
whether or not movement objects [5] [6]. The development of
three frames algorithm method is used for image processing. N
This method can observe the threshold under the condition of
existing movement objects and static objects and then identify Fig 2 flow chart of movement object extraction
400
3. Movement target in the judgment process is divided into the Table 2 and table 3 each represent a difference image, it can
following several steps: be seen in Table 2 that the pixel difference is not much, so it
can be concluded that the two pictures did not change and
(1) to take three successive frames of image difference adaptive method to obtain the threshold is also small. The pixel
image and threshold; difference in Table 3 is large, it can be concluded that
(2) to determine whether threshold is more than 20 or movement object exits and adaptive method to obtain the
difference of threshold is more than 10, if a jump to (7 )or not threshold is large too.
to jump to ( 3);
(3) to capture image difference and threshold of three V. SUMMARY
consecutive image frames; This paper focused on the study of information processing
and movement information detection based on the platform of
(4) to determine whether threshold is more than 20 or
ARM+Linux. The main results are as follows:
difference of threshold is more than 10, if a jump to (7 )or not
to jump to ( 5); (1) a video surveillance system is developed based on the
embedded development platform of ARM+Linux, which could
(5) to calculate respectively difference image and threshold
be widely used for using USB camera. The results show that,
of two three frame image;
the platform has stable performance, it is easy to use and
(6) to determine whether threshold is more than 15, if a expand, and the image information acquired by the picture is
jump to (7 )or not to jump to ( 5); relatively clear. so it is accord with future development trend of
video monitoring application.
(7) to alarm to the user and jump to ( 1).
(2) using median filter to process image and the de-noising
3, to determine whether there is a movement object effect is much better.
according to threshold.
(3) the modified three frame difference algorithm is used
The threshold represents characteristics of a difference for image processing. the method observe the threshold size of
image. That the threshold is more representative of the two moving objects and moving objects under the condition and
images of the poor more, and it can determine whether a then judge the difference image threshold to identify whether
movement object exists. Table and table can shows it as there is a moving object method. The experimental results
follows: show that the extraction effect of moving target information is
quite good.
TABLE II. PIXEL ROWS LIST
1 2 0 1 1
REFERENCES
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technology 2004.
2 1 0 3 2
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TABLE III. PIXEL ROWS LIST [4] Zhou Xi-Han, Liu Bo,Zhou Heqin. Based on a symmetric difference and
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[5] Wang Yaming, Huang Wenqing, Zhou Hai-ying. Dynamic image
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