Gpu implementation of satellite image filtering
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This document discusses implementing satellite image filtering algorithms using GPUs with OpenCL. It evaluates the Sobel and Laplacian edge detection algorithms for efficiency and quality. The host code uses C++ and OpenCL API to transfer image data to the GPU device, compile and run the OpenCL program, then retrieve results. Testing on Landsat imagery shows the GPU outperforms the CPU for both Sobel and Laplacian filtering. An open issue is applying preprocessing like smoothing and contrast adjustment during filtering.
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Automatic License Plate Recognition system is a real time embedded system which automatically recognizes the license plate of vehicles. There are many applications ranging from complex security systems to common areas and from parking admission to urban traffic control. Automatic license plate recognition (ALPR) has complex characteristics due to diverse effects such as of light and speed. Most of the ALPR systems are built using proprietary tools like Matlab. This paper presents an alternative method of implementing ALPR systems using Free Software including Python and the Open Computer Vision Library.
Automatic License Plate Recognition using OpenCV
Automatic License Plate Recognition system is a real time embedded system which automatically recognizes the license plate of vehicles. There are many applications ranging from complex security systems to common areas and from parking admission to urban traffic control. Automatic license plate recognition (ALPR) has complex characteristics due to diverse effects such as of light and speed. Most of the ALPR systems are built using proprietary tools like Matlab. This paper presents an alternative method of implementing ALPR systems using Free Software including Python and the Open Computer Vision Library.
Webinar: Começando seus trabalhos com Machine Learning utilizando ferramentas...
Nesse webinar será apresentado o passo a passo de como criar projetos com Machine Learning utilizando ferramentas de terceiros como Sensi ML e Edge Impulse.
Tópicos que serão apresentados:
Kits de desenvolvimento para Machine Learning:
EV18H79A: SAMD21 ML Evaluation Kit with TDK 6-axis MEMS
EV45Y33A: SAMD21 ML Evaluation Kit with BOSCH IMU
SAMC21 xPlained Pro evaluation kit (ATSAMC21-XPRO) plus its QT8 xPlained Pro Extension Kit (AC164161)
Ferramentas de desenvolvimento:
MPLAB X
Data Visualizer
Ambiente de terceiros: Sensi ML e Edge Impulse
Coleta de dados
Como desenvolver um projeto usando Machine Learning sem conhecimentos específicos sobre o assunto e com conhecimentos sobre Machine Learning.
PT-4053, Advanced OpenCL - Debugging and Profiling Using AMD CodeXL, by Uri S...
PT-4053, Advanced OpenCL - Debugging and Profiling Using AMD CodeXL, by Uri S...AMD Developer Central
This document discusses debugging and profiling challenges with OpenCL and how AMD CodeXL addresses them. It provides an overview of CodeXL's debugging and profiling capabilities for OpenCL, including API-level debugging, kernel source debugging, profiling views for APIs, objects, and kernel variables, and integrated support in Visual Studio. Demo code is included to illustrate pinpointing OpenCL errors and optimizing work item loads.Current & Future Use-Cases of OpenDaylight
OpenDaylight Overview and Architecture
• OpenDaylight Use Cases (Partial List)
I. Network Abstraction
II. ONAP
III. Network Virtualization
IV. AI/ML with OpenDaylight
V. ODL in OSS
• OpenDaylight: Getting Involved
Graphical packet generator
This document describes a Graphical Packet Generator (GPG) software application that was developed using onePK API to enable network administration from a central point. GPG allows for automatic discovery of network topology by using CDP or LLDP protocols. It offers packet generation capabilities for troubleshooting networks as well as ICMP, UDP, and IP packet generation. The design of GPG includes modules for topology discovery, a packet generator window for the GUI, and a packet generator to establish a connection between onePK API and virtual routers. The GPG respects SDN principles and provides abstraction at the software level for network administration.
"OpenCV for High-performance, Low-power Vision Applications on Snapdragon," a...
"OpenCV for High-performance, Low-power Vision Applications on Snapdragon," a...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/opencv-for-high-performance-low-power-vision-applications-on-snapdragon-a-presentation-from-qualcomm/
Xin Zhong, Computer Vision Product Manager at Qualcomm Technologies, presents the “OpenCV for High-performance, Low-power Vision Applications on Snapdragon” tutorial at the May 2024 Embedded Vision Summit.
For decades, the OpenCV software library has been popular for developing computer vision applications. However, developers have found it challenging to create efficient implementations of their OpenCV applications on processors optimized for edge applications, like the Qualcomm Snapdragon family. As part of its comprehensive support for computer vision application developers, Qualcomm provides a variety of tools to enable developers to take full advantage of the heterogeneous computing resources in the Snapdragon processors.
In this talk, Zhong introduces a new element of Qualcomm’s computer vision tools suite: a version of OpenCV optimized for Snapdragon platforms, which allows developers to leverage and port their existing OpenCV-based applications seamlessly to Snapdragon platforms. Supporting OpenCV v4.x and later releases, this implementation contains unique Qualcomm-specific accelerations of OpenCV and OpenCV extension APIs. Zhong explains how this library enables developers to leverage existing OpenCV code to achieve superior performance and power savings on Snapdragon platforms.lecture_GPUArchCUDA04-OpenMPHOMP.pdf
The lecture discusses manycore GPU architectures and programming using OpenMP and HOMP. It introduces OpenMP directives for offloading computation to accelerators and covers data mapping between the host and device. It also discusses HOMP for automated distribution of parallel loops and data across multiple accelerators to improve load balancing and performance. The document provides examples of using OpenMP target directives and data mapping for problems like AXPY and Jacobi iteration on a GPU. It evaluates performance of different loop scheduling algorithms in HOMP on a system with CPUs, GPUs and MICs.
Development of Signal Processing Algorithms using OpenCL for FPGA based Archi...
This document provides an overview of using OpenCL to develop signal processing algorithms for an FPGA. It first introduces OpenCL and discusses its memory model and programming model, including host and kernel programming. It then discusses using OpenCL specifically for FPGAs and describes implementing some example algorithms like vector addition, N-point DFT and 8-point FFT using Vivado HLS. The document also discusses problems encountered and future work, and provides results and conclusions from implementing the algorithms on the FPGA using OpenCL.
Introduction to OpenCL, 2010
This document provides an introduction to OpenCL, including:
- An overview of the OpenCL model and how work is distributed across CPUs and GPUs.
- A demonstration of an N-body simulation and how it can be parallelized with OpenCL.
- Details on OpenCL concepts like platforms, devices, memory model, and how applications are organized with host code and kernels.
REAL TIME FACE DETECTION ON GPU USING OPENCL
This paper presents a novel approach for real time face detection using heterogeneous
computing. The algorithm uses local binary pattern (LBP) as feature vector for face detection.
OpenCL is used to accelerate the code using GPU[1]. Illuminance invariance is achieved using
gamma correction and Difference of Gaussian(DOG) to make the algorithm robust against
varying lighting conditions. This implementation is compared with previous parallel
implementation and is found to perform faster.
Boosting your HTML Apps – Overview of OpenCL and Hello World of WebCL
WebCL enables you boost the performance of select HTML application where lots of computation is involved. For example, fluid simulation, image manipulation, video manipulation.
Since, OpenCL is the underlying platform, the same has been introduced in the beginning and then WebCL.
Contents:
Motivation
Introduction to OpenCL
Introduction to WebCL
Hello World Program of WebCL
Real Time Face Detection on GPU Using OPENCL
This document summarizes a research paper that presents a real-time face detection algorithm using local binary patterns (LBP) as features. The algorithm is implemented on a GPU using OpenCL to achieve faster processing speeds compared to CPU implementations. LBP features are extracted from divided image blocks in parallel on the GPU. Histograms of the LBP codes from each block are concatenated to form an overall feature vector for classification. Experimental results show the GPU implementation achieves a 5x speed improvement over CPU for 640x480 images.
"Making Computer Vision Software Run Fast on Your Embedded Platform," a Prese...
"Making Computer Vision Software Run Fast on Your Embedded Platform," a Prese...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/luxoft/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Alexey Rybakov, Senior Director at LUXOFT, presents the "Making Computer Vision Software Run Fast on Your Embedded Platform" tutorial at the May 2016 Embedded Vision Summit.
Many computer vision algorithms perform well on desktop class systems, but struggle on resource constrained embedded platforms. This how-to talk provides a comprehensive overview of various optimization methods that make vision software run fast on low power, small footprint hardware that is widely used in automotive, surveillance, and mobile devices. The presentation explores practical aspects of deep algorithm and software optimization such as thinning of input data, using dynamic regions of interest, mastering data pipelines and memory access, overcoming compiler inefficiencies, and more.OpenCV @ Droidcon 2012
The document discusses OpenCV and its suitability for image processing on Android devices, noting that OpenCV is an open source library for computer vision and image processing that allows treating images as matrices and provides functions for tasks like blurring, edge detection, and object recognition; it provides an overview of some key OpenCV classes for Android and approaches for building image processing applications using OpenCV on Android.
Similar to Gpu implementation of satellite image filtering (20)
WT-4069, WebCL: Enabling OpenCL Acceleration of Web Applications, by Mikael ...
WT-4069, WebCL: Enabling OpenCL Acceleration of Web Applications, by Mikael ...
Webinar: Começando seus trabalhos com Machine Learning utilizando ferramentas...
Webinar: Começando seus trabalhos com Machine Learning utilizando ferramentas...
PT-4053, Advanced OpenCL - Debugging and Profiling Using AMD CodeXL, by Uri S...
PT-4053, Advanced OpenCL - Debugging and Profiling Using AMD CodeXL, by Uri S...
"OpenCV for High-performance, Low-power Vision Applications on Snapdragon," a...
"OpenCV for High-performance, Low-power Vision Applications on Snapdragon," a...
Development of Signal Processing Algorithms using OpenCL for FPGA based Archi...
Development of Signal Processing Algorithms using OpenCL for FPGA based Archi...
Boosting your HTML Apps – Overview of OpenCL and Hello World of WebCL
Boosting your HTML Apps – Overview of OpenCL and Hello World of WebCL
"Making Computer Vision Software Run Fast on Your Embedded Platform," a Prese...
"Making Computer Vision Software Run Fast on Your Embedded Platform," a Prese...
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Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
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- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Recently uploaded (20)
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
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Climate Impact of Software Testing at Nordic Testing Days
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Choosing The Best AWS Service For Your Website + API.pptx
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Gpu implementation of satellite image filtering
- 1. GPU Implementation of Satellite Image Filtering using OpenCL Institute for Geoinformatics Advanced processing of geospatial data- GPU, Grid and Cloud computing Ermias Beyene Tesfamariam 15 July 2010
- 2. Objective • Efficient edge detection method for remote sensing imageries. • implementation of image filtering on programmable GPU using the openCL language • Comparison of different algorithms (Sobel vs. Laplacian) for their efficiency and quality
- 3. Application • The HOST executes the code written as usual, using C++. • The DEVICES execute OpenCL code. • Use specific OpenCL compiler for the CPU & for the GPU (ATI Stream). • The OpenCL API has functions to identify devices, compile programs, send and receive information and run OpenCL program on the chosen device.
- 4. Application OpenCL Code: 1 - Create the OpenCL code using OpenCL language; Host Code: 2 - Create program using C++; 3 - Import the data to be processed; 4 - Use the OpenCL API to transfer data to the devices; 5 - Use the OpenCL API to call executions; 6 - Retrieve processed data.
- 5. Main OpenCL API Commands • Memory allocation via API – clCreateBuffer • Accessing device memory via API – clEnqueueWriteBuffer – clEnqueueReadBuffer
- 6. Method • Landsat imageries – Bands with high contrast, e.g. Band 4 • Image Convolution – 3X3 Filtering Mask convolves over the image • Sobel Algorithm • Laplacian Algorithm
- 7. Sobel Filter • 2-D anisotropic measure of the 1st spatial derivative of an image.
- 8. Sobel filter • consists of two kernels (Masks) which detect horizontal and vertical changes in an image • The 3x3 Sobel kernels are: – Horizontal – Vertical
- 9. Sobel filter
- 10. Laplacian filter 2-D isotropic measure of the 2nd spatial derivative of an image.
- 12. Sobel Filter Output Image
- 13. Sobel Filter Output Image
- 14. GPU vs CPU performance Sobel Filter Laplacian Filter
- 15. Open Issue • Applying Image smoothing and contrast enhancement before/during filtering