Introduction

•Download as PPT, PDF•

1 like•497 views

Kh Ravy

Multimedia

Technology

Multimedia System ,[object Object],[object Object],[object Object]

Challenges of Multimedia Computing ,[object Object],[object Object],[object Object],[object Object],[object Object]

Multimedia is Multidisciplinary Image, audio, speech processing Computer vision, pattern recognition Computer networks, operating system Human computer interaction Computer graphics Multimedia computing

Example Multimedia Systems Real Estate in MIT’s Project Athena

Example Multimedia Systems Chronoscope in MIT’s Project Athena

Example Multimedia Systems Navigation Learning Environment in MIT’s Project Athena

Example Multimedia Systems Photosynth of Microsoft Live Labs.

Multimedia Computing ,[object Object],[object Object],[object Object],[object Object],[object Object]

Data Compression Raw image takes about 6M bytes (without header information) 24k bytes with jpeg, Q=50

“JPEG” Illustration r g b Transform to frequency domain Quantization Lossless compression … 0010001 …. decoder r’ g’ b’ u v

Media Processing and Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Multiple Object Tracking Toys Squash Lab Double Squash

Object Recognition David G. Lowe, "Object recognition from local scale-invariant features," ICCV99.

Posture Detection in Images Template Image

Finding Action Clusters in Image Database

Action Recognition video1 video2 Ramanan, D., Forsyth, D. A., Zisserman, A. " Strike a Pose: Tracking People by Finding Stylized Poses. " Computer Vision and Pattern Recognition (CVPR), San Diego, CA, June 2005. video3

Media Delivery ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Multimedia Database and Indexing ,[object Object],[object Object],[object Object]

Useful Resources ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

What's hot

We propose a new design for large-scale multimedia content protection systems. The proposed system can be used to protect different multimedia content types, including 2-D videos, 3-D videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of 3-D videos, and (ii) distributed matching engine for multimedia objects.

Cloud-Based Multimedia Content Protection System

reshma reshu

Machine Learning Meetup SOF: Intro to ML

Imagga Technology

A robust watermarking algorithm based on image normalization and dc coefficients

Harshal Ladhe

Mc 004

CroninKe

ESWC SS 2013 - Monday Keynote Stefan Decker: From Linked Data to Networked Kn...

eswcsummerschool

KIoT - Kit for the internet of things

carlo zapponi

Internet of Things: Concepts and Technologies

PayamBarnaghi

Nowadays, Internet users use many social websites for communication with each other, and some of the users may live in another part of the world. Thus, communication becomes much easier and faster. However, with internet growing and their users, it becomes more likely to be attacked by hackers and intruders to access people personal data. Also, some professional hackers can detect the entire communication component for instance; text, image, video and voice messages. In this paper, it is focused on one component, which is voice communication; and the main idea of this work is to hide the voice message that generated from one side and convert to a wave data, then to the binary code consequently on the receiver side. Later, the generated code will hide inside an image before reaching the destination. The final step is to reverse the whole security process that has been done in the source side. Therefore it can provide a safe environment for voice communication among the internet client.

An Approach for Securing Voice Communication via Image Watermarking Technique

CSCJournals

IoT-Lite: A Lightweight Semantic Model for the Internet of Things

PayamBarnaghi

What's hot (9)

Cloud-Based Multimedia Content Protection System

Machine Learning Meetup SOF: Intro to ML

A robust watermarking algorithm based on image normalization and dc coefficients

Mc 004

ESWC SS 2013 - Monday Keynote Stefan Decker: From Linked Data to Networked Kn...

KIoT - Kit for the internet of things

Internet of Things: Concepts and Technologies

An Approach for Securing Voice Communication via Image Watermarking Technique

IoT-Lite: A Lightweight Semantic Model for the Internet of Things

Similar to Introduction

Multimediaexercise

Rony Mohamed

Emerging database technology multimedia database

Salama Al Busaidi

Unit 1

swapnasalil

Are you ready to take your smart solutions to the next level? During this session, you will learn how to integrate AI and ML platforms for analysis and processing of digital twin data in systems based on FIWARE. In this session, we'll cover all aspects of ML Ops, including how to deploy ML in edge systems, and show you how to use AI and ML to turn data into actionable insights and business value. You'll discover how to leverage the power of AI and ML to optimize your smart solutions and gain a competitive edge. You'll learn how to implement ML Ops in solutions powered by FIWARE, enabling you to easily deploy machine learning models and update them in real-time. We'll also discuss how to deploy ML in edge systems, allowing you to process data locally and avoid the latency of sending it to a central server. This session will invite participants to discover how to put data to work and turn it into wisdom that drives business value. Moreover, different experiences on how to automatize ML, especially regarding training and deploying ML models into solutions powered by FIWARE in different real scenarios. If you are interested in learning how to turn machine learning models towards perfection and deliver ML solutions more easily as part of solutions powered by FIWARE, to extract the most out of data, this session is for you.

AI-MiguelGonzalez.pdf

FIWARE

Guru_poster

Christopher Clarke

Chapter 1-Introduction to Media-Past, Present and Future.ppt

VasanthiMuniasamy2

2018 learning approach-digitaltrends

Abhilash Gopalakrishnan

VIZBI 2015 Tutorial: Cytoscape, IPython, Docker, and Reproducible Network Dat...

Keiichiro Ono

Intrusive Images, Neural Mechanisms, And Treatment...

Angie Lee

Emerging Technologies 3.0

Naveed Ahmed Siddiqui

So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it. As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop. Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #datasciencehappiness.

Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...

Shirshanka Das

2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047 Description: So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it. As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop. Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.

Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...

Yael Garten

Science Big, Science Connected

Deepak Singh

Information_Systems

Amitave Banik

B017250715

IOSR Journals

IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.

DIP Using Image Encryption and XOR Operation Affine Transform

iosrjce

Deep dive in to modern it technology

Viknaraj Manogararajah

Redundancy avoidance for big data in data centers a conventional neural netwo...

nexgentechnology

Interaction Paradigms

Sebastian Weinhold

Evaluation Of Proposed Design And Necessary Corrective Action

Sandra Arveseth

Similar to Introduction (20)

Multimediaexercise

Emerging database technology multimedia database

Unit 1

AI-MiguelGonzalez.pdf

Guru_poster

Chapter 1-Introduction to Media-Past, Present and Future.ppt

2018 learning approach-digitaltrends

VIZBI 2015 Tutorial: Cytoscape, IPython, Docker, and Reproducible Network Dat...

Intrusive Images, Neural Mechanisms, And Treatment...

Emerging Technologies 3.0

Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...

Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...

Science Big, Science Connected

Information_Systems

B017250715

DIP Using Image Encryption and XOR Operation Affine Transform

Deep dive in to modern it technology

Redundancy avoidance for big data in data centers a conventional neural netwo...

Interaction Paradigms

Evaluation Of Proposed Design And Necessary Corrective Action

Recently uploaded

Real Time Object Detection Using Open CV

Khem

AXA XL - Insurer Innovation Award Americas 2024

The Digital Insurer

A Beginners Guide to Building a RAG App Using Open Source Milvus

Zilliz

Data Cloud, More than a CDP by Matt Robison

Anna Loughnan Colquhoun

Whatsapp Number Escorts Call girls 8617370543 Available 24x7 Navi Mumbai Call Girls Service Offer Genuine VIP Model Escorts Call Girls in Your Budget. Navi Mumbai Call Girls Service Provide Real Call Girls Number. Make Your Sexual Pleasure Memorable with Our Navi Mumbai Call Girls at Affordable Price. Top VIP Escorts Call Girls, High Profile Independent Escorts Call Girls, Housewife Women Escorts Call Girl, College Girls Escorts Call Girls, Russian Escorts Call girls Service in Your Budget.

Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model

Deepika Singh

Scalable LLM APIs for AI and Generative AI Application Development Ettikan Karuppiah, Director/Technologist - NVIDIA Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...

apidays

Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...

Zilliz

Accelerating FinTech Innovation: Unleashing API Economy and GenAI Vasa Krishnan, Chief Technology Officer - FinResults Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

apidays

Automating Google Workspace (GWS) & more with Apps Script

wesley chun

Artificial Intelligence Chap.5 : Uncertainty

Khushali Kathiriya

The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

apidays

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

GenAI Risks & Security Meetup 01052024.pdf

lior mazor

Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.

Corporate and higher education May webinar.pptx

Rustici Software

Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

debabhi2

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

2024: Domino Containers - The Next Step. News from the Domino Container commu...

Martijn de Jong

DBX First Quarter 2024 Investor Presentation

Dropbox

presentation ICT roal in 21st century education

jfdjdjcjdnsjd

Recently uploaded (20)

Real Time Object Detection Using Open CV

AXA XL - Insurer Innovation Award Americas 2024

A Beginners Guide to Building a RAG App Using Open Source Milvus

Data Cloud, More than a CDP by Matt Robison

Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model

Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...

Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

Automating Google Workspace (GWS) & more with Apps Script

Artificial Intelligence Chap.5 : Uncertainty

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

GenAI Risks & Security Meetup 01052024.pdf

Corporate and higher education May webinar.pptx

Exploring the Future Potential of AI-Enabled Smartphone Processors

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

2024: Domino Containers - The Next Step. News from the Domino Container commu...

DBX First Quarter 2024 Investor Presentation

presentation ICT roal in 21st century education

Introduction

1. Introduction Principles of Multimedia Systems Sethserey SAM Computer Science Department

2. `

5. Multimedia is Multidisciplinary Image, audio, speech processing Computer vision, pattern recognition Computer networks, operating system Human computer interaction Computer graphics Multimedia computing

6. Example Multimedia Systems Real Estate in MIT’s Project Athena

7. Example Multimedia Systems Chronoscope in MIT’s Project Athena

8. Example Multimedia Systems Navigation Learning Environment in MIT’s Project Athena

9. Example Multimedia Systems Photosynth of Microsoft Live Labs.

10.

11. Data Compression Raw image takes about 6M bytes (without header information) 24k bytes with jpeg, Q=50

12. “JPEG” Illustration r g b Transform to frequency domain Quantization Lossless compression … 0010001 …. decoder r’ g’ b’ u v

13.

14. Tracking Object by Image Matching

15. Multiple Object Tracking Toys Squash Lab Double Squash

16. Finding Objects in Clutter

17. Object Recognition David G. Lowe, "Object recognition from local scale-invariant features," ICCV99.

18. Posture Detection in Images Template Image

19. Dealing with Multiple Objects

20.

21. Finding Action Clusters in Image Database

22. Finding Action Clusters (con’t)

23. Action Recognition video1 video2 Ramanan, D., Forsyth, D. A., Zisserman, A. " Strike a Pose: Tracking People by Finding Stylized Poses. " Computer Vision and Pattern Recognition (CVPR), San Diego, CA, June 2005. video3

24.

25.

26.

27. Q & A

Introduction

Recommended

Recommended

More Related Content

What's hot

What's hot (9)

Similar to Introduction

Similar to Introduction (20)

More from Kh Ravy

More from Kh Ravy (9)

Recently uploaded

Recently uploaded (20)

Introduction