This document discusses compact descriptors for visual search. It begins by providing context on visual search and content-based image recognition. It then discusses MPEG's initiative to standardize compact descriptors to enable interoperable visual search applications. The presentation describes the requirements and techniques for developing compact yet robust descriptors, including achieving various types of invariance. It presents different methods for compacting descriptors and their properties. Finally, it discusses potential use cases and the evolution of visual search to include video, 3D objects, and augmented reality.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
A Segmentation based Sequential Pattern Matching for Efficient Video Copy De...SWAMI06
Â
A considerable number of videos are illegal copies or manipulated versions of existing media, making copyright management a complicated process.
Call for Change:-
Today’s widespread video copyright infringement calls for the development of fast and accurate copy-detection algorithms.
As video is the most complex type of digital media, it has so far received the least attention regarding copyright management.
Protect Data:-
Content-based copy detection (CBCD) ,a promising technique for video monitoring and copyright protection.
In this presentation, keynoting a session at the 2012 Model Based Enterprise Summit 2012 on lightweight visualization, Consortium Executive Director Dave Opsahl informs the audience on how what we typically refer to as "visualization", while a needed and extremely valuable category of solutions, is not the same as "communication". The MBE Summit, hosted annually by the National Institue of Standards and Technology, is a gathering of managers, technologists, engineers, and thought leaders on how to enable the Model Based Enterpris (MBE).
Efficient use of Standards-based Interfaces and Encodings in Geospatial Intel...Luis Bermudez
Â
Presentation provided at GEOINT 2019. This training session Provides an overview of OGC standards that have been adapted by National System for Geospatial Intelligence (NSG) specifications. It will enable GEOINT professionals to more efficiently use standards-based interfaces and encoding formats to solve geospatial problems. By equipping GEOINT professionals with the skills to identify and apply OGC standards, this training course will improve the professionals’ ability to meet challenges within their day-to-day work.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
A Segmentation based Sequential Pattern Matching for Efficient Video Copy De...SWAMI06
Â
A considerable number of videos are illegal copies or manipulated versions of existing media, making copyright management a complicated process.
Call for Change:-
Today’s widespread video copyright infringement calls for the development of fast and accurate copy-detection algorithms.
As video is the most complex type of digital media, it has so far received the least attention regarding copyright management.
Protect Data:-
Content-based copy detection (CBCD) ,a promising technique for video monitoring and copyright protection.
In this presentation, keynoting a session at the 2012 Model Based Enterprise Summit 2012 on lightweight visualization, Consortium Executive Director Dave Opsahl informs the audience on how what we typically refer to as "visualization", while a needed and extremely valuable category of solutions, is not the same as "communication". The MBE Summit, hosted annually by the National Institue of Standards and Technology, is a gathering of managers, technologists, engineers, and thought leaders on how to enable the Model Based Enterpris (MBE).
Efficient use of Standards-based Interfaces and Encodings in Geospatial Intel...Luis Bermudez
Â
Presentation provided at GEOINT 2019. This training session Provides an overview of OGC standards that have been adapted by National System for Geospatial Intelligence (NSG) specifications. It will enable GEOINT professionals to more efficiently use standards-based interfaces and encoding formats to solve geospatial problems. By equipping GEOINT professionals with the skills to identify and apply OGC standards, this training course will improve the professionals’ ability to meet challenges within their day-to-day work.
This presentation was given at the doctoral days at ENSIAS Morocco. The goal was to show how the innovation process goes and a particular example through what Cisco is doing for the media networks.
CGM (Computer Graphics Metafile) v SVG (Scalable Vector Graphic)Vizualsite LLC
Â
The slides provide an overview of the two open graphics formats CGM and SVG. Included is a comparison of the formats and the benefits and limitations of both. They slides were part of a webinar, please follow the link to see a recording. https://attendee.gotowebinar.com/recording/9059357153062897153
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-gallagher
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Paul Gallagher, Senior Director of Technology and Product Planning for LG, presents the "Coming Shift from Image Sensors to Image Sensing" tutorial at the May 2017 Embedded Vision Summit.
The image sensor space is entering the fourth disruption in its evolution. The first three disruptions primarily focused on taking “pretty pictures” for human consumption, evaluation, and storage. The coming disruption will be driven by machine vision moving into the mainstream. Smart homes, offices, cars, devices – as well as AR/MR, biometrics and crowd monitoring – all need to run image data through a processor to activate responses without human viewing. The opportunity this presents is massive, but as the growth efficiencies come into play the solutions will become specialized.
This talk highlights the opportunities that the emerging shift to image-based sensing will bring throughout the imaging and vision industry. It explores the ingredients that industry participants will need in order to capitalize on these opportunities, and why the entrenched players may not be at as great an advantage as might be expected.
Data Engineer, Patterns & Architecture The future: Deep-dive into Microservic...Igor De Souza
Â
With Industry 4.0, several technologies are used to have data analysis in real-time, maintaining, organizing, and building this on the other hand is a complex and complicated job. Over the past 30 years, we saw several ideas to centralize the database in a single place as the united and true source of data has been implemented in companies, such as Data wareHouse, NoSQL, Data Lake, Lambda & Kappa Architecture.
On the other hand, Software Engineering has been applying ideas to separate applications to facilitate and improve application performance, such as microservices.
The idea is to use the MicroService patterns on the date and divide the model into several smaller ones. And a good way to split it up is to use the model using the DDD principles. And that's how I try to explain and define DataMesh & Data Fabric.
“Selecting the Right Camera for Your Embedded Computer Vision Project,” a Pre...Edge AI and Vision Alliance
Â
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/09/selecting-the-right-camera-for-your-embedded-computer-vision-project-a-presentation-from-digital-sense/
Adrián Márques , Managing Partner at Digital Sense, presents the “Selecting the Right Camera for Your Embedded Computer Vision Project” tutorial at the May 2022 Embedded Vision Summit.
The right camera can be key to the success of a computer vision project. For example, in an object detection or segmentation task, you can gain much more in performance by producing images with high contrast between your objects of interest and the background than by having your deep neural network do its best with sub-par footage.
It’s easy to underestimate the complexity of selecting and integrating the right camera for your application, and the numerous important considerations can seem overwhelming. In this talk, Márques provides an introduction to the main factors to consider when choosing a camera, and share practical considerations you can apply to your project.
Is Dynamic Cubes now ready to replace Transformer implementations? The business analytics experts at Senturus take an unbiased look at the pros and cons of switching. View the webinar video recording and download this deck: http://www.senturus.com/resources/cognos-dynamic-cubes-set-to-retire-transformer/.
Topics discussed include the types of Transformer implementations that could benefit by switching to Dynamic Cubes, pre-requisites for replacing a Transformer implementation with Dynamic Cubes and typical pitfalls you may encounter in the process.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
It is mandatory for every medicine or pharma packaging to have a unique serial code or UID. Project is to build a web application that will provide tracking capabilities for the UID for pharma packaging of drugs. The track feature (TRACK n trace) will track the UID of each package by using vision based scanners, RFIDs, etc. and store the data into a local server. The server will be synced daily with a global server (we are looking for cloud based hosting platforms such as Windows Azure or amazon web services). We have to build the trace functionality (Track n TRACE) by building a web interface where a person with the UID can trace the shipment.
We have to keep historical records for as long as 10 years and build logic on basis of the UID state. We have to provide the details from the database as in when was this package manufactured, when was it shipped, etc. If the UID entered is faulty for example; it wasn’t ever manufactured or if it is over its expiration date then we have to generate corresponding errors and also maintain a log of such entries and send notification to the admins with details of IP, Geography or where the error generated.
It is a presentation for the concept of deep lap which is machine learning and artificial intelligence
DeepLab is a series of state-of-the-art deep learning models developed for semantic image segmentation, which is the process of partitioning an image into segments where each segment corresponds to a specific object or region within the image. This detailed exploration will cover the evolution of DeepLab, its architecture, core techniques, applications, and its impact on the field of computer vision.
### Evolution of DeepLab
DeepLab has undergone multiple iterations, each improving upon the previous in terms of accuracy and efficiency. The major versions are:
1. **DeepLabv1** (2014)
2. **DeepLabv2** (2015)
3. **DeepLabv3** (2017)
4. **DeepLabv3+** (2018)
#### DeepLabv1
The first version of DeepLab introduced the idea of employing atrous (dilated) convolutions in convolutional neural networks (CNNs). Atrous convolutions allow for control over the resolution at which feature responses are computed within the network, effectively enabling the network to have a larger receptive field without increasing the number of parameters or the amount of computation required. This approach helps to capture more contextual information, which is crucial for accurately segmenting images.
**Key Features:**
- **Atrous Convolutions**: By inserting spaces (or holes) between the convolutional kernel elements, atrous convolutions enlarge the field of view of filters without increasing the number of parameters or computational cost.
- **Fully Convolutional Networks (FCNs)**: DeepLabv1 leverages FCNs to ensure that the input image's spatial dimensions are preserved, facilitating dense predictions needed for segmentation.
- **CRF (Conditional Random Fields)**: Post-processing with CRFs is used to refine the boundaries of the segmented regions, leveraging spatial consistency and smoothness.
#### DeepLabv2
DeepLabv2 builds on the success of the first version by introducing the Atrous Spatial Pyramid Pooling (ASPP) module. This module helps capture multi-scale contextual information by applying atrous convolutions with different rates, which essentially probes the input image with filters of multiple effective fields of view.
**Key Features:**
- **ASPP Module**: It combines several parallel atrous convolution layers with different rates, capturing information at multiple scales.
- **Improved CRF**: The CRF used in DeepLabv2 is more deeply integrated and fine-tuned to enhance the segmentation performance.
#### DeepLabv3
DeepLabv3 further improves the model by addressing some limitations of the previous versions. It refines the ASPP module and removes the need for CRF post-processing by integrating stronger and more effective feature extraction techniques within the network itself.
**Key Features:**
- **Enhanced ASPP**: This version of ASPP includes batch normalization and image-level features, which improve the overall robustness and accuracy.
This presentation was given at the doctoral days at ENSIAS Morocco. The goal was to show how the innovation process goes and a particular example through what Cisco is doing for the media networks.
CGM (Computer Graphics Metafile) v SVG (Scalable Vector Graphic)Vizualsite LLC
Â
The slides provide an overview of the two open graphics formats CGM and SVG. Included is a comparison of the formats and the benefits and limitations of both. They slides were part of a webinar, please follow the link to see a recording. https://attendee.gotowebinar.com/recording/9059357153062897153
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-gallagher
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Paul Gallagher, Senior Director of Technology and Product Planning for LG, presents the "Coming Shift from Image Sensors to Image Sensing" tutorial at the May 2017 Embedded Vision Summit.
The image sensor space is entering the fourth disruption in its evolution. The first three disruptions primarily focused on taking “pretty pictures” for human consumption, evaluation, and storage. The coming disruption will be driven by machine vision moving into the mainstream. Smart homes, offices, cars, devices – as well as AR/MR, biometrics and crowd monitoring – all need to run image data through a processor to activate responses without human viewing. The opportunity this presents is massive, but as the growth efficiencies come into play the solutions will become specialized.
This talk highlights the opportunities that the emerging shift to image-based sensing will bring throughout the imaging and vision industry. It explores the ingredients that industry participants will need in order to capitalize on these opportunities, and why the entrenched players may not be at as great an advantage as might be expected.
Data Engineer, Patterns & Architecture The future: Deep-dive into Microservic...Igor De Souza
Â
With Industry 4.0, several technologies are used to have data analysis in real-time, maintaining, organizing, and building this on the other hand is a complex and complicated job. Over the past 30 years, we saw several ideas to centralize the database in a single place as the united and true source of data has been implemented in companies, such as Data wareHouse, NoSQL, Data Lake, Lambda & Kappa Architecture.
On the other hand, Software Engineering has been applying ideas to separate applications to facilitate and improve application performance, such as microservices.
The idea is to use the MicroService patterns on the date and divide the model into several smaller ones. And a good way to split it up is to use the model using the DDD principles. And that's how I try to explain and define DataMesh & Data Fabric.
“Selecting the Right Camera for Your Embedded Computer Vision Project,” a Pre...Edge AI and Vision Alliance
Â
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/09/selecting-the-right-camera-for-your-embedded-computer-vision-project-a-presentation-from-digital-sense/
Adrián Márques , Managing Partner at Digital Sense, presents the “Selecting the Right Camera for Your Embedded Computer Vision Project” tutorial at the May 2022 Embedded Vision Summit.
The right camera can be key to the success of a computer vision project. For example, in an object detection or segmentation task, you can gain much more in performance by producing images with high contrast between your objects of interest and the background than by having your deep neural network do its best with sub-par footage.
It’s easy to underestimate the complexity of selecting and integrating the right camera for your application, and the numerous important considerations can seem overwhelming. In this talk, Márques provides an introduction to the main factors to consider when choosing a camera, and share practical considerations you can apply to your project.
Is Dynamic Cubes now ready to replace Transformer implementations? The business analytics experts at Senturus take an unbiased look at the pros and cons of switching. View the webinar video recording and download this deck: http://www.senturus.com/resources/cognos-dynamic-cubes-set-to-retire-transformer/.
Topics discussed include the types of Transformer implementations that could benefit by switching to Dynamic Cubes, pre-requisites for replacing a Transformer implementation with Dynamic Cubes and typical pitfalls you may encounter in the process.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
It is mandatory for every medicine or pharma packaging to have a unique serial code or UID. Project is to build a web application that will provide tracking capabilities for the UID for pharma packaging of drugs. The track feature (TRACK n trace) will track the UID of each package by using vision based scanners, RFIDs, etc. and store the data into a local server. The server will be synced daily with a global server (we are looking for cloud based hosting platforms such as Windows Azure or amazon web services). We have to build the trace functionality (Track n TRACE) by building a web interface where a person with the UID can trace the shipment.
We have to keep historical records for as long as 10 years and build logic on basis of the UID state. We have to provide the details from the database as in when was this package manufactured, when was it shipped, etc. If the UID entered is faulty for example; it wasn’t ever manufactured or if it is over its expiration date then we have to generate corresponding errors and also maintain a log of such entries and send notification to the admins with details of IP, Geography or where the error generated.
It is a presentation for the concept of deep lap which is machine learning and artificial intelligence
DeepLab is a series of state-of-the-art deep learning models developed for semantic image segmentation, which is the process of partitioning an image into segments where each segment corresponds to a specific object or region within the image. This detailed exploration will cover the evolution of DeepLab, its architecture, core techniques, applications, and its impact on the field of computer vision.
### Evolution of DeepLab
DeepLab has undergone multiple iterations, each improving upon the previous in terms of accuracy and efficiency. The major versions are:
1. **DeepLabv1** (2014)
2. **DeepLabv2** (2015)
3. **DeepLabv3** (2017)
4. **DeepLabv3+** (2018)
#### DeepLabv1
The first version of DeepLab introduced the idea of employing atrous (dilated) convolutions in convolutional neural networks (CNNs). Atrous convolutions allow for control over the resolution at which feature responses are computed within the network, effectively enabling the network to have a larger receptive field without increasing the number of parameters or the amount of computation required. This approach helps to capture more contextual information, which is crucial for accurately segmenting images.
**Key Features:**
- **Atrous Convolutions**: By inserting spaces (or holes) between the convolutional kernel elements, atrous convolutions enlarge the field of view of filters without increasing the number of parameters or computational cost.
- **Fully Convolutional Networks (FCNs)**: DeepLabv1 leverages FCNs to ensure that the input image's spatial dimensions are preserved, facilitating dense predictions needed for segmentation.
- **CRF (Conditional Random Fields)**: Post-processing with CRFs is used to refine the boundaries of the segmented regions, leveraging spatial consistency and smoothness.
#### DeepLabv2
DeepLabv2 builds on the success of the first version by introducing the Atrous Spatial Pyramid Pooling (ASPP) module. This module helps capture multi-scale contextual information by applying atrous convolutions with different rates, which essentially probes the input image with filters of multiple effective fields of view.
**Key Features:**
- **ASPP Module**: It combines several parallel atrous convolution layers with different rates, capturing information at multiple scales.
- **Improved CRF**: The CRF used in DeepLabv2 is more deeply integrated and fine-tuned to enhance the segmentation performance.
#### DeepLabv3
DeepLabv3 further improves the model by addressing some limitations of the previous versions. It refines the ASPP module and removes the need for CRF post-processing by integrating stronger and more effective feature extraction techniques within the network itself.
**Key Features:**
- **Enhanced ASPP**: This version of ASPP includes batch normalization and image-level features, which improve the overall robustness and accuracy.
Similar to Compact Descriptors for Visual Search (20)
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
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10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
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The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
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In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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Compact Descriptors for Visual Search
1. Compact Descriptors 4 Visual Search
Danilo Pau (danilo.pau@st.com)
Senior Principal Engineer
Senior Member of Technical Staff
SMIEEE
SI/CVRP
STMicroelectronics/AST
Courtesy: M. Funamizu
2. Agenda 2
• Visual Search: Context
• MPEG initiative on Visual Search
• Compact Descriptors for Visual Search
• Implementation
• Use Cases
• Visual Search Evolution: Moving Pictures and 3D
• Question and Answers
Presentation Title 15/01/2013
3. Agenda 3
• Visual Search: Context
• MPEG initiative on Visual Search
• Compact Descriptors for Visual Search
• Implementation
• Use Cases
• Visual Search Evolution: Moving Pictures and 3D
• Question and Answers
Presentation Title 15/01/2013
4. Visual Search Context 4
• Millions of images and videos continue being uploaded all over the
world on remote servers
• Each day on Facebook 300 million photos are uploaded
• roughly 58 photos uploaded each second
• One hour of video uploaded to YouTube every second
Presentation Title 15/01/2013
5. Content Based Image Recognition 5
• CBIR covers the concept of search that analyzes the actual content in
the image, rather than relying on metadata.
• The development of this concept incorporated many algorithms and
techniques from fields such as statistics, pattern recognition and
computer vision.
• CBIR attracted a lot of attention and after many years of research, it
has expanded towards the marketplace.
• CBIR’s application on mobile market is called Mobile Visual Search
• Visual Search is about the capability to initiate a search using an
image as a query that captures a rigid object
• Market potential of mobile visual search considers any mobile device with camera
(phones, tablets and hybrids).
Presentation Title 15/01/2013
6. CBIR vs QR Codes 6
• Quick Response codes, a type of two-dimensional barcode.
• The code is scanned by the mobile imager to produce a URL address
for re-direction and browsing.
• QR codes are being used by 6.2% of the smart phone users in USA
Presentation Title 15/01/2013
7. Lots of Existing Applications 7
• Google’s Goggles
• Nokia’s Point and Find
• oMoby
• Like.com
• Kooaba
• Moodstocks
• Snaptell
• pixlinQ
• Bing
Presentation Title 15/01/2013
8. Existing Apps use Jpeg 8
• Previous applications use mobile imager that send JPEG compressed
queries
Mobile device
Send Jpeg images Remote server
Visual search result
Database
Presentation Title 15/01/2013
9. An Example of Visual Search 9
Interest Point Description
Descriptor pairing
Inliers
Query
Courtesy Telecom Italia
10. The Rise of Compressed Descriptors 10
• Alternatively send “compact features” extracted from raw images
• For example Scale Invariant Feature Transform – SIFT visual
descriptors
• Consider 1200 descriptors, each one 128 Bytes, 4 bytes for
coordinates, times 30 fps network load nearly 38 Mbit/s
unacceptable VGA Image
160
140
120
100 JPEG High
KB 80 JPEG Low
SIFT
60
40
20
0
JPEG High JPEG Low SIFT Presentation Title 15/01/2013
11. Systems Considered 11
• Instead of sending images
(a)
• application can send
compact descriptors (b)
• and even perform search
locally (c).
12. Previous Attempts 12
• Hashing
• Locality Sensitive Hashing [Yeo et ali., 2008]
• Similarity Sensitive Coding [Torralba et ali., 2008]
• Spectral Hashing [Weiss et ali, 2008]
• Transform Coding
• Karunen-love Transform [Chandrasekhar et ali. 2009]
• ICA based Transform [Narozny et ali., 2008]
• Vector Quantization
• Product Quantization [Jegou et ali., 2010]
• Tree Structured Vector Quantization [Nistr et ali., 2006]
• Alternative to SIFT
• Compressed Histogram of Gradients [Chandrasekhar et ali. 2011]
Presentation Title 15/01/2013
13. Agenda 13
• Visual Search: Context
• MPEG initiative on Visual Search
• Compact Descriptors for Visual Search
• Implementation
• Use Cases
• Visual Search Evolution: Moving Pictures and 3D
• Question and Answers
Presentation Title 15/01/2013
14. Is a standard on Visual Search needed ? 14
• Reduce load on wireless networks carrying visual search-related
information.
• Ensure interoperability of visual search applications and databases,
• Enable hardware support for descriptor extraction and matching in
mobile devices,
• Enable high level of performance of implementations conformant to
the standard,
• Simplify design of descriptor extraction and matching for visual search
applications,
15. What is a suitable standardization
15
body ?
• Informal title:
• Moving Picture Experts Group (MPEG)
• Formal title:
• ISO/IEC JTC1 SC29 WG11 (Coding of Moving Pictures and Audio)
JTC 1
• Parent SDOs:
• ISO: International Organization for Standardization SC29
• IEC: International Electro technical Commission
• JTC 1: Joint Technical Committee One
• SC29: Study Committee 29: Coding of Audio, Picture, WG11 (MPEG)
Multimedia and Hypermedia Information
• Members: National Bodies (25 voting, 16 observers)
17. Agenda 17
• Visual Search: Context
• MPEG initiative on Visual Search
• Compact Descriptors for Visual Search
• Implementation
• Use Cases
• Visual Search Evolution: Moving Pictures and 3D
• Question and Answers
Presentation Title 15/01/2013
18. CDVS : Scope 18
• Descriptor extraction process needed to ensure interoperability.
• Bitstream of compact descriptors
Standard
Query Descriptor Descriptor Descriptor Geometric List of
Image extraction bitstream matching verification results
Database
19. Requirements 19
Robustness
High matching accuracy shall be achieved at least for images of textured
rigid objects, landmarks, and printed documents.
The matching accuracy shall be robust to changes in vantage points,
camera parameters, lighting conditions, as well as in the presence of partial
occlusions.
Sufficiency
Descriptors shall be self-contained, in the sense that no other data are
necessary for matching.
Compactness
Shall minimize lengths/size of image descriptors
Scalability
Shall allow adaptation of descriptor lengths to support the required
performance level and database size.
Shall enable design of web-scale visual search applications and
databases.
20. How to achieve robustness 20
• Image content is transformed into visual feature with coordinates
that are invariant to illumination, scale, rotation, affine and
perspective transforms
27. Extraction Pipeline 27
Encoding
Local Description Transfor Arithmetic
m & SQ coding
Extraction
Image Keypoint MSVQ
Resizing DoG SIFT H Mode
selection encoding Compact
descriptors
S Mode
Coordinate
coding
H-Mode uses SQ encoding (256B) SCFV
S-Mode uses MSVQ encoding (38KB) Descriptor
Both Mode uses SCFV (49KB)
28. Properties of SIFT 28
David Lowe’s local descriptor detection extraction (1999-2004)
Extraordinarily robust matching technique
• Can handle changes in viewpoint
• Up to about 30 degree out of plane rotation
• Can handle significant changes in illumination
• Sometimes even day vs. night (below)
• Lots of code available http://www.vlfeat.org (BSD license)
29. Scale 1
Pyramid of DoG
Scale m
29
Octave 1
DoGs
DoGs
Octave n
DoGs
31. Building a Descriptor 31
• Take 16x16 patch window around detected interest point
• Subdivide patch with 4x4 sub-patches
• Create per sub patch 8 bin-histogram over edge orientations weighted
by magnitude
angle histogram
0 π
2Ď€
• These lead to a 4x4x8=128 element vector the SIFT descriptor
Presentation Title 15/01/2013
32. Key point selection 32
• Basic idea: inlier features do not behave, in a statistical sense, as do
the outlier features.
• Relevance value that results from taking into account distance from
center, scale, orientation, peak, mean and variance of the SIFT
descriptor.
33. Local Descriptor Compression H mode 33
• Main idea is to generate a compressed descriptor from
uncompressed SIFT by
• Simple linear combinations of histograms
• Scalar quantisation of resultant values
• Adaptive Arithmetic coding
• Main benefits
• Very low computational complexity
• Negligible memory requirements
• Highly scalable
• Allows for very efficient matching and retrieval
35. Location Encoding 35
• Histogram Map: The positions of the nonzero bins are encoded as
binary words through scanning columns and compressing the words by
arithmetic coding.
• Histogram Count: The number of coordinates in the nonzero bins is
encoded in an iterative fashion, by specifying first which bins contain
more than 1 key point, then by specifying which among these that
contain more than 2 keypoints, and so forth
36. Agenda 36
• Visual Search: Context
• MPEG initiative on Visual Search
• Compact Descriptors for Visual Search
• Implementation
• Use Cases
• Visual Search Evolution: Moving Pictures and 3D
• Question and Answers
Presentation Title 15/01/2013
37. Extraction times 37
• SIFT interest point detection and feature extraction made the biggest
contribution
• Global descriptors as complex as Interest Point Detection
• Very fast local descriptors and coordinate encoding
Quantitative evaluation of CDVS extraction and pairwise matching 15/01/2013
38. Agenda 38
• Visual Search: Context
• MPEG initiative on Visual Search
• Compact Descriptors for Visual Search
• Implementation
• Use Cases
• Visual Search Evolution: Moving Pictures and 3D
• Question and Answers
Presentation Title 15/01/2013
40. Visual Search: eReaders, Printers 40
Snapshot Mass Storage
Augmentation
Paper-copy Initiate Visual 3D models and markers
Search Send
Compact Transmission of
markers and 3D
Query models
Augmentation
Rendering
2D / 3D
Rendering
Selective quality&content Multimedia Content Retrieval Composition of
printing From the cloud augmentations
and image
Content Augmentation
41. News Finder
41
Still Pictures - Visual Search
Presentation Title 15/01/2013
42. Application and Use Cases from
42
Broadcaster point of view
• Logo Detection
• Interactive Fruition
Courtesy RAI Presentation Title 15/01/2013
46. Agenda 46
• Visual Search: Context
• MPEG initiative on Visual Search
• Compact Descriptors for Visual Search
• Implementation
• Use Cases
• Visual Search Evolution: Moving Pictures and 3D
• Question and Answers
Presentation Title 15/01/2013
47. Intra Predicted Descriptors 47
Desirable Properties:
An inter descriptor coded in a
compact visual stream
Expressed in terms of one or
more temporally neighboring
descriptors.
The "inter" part of the term
refers to the use of Inter Frame
Prediction.
Designed to achieve higher
compression rates and/or better
precision-recall performances
Presentation Title 15/01/2013
48. 3D Mobile Devices Will Surpass 148 Million
48
in 2015
• Advances in the 3D technology are very fast
• Industry adoption opens new opportunities 3D Visual Search
• From In-Stat studies:
• ~ 30 % of all handheld game consoles will be 3D by 2015.
• 3D mobile devices will increase demand for image sensors by 130 %.
• In 2012, Notebook will be the first 3D enabled mobile device to reach 1 million
units.
• By 2014, 18 % of all tablets will be 3D.
• Nintendo, Fuji, GoPro, Sony, ViewSonic, LG, Origin, Toshiba, Fujitsu, HP, ASUS,
Lenovo, Dell, Alienware, HTC and Sharp focusing on autostereoscopy mobile
technologies
Presentation Title 15/01/2013
49. Microsoft Kinect Asus Xtion
49
LG Optimus 3D P920
LG Optimus Pad
3DS by Nintendo
Google 3D Warehouse
HTC EVO 3D Sharp Aquos SH-12C
Presentation Title 15/01/2013
50. 3D Object Recognition with Kinect 50
SHOT: Unique Signatures of Histograms for Local Surface Description
http://www.youtube.com/watch?v=eRW1zG_aONk
Courtesy: CV laboratory University of Bologna
Presentation Title 15/01/2013
51. Agenda 51
• Visual Search: Context
• MPEG initiative on Visual Search
• Compact Descriptors for Visual Search
• Implementation
• Use Cases
• Visual Search Evolution: Moving Pictures and 3D
• Question and Answers
Presentation Title 15/01/2013