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Traditional cross-ratio methods (TCR) project a light pattern and use invariant properties of projective geometry to estimate the gaze position. Advantages of the TCR methods include robustness to large head movements and in general requires just a one time per user calibration. However, the accuracy of TCR methods decay significantly for head movements along the camera optical axis, mainly due to the angular difference between the optical and visual axis of the eye. In this paper we propose a depth compensation cross-ratio (DCR) method that improves the accuracy of TCR methods for large head depth variations. Our solution compensates the angular offset using a 2D onscreen vector computed from a simple calibration procedure. The length of the 2D vector, which varies with head distance, is adjusted by a scale factor that is estimated from relative size variations of the corneal reflection pattern. The proposed DCR solution was compared to a TCR method using synthetic and real data from 2 users. An average improvement of 40% was observed with synthetic data, and 8% with the real data.
Explains the fundamental Transport Equation. Shows the transportation industry perspective on Transport Quality. Discusses trends for the transportation industry and shows strategies to meet these trends.
Customer Survey Results as presented by Richard Jimmerson at ARIN's Public Policy and Members Meeting in April 2014. All ARIN 33 presentations are posted online at: https://www.arin.net/ARIN33_materials
Coutinho A Depth Compensation Method For Cross Ratio Based Eye TrackingKalle
Traditional cross-ratio methods (TCR) project a light pattern and use invariant properties of projective geometry to estimate the gaze position. Advantages of the TCR methods include robustness to large head movements and in general requires just a one time per user calibration. However, the accuracy of TCR methods decay significantly for head movements along the camera optical axis, mainly due to the angular difference between the optical and visual axis of the eye. In this paper we propose a depth compensation cross-ratio (DCR) method that improves the accuracy of TCR methods for large head depth variations. Our solution compensates the angular offset using a 2D onscreen vector computed from a simple calibration procedure. The length of the 2D vector, which varies with head distance, is adjusted by a scale factor that is estimated from relative size variations of the corneal reflection pattern. The proposed DCR solution was compared to a TCR method using synthetic and real data from 2 users. An average improvement of 40% was observed with synthetic data, and 8% with the real data.
Explains the fundamental Transport Equation. Shows the transportation industry perspective on Transport Quality. Discusses trends for the transportation industry and shows strategies to meet these trends.
Customer Survey Results as presented by Richard Jimmerson at ARIN's Public Policy and Members Meeting in April 2014. All ARIN 33 presentations are posted online at: https://www.arin.net/ARIN33_materials
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
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Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
2015EDM: A Framework for Multifaceted Evaluation of Student Models (Polygon)Yun Huang
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Multimodal behavior signal analysis and interpretation for young kids with ASDdiannepatricia
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CDS is the criminal face identification by capsule neural network.
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Deep learning, enabled by powerful compute, and fuelled by massive data, has delivered unprecedented data analytics capabilities. However, major limitations remain. Chiefly among those is that deep neural networks tend to exploit the surface statistics in the data, creating short-cuts from the input to the output, without really deeply understanding of the data. As a result, these networks fail miserably to generalize to novel combinations. This is because the networks perform shallow pattern matching but not deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. Second, machine learning is often trained to do just one task at a time, making it impossible to re-define tasks on the fly as needed in a complex operating environment. This talk presents our recent developments to extend the capacity of neural networks to remove these limitations. Our main focus is on learning to reason from data, that is, learning to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.
Professor Steve Roberts; The Bayesian Crowd: scalable information combinati...Ian Morgan
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Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
2015EDM: A Framework for Multifaceted Evaluation of Student Models (Polygon)Yun Huang
Presented in the 8th International Conference on Educational Data Mining as full paper. This is the first work that bring together predictive performance, plausibility and consistency three dimensions for evaluating student models, which is related to the general issues of appling machine learning to education domain.
Multimodal behavior signal analysis and interpretation for young kids with ASDdiannepatricia
Dr. Ming Li from Sun Yat-sen University CMU Joint Institute of Engineering presented “Multimodal behavior signal analysis and interpretation for young kids with ASD.” as part of the Cognitive Systems Institute Speaker Series.
CDS is the criminal face identification by capsule neural network.
Solving the common problems in image recognition such as illumination problem, scale variability, and to fight against a most common problem like pose problem, we are introducing Face Reconstruction System.
Deep learning, enabled by powerful compute, and fuelled by massive data, has delivered unprecedented data analytics capabilities. However, major limitations remain. Chiefly among those is that deep neural networks tend to exploit the surface statistics in the data, creating short-cuts from the input to the output, without really deeply understanding of the data. As a result, these networks fail miserably to generalize to novel combinations. This is because the networks perform shallow pattern matching but not deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. Second, machine learning is often trained to do just one task at a time, making it impossible to re-define tasks on the fly as needed in a complex operating environment. This talk presents our recent developments to extend the capacity of neural networks to remove these limitations. Our main focus is on learning to reason from data, that is, learning to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.
Professor Steve Roberts; The Bayesian Crowd: scalable information combinati...Ian Morgan
Professor Steve Roberts, Machine learning research group and Oxford-Man Institute + Alan Turing Institute. Steve gave this talk on the 24th January at the London Bayes Nets meetup.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
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Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
1. Knowledge Augmented
Visual Learning
Qiang Ji
Rensselaer Polytechnic Institute
qji@ecse.rpi.edu
1
2. Motivation
• Machine learning (ML) is playing an
increasingly important role in computer vision.
• As an enabler for computer vision, it allows
automatically extracting pattern from the data,
a significant progress over traditional hand-
crafted AI-based knowledge acquisition
models
• Current wisdom: powerful image features +
large amount of data+ advanced learning
techniques is the solution to CV ?
2
3. Motivation (cont’d)
• Current ML methods are mostly data-driven, and
they are brittle, lack of robustness, and cannot
generalize well when the training data is
inadequate in either quality or quantity.
• Current ML learning methods cannot lend
themselves easily to exploit the readily available
prior knowledge.
• Prior knowledge is essential to alleviating the
problems with data and to regularize the ill-
posed vision problems.
3
4. Knowledge-Augmented
Visual Learning
• Identify the related prior knowledge from
different sources
• Use the Probabilistic Graphical Models (PGM)
to capture and encode such knowledge
systematically and automatically to produce a
prior model
• Combine the prior model with image
measurements (features) in a principle manner
to perform visual understanding
4
5. Sources of Knowledge
• Permanent theoretical knowledge
– Various theories or principles or laws that govern the properties and
behavior of the objects (e.g physics for body tracking)
– Tend to be generic, applicable to different objects and different
situations, but hard to capture
• Subjective and experiential knowledge (expert)
– Knowledge gained from experience based on long time observations
– Tend to be qualitative, inexact, and approximate
• Circumstantial and contextual knowledge
– Auxiliary information or context that is available during training or testing
• Temporary-statistical pattern-based
– Tend to be object, situation or database specific
– widely used in CV. 5
6. Methods for Knowledge
Representation and Encoding
• Convert knowledge into constraints on parameters
or structure of the PGM
– Model learning can then be formulated as constrained
ML/EM (either closed form or iterative )
• Numerically sample the knowledge to generate
pseudo-data
– Propose a MCMC sampling approach to efficiently
explore the parameter space to acquire samples that
satisfy the knowledge.
– Encode the knowledge by the distribution of synthetic
samples
– Combine the real data with the pseudo-data to train the
6
model
7. Knowledge Representation
MCMC Sampling
– Determine the valid range for each parameter
– Generate new sample in the valid parameter space,
using the proposal distribution
– Reject samples inconsistent with the knowledge
– Repeat until enough samples are collected
The proposal distribution allows efficiently exploring the parameter
space by associating high probability for unexplored regions to
7
produce representative samples.
8. Facial Action Recognition
(Tong and Ji, CVPR07, PAMI07, and PAMI 10)
Facial Action Units (AUs) capture the non-rigid muscular activities
that produce facial appearance changes (defined in Facial Action
Coding System)
• Each AU is related to the contraction of a set of facial muscles.
A small set of AUs can describe a large number of facial behaviors
(a) A list of AUs and their interpretations (b) Muscles underlying facial AUs
8
9. AU Knowledge
– Positive and negative causal influences
• Mouth stretch increases the chance of lips apart; it decreases the chance
of cheek raiser and lip presser.
• Cheek raiser and lid compressor increases the chance of lip corner puller.
• Outer brow raiser increases the chance of inner brow raiser.
• Upper lid raiser increases the chance of inner brow raiser and decreases
the chance of nose wrinkler.
• Lip tightener increases the chance of lip presser.
• Lip presser increases the chance of lip corner depressor and chin raiser.
– Group AU constraints
• Group of AUs happen together or never happen together to produce a
meaningful or spontaneous expression due to underlying facial anatomy
– Dynamic knowledge
• Each AU evolves smoothly over time 9
• Dynamic dependencies among AUs
10. Positive and Negative Influences
For an AUi with positive influence by its parent node AUjP(AUi =1| AUj
=1)>P(AUi =1| AUj =0)
For an AUi with negative influence by its parent node AUj 10
P(AUi =1| AUj =1)<P(AUi =1| AUj =0)
11. AU Prior Model Learning
• Use a DBN to encode the knowledge on
the relationships among AUs
• Convert the knowledge into constraints on
DBN or into pseudo-data
• Learn the DBN with both pseudo and real
data under constraints
11
12. The Learnt DBN for AU
Relationship Modeling
• Solid line: spatial
relationship among AUs
• Self-arrow: temporal
evolution of a single AU
• Dashed line from time t-
1 to time t: temporal
relationship between two
different AUs
AU1*.. N = arg max P( AU1.. N | OAU1.. N )
AU1.. N 12
14. Human Body Tracking
• Goal: Recover the 3D upper-body pose given the image
observation .
2
3
5
6
1
O: Image observation S : 3D upper-body pose
from multiple views
The pose state is represented as the joint angles among the six rigid
body parts:
14
15. Our Approach
• Bayesian Approach
– Pose estimation is interpreted as the maximization of the
posterior probability: .
– Based on Bayes rule, the posterior can be factorized as
Image likelihood Prior model of the body pose
A good prior model can handle the uncertainty and
ambiguity of the image observation
15
16. Human Body Pose Prior Model
We construct a Bayesian Network (BN) to model the
prior probability of upper body pose.
2
5
1
4
6
• Node : represent the joint angle.
• Link : represent the probabilistic relationship (mixture of Gaussians) :
• Probability of body pose : 16
17. Human Body Knowledge
• Anatomical Constraints
– Restrict body structure based on anatomy.
• Connectivity, kinesiology, symmetric, etc.
• Biomechanics Constraints
– Restrict the body joint angle ranges.
• Physical Constraints
– Exclude the physically infeasible pose
• Non-penetrating constraint
• Dynamics Constraints
– Restrict the body movement 17
• movement speed and movement smoothness
18. Knowledge-driven Model Learning
– Using the pseudo-data and constraints, learn a
DBN by maximizing the score of the DBN
structure (B), given pseudo data (D):
d
Score( B) = P( B) + p ( D | θ B , B) − log( K )
2
18
19. Body Tracking Experiment
Comparison with Model from Training Data.
Table 1. Result of baseline system (particle filter) on 5 test sequences.
Table 2. Results of different models .
BN_Activity is learned from specific activity. BN_HumanEva is learned from 5 activities.
19
BN_CMU is learned from CMU database. BN_C is learned from Constraints.
20. Conclusions
• Knowledge is a crucial component of visual
understanding, and that the long-term success of
computer vision requires a union of domain knowledge
and the data.
• We advocate for a hybrid approach for machine learning,
whereby both knowledge and data can be integrated to
result in a robust and generalizable learning.
• We propose to systemically identify related knowledge
from different sources that govern the functions,
properties, and behaviors of the objects being studied
• We propose to use the probabilistic graphical models to
automatically and systematically capture the related
knowledge and to combine with image measurements. 20