practical computer vision-- a problem-driven approach towards learning cv/ml/dl

Practical Computer Vision A problem-driven approach towards learning CV/ML/DL

Albert Y. C. Chen, Ph.D.Vice President, R&D

Viscovery

Albert Y. C. Chen, Ph.D.

• Experience 2017-present: Vice President of R&D @ Viscovery 2016-2017: Chief Scientist @ Viscovery 2015: Principal Scientist @ Nervve Technologies 2013-2014 Computer Vision Scientist @ Tandent 2011-2012 @ GE Global Research

• Education Ph.D. in Computer Science, SUNY-Buffalo M.S. in Computer Science, NTNU B.S. in Computer Science, NTHU

1. W. Wu, A. Y. C. Chen, L. Zhao, and J. J. Corso. Brain tumor detection and segmentation in a CRF framework with pixel-wise affinity and superpixel-level features. International Journal of Computer Assisted Radiology and Surgery, 2015.

2. S. N. Lim, A. Y. C. Chen and X. Yang. Parameter Inference Engine (PIE) on the Pareto Front. In Proceedings of International Conference of Machine Learning, Auto ML Workshop, 2014.

3. A. Y. C. Chen, S. Whitt, C. Xu, and J. J. Corso. Hierarchical supervoxel fusion for robust pixel label propagation in videos. In Submission to ACM Multimedia, 2013.

4. A.Y.C. Chen and J.J. Corso. Temporally consistent multi-class video-object segmentation with the video graph-shifts algorithm. In Proceedings of IEEE Workshop on Applications of Computer Vision, 2011.

5. D.R. Schlegel, A.Y.C. Chen, C. Xiong, J.A. Delmerico, and J.J. Corso. Airtouch: Interacting with computer systems at a distance. In Proceedings of IEEE Workshop on Applications of Computer Vision, 2011.

6. A.Y.C. Chen and J.J. Corso. On the effects of normalization in adaptive MRF Hierarchies. In Proceedings of International Symposium CompIMAGE, 2010.

7. A.Y.C. Chen and J.J. Corso. Propagating multi-class pixel labels throughout video frames. In Proceedings of IEEE Western New York Image Processing Workshop, 2010.

8. A. Y. C. Chen and J. J. Corso. On the effects of normalization in adaptive MRF Hierarchies. Computational Modeling of Objects Represented in Images, pages 275–286, 2010.

9. Y. Tao, L. Lu, M. Dewan, A. Y. C. Chen, J. J. Corso, J. Xuan, M. Salganicoff, and A. Krishnan. Multi-level ground glass nodule detection and segmentation in ct lung images. Medical Image Computing and Computer-Assisted Intervention, 2009.

10. A.Y.C. Chen, J.J. Corso, and L. Wang. Hops: Efficient region labeling using higher order proxy neighborhoods. In Proceedings of IEEE International Conference on Pattern Recognition, 2008.

Some work done before I caught the startup fever

Freestyle Sketching Stage

AirTouch waits in background for the initialization signal

Initialize

Terminate

Output

imagedatabase

Start:Results

CBIRquery

Airtouch HCI interface for Content-based Image Retrieval

Interactive Segmentation & Classification• Segmentation then classification:

• computationally more efficient, • results in much higher classification accuracy.

• Pioneered the “pixel label propagation” field. • First to utilize superpixels and supervoxels for the task.

FG

Traditional Spatial Propagation

Pixel label map

Label a subset of pixels

BG

Spatio-temporal Propagation

time

Image/Video Object Recognition and Content Understanding

approaches

person carries

gives

recieves

Ontology

object

Person 1 Person 1Person 2

High-Level

Mid-Level

approachactivity

receives givescarries

activityactivity activity

Time

Reasoning

xx

x

Low-Level

x x

x

x

Learning and Adapting Optimal Classifier Parameters

subspace B

subsp

ace A

subspace C

Image-level feature space

priors

Patch-level feature space

posteriorprobability

suggest optimal parameter configuration

Graphical Models and Stochastic Optimization

A

(a) The space-time volume of a video showing the objects (A--F) and their appearing time-span.

spac

e

time

AB

C

D

E F

B E

F

C

D

(b) The temporal relationship graph. An edge between two vertices mean that the two objects overlap in time.

(c) The goal is: cover all objects with the smallest number of "ground truth key frames".

spac

e

time

AB

C

D

E F

key 1 key 2

A

B E

F

C

D

(d) This translates to: iteratively solving the max clique problem until all vertices belong to a clique.

A

B E

F

C

Dkey 2

key 1frame t-1 frame t

layer n layer n

layer n+1 layer n+1

TemporalShift

Shift

µ

Medical Imaging and Geospatial Imaging

GNN detection and segmentation

in Lung CT geospatial imaging: building detection

Brain tumor detection and segmentation in MR images.

Why are we here today?

To make a better change for our future.

Change is the only constant-Heraclitus (535 BC - 475 BC)

Change is the only constant-Heraclitus (535 BC - 475 BC)

Why Risk Innovating?

• Good business model NEVER last forever.

• Average “shelf life” on S&P 500: 20 years.

• 100-year old companies constantly reinvent themselves every 10-20 years

• Startups contribute to 20% of USA’s GDP.

The Death of a Good Business Model

• Foxconn 20 year revenue v.s. net profit (now at 5%)

What do 100 year old corporations do?

GE Schenectady, 1896

History of change at GE• 1886: one of the 12 original companies on the Dow

Jone Industrial Average (also the only one remaining). • 1889: lightbulbs • 1919: radios • 1927: TV • 1941: jet engine • 1960: nuclear power • 1971: room AC units • 1995: MRI

History of change at IBM• 1960s: mainframe computer • 1980s: personal computer • 2000s: integrated solutions • 2020s: AI, Watson

How about the leading Semiconductor companies?

NVidia reinventing itself —2 times in 20 years

“Bad money drives out good” in the desktop GPU market

The rise of mobile computing, and how NVidia missed the boat!

NVidia’s Tegra mobile processors never took off

then, the market saturated…

NVidia not just survived. NVidia is thriving!

Meet the new NVidia: Deep Learning, Deep Learning, and still, Deep Learning

The king is dead, long live the king!

Now, again, do we want to do OEM/ODM forever?

Optimizing an old business model is just delaying its eventual death.

Computer Vision, it can’t be that hard, right?

hmm… grayscale color can’t work alone… maybe color works better?

Computer Vision, it can’t be that hard, right?

White and Gold or

Blue and Black?

The Dress 2015/02/26

Computer Vision, it can’t be that hard, right?

Even if we can auto-correct all lighting and color temperature

[w w w w] [w r r w] [w r r w] [w w w w]

and force all apples to be encoded as:

we’d still have all these “affine transformation” issues:

Even if lighting, color, affine transformation are not an issue

• Our 3D world can’t simply be represented by fixed 2D encoding:

Brief History

Marvin Minsky

“In 1966, Minsky hired a first-year undergraduate student and assigned him a problem to solve over the summer: connect a television camera to a computer and get the machine to describe what it sees.”

Gerald SussmanThe student never worked on Computer Vision problems again.

Brief History• 1960’s: interpretation of synthetic worlds • 1970’s: some progress on interpreting selected images • 1980’s: ANNs come and go; shift toward geometry and increased

mathematical rigor • 1990’s: face recognition; statistical analysis in vogue • 2000’s: broader recognition; large annotated datasets available; video

processing starts

Guzman ‘68 Ohta Kanade ‘78 Turk and Pentland ‘91

What was in our arsenal?

• Image filters

• Feature descriptors

• Classifiers

Filters: blurring

Filters: sharpening

Filters: Canny Edge Detector

Filters: straight lines

Features: a compact and (hopefully) invariant representation

Features: Gabor

Features: Harris Corners

Features: Laplacian of Gaussian (LoG; scale detection)

Features: OrientationHow to compute the rotation?

Create edge orientation histogram and find peak.

Features: SIFT

Features: SIFT

Classifier Training in Machine Learning

Classification Clustering

Regression DimensionReduction

supervised unsupervised

cont

inuo

usdi

scre

te

Classifiers: SVM

Classifiers: Ensemble

Classifiers: Random Fields

Classifiers: Deformable Parts Model (DPM)

Meta-Learning• Different use

cases calls for different ML algorithms.

• Meta-Learning: learning how to learn.

• Requires plenty of domain-specific know-how.

Neural Network (NN) Why didn’t it work; why now?

• MNIST digit data 28x28 • LeCunn’s 3 layer NN:

1170 variables. • Require tens of

thousands of samples. • Only learn simple line/

curve combinations

AI Winter (1970-1980, 1990-2000) • Early NN problems:

• redundant structure, • slow learning speed • need too much data • bad learning

stability.

What’s in a NN

( )zσ+

( )zσ+

( )zσ+

( )zσ+Input

weights

bias

activation function

NN breakthroughs since 1970’s 1. Better Network Structure

• Convolutional Neural Network greatly reduces the number of variables in NN’s designed for images and videos. —> Improved convergence speed, reduced data requirements.

Upper-left corner Bird Beak Detector

Center Bird Beak Detector

Almost identical, can be shared across regions

NN breakthroughs since 1970’s 1. Network Structure

NN breakthroughs since 1970’s 2. Improved Activation Functions

Large

Small

1x

2x

……

Nx

……

……

……

……

……

……

……

y1

y2

yM

NN breakthroughs since 1970’s 3. Effective Backpropagation

w1

w2

Clipping

[Razvan Pascanu, ICML’13]

NN breakthroughs since 1970’s 4. Efficient Training Methods

• Mini-batch

• Adaptive Learning Rate

• Dropout, Batch-normalization

minibatchminibatch

1 epoch

Deep Neural Networks (DNN) way more complex and capable!

What do DNNs learn?

• Neurons act like “custom-trained filters”; react to very different visual cues, depending on data.

What do DNNs learn?

• Neurons act like “custom-trained filters”; react to very different visual cues, depending on data.

• Does not “memorize” millions of viewed images. • Extracts greatly reduced number of features that

are vital to classify different classes of data. • Classifying data becomes a simple task when

the features measured are “”good”.

What do DNNs learn?

Mature/Maturing Computer Vision Applications

• Final inspection cells • Robot guidance and

checking orientation of components

• Packaging Inspection • Medical vial inspection • Food pack checks • Verifying engineered

components[5] • Wafer Dicing • Reading of Serial

Numbers • Inspection of Saw

Blades

• Inspection of Ball Grid Arrays (BGAs)

• Surface Inspection • Measuring of Spark

Plugs • Molding Flash Detection • Inspection of Punched

Sheets • 3D Plane

Reconstruction with Stereo

• Pose Verification of Resistors

• Classification of Non-Woven Fabrics

1970s-now: Machine Vision for Industrial Inspection

• Automated Train Examiner (ATEx) Systems

• Automatic PCB inspection

• Wood quality inspection

• Final inspection of sub-assemblies

• Engine part inspection • \Label inspection on

products • Checking medical

devices for defects

Industrial Inspection: turbofan jet engine blade maintenance• Some seemingly daunting

machine vision tasks actually works with relatively simple image processing algorithms.

Industrial Inspection: Cognex Omniview

Industrial Inspection: Cognex Omniview

License Plate Recognition (1979-now)

License Plate Readers with Text Detection and Neural Networks

Biometrics

Automated Fingerprint Identification (1970s-now)

Face Recognition (1990s-now)

• Face Detection (Viola and Jones, 2001)

• Face Verification (1:1) v.s. Identification (1:N)

Face Verification and Identification, Labeled Faces in the Wild (LFW)

Recognition Accuracy: • 1 to 1: 99%+ • 1 to 100: 90% • 1 to 10,000:

50%-70%. • 1 to 1M: 30%.

LFW dataset, common FN↑, FP↓

Sports—NFL first down line (1995-now)

Sports—NFL first down line

minus

equals

3D Reconstruction(As old as CV; became practical since SIFT)

3D Reconstruction with Feature Matching, Structure from Motion

3D Reconstruction with Feature Matching, Structure from Motion

Image Panoramas (1980s - now)

Solving Panorama Problem with Markov Random Fields

Input:

Solving Panorama Problem with Markov Random Fields

Input:

Solving Panorama Problem with Markov Random Fields

Input:

Solving Panorama Problem with Markov Random Fields

Input:

Solving Panorama Problem with Markov Random Fields

Input:

Solving Panorama Problem with Markov Random Fields

Input:

Solving Panorama Problem with Markov Random Fields

Input:

Solving Panorama Problem with Markov Random Fields

Solving Panorama Problem with Markov Random Fields

ICM (Iterated Conditional Modes), 1986

Solving Panorama Problem with Markov Random Fields

Belief Propagation (1980-2000)

Solving Panorama Problem with Markov Random Fields

Graph-Cuts (alpha expansion), 2001

Photosynthesis

Solving Photosynthesis Problems with Alpha-matting (2000s-now)

Object Detection & Classification state-of-the-art

• ImageNet Large Scale Visual Recognition Challenge (ILSVRC) • 1000+ classes, 1.2M images.

0

0.125

0.25

0.375

0.5

11 12 13 14 11 12 13 14classification

errorclassification

+localization error

Image Scene Classification• MIT Places 401

dataset.

• top-5 accuracy rates >80%.

Self-driving cars (2000s-now)

DARPA Grand Challenge (2005)

2005 winner, Stanley (Stanford), 3mph through desert

DARPA Urban Challenge (2007)

2007 winner, Boss (CMU), 13mpg through the city

Self Driving Cadillac, US congressman to airport, 2013

Google Self Driving Car, 2015

Google Self-Driving Car, 2016

Google Self-Driving Car, 2016

NVidia Self Driving Car, 2016

How did we come this far? Race car drivers know the trick

Focus on Free Space / Drivable Area, not Obstacles!

Up-and-coming Computer Vision

Applications

Structure from X, Floored

Structure from X, PIX4D

Object Recognition Blue River Technology

Augmented Reality Magic Leap

IMRSV

Retail Insights

Source: Prism Skylabs

Other Applications in Business Intelligence

• Measure brand exposure. • Measure sponsorship effectiveness. • Loss prevention and retail layout optimization.

How about Smart Surveillance?

Exciting applications many of you might be

attempting to SOLVE!!!

Problem Solving WorkflowClassical Workflow: 1. Data collection 2. Feature Extraction 3. Dimension Reduction 4. Classifier (re)Design 5. Classifier Verification 6. Deploy

Modern Brute-force workflow 1. Data collection 2. Throw everything into a Deep Neural Network 3. Mommy, why doesn’t it work ???

Classical Problem #1: Curse of Dimensionality

zesit

앉다

sentarse

• Number of Variables vs Number of Samples

Q. Who would make such naive mistakes? A. Many “newbies” repeatedly do so.

Example 1-1: illegal parking detection

legal parking samples x100 illegal parking samples x100Let’s train a 150-layer Res-Net!!!What could possibly go wrong?

Example 1-1: illegal parking detection

• Data: try cleaner data

• Feature: fine-tune with pre-trained model; don’t train from scratch

• Classifier overfitting: beware of statistical coincidences,

Example 1-2: Smart Photo Album with Google Cloud Vision

Example 1-2: Smart Photo Album with Google Cloud Vision

No effective distance measure for thousands, if not millions of dimensions (tags); would be

approximately zero most of the time.

Classical Problem #2: Overfitting Data

• Make sure your deep learning algorithm is learning better features for data, not overfitting the data with complex classifiers.

Deep Learning Cookbook

GoodResultsonTestingData?

GoodResultsonTrainingData?

YES

YES

Newactivationfunction

AdaptiveLearningRate

EarlyStopping

Regularization

Dropout

(credit: Prof. H.Y. Lee, NTU)

Example: AOI breakthroughs with Deep Learning—Metal Inspection

D Weimer et al. 2017

Example: AOI breakthroughs with Deep Learning—Textile Inspection

X

Funding Li et al. / IEEE Tran Automation Science and Engineer 2017 (to appear)

Example: AOI breakthroughs with Deep Learning—Laser Welding

Johannes Günther et al. / Procedia Technology 15 (2014) 474 – 483

Example: AOI breakthroughs with Deep Learning—Laser Welding

Johannes Günther et al. / Procedia Technology 15 (2014) 474 – 483

Example: AOI breakthroughs with Deep Learning—Serial Number Processing

S. N. Lim et al. / GE Global Research

S. N. Lim et al. / GE Global Research

Example: AOI breakthroughs with Deep Learning—Serial Number Processing

Example: AOI breakthroughs with Deep Learning—Corrosion Detection

S. N. Lim et al. / GE Global Research

Example: Dermatologist-level Skin Cancer Diagnosis with DNN+Smartphones

• 5.4M cancer cases, 58M pre-cancer cases diagnosed every year in the US.

(Andre Esteva, Sebastian Thrun, 2017)

Example: Dermatologist-level Skin Cancer Diagnosis with DNN+Smartphones

Example: Dermatologist-level Skin Cancer Diagnosis with DNN+Smartphones

Example: Hippocampus Segmentation in 7T MR Images

(Dinggang Shen, 2017)

(Dinggang Shen, 2017)

Example: Hippocampus Segmentation in 7T MR Images

(Dinggang Shen, 2017)

Example: Hippocampus Segmentation in 7T MR Images

Example: Histopathological Image Classification w. DNN

Microscopic view of Breast malignant tumor

40x 100x

200x 400x(FA Spanhol, IJCNN 2016)

Example: Histopathological Image Classification w. DNN

Example: Histopathological Image Classification w. DNN

Example: DNN for Plant Disease Detection

(S Mohanty, 2016)

Example: DNN for Plant Disease Detection

Example: DNN for Plant Disease Detection

Thank You!albert@viscovery.com

Appendix 1: Startups• A company, partnership, or temporary

organization designed to search for a new, repeatable and scalable business model.

Your Idea• Are you passionate about it? • Is it disruptive enough? • What is your business plan?

• What is it? • Can it make money? • What is the future of the idea?

• What is your competitive advantage? • How do you build up your entry barrier?

A minimal startup team

• A hacker

• A hustler

• A hipster

Startup Timeline

Prototype• Hack out a prototype

• Spend 2-10 weeks max.

• Investors are much more likely to fund you if you have a minimal initial version of your idea.

• Hackathons are a good place to start.

• Iteratively improve the prototype

Money!

Buildup your entry barrier!

• Market (users)

• Speed

• Team

• Technology

Building entry barrier with Technology!!

Angel.co

Appendix 2: My humble attempts at putting the latest Computer

Vision algorithms to work

Intrinsic Imaging at Tandent Vision Science

Computer Vision would be half-solved without shadows!

LightOriginal Image Surface

Tandent Lightbrush

Video Tutorial for Tandent Lightbrush: https://vimeo.com/47009123

Issues• Highly anticipated, highly acclaimed, but small

crowd at $500 a license.

• Adobe Photoshop monopoly and the “not invented here” syndrome.

• Adobe’s arch-rival, Corel (Corel Draw, Paint Shop Pro, Ulead PhotoImpact) was DYING and asked too much from the botched deal.

Have fun scribbling out your shadows in photoshop!

Poor Bob from Adobe wasted 9 minutes removing just 1 shadow

Intrinsic Imaging for improving the RGB signal in autonomous driving

Intrinsic Imaging’s other applications

Retrospect

• 20 researchers burned 25 million in 8 years; investors got 50 patents in return, period.

• Overestimated the total addressable market size, in a market with existing monopoly.

• Many missed opportunities. Counterexample of the lean startup model.

Some SfM, SLAM startups

Satellite/Aerial Imagery Analysis

• 40cm resolution at 30fps for 90 sec for any location on earth. • One LEO satellite revisits any place on Earth every 3 days. • Need 24 satellites to revisit any place on Earth every 3 hours.

Challenges for Single satellite depth estimation and 3D reconstruction

• At 30fps, a LEO satellite travels 250m between two consecutive frames —> theoretically sufficient for cm-level depth estimation.

• Sources of Noise: • Camera distortions • Atmospheric Disturbance • Ground vegetation • Sub-pixel sampling noise

1 2

What happened?

• B2B customers takes too long to strike deals.

• Google ate us alive in just 3 months, while we were still pitching for VC-funding with our prototype.

Visual Search at Nervve

Retrospect• Growth pains expanding from intelligence

community clients to advertisement clients. • Forming the right team of engineers and

researchers and moving at the right pace. • For any Computer Vision/Machine Learning

company: • Researchers that cannot program—> OUT • Engineers that don’t know math —> OUT

Visual Search, Simply Smarter

Once in a lifetime opportunity in China’s video streaming market

What do we need?

Face MotionImage scene Text Audio Object

Semantics

Viscovery VDS (Video Discovery Service)

Viscovery VDS (Video Discovery Service)

Viscovery VDS (Video Discovery Service)

Challenges Encountered Along the Way

• From Product Recognition in Images, to Face, Logo, Object, Scene recognition in Videos. • Number of Categories • Recognition Accuracy • Recognition Speed

• System Architecture

• Business Model

Viscovery’s Edge• Market: first mover’s advantage in China’s video

streaming market. • Speed: we built the whole VDS thing in a few months! • Team: You! Seriously! • Technology:

• Depth • Breadth • Cloud • Customizability • Self-Learning

Life is not all rosy at startups

• High Risk, High Pressure, High Uncertainty!

• Resources are scarce, but you MUST DELIVER!

• Forming your all-star team is not that easy…

• Focus, and persistence.

Appendix 3: What can Taiwan’s academia do to help bridge the gap?

HMM….

Academia

IndustryGeneral Public

reputation and policy support

improved living standards

students

opportunity

well-trained graduates

grants and collaborations

A healthy cycle

Academia

IndustryGeneral Public

unsupportive policies

stagnant wages

useless education

unemployable graduates

A vicious cycle

no grants

no students

Where should we start? Maybe with a few more stories.

Where should we start? Maybe with a few more stories.

Where should we start? Maybe with a few more stories.

The Goldilocks zone of innovation

The Goldilocks zone of innovation

Business Relevance

Academic Relevance

plentiful resources; hierarchical organization

lack of resources; responsive organization

traditional corporations talking “innovation”

corporate research

startups struggling to survive

academic spinoffs

MSR