Richard RussellGraduate Studentrrussell@mit.edu

The Problems:

Which computational image similarity metrics correspond most closely to human impressions of image similarity?

How can image compression be optimized?

What makes images look similar or dissimilar?

How do humans perform comparisons of images?

Comparisons of different image similarity metrics

L1 matchΣ | xi-yi |

L2 matchΣ (xi-yi)2

Original Image

Vector quantization style image compression using different similarity metrics

L1 Metric L2 MetricOriginal

What I like most about MIT/Cambridge:- ‘All under one roof’- living in Boston - free espresso

What I like least about MIT/Cambridge:- expensive - winter - can’t buy booze on Sunday

Yuri OstrovskyGraduate Student

yo@mit.edu

What is the role of 3D in image processing?

Does 3D scene information facilitate object search?

How are different 3D scene attributes, such as illumination,represented by the visual system?

Does object recognition influence 3D form perception?

The Problems:

Exploring the influence of 3D scene layout on visual search

Good3D

Bad3D

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Good 3D

Bad 3D

Instant Good3D advantage

Amount of training

How fast do people find objects in Good3D vs. Bad3D?

Exploring the representation of 3D scene attributes such as illumination

What I like most about MIT/Cambridge:- The inexhaustible supply of nerdy rhetoric- Those charming long-time residents that scream at you for riding your bike on the sidewalk

- Parades, parades, parades

What I like least about MIT/Cambridge:- Too much sunshine

Javid Q. SadrGraduate Student

sadr@mit.edu

Can we develop a unified experimental paradigm for exploring different issues in object perception?

What are the neural correlates of conscious object percepts?

Can we develop novel, quantitative measures of objectagnosias, development, and priming?

How are object concepts learned, especially in the absence of supervision and image normalization?

The Problems:

The RISE Paradigm for Exploring Issues in Object Perception

Idea of Object-Image Space andRandom Image Structure Evolution....

... Bit-Flip RISE

... Phase RISE

manipulatephase

A sample RISE sequence

What I like most about MIT/Cambridge:- the Rev. Sayan Mukherjee, Ph.D.- Top-Notch Local Weather Coverage- Dirty Deeds, Done Dirt Cheap- The Cambridgeport Saloon- Community-Based Policing

What I like least about MIT/Cambridge:- WHAT !- SOMEONE SET UP US THE BOMB !

Jodi DavenportGraduate Student

jodi@mit.edu

How do we recognize faces?

What aspects of a face do caricaturists capture to make super-recognizable portraits?

What features are important for recognizing faces that are blurred or far away?

How are objects represented in the brain?

The Problems:

Caricaturists have an intuitive sense of what features make a face recognizable as a unique individual.

By creating a database of caricatures and real faces and developing computational schemes for extracting consistencies across the images of specific individuals, we hope to determine what aspects of a face define its recognizability.

Even when faces are far away or blurred and individual features (such as eyes, nose, lips) are difficult to discern, people are still able to recognize specific individuals.

One goal is to define the heuristics used by human observers by determining the capabilities and limitations in face recognitionwith degraded images.

What I like most about MIT/Cambridge:• Students and Faculty with diverse interests.• Academic resources and talks.• Art loan program.

What I like least about MIT/Cambridge:• Tow trucks.• Lack of late night coffee-shops.• Slush.

Keith ThoreszGraduate Student

thorek@ai.mit.edu

How can one extract automatically the invariant features of an arbitrary class of objects?

Can a reliable object invariant be learned from a handful of examples?

What kind of object model could be used to encode low resolution images to facilitate detection at a distance?

The Problems:

A qualitative invariant object representation.

The ratio template is a compact object representation, robust to changes in illumination, noise and image degradation.

Emphasizes low-frequency (low-res) spectral components.

Detection results on faces.

Note the range of spatial resolution and illumination differences.

What I like most about MIT/Cambridge:- New England sports (SCUBA, snowboarding)- Student-sponsored activities- Variety of lectures and talks institute-wide- Academic freedom and resources

What I like least about MIT/Cambridge:- No late-night food options- Classes

Antonio TorralbaPostdoctoral Associate

torralba@ai.mit.edu

What is the role of contextual information inobject recognition?

How can we efficiently represent context structure?

How can we computationally model contextual influenceson object recognition?

What are the neural correlates of contextual processing?

The Problems:

Contextual influences in object recognitionObject recognitionperformances

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Object intrinsic features

Object background features

Object in context

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A statistical framework for modeling contextual influenceson object recognition

Outdoor scene.Urban environment.Street.50-100 meters.

Object priming: Car Person1

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FurnitureTraffic signs

Context driven focus of attention

categories

Location of Pedestrian area derived by context-based processing.

Context driven scale selection

Likely pedestriansize derived viacontextual cues

…

What I like most about MIT/Cambridge:

What I like least about MIT/Cambridge:- this guy