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Detection
of Human Motion in a Cluttered Scene
Yang Song, Xiaolin Feng, Luis Goncalves, Pietro Perona
Detecting
humans in images is a useful application of computer vision. Loose and
textured clothing, occlusion and scene clutter make it a difficult problem
because bottom-up segmentation and grouping do not always work. We address
the problem of detecting humans from their motion pattern in monocular
image sequences; extraneous motions and occlusion may be present. We
assume that we may not rely on segmentation, nor grouping and that the
vision front-end is limited to observing the motion of key points and
textured patches in between pairs of frames. We do not assume that we
are able to track features for more than two frames. Our method is based
on learning an approximate probabilistic model of the joint position
and velocity of different body features. Detection is performed by hypothesis
testing on the maximum a posteriori estimate of the pose and motion
of the body. Our experiments on a dozen of walking sequences indicate
that our algorithm is accurate and efficient. (full
report)
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Configurable
Architectures and Systems for Real-Time Low-level Vision
Arrigo Benedetti,
Pietro Perona
The long-term
goal of this project is to build an infrastructure for the design and
implementation of real-time computer vision systems. Since vision algorithms
are compute-bound we have chosen the technology of Field Programmable
Gate Array (FPGAs), that allow to exploit the parallelism inherent to
the first stages of low-level vision tasks. The first problem that we
have considered is the real-time computation of the optical flow measured
from the sequence of images captured by a video camera. We have designed,
built and demonstrated a system able to select in real-time 2-D visual
features on a commercially available platform. During this process we
have learned that the system level architectures of commercially available
configurable systems are not optimized for low level vision tasks, therefore,
we have designed a novel architecture dedicated to real-time processing
of video streams. A system based on this architecture has been built
and is currently being tested. More recently, we have studied the problem
of bit-width computation for the optimization of the data paths found
in digital video signal processors. (full
report)
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Learning
Object Class Models
Markus Weber, Max
Welling, Robert Fergus, Pietro
Perona
We have
developed a method to automatically learn models of visual object classes
from sets of unlabeled and unsegmented training images. The method has
been demonstrated to work on images of cars and handwritten characters
and it is being adapted to human faces. (full
report)
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Finding
Faces in Cluttered Scenes
Markus Weber, Michael Burl,
Pietro Perona
We have
designed algorithms that learn a probabilistic description of human
faces and other object classes. We have implemented a real-time face
detection system which runs at 1Hz and demonstrates the ability to handle
deformations, occlusions and background clutter. (full
report)
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Perception
and 3D Reconstruction of Specular Surfaces
Silvio
Savarese, Pietro Perona
The aim
of our work is to investigate how the human visual system perceives
specular surfaces and which cues can be used to recover the shape of
such class of objects. (full report)
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3D Photography
on Your Desk
Jean-Yves Bouguet, Pietro
Perona
We are
developing a simple and inexpensive method for extracting the three-dimensional
shape of objects by using weak-structured lighting. Experimental results
demonstrate that the error in reconstructing the surface is less than
1%. (full report)
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Visual
Input for Pen-Based Computers
Mario E. Munich,
Pietro Perona
Our work
focuses on the development of a visual interface for pen-based computers.
We are building a system that visually tracks the trajectory of a pen
in real-time and recovers the handwritten strokes with sufficient spatio-temporal
resolution and accuracy to enable handwritten character recognition.
(full report)
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Camera-Based
ID Verification by Signature Tracking
Mario E. Munich,
Pietro Perona
The goal
of this project is to develop a vision-based biometric technique based
on visual capturing of signatures and to evaluate the performance of
the system. (full report)
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Little
Piece of Cortex
George Panotopoulos, Demetri
Psaltis, Pietro Perona
We introduce
a model of the V1 cortex. This model is composed by an initial filter
stage and two interaction stages, inspired by their biological counterparts.
The model produces results matching the ones obtained by physiological
experiments. (full report)
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Optically
Programmable FPGA Systems
Jose Mumbru, Gan Zhou,
Arrigo Benedetti, Xin An,
George Panotopoulos,
Fai Mok, Demetri Psaltis, Pietro
Perona
Reconfigurable
processors bring a new computational paradigm where the processor modifies
its structure to suit a given application, rather than having to modify
the application to fit the device. The Optically Programmable Gate Array
(OPGA), an enhanced version of a conventional FPGA, utilizes a holographic
memory accessed by an array of VCSELs to program its logic. Combining
spatial and shift multiplexing to store the configuration pages in the
memory, the OPGA module is very compact and has extremely short configuration
time allowing for dynamic reconfiguration. The reconfiguration capability
of the OPGA can be applied to solve more efficiently problems in pattern
recognition and searches in databases. (full
report)
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Learning
in Hardware
Alexander Nicholson,
Arrigo Benedetti, Yaser
Abu-Mostafa, Pietro Perona
We investigate
the use of learning and adaptation for digital hardware design. We use
reconfigurable hardware devices and discrete optimization methods to
learn circuits from a set of examples. We have shown that this approach
works well for the design of small arithmetic circuits and that significant
performance improvements may be achieved by moving away from a strictly
evolvable (genetic algorithms) approach. (full
report)
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