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Atlanta Magazine Features, Thad Starner, “Magnifying glass”

March 3rd, 2014 Irfan Essa Posted in In The News, Thad Starner, Ubiquitous Computing No Comments »

A wonderful write up on my friend and colleague, Thad Starner in the Atlanta Magazine.  Worth a read for sure

“The guy with the computer on his face.” This would have been a fair description of Starner at almost any time over the past twenty years. He first built his own wearable computer with a head-mounted display in 1993, and has donned some version or another of the computer-eyepiece-Internet system most days since then. But over the previous year, something changed.

via Magnifying glass – Features – Atlanta Magazine.

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Paper (2009): ICASSP “Learning Basic Units in American Sign Language using Discriminative Segmental Feature Selection”

February 4th, 2009 Irfan Essa Posted in 0205507, Face and Gesture, ICASSP, James Rehg, Numerical Machine Learning, Pei Yin, Thad Starner No Comments »

Pei Yin, Thad Starner, Harley Hamilton, Irfan Essa, James M. Rehg (2009), ”Learning Basic Units in American Sign Language using Discriminative Segmental Feature Selection” in IEEE Conference on Acoustics, Speech, and Signal Processing 2009 (ICASSP 2009). Session: Spoken Language Understanding I, Tuesday, April 21, 11:00 – 13:00, Taipei, Taiwan.

ABSTRACT

The natural language for most deaf signers in the United States is American Sign Language (ASL). ASL has internal structure like spoken languages, and ASL linguists have introduced several phonemic models. The study of ASL phonemes is not only interesting to linguists, but also useful for scalability in recognition by machines. Since machine perception is different than human perception, this paper learns the basic units for ASL directly from data. Comparing with previous studies, our approach computes a set of data-driven units (fenemes) discriminatively from the results of segmental feature selection. The learning iterates the following two steps: first apply discriminative feature selection segmentally to the signs, and then tie the most similar temporal segments to re-train. Intuitively, the sign parts indistinguishable to machines are merged to form basic units, which we call ASL fenemes. Experiments on publicly available ASL recognition data show that the extracted data-driven fenemes are meaningful, and recognition using those fenemes achieves improved accuracy at reduced model complexity

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Paper: ISWC (2008) “Localization and 3D Reconstruction of Urban Scenes Using GPS”

September 28th, 2008 Irfan Essa Posted in ISWC, Kihwan Kim, Mobile Computing, Papers, Thad Starner No Comments »

Kihwan Kim, Jay Summet, Thad Starner, Daniel Ashbrook, Mrunal Kapade and Irfan Essa  (2008) “Localization and 3D Reconstruction of Urban Scenes Using GPS” In Proceedings of IEEE Symposium on Wearable Computing (ISWC) 2008 (To Appear). [PDF]

ABSTRACT

research_gpsray

Using off-the-shelf Global Positioning System (GPS) units, we reconstruct buildings in 3D by exploiting the reduction in signal to noise ratio (SNR) that occurs when the buildings obstruct the line-of-sight between the moving units and the orbiting satellites. We measure the size and height of skyscrapers as well as automatically constructing a density map representing the location of multiple buildings in an urban landscape.  If deployed on a large scale, via a cellular service provider’s GPS-enabled mobile phones or GPS-tracked delivery vehicles, the system could provide an inexpensive means of continuously creating and updating 3D maps of urban environments.

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Thesis David Minnen PhD (2008): “Unsupervised Discovery of Activity Primitives from Multivariate Sensor Data”

June 18th, 2008 Irfan Essa Posted in Activity Recognition, David Minnen, PhD, Thad Starner No Comments »

Unsupervised Discovery of Activity Primitives from Multivariate Sensor Data

Abstract

 

This research addresses the problem of temporal pattern discovery in real-valued, multivariate sensor data. Several algorithms were developed, and subsequent evaluation demonstrates that they can efficiently and accurately discover unknown recurring patterns in time series data taken from many different domains. Different data representations and motif models were investigated in order to design an algorithm with an improved balance between run-time and detection accuracy. The different data representations are used to quickly filter large data sets in order to detect potential patterns that form the basis of a more detailed analysis. The representations include global discretization, which can be efficiently analyzed using a suffix tree, local discretization with a corresponding random projection algorithm for locating similar pairs of subsequences, and a density-based detection method that operates on the original, real-valued data. In addition, a new variation of the multivariate motif discovery problem is proposed in which each pattern may span only a subset of the input features. An algorithm that can efficiently discover such “subdimensional” patterns was developed and evaluated. The discovery algorithms are evaluated by measuring the detection accuracy of discovered patterns relative to a set of expected patterns for each data set. The data sets used for evaluation are drawn from a variety of domains including speech, on-body inertial sensors, music, American Sign Language video, and GPS tracks.

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Paper: ICASSP (2008) “Discriminative Feature Selection for Hidden Markov Models using Segmental Boosting”

April 3rd, 2008 Irfan Essa Posted in 0205507, Face and Gesture, Funding, James Rehg, Numerical Machine Learning, PAMI/ICCV/CVPR/ECCV, Papers, Pei Yin, Thad Starner No Comments »

Pei Yin, Irfan Essa, James Rehg, Thad Starner (2008) “Discriminative Feature Selection for Hidden Markov Models using Segmental Boosting”, ICASSP 2008 – March 30 – April 4, 2008 – Las Vegas, Nevada, U.S.A. (Paper: MLSP-P3.D8, Session: Pattern Recognition and Classification II, Time: Thursday, April 3, 15:30 – 17:30, Topic: Machine Learning for Signal Processing: Learning Theory and Modeling) (PDF|Project Site)

ABSTRACT

icassp08We address the feature selection problem for hidden Markov models (HMMs) in sequence classification. Temporal correlation in sequences often causes difficulty in applying feature selection techniques. Inspired by segmental k-means segmentation (SKS), we propose Segmentally Boosted HMMs (SBHMMs), where the state-optimized features are constructed in a segmental and discriminative manner. The contributions are twofold. First, we introduce a novel feature selection algorithm, where the temporal dynamics are decoupled from the static learning procedure by assuming that the sequential data are piecewise independent and identically distributed. Second, we show that the SBHMM consistently improves traditional HMM recognition in various domains. The reduction of error compared to traditional HMMs ranges from 17% to 70% in American Sign Language recognition, human gait identification, lip reading, and speech recognition.

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Paper: IEEE Data Mining Conference 2007 “Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery”

October 28th, 2007 Irfan Essa Posted in Activity Recognition, Charles Isbell, David Minnen, Papers, Research, Thad Starner No Comments »

Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery

  • Minnen, Essa, Isbell, and Starner (2007), “Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery,” in Proceedings of IEEE International Conference on Data Mining (ICDM), 2007. [PDF] [DOI]
    [BIBTEX]

    @inproceedings{2007-Minnen-DSMEAGMPD,
      Author = {D. Minnen and I. Essa and C. Isbell and T. Starner},
      Booktitle = {Proceedings of IEEE International Conference on Data Mining (ICDM)},
      Doi = {10.1109/ICDM.2007.52},
      Month = {October},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2007-Minnen-DSMEAGMPD.pdf},
      Title = {Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery},
      Year = {2007}}

Abstract

ICDMPaper Discovering recurring patterns in time series data is a fundamental problem for temporal data mining. This paper addresses the problem of locating subdimensional motifs in real-valued, multivariate time series, which requires the simultaneous discovery of sets of recurring patterns along with the corresponding relevant dimensions. While many approaches to motif discovery have been developed, most are restricted to categorical data, univariate time series, or multivariate data in which the temporal patterns span all of the dimensions. In this paper, we present an expected linear-time algorithm that addresses a generalization of multivariate pattern discovery in which each motif may span only a subset of the dimensions. To validate our algorithm, we discuss its theoretical properties and empirically evaluate it using several data sets including synthetic data and motion capture data collected by an on-body inertial sensor.

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Paper: AAAI 2007: “Discovering Multivariate Motifs using Subsequence Density Estimation and Greedy Mixture Learning”

July 24th, 2007 Irfan Essa Posted in Activity Recognition, Charles Isbell, David Minnen, Papers, Research, Thad Starner No Comments »

Discovering Multivariate Motifs using Subsequence Density Estimation and Greedy Mixture Learning

Abstract
The problem of locating motifs in real-valued, multivariate time series data involves the discovery of sets of recurring patterns embedded in the time series. Each set is composed of several non-overlapping subsequences and constitutes a motif because all of the included subsequences are similar. The ability to automatically discover such motifs allows intelligent systems to form endogenously meaningful representations of their environment through unsupervised sensor analysis. In this paper, we formulate a unifying view of motif discovery as a problem of locating regions of high density in the space of all time series subsequences. Our approach is efficient (sub-quadratic in the length of the data), requires fewer user-specified parameters than previous methods, and naturally allows variable length motif occurrences and nonlinear temporal warping. We evaluate the performance of our approach using four data sets from different domains including on-body inertial sensors and speech.

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Paper: IEEE ISWC (2006) “Discovering Characteristic Actions from On-Body Sensor Data”

October 14th, 2006 Irfan Essa Posted in Activity Recognition, Charles Isbell, David Minnen, Papers, Research, Thad Starner No Comments »

Discovering Characteristic Actions from On-Body Sensor Data (IEEEXplore)

Minnen, D. Starner, T. Essa, I. Isbell, C.
College of Computing, Georgia Institute of Technology, Atlanta, GA 30332 USA.
This paper appears in: Wearable Computers, 2006 10th IEEE International Symposium on
Publication Date: Oct. 2006
On page(s): 11 – 18
Number of Pages: 11 – 18
Location: Montreux, Switzerland
ISSN: 1550-4816
ISBN: 1-4244-0598-x
Digital Object Identifier: 10.1109/ISWC.2006.286337
Posted online: 2007-01-22 09:58:15.0

Abstract

We present an approach to activity discovery, the unsupervised identification and modeling of human actions embedded in a larger sensor stream. Activity discovery can be seen as the inverse of the activity recognition problem. Rather than learn models from hand-labeled sequences, we attempt to discover motifs, sets of similar subsequences within the raw sensor stream, without the benefit of labels or manual segmentation. These motifs are statistically unlikely and thus typically correspond to important or characteristic actions within the activity. The problem of activity discovery differs from typicalmotif discovery, such as locating protein binding sites, because of the nature of time series data representing human activity. For example, in activity data, motifs will tend to be sparsely distributed, vary in length, and may only exhibit intra-motif similarity after appropriate time warping. In this paper, we motivate the activity discovery problem and present our approach for efficient discovery of meaningful actions from sensor data representing human activity. We empirically evaluate the approach on an exercise data set captured by a wrist-mounted, three-axis inertial sensor. Our algorithm successfully discovers motifs that correspond to the real exercises with a recall rate of 96.3% and overall accuracy of 86.7% over six exercises and 864 occurrences.

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Paper (1999) in CoBuild: “The Aware Home: A Living Laboratory for Ubiquitous Computing Research”

October 28th, 1999 Irfan Essa Posted in Aware Home, Beth Mynatt, Collaborators, Gregory Abowd, Intelligent Environments, Thad Starner, Wendy Rogers No Comments »

Cory D. Kidd, Robert Orr, Gregory D. Abowd, Christopher G. Atkeson, Irfan A. Essa, Blair MacIntyre, Elizabeth Mynatt, Thad E. Starner and Wendy Newstetter (1999) “The Aware Home: A Living Laboratory for Ubiquitous Computing Research”, In Cooperative Buildings. Integrating Information, Organizations and Architecture , Volume 1670/1999, Springer Berlin / Heidelberg, Lecture Notes in Computer Science, ISBN: 978-3-540-66596-0. [PDF | DOI | Project Site]

Abstract

We are building a home, called the Aware Home, to create a living laboratory for research in ubiquitous computing for everyday activities. This paper introduces the Aware Home project and outlines some of our technology-and human-centered research objectives in creating the Aware Home.

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