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Suman Shrestha - IEEE Xplore Author Profile

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Author details

Suman Shrestha

Also published under: S. Shrestha, Simian Shrestha

Publications

19

Citations

59

Publications by Year

20122019

Co-Authors:

Marios AntonakakisGloria BaumanE. S. BeiAnthony BeninatiAnthony Benitati

Show All Co-Authors (53)

Suman Shrestha

Also published under: S. Shrestha, Simian Shrestha

Affiliation

Department of Electrical and Computer Engineering

Manhattan College

NY, USA

Publication Topics

Dynamic Vision Sensor,
Linearly Polarized,
Vision Sensors,
Visual System,
Deep Learning,
Polarimetric Images,
Polarized Light,
Study Outcomes,
Artificial Neural Network,
Dynamic Range,
High Dynamic Range,
Low Earth Orbit

Biography

Suman Shrestha received the M.S. degree in electrical engineering from the University of Akron, Akron, OH, USA, in 2013.,He is currently a Research Engineer with the Department of Radiology, University of Massachusetts Medical School, Worcester, MA, USA. He has authored or co-authored 25 peer-reviewed journal and conference articles.,Mr. Shrestha was the recipient of the Best Reviewer Award for 2014 IEEE International Conference on Imaging Systems and Techniques. His current research interests include polarimetry, optical electronics, medical instrumentation, and medical imaging.(Based on document published on 8 June 2015).

Publications

19

Citations

59

Publications by Year

20122019

Co-Authors:

Marios Antonakakis
Gloria Bauman
E. S. Bei
Anthony Beninati
Anthony Benitati

Show All Co-Authors (53)

Author's Published Works

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Showing 1-10 of 10 results

Conferences (9)

Magazines (1)

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I. Guyon(10)
V. Vapnik(4)
L. Bottou(3)
J. Denker(3)
L.D. Jackel(3)
AT and T Bell Laboratories, Inc., Holmdel, NJ, USA(6)
AT and T Bell Laboratories, Inc., USA(2)
AT&T Bell Labs., Holmdel, NJ, USA(1)
Heuristique, Paris, France(1)
Swiss Federal Institute of Technology, Zurich, Switzerland(1)
Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5)(2)
Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems(2)
1990 IJCNN International Joint Conference on Neural Networks(1)
IEEE Communications Magazine(1)
International 1989 Joint Conference on Neural Networks(1)
IEEE(10)
Jerusalem, Israel(2)
The Hague, Netherlands(2)
Adelaide, SA, Australia(1)
Montreal, QC, Canada(1)
San Diego, CA, USA(1)
Neural Network(6)
Optical Character Recognition(5)
Training Set(5)
Error Rate(3)
Linear Classifier(3)

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Design of a linguistic postprocessor using variable memory length Markov models

I. Guyon;F. Pereira

Proceedings of 3rd International Conference on Document Analysis and Recognition

Year: 1995 | Conference Paper |

Cited by: Papers (23) | Patents (1)

We describe a linguistic postprocessor for character recognizers. The central module of our system is a trainable variable memory length Markov model (VLMM) that predicts the next character given a variable length window of past characters. The overall system is composed of several finite state automata, including the main VLMM and a proper noun VLMM. The best model reported in the literature (Bro...Show More

Design of a linguistic postprocessor using variable memory length Markov models

I. Guyon;F. Pereira

Proceedings of 3rd International Conference on Document Analysis and Recognition

Year: 1995 | Volume: 1 | Conference Paper |

UNIPEN project of on-line data exchange and recognizer benchmarks

I. Guyon;L. Schomaker;R. Plamondon;M. Liberman;S. Janet

Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5)

Year: 1994 | Conference Paper |

Cited by: Papers (219) | Patents (4)

We report the status of the UNIPEN project of data exchange and recognizer benchmarks started two years ago at the initiative of the International Association of Pattern Recognition (Technical Committee 11). The purpose of the project is to propose and implement solutions to the growing need of handwriting samples for online handwriting recognizers used by pen-based computers. Researchers from sev...Show More

UNIPEN project of on-line data exchange and recognizer benchmarks

I. Guyon;L. Schomaker;R. Plamondon;M. Liberman;S. Janet

Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5)

Year: 1994 | Volume: 2 | Conference Paper |

Comparison of classifier methods: a case study in handwritten digit recognition

L. Bottou;C. Cortes;J.S. Denker;H. Drucker;I. Guyon;L.D. Jackel;Y. LeCun;U.A. Muller;E. Sackinger;P. Simard;V. Vapnik

Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5)

Year: 1994 | Conference Paper |

Cited by: Papers (330) | Patents (3)

This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, and memory requirements. When available, we report measurements of the fraction of patterns that must be rejected so that the remaining patterns have misclassification rates less than a given threshold.Show More

Comparison of classifier methods: a case study in handwritten digit recognition

L. Bottou;C. Cortes;J.S. Denker;H. Drucker;I. Guyon;L.D. Jackel;Y. LeCun;U.A. Muller;E. Sackinger;P. Simard;V. Vapnik

Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5)

Year: 1994 | Volume: 2 | Conference Paper |

On-line cursive script recognition using time delay neural networks and hidden Markov models

M. Schenkel;I. Guyon;D. Henderson

Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing

Year: 1994 | Conference Paper |

Cited by: Papers (31) | Patents (1)

Presents a writer independent system for on-line handwriting recognition which can handle both cursive script and hand-print. The pen trajectory is recorded by a touch sensitive pad, such as those used by note-pad computers. The input to the system contains the pen trajectory information, encoded as a time-ordered sequence of feature vectors. Features include X and Y coordinates, pen-lifts, speed,...Show More

On-line cursive script recognition using time delay neural networks and hidden Markov models

M. Schenkel;I. Guyon;D. Henderson

Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing

Year: 1994 | Volume: ii | Conference Paper |

Writer-adaptation for on-line handwritten character recognition

N. Matic;I. Guyon;J. Denker;V. Vapnik

Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93)

Year: 1993 | Conference Paper |

Cited by: Papers (15) | Patents (1)

The authors have designed a writer-adaptable character recognition system for online characters entered on a touch terminal. It is based on a Time Delay Neural Network (TDNN) that is pre-trained on examples from many writers to recognize digits and uppercase letters. The TDNN without its last layer serves as a preprocessor for an optimal hyperplane classifier that can be easily retrained to peculi...Show More

Writer-adaptation for on-line handwritten character recognition

N. Matic;I. Guyon;J. Denker;V. Vapnik

Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93)

Year: 1993 | Conference Paper |

Computer aided cleaning of large databases for character recognition

N. Matic;I. Guyon;L. Bottou;J. Denker;V. Vapnik

Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems

Year: 1992 | Conference Paper |

Cited by: Papers (16)

A method for computer-aided cleaning of undesirable patterns in large training databases has been developed. The method uses the trainable classifier itself, to point out patterns that are suspicious, and should be checked by the human supervisor. While suspicious patterns that are meaningless or mislabeled are considered garbage, and removed from the database, the remaining patterns, like ambiguo...Show More

Computer aided cleaning of large databases for character recognition

N. Matic;I. Guyon;L. Bottou;J. Denker;V. Vapnik

Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems

Year: 1992 | Conference Paper |

Capacity control in linear classifiers for pattern recognition

I. Guyon;V. Vapnik;B. Boser;L. Bottou;S.A. Solla

Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems

Year: 1992 | Conference Paper |

Cited by: Papers (7) | Patents (3)

Achieving good performance in statistical pattern recognition requires matching the capacity of the classifier to the amount of training data. If the classifier has too many adjustable parameters (large capacity), it is likely to learn the training data without difficulty, but will probably not generalize properly to patterns that do not belong to the training set. Conversely, if the capacity of t...Show More

Capacity control in linear classifiers for pattern recognition

I. Guyon;V. Vapnik;B. Boser;L. Bottou;S.A. Solla

Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems

Year: 1992 | Conference Paper |

Hardware requirements for neural-net optical character recognition

L.D. Jackel;B. Boser;J.S. Denker;H.P. Graf;Y. Le Cun;I. Guyon;D. Henderson;R.E. Howard;W. Hubbard;S.A. Solla

1990 IJCNN International Joint Conference on Neural Networks

Year: 1990 | Conference Paper |

Cited by: Papers (5)

Hardware architectures for character recognition are discussed, and choices for possible circuits are outlined. An advanced (and working) reconfigurable neural-net chip that mixes analog and digital processing is described. It is found that different approaches to image recognition often lead to neural-net architectures that have limited connectivity and repeated use of the same set of weights. Th...Show More

Hardware requirements for neural-net optical character recognition

L.D. Jackel;B. Boser;J.S. Denker;H.P. Graf;Y. Le Cun;I. Guyon;D. Henderson;R.E. Howard;W. Hubbard;S.A. Solla

1990 IJCNN International Joint Conference on Neural Networks

Year: 1990 | Conference Paper |

Handwritten digit recognition: applications of neural network chips and automatic learning

Y. Le Cun;L.D. Jackel;B. Boser;J.S. Denker;H.P. Graf;I. Guyon;D. Henderson;R.E. Howard;W. Hubbard

IEEE Communications Magazine

Year: 1989 | Volume: 27, Issue: 11 | Magazine Article |

Cited by: Papers (317) | Patents (15)

Two novel methods for achieving handwritten digit recognition are described. The first method is based on a neural network chip that performs line thinning and feature extraction using local template matching. The second method is implemented on a digital signal processor and makes extensive use of constrained automatic learning. Experimental results obtained using isolated handwritten digits take...Show More

Handwritten digit recognition: applications of neural network chips and automatic learning

Y. Le Cun;L.D. Jackel;B. Boser;J.S. Denker;H.P. Graf;I. Guyon;D. Henderson;R.E. Howard;W. Hubbard

IEEE Communications Magazine

Year: 1989 | Volume: 27, Issue: 11 | Magazine Article |

Comparing different neural network architectures for classifying handwritten digits

I. Guyon;I. Poujaud;L. Personnaz;G. Dreyfus;J. Denker;Y. Le Cun

International 1989 Joint Conference on Neural Networks

Year: 1989 | Conference Paper |

Cited by: Papers (20)

An evaluation is made of several neural network classifiers, comparing their performance on a typical problem, namely handwritten digit recognition. For this purpose, the authors use a database of handwritten digits, with relatively uniform handwriting styles. The authors propose a novel way of organizing the network architectures by training several small networks so as to deal separately with su...Show More

Comparing different neural network architectures for classifying handwritten digits

I. Guyon;I. Poujaud;L. Personnaz;G. Dreyfus;J. Denker;Y. Le Cun

International 1989 Joint Conference on Neural Networks

Year: 1989 | Conference Paper |

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