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Robust Head Detection in Collaborative Learning Environments Using AM-FM Representations

Published on Apr 1, 2018
· DOI :10.1109/ssiai.2018.8470355
Wenjing Shi (UNM: University of New Mexico), Marios S. Pattichis25
Estimated H-index: 25
(UNM: University of New Mexico)
+ 1 AuthorsCarlos LopezLeiva2
Estimated H-index: 2
(UNM: University of New Mexico)
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Abstract
The paper introduces the problem of robust head detection in collaborative learning environments. In such environments, the camera remains fixed while the students are allowed to sit at different parts of a table. Example challenges include the fact that students may be facing away from the camera or exposing different parts of their face to the camera. To address these issues, the paper proposes the development of two new methods based on Amplitude Modulation-Frequency Modulation (AM-FM) models. First, a combined approach based on color and FM texture is developed for robust face detection. Secondly, a combined approach based on processing the AM and FM components is developed for robust, back of the head detection. The results of the two approaches are also combined to detect all of the students sitting at each table. The robust face detector achieved 79% accuracy on a set of 1000 face image examples. The back of the head detector achieved 91% accuracy on a set of 363 test image examples.
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Published on May 1, 2017in IEEE Transactions on Image Processing 6.79
Cesar Carranza5
Estimated H-index: 5
(PUCP: Pontifical Catholic University of Peru),
Daniel Llamocca8
Estimated H-index: 8
(UR: University of Rochester),
Marios S. Pattichis25
Estimated H-index: 25
(UNM: University of New Mexico)
The manuscript describes fast and scalable architectures and associated algorithms for computing convolutions and cross-correlations. The basic idea is to map 2D convolutions and cross-correlations to a collection of 1D convolutions and cross-correlations in the transform domain. This is accomplished through the use of the discrete periodic radon transform for general kernels and the use of singular value decomposition -LU decompositions for low-rank kernels. The approach uses scalable architect...
Published on Jan 1, 2014
Mohamed Dahmane1
Estimated H-index: 1
,
Langis Gagnon24
Estimated H-index: 24
Abstract In this paper, we address the problem of face recognition of low-resolution images under varying light, illumination and blur using local texture based face representation. The main contribution is the texture representation using Phase-Context which is based on four-quadrant mask of the Fourier transform phase in local neighborhoods. The contextual phase generates a more discriminative code filtering responses, and a more effective feature set than the Local Phase Quantization (LPQ) de...
Published on Aug 1, 2010 in ICPR (International Conference on Pattern Recognition)
Pauline Julian1
Estimated H-index: 1
,
Christophe Dehais2
Estimated H-index: 2
+ 3 AuthorsAriel Choukroun2
Estimated H-index: 2
This paper presents an algorithm for segmenting the hair region in uncontrolled, real life conditions images. Our method is based on a simple statistical hair shape model representing the upper hair part. We detect this region by minimizing an energy which uses active shape and active contour. The upper hair region then allows us to learn the hair appearance parameters (color and texture) for the image considered. Finally, those parameters drive a pixel-wise segmentation technique that yields th...
Published on May 1, 2010in IEEE Transactions on Image Processing 6.79
Victor Murray12
Estimated H-index: 12
(UNM: University of New Mexico),
Paul V. Rodriguez3
Estimated H-index: 3
(PUCP: Pontifical Catholic University of Peru),
Marios S. Pattichis25
Estimated H-index: 25
(UNM: University of New Mexico)
We develop new multiscale amplitude-modulation frequency-modulation (AM-FM) demodulation methods for image processing. The approach is based on three basic ideas: (i) AM-FM demodulation using a new multiscale filterbank, (ii) new, accurate methods for instantaneous frequency (IF) estimation, and (iii) multiscale least squares AM-FM reconstructions. In particular, we introduce a variable-spacing local linear phase (VS-LLP) method for improved instantaneous frequency (IF) estimation and compare it...
Published on Jan 1, 2006
N. A. Abdul Rahim1
Estimated H-index: 1
,
C. W. Kit J. See1
Estimated H-index: 1
Published on Jan 1, 2004
Yohei Ishii5
Estimated H-index: 5
,
Hitoshi Hongo6
Estimated H-index: 6
+ 1 AuthorsYoshinori Niwa11
Estimated H-index: 11
Real-time human detection is an important part in a surveillance system using computer vision. In this paper, a real-time face and head detection method is proposed for such human detection. The method has an advantage of detecting peoples who are not facing a camera, by detecting their heads. It employs four directional features (FDF) and linear discriminant analysis in order to save computation cost for scanning and classification. Since FDF represents edge directional information in low resol...
Published on Jan 1, 2000
Saeed Shiry Ghidary13
Estimated H-index: 13
(Kobe University),
Yasushi Nakata5
Estimated H-index: 5
(Kobe University)
+ 1 AuthorsMotofumi Hattori9
Estimated H-index: 9
(Kobe University)
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