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Dynamic Group Interactions in Collaborative Learning Videos

Published on Oct 1, 2018
· DOI :10.1109/acssc.2018.8645132
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
We introduce a new method to detect student group interactions in collaborative learning videos. We consider the following video activities: (i) human to human, (ii) human to others, and (iii) lack of any interaction. The system uses multidimensional AM-FM methods to detect student faces, hair, and then use the results to detect possible interactions. We use dynamic graphs to represent group interactions within each video. We tested our methods with 15 videos and achieved an 84% accuracy for students facing the camera and 76% for students facing both towards and away from the camera.
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Published on Oct 1, 2018 in ICIP (International Conference on Image Processing)
Cesar Carranza5
Estimated H-index: 5
(PUCP: Pontifical Catholic University of Peru),
Marios S. Pattichis25
Estimated H-index: 25
(UNM: University of New Mexico),
Daniel Llamocca8
Estimated H-index: 8
(UR: University of Rochester)
The Discrete Periodic Radon Transform (DPRT) has many important applications in reconstructing images from their projections and has recently been used in fast and scalable architectures for computing 2D convolutions. Unfortunately, the direct computation of the DPRT involves O(N^{3})additions and memory accesses that can be very costly in single-core architectures. The current paper presents new and efficient algorithms for computing the DPRT and its inverse on multi-core CPUs and GPUs. The ...
Published on Apr 1, 2018
Abigail Ruth Jacoby (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)
Human activity classification remains challenging due to the strong need to eliminate structural noise, the multitude of possible activities, and the strong variations in video acquisition. The current paper explores the study of human activity classification in a collaborative learning environment.This paper explores the use of color based object detection in conjunction with contextualization of object interaction to isolate motion vectors specific to each human activity. The basic approach is...
Limin Wang19
Estimated H-index: 19
(NU: Nanjing University),
Yuanjun Xiong13
Estimated H-index: 13
(CUHK: The Chinese University of Hong Kong)
+ -3 AuthorsLuc Van Gool98
Estimated H-index: 98
(ETH Zurich)
Deep convolutional networks have achieved great success for image recognition. However, for action recognition in videos, their advantage over traditional methods is not so evident. We present a general and flexible video-level framework for learning action models in videos. This method, called temporal segment network (TSN), aims to model long-range temporal structures with a new segment-based sampling and aggregation module. This unique design enables our TSN to efficiently learn action models...
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, 2016in IEEE Transactions on Image Processing 6.79
Cesar Carranza5
Estimated H-index: 5
(UNM: University of New Mexico),
Daniel Llamocca8
Estimated H-index: 8
(UR: University of Rochester),
Marios S. Pattichis25
Estimated H-index: 25
(UNM: University of New Mexico)
The discrete periodic radon transform (DPRT) has extensively been used in applications that involve image reconstructions from projections. Beyond classic applications, the DPRT can also be used to compute fast convolutions that avoids the use of floating-point arithmetic associated with the use of the fast Fourier transform. Unfortunately, the use of the DPRT has been limited by the need to compute a large number of additions and the need for a large number of memory accesses. This paper introd...
Daniel Llamocca8
Estimated H-index: 8
(UR: University of Rochester),
Marios S. Pattichis25
Estimated H-index: 25
(UNM: University of New Mexico)
There is strong interest in the development of dynamically reconfigurable systems that can meet real-time constraints on energy, performance, and accuracy. The generation of real-time constraints will significantly expand the applicability of dynamically reconfigurable systems to new domains, such as digital video processing. We develop a dynamically reconfigurable 2D FIR filtering system that can meet real-time constraints in energy, performance, and accuracy (EPA). The real-time constraints ar...
Published on Dec 1, 2013 in ICCV (International Conference on Computer Vision)
Heng Wang6
Estimated H-index: 6
(IRIA: French Institute for Research in Computer Science and Automation),
Cordelia Schmid94
Estimated H-index: 94
(IRIA: French Institute for Research in Computer Science and Automation)
Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This paper improves their performance by taking into account camera motion to correct them. To estimate camera motion, we match feature points between frames using SURF descriptors and dense optical flow, which are shown to be complementary. These matches are, then, used to robustly estimate a homography with RANSAC. Human motion is...
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...
Marios S. Pattichis25
Estimated H-index: 25
,
Alan C. Bovik80
Estimated H-index: 80
We develop a mathematical framework for quantifying and understanding multidimensional frequency modulations in digital images. We begin with the widely accepted definition of the instantaneous frequency vector (IF) as the gradient of the phase and define the instantaneous frequency gradient tensor (IFGT) as the tensor of component derivatives of the IF vector. Frequency modulation bounds are derived and interpreted in terms of the eigendecomposition of the IFGT. Using the IFGT, we derive the or...
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