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19th International Conference on Computer Analysis of images and Patterns, CAIP 2021

作者： Shi, Wenjing Pattichis, Marios S. Celedón-Pattichis, Sylvia LópezLeiva, Carlos Image and Video Processing and Communications Lab Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States Departtment of Language Literacy and Sociocultural Studies University of New Mexico AlbuquerqueNM United States

ISBN: (纸本)9783030891305

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves. © 2021, Springer Nature Switzerland AG.

关键词： Computer aided instruction

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Stochastic Recursive Gradient Descent Optimization-based on Foreground Features of Fisher Vector 14

Stochastic Recursive Gradient Descent Optimization-based on ...

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14th International Conference on Digital image processing, ICDIP 2022

作者： Kamaleldin, Mohamed Gamal M. Abu-Bakar, Syed A.R. Sheikh, Usman Ullah Electronics and Communications Engineering Department Arab Academy for Science and Technology Cairo11736 Egypt Computer Vision Video and Image Processing Research Lab School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81300 Malaysia

ISBN: (纸本)9781510657564

Human action recognition has been one of the hot topics in computer vision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size of these features. Amonsgt the state-of-the-art approaches is to encode these visual features using the Gaussian mixture model. However, the size of the codebook is an issue in terms of the computation complexity, especially for large-scale data as it requires encoding using a large codebook. In this paper, we introduced the use of different optimizers to reduce the codebook size while boosting its accuracy. To illustrate the performance, first we use the improved dense trajectories (IDT) to extract the handcrafted features. This is followed with encoding the descriptor using Fisher kernel-based codebook using the Gaussian mixture model. Next, the support vector machine is used to classify the categories. We then use and compare five different Stochastic gradient descent optimization techniques to modify the number of Gaussian components. In this manner we are able to select the discriminative foreground features (as represented by the final number of Gaussian components), and omit the background features. Finally, to show the performance improvement of the proposed method, we implement this technique to two datasets UCF101 and HMDB51. © 2022 SPIE.

关键词： Support vector machines

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Real time AM-FM analysis of ultrasound video

Real time AM-FM analysis of ultrasound video

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： P. Rodriguez V M.S. Pattichis Image and video Processing and Communications Lab Department of EECE University of New Mexico Albuquerque NM USA

In this paper, we present a real-time amplitude-modulation frequency-modulation (AM-FM) analysis system, and apply the system to M-mode ultrasound video. The estimated frequency-modulation (FM) process is shown to capture the cardiac wall deformation, while the amplitude modulation process is shown to follow changes in cardiac wall material. Results from segmenting the septum and left-ventricle walls are also presented.

关键词： Ultrasonic imaging Frequency estimation Amplitude estimation Phase estimation Demodulation Band pass filters Frequency modulation Gabor filters image analysis Amplitude modulation

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Robust segmentation and volumetric registration in a multi-view 3D freehand ultrasound reconstruction system

Robust segmentation and volumetric registration in a multi-v...

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40th Asilomar Conference on Signals, Systems, and Computers, ACSSC '06

作者： Yu, Honggang Pattichis, Marios S. Goens, M. Beth Image and Video Processing and Communications Lab. Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM 87131 United States Pediatric Cardiology Clinic University of New Mexico Children's Hospital Albuquerque NM 87131 United States

ISBN: (纸本)1424407850

In this paper, we present a new reconstruction method with volumetric registration and semi-automatic segmentation in a multi-view 3D freehand ultrasound imaging system. The new volumetric registration approach is performed on binarized walls using non-linear least squares. It can provide accurate multi-view reconstructions despite significant rigid target motion between different acoustic window acquisitions. A hybrid adaptive gradient vector flow (GVF) geometric active contour (GAC) model is used for image sequence segmentation. It allows for relatively simple initialization of the deformable model, while avoiding edge leaking at poor edges and small boundary gaps. The algorithms were validated on four ultrasound phantom data sets (eight sequences of a total of 336 images) and two echocardiography data sets (four sequences for a total of 75 images). Quantitative evaluation shows that automatic segmentation is comparable with manual segmentation. Using breath-holding and cardiac gating, volume estimates from multiview reconstructions of the left-ventricle were found to be in better agreement with clinical estimates than volumes estimated from single view reconstructions.

关键词： Echocardiography

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Person Detection in Collaborative Group Learning Environments Using Multiple Representations

Person Detection in Collaborative Group Learning Environment...

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Asilomar Conference on Signals, Systems & Computers

作者： Wenjing Shi Marios S. Pattichis Sylvia Celedón-Pattichis Carlos LópezLeiva Image and Video Processing and Communications Lab (ivpcl.unm.edu) University of New Mexico United States University of New Mexico United States

We introduce the problem of detecting a group of students from classroom videos. The problem requires the detection of students from different angles and the separation of the group from other groups in long videos (one to one and a half hours).We use multiple image representations to solve the problem. We use FM components to separate each group from background groups, AM-FM components for detecting the back-of-the-head, and YOLO for face detection. We use classroom videos from four different groups to validate our approach. Our use of multiple representations is shown to be significantly more accurate than the use of YOLO alone.

关键词： Computers Frequency modulation Face recognition Collaboration image representation Face detection videos

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Person Detection in Collaborative Group Learning Environments Using Multiple Representations

arXiv

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arXiv 2021年

作者： Shi, Wenjing Pattichis, Marios S. Celedón-Pattichis, Sylvia LópezLeiva, Carlos image and video Processing and Communications Lab [ivpcl.unm.edu Dept. of Language Literacy and Sociocultural Studies University of New Mexico United States

We introduce the problem of detecting a group of students from classroom videos. The problem requires the detection of students from different angles and the separation of the group from other groups in long videos (one to one and a half hours). We use multiple image representations to solve the problem. We use FM components to separate each group from background groups, AM-FM components for detecting the back-of-the-head, and YOLO for face detection. We use classroom videos from four different groups to validate our approach. Our use of multiple representations is shown to be significantly more accurate than the use of YOLO alone. © 2021, CC BY.

关键词： Students

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Robust Segmentation of Freehand Ultrasound image Slices Using Gradient Vector Flow Fast Geometric Active Contours

Robust Segmentation of Freehand Ultrasound Image Slices Usin...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Honggang Yu M.S. Pattichis M.B. Goens Department of Electrical and Computer Engineering image and video Processing and Communications Lab (ivPCL) Albuquerque NM USA Children's Hospital Heart Center The University of New Mexico Albuquerque NM USA

ISBN: (纸本)1424400694

We propose a new semi-automatic segmentation strategy on echocardiographic images, which combines a recently introduced gradient vector flow (GVF) fast geometric active contour (GAC) model and a modified level sets methods applied to echocardiographic data by Corsi et al.. We call it adaptive GVF GAC model. We note that echocardiographic images are characterized by high levels of speckle noise, weakly-defined boundaries and severe gaps. We show that the new method, adapted for single object segmentation, can provide significantly improved performance over a competing level set method, and that was in turn shown to perform better than the original gradient vector flow method. The new method modifies the advection term in the speed function adoptively by estimating how close the propagated curve is to the target boundaries. We show both synthetic and real, freehand ultrasound image and echocardiographic image examples to illustrate the robustness and accuracy of the new segmentation method

关键词： Robustness image segmentation Ultrasonic imaging Active contours Level set Deformable models image reconstruction Solid modeling Heart Speckle

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

Robust Head Detection in Collaborative Learning Environments...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Wenjing Shi Marios S. Pattichis Sylvia Celedón-Pattichis Carlos LópezLeiva image and video Processing and Communications Lab (ivpcl.unm.edu) University of New Mexico United States Dept. of Language Literacy and Sociocultural Studies University of New Mexico United States

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.

关键词： Hair Frequency modulation Face Detectors Robustness image color analysis

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Context-Sensitive Human Activity Classification in Collaborative Learning Environments

Context-Sensitive Human Activity Classification in Collabora...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Abigail Ruth Jacoby Marios S. Pattichis Sylvia Celedón-Pattichis Carlos LópezLeiva image and video Processing and Communications Lab (ivpcl.unm.edu) University of New Mexico United States Dept. of Language Literacy and Sociocultural Studies University of New Mexico United States

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 *** 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 to make use of separate classifiers for each activity. Here, we consider the detection of typing, writing, and talking activities in raw *** method was tested using 43 uncropped video clips with 620 video frames for writing, 1050 for typing, and 1755 frames for talking. Using simple KNN classifiers, the method gave accuracies of 72.6% for writing, 71% for typing and 84.6% for talking. Classification accuracy improved to 92.5% (writing), 82.5% (typing) and 99.7% (talking) with the use of Deep Neural Networks.

关键词： Writing Keyboards Feature extraction image color analysis Neural networks Collaborative work Monitoring

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Talking detection in collaborative learning environments

arXiv

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arXiv 2021年

作者： Shi, Wenjing Pattichis, Marios S. Celedón-Pattichis, Sylvia LópezLeiva, Carlos Image and Video Processing and Communications Lab Dept. of Electrical and Computer Engineering University of New Mexico United States Dept. of Language Literacy and Sociocultural Studies University of New Mexico United States

关键词： Speech recognition

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