scholarly journals Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

2021 ◽  
Vol 11 (4) ◽  
pp. 1933
Author(s):  
Hiroomi Hikawa ◽  
Yuta Ichikawa ◽  
Hidetaka Ito ◽  
Yutaka Maeda
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Weight Vector ◽  
Recognition System ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Dynamic ◽  
System Input ◽  
Self Organizing ◽  
Gesture Spotting

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.

scholarly journals Trajectory Analysis on Spherical Self-Organizing Maps With Application to Gesture Recognition

2021 ◽  
Author(s):  
Artur Oliva Gonsales
Keyword(s):  
Gesture Recognition ◽  
Trajectory Analysis ◽  
Microsoft Kinect ◽  
Data Sets ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
New Approach ◽  
Spatiotemporal Information ◽  
Full Body ◽  
Self Organizing

In this work, a new approach to gesture recognition using the properties of Spherical Self- Organizing Map (SSOM) is investigated. Bounded mapping of data onto a SSOM creates not only a powerful tool for visualization but also for modeling spatiotemporal information of gesture data. The SSOM allows for the automated decomposition of a variety of gestures into a set of distinct postures. The decomposition naturally organizes this set into a spatial map that preserves associations between postures, upon which we formalize the notion of a gesture as a trajectory through learned posture space. Trajectories from different gestures may share postures. However, the path traversed through posture space is relatively unique. Different variations of posture transitions occurring within a gesture trajectory are used to classify new unknown gestures. Four mechanisms for detecting the occurrence of a trajectory of an unknown gesture are proposed and evaluated on two data sets involving both hand gestures (public sign language database) and full body gestures (Microsoft Kinect database collected in-house) showing the effectiveness of the proposed approach.

scholarly journals Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

2019 ◽  
Vol 9 (3) ◽  
pp. 528 ◽  
Author(s):  
Fahn Chin-Shyurng ◽  
Shih-En Lee ◽  
Meng-Luen Wu
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Dynamic Time Warping ◽  
Recognition System ◽  
Experimental Result ◽  
Depth Camera ◽  
Time Warping ◽  
Skeleton Model ◽  
Time Dynamic ◽  
Dynamic Time

Gesture recognition is a human−computer interaction method, which is widely used for educational, medical, and entertainment purposes. Humans also use gestures to communicate with each other, and musical conducting uses gestures in this way. In musical conducting, conductors wave their hands to control the speed and strength of the music played. However, beginners may have a limited comprehension of the gestures and might not be able to properly follow the ensembles. Therefore, this paper proposes a real-time musical conducting gesture recognition system to help music players improve their performance. We used a single-depth camera to capture image inputs and establish a real-time dynamic gesture recognition system. The Kinect software development kit created a skeleton model by capturing the palm position. Different palm gestures were collected to develop training templates for musical conducting. The dynamic time warping algorithm was applied to recognize the different conducting gestures at various conducting speeds, thereby achieving real-time dynamic musical conducting gesture recognition. In the experiment, we used 5600 examples of three basic types of musical conducting gestures, including seven capturing angles and five performing speeds for evaluation. The experimental result showed that the average accuracy was 89.17% in 30 frames per second.

scholarly journals Trajectory Analysis on Spherical Self-Organizing Maps With Application to Gesture Recognition

2021 ◽  
Author(s):  
Artur Oliva Gonsales
Keyword(s):  
Gesture Recognition ◽  
Trajectory Analysis ◽  
Microsoft Kinect ◽  
Data Sets ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
New Approach ◽  
Spatiotemporal Information ◽  
Full Body ◽  
Self Organizing

In this work, a new approach to gesture recognition using the properties of Spherical Self- Organizing Map (SSOM) is investigated. Bounded mapping of data onto a SSOM creates not only a powerful tool for visualization but also for modeling spatiotemporal information of gesture data. The SSOM allows for the automated decomposition of a variety of gestures into a set of distinct postures. The decomposition naturally organizes this set into a spatial map that preserves associations between postures, upon which we formalize the notion of a gesture as a trajectory through learned posture space. Trajectories from different gestures may share postures. However, the path traversed through posture space is relatively unique. Different variations of posture transitions occurring within a gesture trajectory are used to classify new unknown gestures. Four mechanisms for detecting the occurrence of a trajectory of an unknown gesture are proposed and evaluated on two data sets involving both hand gestures (public sign language database) and full body gestures (Microsoft Kinect database collected in-house) showing the effectiveness of the proposed approach.

Measuring and Modeling Real-Time Responses to Music: The Dynamics of Tonality Induction

Perception ◽  
10.1068/p3312 ◽  
2003 ◽  
Vol 32 (6) ◽  
pp. 741-766 ◽  
Author(s):  
Petri Toiviainen ◽  
Carol L Krumhansl
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Primary Data ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Probe Tone ◽  
Additional Information ◽  
Chromatic Scale ◽  
Dynamic Components ◽  
Self Organizing

We examined a variety of real-time responses evoked by a single piece of music, the organ Duetto BWV 805 by J S Bach. The primary data came from a concurrent probe-tone method in which the probe-tone is sounded continuously with the music. Listeners judged how well the probe tone fit with the music at each point in time. The process was repeated for all probe tones of the chromatic scale. A self-organizing map (SOM) [Kohonen 1997 Self-organizing Maps (Berlin: Springer)] was used to represent the developing and changing sense of key reflected in these judgments. The SOM was trained on the probe-tone profiles for 24 major and minor keys (Krumhansl and Kessler 1982 Psychological Review89 334–368). Projecting the concurrent probe-tone data onto the map showed changes both in the perceived keys and in their strengths. Two dynamic models of tonality induction were tested. Model 1 is based on pitch class distributions. Model 2 is based on the tone-transition distributions; it tested the idea that the order of tones might provide additional information about tonality. Both models contained dynamic components for characterizing pitch strength and creating pitch memory representations. Both models produced results closely matching those of the concurrent probe-tone data. Finally realtime judgments of tension were measured. Tension correlated with distance away from the predominant key in the direction of keys built on the dominant and supertonic tones, and also correlated with dissonance.

A Real-Time Dynamic Gesture Recognition System

2013 ◽  
Vol 333-335 ◽  
pp. 849-855 ◽  
Author(s):  
Jiang Guo ◽  
Jun Cheng ◽  
Yu Guo ◽  
Jian Xin Pang
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Sensory Information ◽  
Recognition System ◽  
Correction Method ◽  
Hand Detection ◽  
Time Dynamic ◽  
Trajectory Correction ◽  
Dynamic Time ◽  
Dynamic Gesture Recognition

In this paper, we present a dynamic gesture recognition system. We focus on the visual sensory information to recognize human activity in form of hand movements from a small, predefined vocabulary. A fast and effective method is presented for hand detection and tracking at first for the trajectory extraction. A novel trajectory correction method is applied for simply but effectively trajectory correction. Gesture recognition is achieved by means of a matching technique by determining the distance between the unknown input direction code sequence and a set of previously defined templates. A dynamic time warping (DTW) algorithm is used to perform the time alignment and normalization by computing a temporal transformation allowing the two signals to be matched. Experiment results show our proposed gesture recognition system achieve well result in real time.

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

scholarly journals Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adeoluwa Akande ◽  
Ana Cristina Costa ◽  
Jorge Mateu ◽  
Roberto Henriques
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Northern Region ◽  
Extreme Weather Events ◽  
Self Organizing Map ◽  
Spatial And Temporal Patterns ◽  
Self Organizing Maps ◽  
The North ◽  
Precipitation Regimes ◽  
Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

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