Pub Date : 2014-05-05DOI: 10.1109/CIVEMSA.2014.6841431
Muhammad R. Abid, Philippe E. Meszaros, Ricardo F. D. Silva, E. Petriu
This paper discusses inter-robot and human-robot communication by bare hand dynamic gestures. We use a Bag-of-Features and a local part model approach for bare hand dynamic hand gesture recognition from video. We used dense sampling to extract local 3D multiscale whole-part features. We adopted three dimensional histograms of a gradient orientation (3D HOG) descriptor to represent features. The K-means++ method was applied to cluster the visual words. Dynamic hand gesture classification was completed by using a Bag-of-features (BOF) and non-linear support vector machine (SVM) method. A BOF does not track the order of events. To counter the unordered events of the BOF approach, we used a multiscale local part model to preserve temporal context. Initial experimental results on the newly collected complex dataset show a higher level of recognition. We used the same above mentioned approach for inter-robot communication by using two sample hand models.
{"title":"Dynamic hand gesture recognition for human-robot and inter-robot communication","authors":"Muhammad R. Abid, Philippe E. Meszaros, Ricardo F. D. Silva, E. Petriu","doi":"10.1109/CIVEMSA.2014.6841431","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2014.6841431","url":null,"abstract":"This paper discusses inter-robot and human-robot communication by bare hand dynamic gestures. We use a Bag-of-Features and a local part model approach for bare hand dynamic hand gesture recognition from video. We used dense sampling to extract local 3D multiscale whole-part features. We adopted three dimensional histograms of a gradient orientation (3D HOG) descriptor to represent features. The K-means++ method was applied to cluster the visual words. Dynamic hand gesture classification was completed by using a Bag-of-features (BOF) and non-linear support vector machine (SVM) method. A BOF does not track the order of events. To counter the unordered events of the BOF approach, we used a multiscale local part model to preserve temporal context. Initial experimental results on the newly collected complex dataset show a higher level of recognition. We used the same above mentioned approach for inter-robot communication by using two sample hand models.","PeriodicalId":228132,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124104533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-05DOI: 10.1109/CIVEMSA.2014.6841450
E. Huluta, Ricardo Freire da Silva, T. E. D. Oliveira
This paper presents a novel method to provide Neural Network Based Control of a Multi-finger Robot Hand. There are several challenges known from the literature that researchers are facing when they are trying to produce a human-like trainable robotic hand due to the complexity of building it and controlling its 3D movement. The authors of this article are providing an improved solution to this problem by developing a framework that enables easy training and control of a Robotic Hand by using Artificial Neural Networks.
{"title":"Neural network-Based hand posture control of a humanoid Robot Hand","authors":"E. Huluta, Ricardo Freire da Silva, T. E. D. Oliveira","doi":"10.1109/CIVEMSA.2014.6841450","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2014.6841450","url":null,"abstract":"This paper presents a novel method to provide Neural Network Based Control of a Multi-finger Robot Hand. There are several challenges known from the literature that researchers are facing when they are trying to produce a human-like trainable robotic hand due to the complexity of building it and controlling its 3D movement. The authors of this article are providing an improved solution to this problem by developing a framework that enables easy training and control of a Robotic Hand by using Artificial Neural Networks.","PeriodicalId":228132,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124596188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-05DOI: 10.1109/CIVEMSA.2014.6841448
E. Paquet, Dela De Youngster, H. Viktor, E. Petriu
Non-rigid shapes are generally known as objects where the three dimensional geometry may deform by internal and/or external forces. Deformable shapes are all around us, ranging from macromolecules, to natural objects such as the trees in the forest or the fruits in our gardens, and even human bodies. The development of measurements to accurately describe such non-rigid shapes has wide application. It follows that, when aiming to perform measurement of non-rigid shapes, one need to ensure that the intrinsic geometries are preserved in the presence of deformations. This paper introduces physics-based techniques for the measurements of such deformable shapes. Our work is based on the idea of heat propagation through a surface. Specifically, we introduce so-called multi-measurements to measure the distance between two points on an object and reflective measurements that allows for the exploration of a region around a point. Further, we introduce two novel meta-measurements that extend the bag of features concept. We illustrate our methodology in the context of structural proteomics and molecular docking.
{"title":"Physics-based measurements, reflective measurements and meta-measurements for nonrigid and deformable shapes with application to structural proteomics and macromolecular docking","authors":"E. Paquet, Dela De Youngster, H. Viktor, E. Petriu","doi":"10.1109/CIVEMSA.2014.6841448","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2014.6841448","url":null,"abstract":"Non-rigid shapes are generally known as objects where the three dimensional geometry may deform by internal and/or external forces. Deformable shapes are all around us, ranging from macromolecules, to natural objects such as the trees in the forest or the fruits in our gardens, and even human bodies. The development of measurements to accurately describe such non-rigid shapes has wide application. It follows that, when aiming to perform measurement of non-rigid shapes, one need to ensure that the intrinsic geometries are preserved in the presence of deformations. This paper introduces physics-based techniques for the measurements of such deformable shapes. Our work is based on the idea of heat propagation through a surface. Specifically, we introduce so-called multi-measurements to measure the distance between two points on an object and reflective measurements that allows for the exploration of a region around a point. Further, we introduce two novel meta-measurements that extend the bag of features concept. We illustrate our methodology in the context of structural proteomics and molecular docking.","PeriodicalId":228132,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"29 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123392376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-05DOI: 10.1109/CIVEMSA.2014.6841453
R. Precup, Andrei-Leonard Borza, M. Radac, E. Petriu
This paper deals with the optimal tuning of proportional-integral-derivative (PID) controllers for a pancake direct current (DC) torque motor system that belongs to a Diesel engine exhaust gas recirculation valve in automotive applications. The Bacterial Foraging Optimization (BFO) algorithms solve an optimization problem which targets the minimization of an objective function expressed as the weighted sum of overshoot plus the integral of squared control error, and the parameters of the PID controllers are the variables of the objective function. Our BFO algorithms are characterized by the validation of the position of bacteria only if the PID control system response is in a valid range. A digitally simulated case study which deals with the shaft angle control of a DC torque motor system is considered. The impact of four parameters of one BFO algorithm on the objective function values is discussed.
{"title":"Performance analysis of torque motor systems with PID controllers tuned by Bacterial Foraging Optimization algorithms","authors":"R. Precup, Andrei-Leonard Borza, M. Radac, E. Petriu","doi":"10.1109/CIVEMSA.2014.6841453","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2014.6841453","url":null,"abstract":"This paper deals with the optimal tuning of proportional-integral-derivative (PID) controllers for a pancake direct current (DC) torque motor system that belongs to a Diesel engine exhaust gas recirculation valve in automotive applications. The Bacterial Foraging Optimization (BFO) algorithms solve an optimization problem which targets the minimization of an objective function expressed as the weighted sum of overshoot plus the integral of squared control error, and the parameters of the PID controllers are the variables of the objective function. Our BFO algorithms are characterized by the validation of the position of bacteria only if the PID control system response is in a valid range. A digitally simulated case study which deals with the shaft angle control of a DC torque motor system is considered. The impact of four parameters of one BFO algorithm on the objective function values is discussed.","PeriodicalId":228132,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114174806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-05DOI: 10.1109/CIVEMSA.2014.6841439
G. Boracchi, M. Roveri
Detecting changes in data-generating processes is a primary requirement for adaptive and flexible systems endowed with computational intelligence abilities. In order to maintain/improve their performance in evolving or dynamic environments, these systems have to detect any variation in the data-generating process and react and adapt to the new operating conditions. The problem of detecting changes in streams of data is generally addressed by means of Change-Detection Tests (CDTs) and, recently, a family of CDTs based on the Intersection-of-Confidence-Interval (ICI) rule has been presented. ICI-based CDTs monitor data streams by extracting Gaussian distributed features from non-overlapping data windows. The drawback of such a window-wise operational mode is a structural delay, which is particularly evident when the change magnitude is large. We present a novel ICI-based CDT that overcomes this problem by operating in an element-wise manner thanks to a Gaussian transform of the acquired data. Such an element-wise CDT is characterized by a high change-detection ability and a reduced computational complexity, which makes it suitable for the execution on low-power embedded systems. The proposed CDT is also provided with a reconfiguration mechanism that, after any detected change, allows the CDT to be reconfigured on the new working conditions to detect further changes. A wide experimental campaign shows the effectiveness of the proposed element-wise CDT both on synthetic and real datasets.
{"title":"A reconfigurable and element-wise ICI-based change-detection test for streaming data","authors":"G. Boracchi, M. Roveri","doi":"10.1109/CIVEMSA.2014.6841439","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2014.6841439","url":null,"abstract":"Detecting changes in data-generating processes is a primary requirement for adaptive and flexible systems endowed with computational intelligence abilities. In order to maintain/improve their performance in evolving or dynamic environments, these systems have to detect any variation in the data-generating process and react and adapt to the new operating conditions. The problem of detecting changes in streams of data is generally addressed by means of Change-Detection Tests (CDTs) and, recently, a family of CDTs based on the Intersection-of-Confidence-Interval (ICI) rule has been presented. ICI-based CDTs monitor data streams by extracting Gaussian distributed features from non-overlapping data windows. The drawback of such a window-wise operational mode is a structural delay, which is particularly evident when the change magnitude is large. We present a novel ICI-based CDT that overcomes this problem by operating in an element-wise manner thanks to a Gaussian transform of the acquired data. Such an element-wise CDT is characterized by a high change-detection ability and a reduced computational complexity, which makes it suitable for the execution on low-power embedded systems. The proposed CDT is also provided with a reconfiguration mechanism that, after any detected change, allows the CDT to be reconfigured on the new working conditions to detect further changes. A wide experimental campaign shows the effectiveness of the proposed element-wise CDT both on synthetic and real datasets.","PeriodicalId":228132,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123687970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-05DOI: 10.1109/CIVEMSA.2014.6841446
F. Halal, M. Zaremba
This paper addresses the issue of efficient monitoring of Lake Winnipeg water quality by employing the power of computational intelligence methods in the processing of multi-spectral remote sensing data. The large size of Lake Winnipeg (the 10th largest lake in the world) and its susceptibility to algal blooms makes satellite technologies indispensable in monitoring the quality of the lake's water. The remote sensing data have to be complemented by in-situ measurements due to the requirements for the calibration of satellite imagery, for precise local measurements, as well as because of the variations in water conditions. A method for the path planning of a ship equipped with water sample acquisition and processing facilities is presented. Given the complexity of the planning task (acquisition of different types of samples of different informational values, use of ancillary environment data, dependence on the results of satellite data processing, etc.), an inclusion of the deliberative level in the ship trajectory planning and control scheme is postulated. A deliberative control architecture is proposed which features a multi-model classification/regression system for the determination and forecasting of spatial distribution of water pollutants, in particular chlorophyll-a, and a cost optimizing path planner. A fuzzy system which handles different control strategies depending on the surrounding environment supervises the reactive level operational control.
{"title":"Deliberative control for satellite-guided water quality monitoring","authors":"F. Halal, M. Zaremba","doi":"10.1109/CIVEMSA.2014.6841446","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2014.6841446","url":null,"abstract":"This paper addresses the issue of efficient monitoring of Lake Winnipeg water quality by employing the power of computational intelligence methods in the processing of multi-spectral remote sensing data. The large size of Lake Winnipeg (the 10th largest lake in the world) and its susceptibility to algal blooms makes satellite technologies indispensable in monitoring the quality of the lake's water. The remote sensing data have to be complemented by in-situ measurements due to the requirements for the calibration of satellite imagery, for precise local measurements, as well as because of the variations in water conditions. A method for the path planning of a ship equipped with water sample acquisition and processing facilities is presented. Given the complexity of the planning task (acquisition of different types of samples of different informational values, use of ancillary environment data, dependence on the results of satellite data processing, etc.), an inclusion of the deliberative level in the ship trajectory planning and control scheme is postulated. A deliberative control architecture is proposed which features a multi-model classification/regression system for the determination and forecasting of spatial distribution of water pollutants, in particular chlorophyll-a, and a cost optimizing path planner. A fuzzy system which handles different control strategies depending on the surrounding environment supervises the reactive level operational control.","PeriodicalId":228132,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124918687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-05DOI: 10.1109/CIVEMSA.2014.6841441
G. Jäger, S. Zug, Tino Brade, André Dietrich, Christoph Steup, C. Moewes, A. Crétu
The idea of “smart sensing” includes a permanent monitoring and evaluation of sensor data related to possible measurement faults. This concept requires a fault detection chain covering all relevant fault types of a specific sensor. Additionally, the fault detection components have to provide a high precision in order to generate a reliable quality indicator. Due to the large spectrum of sensor faults and their specific characteristics these goals are difficult to meet and error prone. The developer manually determines the specific sensor characteristics, indicates a set of detection methods, adjusts parameters and evaluates the composition. In this paper we exploit neural-network approaches in order to provide a general solution covering typical sensor faults and to replace complex sets of individual detection methods. For this purpose, we identify an appropriate set of fault relevant features in a first step. Secondly, we determine a generic neural-network structure and learning strategy adaptable for detecting multiple fault types. Afterwards the approach is applied on a common used sensor system and evaluated with deterministic fault injections.
{"title":"Assessing neural networks for sensor fault detection","authors":"G. Jäger, S. Zug, Tino Brade, André Dietrich, Christoph Steup, C. Moewes, A. Crétu","doi":"10.1109/CIVEMSA.2014.6841441","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2014.6841441","url":null,"abstract":"The idea of “smart sensing” includes a permanent monitoring and evaluation of sensor data related to possible measurement faults. This concept requires a fault detection chain covering all relevant fault types of a specific sensor. Additionally, the fault detection components have to provide a high precision in order to generate a reliable quality indicator. Due to the large spectrum of sensor faults and their specific characteristics these goals are difficult to meet and error prone. The developer manually determines the specific sensor characteristics, indicates a set of detection methods, adjusts parameters and evaluates the composition. In this paper we exploit neural-network approaches in order to provide a general solution covering typical sensor faults and to replace complex sets of individual detection methods. For this purpose, we identify an appropriate set of fault relevant features in a first step. Secondly, we determine a generic neural-network structure and learning strategy adaptable for detecting multiple fault types. Afterwards the approach is applied on a common used sensor system and evaluated with deterministic fault injections.","PeriodicalId":228132,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128277198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-05DOI: 10.1109/CIVEMSA.2014.6841445
Ali N. Hasan, Bhekisipho Twala, T. Marwala
Three computational intelligence algorithms (k-nearest neighbors, a naïve Bayes' classifier, and decision trees) were applied on a double pump station mine to monitor and predict the dam levels and energy consumption. This work was carried out to inspect the feasibility of using computational intelligence in certain aspects of the mining industry. If successful, computational intelligence systems could lead to improved safety and reduced electrical energy consumption. The results show k nearest neighbors' technique to be more efficient when compared with decision trees, and naïve Bayes' classifier techniques in terms of predicting underground dam levels and pumps energy consumption.
{"title":"Underground water dam levels and energy consumption prediction using computational intelligence techniques","authors":"Ali N. Hasan, Bhekisipho Twala, T. Marwala","doi":"10.1109/CIVEMSA.2014.6841445","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2014.6841445","url":null,"abstract":"Three computational intelligence algorithms (k-nearest neighbors, a naïve Bayes' classifier, and decision trees) were applied on a double pump station mine to monitor and predict the dam levels and energy consumption. This work was carried out to inspect the feasibility of using computational intelligence in certain aspects of the mining industry. If successful, computational intelligence systems could lead to improved safety and reduced electrical energy consumption. The results show k nearest neighbors' technique to be more efficient when compared with decision trees, and naïve Bayes' classifier techniques in terms of predicting underground dam levels and pumps energy consumption.","PeriodicalId":228132,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134353794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-05DOI: 10.1109/CIVEMSA.2014.6841440
H. Marzi, Arash Marzi
Until recently use of sensors to collect sensitive parameters had only few risk factors such as sensor malfunction, uncertainty of data collection, or missing data coverage. Some issues in these categories were addressed by multi-sensor application or sensor fusion. However, advancement in technology and advent of wireless sensors in a networked environment, brought along a new risk factor related to the security in wireless sensor network. Therefore security in Wireless Sensor Network WSNs is challenging and critical to the functionality of the networked sensors. This is very important in cases of highly secure environment, especially in industrial, military, and medical domains. The standard WSN protocols focus on energy efficiency; transmission efficiency, and routing. WSN is known for limitations on hardware and software and for resources-constrained in general. An adaptive model of security that meets requirements and constraints in WSN is Intrusion Detections. This article investigates security in WSN and presents a design process for achieving optimum security based on requirements and constraints in WSNs. Further, comparative results between a proposed technique and other security current approaches are discussed.
{"title":"A security model for wireless sensor networks","authors":"H. Marzi, Arash Marzi","doi":"10.1109/CIVEMSA.2014.6841440","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2014.6841440","url":null,"abstract":"Until recently use of sensors to collect sensitive parameters had only few risk factors such as sensor malfunction, uncertainty of data collection, or missing data coverage. Some issues in these categories were addressed by multi-sensor application or sensor fusion. However, advancement in technology and advent of wireless sensors in a networked environment, brought along a new risk factor related to the security in wireless sensor network. Therefore security in Wireless Sensor Network WSNs is challenging and critical to the functionality of the networked sensors. This is very important in cases of highly secure environment, especially in industrial, military, and medical domains. The standard WSN protocols focus on energy efficiency; transmission efficiency, and routing. WSN is known for limitations on hardware and software and for resources-constrained in general. An adaptive model of security that meets requirements and constraints in WSN is Intrusion Detections. This article investigates security in WSN and presents a design process for achieving optimum security based on requirements and constraints in WSNs. Further, comparative results between a proposed technique and other security current approaches are discussed.","PeriodicalId":228132,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115712937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-05DOI: 10.1109/CIVEMSA.2014.6841429
D. Ionescu, V. Suse, C. Gadea, B. Solomon, B. Ionescu, S. Islam, M. Cordea
Gesture-based human-computer interaction is presently an important area of research that aims to make reliable touch-free user interfaces a reality. More recent gesture detection technologies use cameras that rely on near-infrared (NIR) illumination to obtain 3D depth information for objects within the camera's field-of-view. These cameras use either structured light, time-of-flight (ToF), or stereoscopy. Depth images allow a person's body and hands to be separated from the background, thereby permitting modern image processing algorithms to be used for greatly improved gesture detection. This paper presents a new depth generation principle that uses a monotonic increasing and decreasing function to control NIR illumination pulses. Reflected light pulses are captured as a series of images and the depth map of the visible objects is calculated in real-time using reconfigurable hardware. Measurements and results are given to explain how the depth map is built and how the camera allows gestures to be used to control a video game console.
{"title":"A 3D NIR camera for gesture control of video game consoles","authors":"D. Ionescu, V. Suse, C. Gadea, B. Solomon, B. Ionescu, S. Islam, M. Cordea","doi":"10.1109/CIVEMSA.2014.6841429","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2014.6841429","url":null,"abstract":"Gesture-based human-computer interaction is presently an important area of research that aims to make reliable touch-free user interfaces a reality. More recent gesture detection technologies use cameras that rely on near-infrared (NIR) illumination to obtain 3D depth information for objects within the camera's field-of-view. These cameras use either structured light, time-of-flight (ToF), or stereoscopy. Depth images allow a person's body and hands to be separated from the background, thereby permitting modern image processing algorithms to be used for greatly improved gesture detection. This paper presents a new depth generation principle that uses a monotonic increasing and decreasing function to control NIR illumination pulses. Reflected light pulses are captured as a series of images and the depth map of the visible objects is calculated in real-time using reconfigurable hardware. Measurements and results are given to explain how the depth map is built and how the camera allows gestures to be used to control a video game console.","PeriodicalId":228132,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125534224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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