Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

Enrollment time as a requirement for biometric hand recognition systems

Biometric systems are increasingly being used as a means for authentication to provide system security in modern technologies. The performance of a biometric system depends on the accuracy, the processing speed, the template size, and the time necessary for enrollment. While much research has focused on the first three factors, enrollment time has not received as much attention. In this work, we present the findings of our research focused upon studying user’s behavior when enrolling in a biometric system. Specifically, we collected information about the user’s availability for enrollment in respect to the hand recognition systems (e.g., hand geometry, palm geometry or any other requiring positioning the hand on an optical scanner). A sample of 19 participants, chosen randomly apart their age, gender, profession and nationality, were used as test subjects in an experiment to study the patience of users enrolling in a biometric hand recognition system.

Automatic generation of ETL physical systems from BPMN conceptual models

ETL conceptual modeling is a very important activity in any data warehousing system project implementation. Owning a high-level system representation allowing for a clear identification of the main parts of a data warehousing system is clearly a great advantage, especially in early stages of design and development. However, the effort to model conceptually an ETL system rarely is properly rewarded. Translating ETL conceptual models directly into something that saves work and time on the concrete implementation of the system process it would be, in fact, a great help. In this paper we present and discuss a hybrid approach to this problem, combining the simplicity of interpretation and power of expression of BPMN on ETL systems conceptualization with the use of ETL patterns to produce automatically an ETL skeleton, a first prototype system, which has the ability to be executed in a commercial ETL tool like Kettle.

Estudo acústico de /a/ acentuado na fala bracarense: potenciais aplicações

Dissertação de mestrado em Ciências da Linguagem

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Automatic distinction of Fernando Pessoas’ heteronyms

Text Mining has opened a vast array of possibilities concerning automatic information retrieval from large amounts of text documents. A variety of themes and types of documents can be easily analyzed. More complex features such as those used in Forensic Linguistics can gather deeper understanding from the documents, making possible performing di cult tasks such as author identi cation. In this work we explore the capabilities of simpler Text Mining approaches to author identification of unstructured documents, in particular the ability to distinguish poetic works from two of Fernando Pessoas' heteronyms: Alvaro de Campos and Ricardo Reis. Several processing options were tested and accuracies of 97% were reached, which encourage further developments.

Electronic tongue: a versatile tool for mineral and fruit-flavored waters recognition

Natural mineral waters (still), effervescent natural mineral waters (sparkling) and aromatized waters with fruit-flavors (still or sparkling) are an emerging market. In this work, the capability of a potentiometric electronic tongue, comprised with lipid polymeric membranes, to quantitatively estimate routinely quality physicochemical parameters (pH and conductivity) as well as to qualitatively classify water samples according to the type of water was evaluated. The study showed that a linear discriminant model, based on 21 sensors selected by the simulated annealing algorithm, could correctly classify 100 % of the water samples (leave-one out cross-validation). This potential was further demonstrated by applying a repeated K-fold cross-validation (guaranteeing that at least 15 % of independent samples were only used for internal-validation) for which 96 % of correct classifications were attained. The satisfactory recognition performance of the E-tongue could be attributed to the pH, conductivity, sugars and organic acids contents of the studied waters, which turned out in significant differences of sweetness perception indexes and total acid flavor. Moreover, the E-tongue combined with multivariate linear regression models, based on sub-sets of sensors selected by the simulated annealing algorithm, could accurately estimate waters pH (25 sensors: R 2 equal to 0.99 and 0.97 for leave-one-out or repeated K-folds cross-validation) and conductivity (23 sensors: R 2 equal to 0.997 and 0.99 for leave-one-out or repeated K-folds cross-validation). So, the overall satisfactory results achieved, allow envisaging a potential future application of electronic tongue devices for bottled water analysis and classification.

Apoptosis y expresión de receptores de reconocimiento de patrones en Polimorfonucleares Neutrófilos de individuos con infección VIH/SIDA. Apoptosis and recognition patterns receptors expression in Polymorphonuclear Neutrophils of individuals with HIV/AIDS infection.

El Virus de la Inmunodeficiencia Humana tipo 1 (VIH-1) afecta principalmente a la respuesta inmune específica causando una pérdida progresiva de los linfocitos T CD4+. Sin embargo, este virus también afecta a células del sistema inmune innato, tales como los Polimorfonucleares Neutrófilos (PMN). Existen evidencias de alteraciones funcionales de los PMN durante la progresión de la infección por VIH y una de las explicaciones de estos defectos, la atribuye a una muerte celular programada o apoptosis constitutiva incrementada. El compromiso de la apoptosis de los PMN en la infección por VIH no está totalmente dilucidado, por ello, los objetivos de este proyecto son investigar el efecto de la infección por VIH sobre la apoptosis de PMN, analizar la expresión de moléculas y receptores de patrones de reconocimiento en estas células y evaluar el impacto de la terapia antirretroviral sobre la apoptosis y expresión de moléculas y receptores en PMN. Se incluirán individuos en distintos estadios clínicos e inmunológicos de la infección con o sin tratamiento antirretroviral y se determinarán parámetros hematológicos, inmunológicos y virológicos a fin de correlacionar el nivel de apoptosis y expresión de moléculas y receptores con el nivel de linfocitos T CD4+ y carga viral. La importancia de los PMN en el control de la infección por el VIH es actualmente un área de mucho interés, ya pueden ejercer un efecto anti-VIH directo, y al mismo tiempo, ser blancos de la infección viral. Los mecanismos que conducen a la muerte acelerada de los PMN no han sido totalmente dilucidados, por ello, su estudio permitirá entender las bases bioquímicas de los cambios morfológicos y determinar los mecanismos que definen su iniciación y regulación. En el presente proyecto, el estudio de la apoptosis de PMN de pacientes con infección VIH/SIDA posibilitará caracterizar la sobrevida de éstas células y su relación con el estado inmunológico, virológico y la terapia antirretroviral. Además, el estudio de los receptores reconocedores de patrones moleculares asociados a patógenos permitirá aclarar algunos aspectos de la activación de la respuesta inmune innata y su conexión con la inmunidad adaptativa. Comprender aspectos claves de la cascada de la apoptosis de PMN y de la expresión de receptores reconocedores de patrones moleculares en la infección VIH/SIDA podría en un futuro aportar posibles blancos terapéuticos para restaurar la función de estas células durante esta infección.

Emotion recognition within spoken dialog systems

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2011

Neural mechanisms for fast recognition of auditory emotion

Emotion, audition, event-related potentials, MMN, multidimensional scaling, timbre, perception

Forearm surface EMG signals recognition and muscoloskeletal system dynamics identification using intelligent computational methods

Biosignals processing, Biological Nonlinear and time-varying systems identification, Electomyograph signals recognition, Pattern classification, Fuzzy logic and neural networks methods

Hand gesture spotting and recognition using HMMs and CRFs in color image sequences

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010