New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component

License Plate recognition plays an important role on the traffic monitoring and parking management systems. In this paper, a fast and real time method has been proposed which has an appropriate application to find tilt and poor quality plates. In the proposed method, at the beginning, the image is converted into binary mode using adaptive threshold. Then, by using some edge detection and morphology operations, plate number location has been specified. Finally, if the plat has tilt, its tilt is removed away. This method has been tested on another paper data set that has different images of the background, considering distance, and angel of view so that the correct extraction rate of plate reached at 98.66%.Comment: 3rd IEEE International Conference on Computer and Knowledge Engineering (ICCKE 2013), October 31 & November 1, 2013, Ferdowsi Universit Mashha

DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation

There is an undeniable communication barrier between deaf people and people with normal hearing ability. Although innovations in sign language translation technology aim to tear down this communication barrier, the majority of existing sign language translation systems are either intrusive or constrained by resolution or ambient lighting conditions. Moreover, these existing systems can only perform single-sign ASL translation rather than sentence-level translation, making them much less useful in daily-life communication scenarios. In this work, we fill this critical gap by presenting DeepASL, a transformative deep learning-based sign language translation technology that enables ubiquitous and non-intrusive American Sign Language (ASL) translation at both word and sentence levels. DeepASL uses infrared light as its sensing mechanism to non-intrusively capture the ASL signs. It incorporates a novel hierarchical bidirectional deep recurrent neural network (HB-RNN) and a probabilistic framework based on Connectionist Temporal Classification (CTC) for word-level and sentence-level ASL translation respectively. To evaluate its performance, we have collected 7,306 samples from 11 participants, covering 56 commonly used ASL words and 100 ASL sentences. DeepASL achieves an average 94.5% word-level translation accuracy and an average 8.2% word error rate on translating unseen ASL sentences. Given its promising performance, we believe DeepASL represents a significant step towards breaking the communication barrier between deaf people and hearing majority, and thus has the significant potential to fundamentally change deaf people's lives

Computational Models for the Automatic Learning and Recognition of Irish Sign Language

This thesis presents a framework for the automatic recognition of Sign Language sentences. In previous sign language recognition works, the issues of; user independent recognition, movement epenthesis modeling and automatic or weakly supervised training have not been fully addressed in a single recognition framework. This work presents three main contributions in order to address these issues. The first contribution is a technique for user independent hand posture recognition. We present a novel eigenspace Size Function feature which is implemented to perform user independent recognition of sign language hand postures. The second contribution is a framework for the classification and spotting of spatiotemporal gestures which appear in sign language. We propose a Gesture Threshold Hidden Markov Model (GT-HMM) to classify gestures and to identify movement epenthesis without the need for explicit epenthesis training. The third contribution is a framework to train the hand posture and spatiotemporal models using only the weak supervision of sign language videos and their corresponding text translations. This is achieved through our proposed Multiple Instance Learning Density Matrix algorithm which automatically extracts isolated signs from full sentences using the weak and noisy supervision of text translations. The automatically extracted isolated samples are then utilised to train our spatiotemporal gesture and hand posture classifiers. The work we present in this thesis is an important and significant contribution to the area of natural sign language recognition as we propose a robust framework for training a recognition system without the need for manual labeling

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"Hand gesture recognition is a natural way of human computer interaction and an area of very 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 applied to Human-Computer Interaction (HCI) is to create systems, which can identify specific human gestures and use them to convey information or controlling devices. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. This paper presents a solution, generic enough, with the help of machine learning algorithms, allowing its application in a wide range of human-computer interfaces, for real-time gesture recognition. Experiments carried out showed that the system was able to achieve an accuracy of 99.4% in terms of hand posture recognition and an average accuracy of 93.72% in terms of dynamic gesture recognition. To validate the proposed framework, two applications were implemented. The first one is a real-time system able to help a robotic soccer referee judge a game in real time. The prototype combines a vision-based hand gesture recognition system with a formal language definition, the Referee CommLang, into what is called the Referee Command Language Interface System (ReCLIS). The second one is a real-time system able to interpret the Portuguese Sign Language. Sign languages are not standard and universal and the grammars differ from country to country. Although the implemented prototype 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.(undefined

Portuguese sign language recognition via computer vision and depth sensor

Sign languages are used worldwide by a multitude of individuals. They are mostly used by the deaf communities and their teachers, or people associated with them by ties of friendship or family. Speakers are a minority of citizens, often segregated, and over the years not much attention has been given to this form of communication, even by the scientific community. In fact, in Computer Science there is some, but limited, research and development in this area. In the particular case of sign Portuguese Sign Language-PSL that fact is more evident and, to our knowledge there isn’t yet an efficient system to perform the automatic recognition of PSL signs. With the advent and wide spreading of devices such as depth sensors, there are new possibilities to address this problem. In this thesis, we have specified, developed, tested and preliminary evaluated, solutions that we think will bring valuable contributions to the problem of Automatic Gesture Recognition, applied to Sign Languages, such as the case of Portuguese Sign Language. In the context of this work, Computer Vision techniques were adapted to the case of Depth Sensors. A proper gesture taxonomy for this problem was proposed, and techniques for feature extraction, representation, storing and classification were presented. Two novel algorithms to solve the problem of real-time recognition of isolated static poses were specified, developed, tested and evaluated. Two other algorithms for isolated dynamic movements for gesture recognition (one of them novel), have been also specified, developed, tested and evaluated. Analyzed results compare well with the literature.As Línguas Gestuais são utilizadas em todo o Mundo por uma imensidão de indivíduos. Trata-se na sua grande maioria de surdos e/ou mudos, ou pessoas a eles associados por laços familiares de amizade ou professores de Língua Gestual. Tratando-se de uma minoria, muitas vezes segregada, não tem vindo a ser dada ao longo dos anos pela comunidade científica, a devida atenção a esta forma de comunicação. Na área das Ciências da Computação existem alguns, mas poucos trabalhos de investigação e desenvolvimento. No caso particular da Língua Gestual Portuguesa - LGP esse facto é ainda mais evidente não sendo nosso conhecimento a existência de um sistema eficaz e efetivo para fazer o reconhecimento automático de gestos da LGP. Com o aparecimento ou massificação de dispositivos, tais como sensores de profundidade, surgem novas possibilidades para abordar este problema. Nesta tese, foram especificadas, desenvolvidas, testadas e efectuada a avaliação preliminar de soluções que acreditamos que trarão valiosas contribuições para o problema do Reconhecimento Automático de Gestos, aplicado às Línguas Gestuais, como é o caso da Língua Gestual Portuguesa. Foram adaptadas técnicas de Visão por Computador ao caso dos Sensores de Profundidade. Foi proposta uma taxonomia adequada ao problema, e apresentadas técnicas para a extração, representação e armazenamento de características. Foram especificados, desenvolvidos, testados e avaliados dois algoritmos para resolver o problema do reconhecimento em tempo real de poses estáticas isoladas. Foram também especificados, desenvolvidos, testados e avaliados outros dois algoritmos para o Reconhecimento de Movimentos Dinâmicos Isolados de Gestos(um deles novo).Os resultados analisados são comparáveis à literatura.Las lenguas de Signos se utilizan en todo el Mundo por una multitud de personas. En su mayoría son personas sordas y/o mudas, o personas asociadas con ellos por vínculos de amistad o familiares y profesores de Lengua de Signos. Es una minoría de personas, a menudo segregadas, y no se ha dado en los últimos años por la comunidad científica, la atención debida a esta forma de comunicación. En el área de Ciencias de la Computación hay alguna pero poca investigación y desarrollo. En el caso particular de la Lengua de Signos Portuguesa - LSP, no es de nuestro conocimiento la existencia de un sistema eficiente y eficaz para el reconocimiento automático. Con la llegada en masa de dispositivos tales como Sensores de Profundidad, hay nuevas posibilidades para abordar el problema del Reconocimiento de Gestos. En esta tesis se han especificado, desarrollado, probado y hecha una evaluación preliminar de soluciones, aplicada a las Lenguas de Signos como el caso de la Lengua de Signos Portuguesa - LSP. Se han adaptado las técnicas de Visión por Ordenador para el caso de los Sensores de Profundidad. Se propone una taxonomía apropiada para el problema y se presentan técnicas para la extracción, representación y el almacenamiento de características. Se desarrollaran, probaran, compararan y analizan los resultados de dos nuevos algoritmos para resolver el problema del Reconocimiento Aislado y Estático de Posturas. Otros dos algoritmos (uno de ellos nuevo) fueran también desarrollados, probados, comparados y analizados los resultados, para el Reconocimiento de Movimientos Dinámicos Aislados de los Gestos

A fast and robust hand-driven 3D mouse

The development of new interaction paradigms requires a natural interaction. This means that people should be able to interact with technology with the same models used to interact with everyday real life, that is through gestures, expressions, voice. Following this idea, in this paper we propose a non intrusive vision based tracking system able to capture hand motion and simple hand gestures. The proposed device allows to use the hand as a "natural" 3D mouse, where the forefinger tip or the palm centre are used to identify a 3D marker and the hand gesture can be used to simulate the mouse buttons. The approach is based on a monoscopic tracking algorithm which is computationally fast and robust against noise and cluttered backgrounds. Two image streams are processed in parallel exploiting multi-core architectures, and their results are combined to obtain a constrained stereoscopic problem. The system has been implemented and thoroughly tested in an experimental environment where the 3D hand mouse has been used to interact with objects in a virtual reality application. We also provide results about the performances of the tracker, which demonstrate precision and robustness of the proposed syste