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.

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.

Dificuldades de expansão da infografia interativa: um problema de produção e receção

Dissertação de mestrado em Media Interativos

Detection of road accident accumulation zones with a visual analytics approach

Nowadays, road accidents are a major public health problem, which increase is forecasted if road safety is not treated properly, dying about 1.2 million people every year around the globe. In 2012, Portugal recorded 573 fatalities in road accidents, on site, revealing the largest decreasing of the European Union for 2011, along with Denmark. Beyond the impact caused by fatalities, it was calculated that the economic and social costs of road accidents weighted about 1.17% of the Portuguese gross domestic product in 2010. Visual Analytics allows the combination of data analysis techniques with interactive visualizations, which facilitates the process of knowledge discovery in sets of large and complex data, while the Geovisual Analytics facilitates the exploration of space-time data through maps with different variables and parameters that are under analysis. In Portugal, the identification of road accident accumulation zones, in this work named black spots, has been restricted to annual fixed windows. In this work, it is presented a dynamic approach based on Visual Analytics techniques that is able to identify the displacement of black spots on sliding windows of 12 months. Moreover, with the use of different parameterizations in the formula usually used to detect black spots, it is possible to identify zones that are almost becoming black spots. Through the proposed visualizations, the study and identification of countermeasures to this social and economic problem can gain new grounds and thus the decision- making process is supported and improved.

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.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

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.

Cross-language influences on new second language vocabulary acquisition: the effect of learning method

Dissertação de mestrado integrado em Psicologia

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

Dissertação de mestrado em Ciências da Linguagem

Light-distortion analysis as a possible indicator of visual quality after refractive lens exchange with diffractive multifocal intraocular lenses

METHODS: Refractive lens exchange was performed with implantation of an AT Lisa 839M (trifocal) or 909MP (bifocal toric) IOL, the latter if corneal astigmatism was more than 0.75 diopter (D). The postoperative visual and refractive outcomes were evaluated. A prototype light-distortion analyzer was used to quantify the postoperative light-distortion indices. A control group of eyes in which a Tecnis ZCB00 1-piece monofocal IOL was implanted had the same examinations. RESULTS: A trifocal or bifocal toric IOL was implanted in 66 eyes. The control IOL was implanted in 18 eyes. All 3 groups obtained a significant improvement in uncorrected distance visual acuity (UDVA) (P < .001) and corrected distance visual acuity (CDVA) (P Z .001). The mean uncorrected near visual acuity (UNVA) was 0.123 logMAR with the trifocal IOL and 0.130 logMAR with the bifocal toric IOL. The residual refractive cylinder was less than 1.00 D in 86.7% of cases with the toric IOL. The mean light-distortion index was significantly higher in the multifocal IOL groups than in the monofocal group (P < .001), although no correlation was found between the light-distortion index and CDVA. CONCLUSIONS: The multifocal IOLs provided excellent UDVA and functional UNVA despite increased light-distortion indices. The light-distortion analyzer reliably quantified a subjective component of vision distinct from visual acuity; it may become a useful adjunct in the evaluation of visual quality obtained with multifocal IOLs.

Cognitive rehabilitation in a visual variant of Alzheimer's disease

Alzheimer's disease (AD) is commonly associated with marked memory deficits; however, nonamnestic variants have been consistently described as well. Posterior cortical atrophy (PCA) is a progressive degenerative condition in which posterior regions of the brain are predominantly affected, therefore resulting in a pattern of distinctive and marked visuospatial symptoms, such as apraxia, alexia, and spatial neglect. Despite the growing number of studies on cognitive and neural bases of the visual variant of AD, intervention studies remain relatively sparse. Current pharmacological treatments offer modest efficacy. Also, there is a scarcity of complementary nonpharmacological interventions with only two previous studies of PCA. Here we describe a highly educated 57-year-old patient diagnosed with a visual variant of AD who participated in a cognitive intervention program (comprising reality orientation, cognitive stimulation, and cognitive training exercises). Neuropsychological assessment was performed across moments (baseline, postintervention, follow-up) and consisted mainly of verbal and visual memory. Baseline neuropsychological assessment showed deficits in perceptive and visual-constructive abilities, learning and memory, and temporal orientation. After neuropsychological rehabilitation, we observed small improvements in the patient's cognitive functioning, namely in verbal memory, attention, and psychomotor abilities. This study shows evidence of small beneficial effects of cognitive intervention in PCA and is the first report of this approach with a highly educated patient in a moderate stage of the disease. Controlled studies are needed to assess the potential efficacy of cognition-focused approaches in these patients, and, if relevant, to grant their availability as a complementary therapy to pharmacological treatment and visual aids.

Visual search for normal color and dichromatic observers using a unique distracter color

It is well known that color coding facilitates search and iden- tification in real-life tasks. The aim of this work was to compare reac- tion times for normal color and dichromatic observers in a visual search experiment. A unique distracter color was used to avoid abnormal color vision vulnerability to background complexity. Reaction times for nor- mal color observers and dichromats were estimated for 2◦ central vision at 48 directions around a white point in CIE L∗a∗b∗ color space for systematic examination on the mechanisms of dichromatic color percep- tion. The results show that mean search times for dichromats were twice larger compared to the normal color observers and for all directions. The difference between the copunctual confusion lines and the confusion direction measure experimentally was 5.5◦ for protanopes and 7.5◦ for deuteranopes.