The Experts below are selected from a list of 261 Experts worldwide ranked by ideXlab platform
Yingli Tian - One of the best experts on this subject based on the ideXlab platform.
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Assistive Clothing Pattern Recognition for Visually Impaired People
IEEE Transactions on Human-Machine Systems, 2014Co-Authors: Xiaodong Yang, S. Sakthimani, C. Hemalatha, Shuai Yuan, Yingli TianAbstract:Choosing clothes with complex Patterns and colors is a challenging task for visually impaired people. Automatic Clothing Pattern recognition is also a challenging research problem due to rotation, scaling, illumination, and especially large intraclass Pattern variations. We have developed a camera-based prototype system that recognizes Clothing Patterns in four categories (plaid, striped, Patternless, and irregular) and identifies 11 Clothing colors. The system integrates a camera, a microphone, a computer, and a Bluetooth earpiece for audio description of Clothing Patterns and colors. A camera mounted upon a pair of sunglasses is used to capture Clothing images. The Clothing Patterns and colors are described to blind users verbally. This system can be controlled by speech input through microphone. To recognize Clothing Patterns, we propose a novel Radon Signature descriptor and a schema to extract statistical properties from wavelet subbands to capture global features of Clothing Patterns. They are combined with local features to recognize complex Clothing Patterns. To evaluate the effectiveness of the proposed approach, we used the CCNY Clothing Pattern dataset. Our approach achieves 92.55% recognition accuracy which significantly outperforms the state-of-the-art texture analysis methods on Clothing Pattern recognition. The prototype was also used by ten visually impaired participants. Most thought such a system would support more independence in their daily life but they also made suggestions for improvements.
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Spring 2012 Colloquium
2012Co-Authors: Yingli TianAbstract:Recent technology developments in computer vision, digital cameras, and portable computers make it possible to develop practical computer vision-based algorithms to help blind persons independently explore unfamiliar environments and improve the quality of their daily life. In this talk, I will introduce the research conducted in CCNY Media Lab for applying computer vision technologies to assist people who are visually impaired including indoor navigation and wayfinding, banknote recognition, and Clothing Pattern recognition.
Jacques Teller - One of the best experts on this subject based on the ideXlab platform.
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Development of thermal comfort models for various climatic zones of North-East India
Sustainable Cities and Society, 2015Co-Authors: Manoj Kumar Singh, Sadhan Mahapatra, Jacques TellerAbstract:Abstract Thermal comfort study provides crucial information about thermal performance of naturally ventilated buildings. Humphreys and Auliciems comfort model uses indoor and outdoor temperatures to predict comfort temperatures. It is found that the comfort temperatures obtained by using these methods do not take into account the occupant behavioural adaptability to a particular climatic zone. This demands development of new set of comfort models based on local environmental parameters, socio-cultural setup and behavioural action. Analysis shows that four major variables like indoor and outdoor temperature, relative humidity and Clothing Pattern plays an important role in defining comfort and greatly influence the occupant's perception and acceptance on thermal comfort. In this study, comfort models are developed based on these variables. The computed neutral temperatures based on the models are compared with the comfort temperatures obtained through comfort survey. The models are developed using the measured data of January and July months and validated with the measured data of April and October months. This study also concludes that it is not possible to obtain a generalized thermal comfort model for all climatic zone because adaptation process, expectation and perception of people are region specific and governed by local socio-cultural requirement.
V. Kamakshi Prasad - One of the best experts on this subject based on the ideXlab platform.
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Automatic Cloth Pattern and Color Recognition for Visually Impaired People Using SVM Algorithm
International Journal of Research, 2017Co-Authors: Sakil Ansari, V. Kamakshi PrasadAbstract:This paper highlights the importance of human Clothing, especially for visually impaired people. Choosing clothes with different colors is a challenging task for color blind people. Automatic Clothing Pattern recognition can bring the much needed independence in their lives. But, this is also a challenging research problem due to rotation, scaling, illumination, and especially large intra-class Pattern variations. In this paper, we propose a camera based system that recognizes Clothing Patterns in four main categories (plaid, striped, Pattern-less, and irregular) and identifies 16 Clothing colors using the support vector machine algorithm. The features and texture of an image can be extracted by the three descriptors. The Radon Signature descriptor is to extract statistical properties, the wavelet subbands are used to extract global features of Clothing Patterns. This gets combined with local features that are obtained from scale invariance feature transform to recognize complex Clothing Patterns. To evaluate the effectiveness of the proposed approach, we used the CCNY Clothing Pattern dataset. The proposed method provides an effective method for visually impaired people that they can identify the Pattern and respective colors easily without any help.
Xiaodong Yang - One of the best experts on this subject based on the ideXlab platform.
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Assistive Clothing Pattern Recognition for Visually Impaired People
IEEE Transactions on Human-Machine Systems, 2014Co-Authors: Xiaodong Yang, S. Sakthimani, C. Hemalatha, Shuai Yuan, Yingli TianAbstract:Choosing clothes with complex Patterns and colors is a challenging task for visually impaired people. Automatic Clothing Pattern recognition is also a challenging research problem due to rotation, scaling, illumination, and especially large intraclass Pattern variations. We have developed a camera-based prototype system that recognizes Clothing Patterns in four categories (plaid, striped, Patternless, and irregular) and identifies 11 Clothing colors. The system integrates a camera, a microphone, a computer, and a Bluetooth earpiece for audio description of Clothing Patterns and colors. A camera mounted upon a pair of sunglasses is used to capture Clothing images. The Clothing Patterns and colors are described to blind users verbally. This system can be controlled by speech input through microphone. To recognize Clothing Patterns, we propose a novel Radon Signature descriptor and a schema to extract statistical properties from wavelet subbands to capture global features of Clothing Patterns. They are combined with local features to recognize complex Clothing Patterns. To evaluate the effectiveness of the proposed approach, we used the CCNY Clothing Pattern dataset. Our approach achieves 92.55% recognition accuracy which significantly outperforms the state-of-the-art texture analysis methods on Clothing Pattern recognition. The prototype was also used by ten visually impaired participants. Most thought such a system would support more independence in their daily life but they also made suggestions for improvements.
Andreas Matzarakis - One of the best experts on this subject based on the ideXlab platform.
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Dynamic modeling of human thermal comfort after the transition from an indoor to an outdoor hot environment
International Journal of Biometeorology, 2015Co-Authors: George Katavoutas, Helena A. Flocas, Andreas MatzarakisAbstract:Thermal comfort under non-steady-state conditions primarily deals with rapid environmental transients and significant alterations of the meteorological conditions, activity, or Clothing Pattern within the time scale of some minutes. In such cases, thermal history plays an important role in respect to time, and thus, a dynamic approach is appropriate. The present study aims to investigate the dynamic thermal adaptation process of a human individual, after his transition from a typical indoor climate to an outdoor hot environment. Three scenarios of thermal transients have been considered for a range of hot outdoor environmental conditions, employing the dynamic two-node IMEM model. The differences among them concern the radiation field, the activity level, and the body position. The temporal Pattern of body temperatures as well as the range of skin wettedness and of water loss have been investigated and compared among the scenarios and the environmental conditions considered. The structure and the temporal course of human energy fluxes as well as the identification of the contribution of body temperatures to energy fluxes have also been studied and compared. In general, the simulation results indicate that the response of a person, coming from the same neutral indoor climate, varies depending on the scenario followed by the individual while being outdoors. The combination of radiation field (shade or not) with the kind of activity (sitting or walking) and the outdoor conditions differentiates significantly the thermal state of the human body. Therefore, 75 % of the skin wettedness values do not exceed the thermal comfort limit at rest for a sitting individual under the shade. This percentage decreases dramatically, less than 25 %, under direct solar radiation and exceeds 75 % for a walking person under direct solar radiation.