The field of computer to help users with special needs, and/or users with disabilities has now reached a very exciting watershed. Users who are physically disabled require a variety of access technology and learning d...
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ISBN:
(纸本)9780769533599
The field of computer to help users with special needs, and/or users with disabilities has now reached a very exciting watershed. Users who are physically disabled require a variety of access technology and learning depending on the nature of their disability. The intention of modern technology policy is to enable users with intellectual disabilities to have as much choice and control as possible over their lives, be involved in their communities, and make a valued contribution to the world at work. However, in order to achieve these aims, more effective educational and training media are needed as well as an intelligent tutoring system (ITS) for improving their skills and removing the barriers that turn cognitive impairments into intellectual disability. This paper proposes an (ITS) framework dedicated to users who are deaf as a solution for such a challenge. The proposed system help to meet these needs solves accessibility problem and enables access to information technologies by users with disabilities in a simple and reliable way.
The research aspiration is to develop a visual framework for the examination timetabling problem. To perform the task we identified visual analytics as a multi-disciplinary field, directly supports the timetable desig...
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ISBN:
(纸本)9780769533599;0769533590
The research aspiration is to develop a visual framework for the examination timetabling problem. To perform the task we identified visual analytics as a multi-disciplinary field, directly supports the timetable designers, assist with visual metaphors, patterns to make decisions. Here we deemed three stages on the problem domain. It deals with pre-processing, during the processing and post-processing; in each of the processes are with transaction on data which transform into timetables. These processes are interrelated with one another for any heuristic based, evolutionary algorithm guided timetabling systems. However, the importance aspect is the significance of visualization among the three processes and how it contributes in each process. This paper discuss two aspects, it wraps the framework with literature and an implication on the pre-processing through visualizing data and knowledge with graph visualization.
In image processing, the curve comparisons can be achieved by extracting the curve information from the bitmapped images. The revealed curves are represented in the forms of the sets of pixel information. Thus, the de...
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ISBN:
(纸本)9780769533599;0769533590
In image processing, the curve comparisons can be achieved by extracting the curve information from the bitmapped images. The revealed curves are represented in the forms of the sets of pixel information. Thus, the determination of curve matching is to compare pixel by pixel, which is time consuming. On the contrary, for vector graphics images, there have been no methods written in the literatures referred about directly comparing the parametric curves either in the rational or non-rational (polynomial) forms. In this paper, the relationships between two curves can be classified into 4 main categories: curve congruence, curve proportion, curve similarity and curve difference. The comparison algorithms for two given parametric curves either non-rational or rational curves are then established. These algorithms can be simply determined by verifying some important geometric features, e.g., the blending functions, the locations of their control points and their weights. In this connection, since a rational curve can be transformed into a non-rational one, the curve comparison algorithms for polynomial curves can also be applied for the cases of the rational curves.
Recently, many inspection systems are using computer vision technology. We also use computer vision to recognize the position error for mounting a wafer in a cleaning process of the semiconductor production processes....
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ISBN:
(纸本)9780769533599;0769533590
Recently, many inspection systems are using computer vision technology. We also use computer vision to recognize the position error for mounting a wafer in a cleaning process of the semiconductor production processes. The robot moves wafer to the cleaning chamber. But sometime, the robot puts the wafer into an incorrect location. In this case, incorrect position becomes cause of the cleaning system trouble or the wafer's broken. In this paper, we propose a position error recognition system using multiple cameras based on Giga Ethernet in multi-cleaning system. This is carried out by capturing image by high frame rate, followed by doing calibration and image processing to check the wafer's position before the cleaning process starts. If the robot puts the wafer in the incorrect location, the machine stops the cleaning process. So, we can prevent the wafer from being broken or the machine from being disturbing the cleaning process.
The fact that the signature is widely used as a means of personal verification emphasizes the need for an automatic recognition system. Recognition can be performed either offline or online based on the application. O...
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ISBN:
(纸本)9780769533599;0769533590
The fact that the signature is widely used as a means of personal verification emphasizes the need for an automatic recognition system. Recognition can be performed either offline or online based on the application. Online systems use dynamic information of a signature captured at the time the signature is made. Offline systems work on the scanned image of a signature. In this paper we present a method for Offline recognition and verification signatures using Principal components analysis. The proposed method consists of image prepossessing , feature extraction, evaluate the Principal components analysis for the extracted feature and the identification step. The identification step contain tow process recognition and verification. In the recognition process we use the K nearest-neighbours classifier and in the verification process we use the neural network classifier.
This paper proposes a new curvelet transform-based method to detect spoof fingerprint attacks in fingerprint biometric systems. It uses only one image to differentiate a real fingerprint from a spoof one. It is based ...
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This paper proposes a new curvelet transform-based method to detect spoof fingerprint attacks in fingerprint biometric systems. It uses only one image to differentiate a real fingerprint from a spoof one. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. Textural measures based on curvelet energy signatures and curvelet co-occurrence signatures are used to characterize fingerprint texture. Dimensionalities of the feature sets are reduced by running pudil's sequential forward floating selection (SFFS) algorithm. We test two feature sets independently on various classifiers like: AdaBoost. M1, support vector machine and k-nearest neighbor; then we fuse all the mentioned classifiers using the "majority voting rule" to form an Ensemble classifier. Classification rates achieved with these classifiers for energy signatures range from ~94.12% to ~97.41%. Likewise, classification rates for co-occurrence signatures range from ~94.35% to ~98.12%. Thus, the performance of a new liveness detection approach is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.
This paper presents a method which able to integrate audio and visual information for action scene analysis in any movie. The approach is top-down for determining and extract action scenes in video by analyzing both a...
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ISBN:
(纸本)9780769533599;0769533590
This paper presents a method which able to integrate audio and visual information for action scene analysis in any movie. The approach is top-down for determining and extract action scenes in video by analyzing both audio and video data. In this paper, we directly modelled the hierarchy and shared structures of human behaviours, and we present a framework of the hidden Markov model based application for the problem of activity recognition. We proposed a framework for recognizing actions by measuring human action-based information from video with the following characteristics: the method deals with both visual and auditory information, and captures both spatial and temporal characteristics; and the extracted features are natural, in the sense that they are closely related to the human perceptual processing. Our effort was to implementing idea of action identification by extracting syntactic properties of a video such as edge feature extraction, colour distribution, audio and motion vectors. In this paper, we present a two layers hierarchical module for action recognition. The first one performs supervised learning to recognize individual actions of participants using low-level visual features. The second layer models actions, using the output of the first layer as observations, and fuse with the high level audio features. Both layers use hidden Markov model-based approaches for action recognition and clustering, respectively. Our proposed technique characterizes the scenes by integration cues obtained from both the video and audio tracks. We are sure that using joint audio and visual information can significantly improve the accuracy for action detection over using audio or visual information only. This is because multimodal features can resolve ambiguities that are present in a single modality. Besides, we modelled them into multidimensional form.
The following topics are dealt with: computergraphics; rendering; digital art; animation; multimedia; augmented, mixed and virtual reality; computer-aided geometric design; forensic digital imaging; image/video analy...
The following topics are dealt with: computergraphics; rendering; digital art; animation; multimedia; augmented, mixed and virtual reality; computer-aided geometric design; forensic digital imaging; image/video analysis for pattern recognition; intelligent recognition techniques; data and information visualisation; biomedical visualisation; and spatial/geographic data visualization.
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