Facial expression is a way of non-verbal communication. A person depicts his/her feelings through facial expressions. In computer systems facial expressions help in verification, identification and authentication. One...
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ISBN:
(纸本)9781479972258
Facial expression is a way of non-verbal communication. A person depicts his/her feelings through facial expressions. In computer systems facial expressions help in verification, identification and authentication. One popular use of facial expression recognition is automatic feedback capture from customers upon reacting to a particular product. Effective recognition technology is in high demand by the common users of today's gadgets and technologies. Facial expression recognition technique is broadly classified into two techniques: Feature basedtechniques and model based techniques. The key contribution of this article is that we have analyzed latest state of the art techniques in Feature basedtechniques and model based techniques. These techniques are analyzed using various standard public face databases: GEMEP-FERA, BU-3DFE, CK+, Bosphorous, MMI, JAFFE, LFW, FERET, CMU-PIE, Georgia tech, AR, eNTERFACE 05 and FRGC. From our analysis we found that for Feature based Curvelet approach performed on FRGCv2 database gave an excellent 97.83% recognition rate and modelbased textured 3D video technique performed on BU-4DFE database gave an excellent 94.34 % recognition rate.
Several tools exist providing support for model-based design of supervisors in high-tech and cyber-physical systems. On the one hand, specifically tools based on finite automata are of interest as they allow to synthe...
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Over the past few years there has been a growing interest in visual interfaces based on gestures. Using gestures as a mean to communicate with a computer can be helpful in applications such as gaming platforms, domoti...
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Over the past few years there has been a growing interest in visual interfaces based on gestures. Using gestures as a mean to communicate with a computer can be helpful in applications such as gaming platforms, domotic environments, augmented reality or sign language interpretation to name a few. However, a serious bottleneck for such interfaces is the current lack of accurate hand localization systems, which are necessary for tracking (re-)initialization and hand pose understanding. In fact, human hand is an articulated object with a very large degree of appearance variability which is difficult to deal with. For instance, recent attempts to solve this problem using machine learning approaches have shown poor generalization capabilities over different viewpoints and finger spatial configurations. In this article we present a modelbased approach to articulated hand detection which splits this variability problem by separately searching for simple finger models in the input image. A generic finger silhouette is localized in the edge map of the input image by combining curve and graph matching techniques. Cluttered backgrounds and thick textured images, which usually make it hard to compare edge information with silhouette models (e.g., using chamfer distance or voting based methods) are dealt with in our approach by simultaneously using connected curves and topological information. Finally, detected fingers are clustered using geometric constraints. Our system is able to localize in real time a hand with variable finger configurations in images with complex backgrounds, different lighting conditions and different positions of the hand with respect to the camera. Experiments with real images and videos and a simple visual interface are presented to validate the proposed method. (C) 2011 Elsevier B.V. All rights reserved.
This paper gives an overview of different methods for automated fault detection. Emphasis will be put on the properties of model based techniques (which we will further divide into analytical modelbased and knowledge...
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ISBN:
(纸本)9781607507543;9781607507536
This paper gives an overview of different methods for automated fault detection. Emphasis will be put on the properties of model based techniques (which we will further divide into analytical modelbased and knowledge based), multivariate statistical process control and machine learning techniques. The machine learning techniques are not traditionally viewed as a standard method for fault detection, so they are especially highlighted in this paper. Each method is presented in detail, and we will also discuss alternative extensions for various applications. The paper is ended by proposing to use machine learning techniques as a robust and well-functioning method in general.
This paper provides a brief review of the history of process control and automated systems and an overview of what we can expect in the future, together with some lessons that may be applicable to the development and ...
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This paper provides a brief review of the history of process control and automated systems and an overview of what we can expect in the future, together with some lessons that may be applicable to the development and operation of highly automated discrete manufacturing systems.
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