105 research outputs found
WEBCAM-BASED LASER DOT DETECTION TECHNIQUE IN COMPUTER REMOTE CONTROL
ABSTRACTIn this paper, the authors propose a method to detect the laser dot in an interactive system using laser pointers. The method is designed for presenters who need to interact with the computer during the presentation by using the laserpointer. The detection technique is developed by using a camera to capture the presentation screen and processing every frames transferred to the ara computer. This paper focuses on the detection and tracking of laser dots, based on their characteristics to distinguish a laser dotfrom other areas on the captured frames. Experimental results showed that the proposed method could reduce the rate of misdetection by light noises of a factor of 10 and achieve an average accuracy of 82% of detection in normal presentation environments. The results point out that the better way to describe the laser dots’ features based on visual concept is to use the HSI color space instead of the normal RGB space.Keywords. laser pointer; laser dot/spot; laser pointer interaction; control; mouse; computer screen/display
ROBUST DYNAMIC ID-BASED REMOTE MUTUAL AUTHENTICATION SCHEME
Dynamic ID based authentication scheme is more and more important in insecure wireless environment and system. Two of kinds of attack that authentication schemes must resist are stealing identity and reflection attack which is a potential way of attacking a challenge- response authentication system using the same protocol in both direcÂtions. It must be guaranteed to prevent attackers from reusing informaÂtion from authentication phase and the scheme of Yoon and Yoo satisfies those requirements. However, their scheme can not resist insider and impersonation attack by using lost or stolen smart card. In this paper, we demonstrate that Yoon and Yoo’s scheme is still vulnerable to those attacks. Then, we present an improvement to their scheme in order to isolate such problems
Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models
Recognition across domains has recently become an active topic in the
research community. However, it has been largely overlooked in the problem of
recognition in new unseen domains. Under this condition, the delivered deep
network models are unable to be updated, adapted or fine-tuned. Therefore,
recent deep learning techniques, such as: domain adaptation, feature
transferring, and fine-tuning, cannot be applied. This paper presents a novel
Universal Non-volume Preserving approach to the problem of domain
generalization in the context of deep learning. The proposed method can be
easily incorporated with any other ConvNet framework within an end-to-end deep
network design to improve the performance. On digit recognition, we benchmark
on four popular digit recognition databases, i.e. MNIST, USPS, SVHN and
MNIST-M. The proposed method is also experimented on face recognition on
Extended Yale-B, CMU-PIE and CMU-MPIE databases and compared against other the
state-of-the-art methods. In the problem of pedestrian detection, we
empirically observe that the proposed method learns models that improve
performance across a priori unknown data distributions
GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation
Recent years have witnessed great advances in object segmentation research.
In addition to generic objects, aquatic animals have attracted research
attention. Deep learning-based methods are widely used for aquatic animal
segmentation and have achieved promising performance. However, there is a lack
of challenging datasets for benchmarking. In this work, we build a new dataset
dubbed "Aquatic Animal Species." We also devise a novel GUided mixup
augmeNtatioN and multi-viEw fusion for aquatic animaL segmentation (GUNNEL)
that leverages the advantages of multiple view segmentation models to
effectively segment aquatic animals and improves the training performance by
synthesizing hard samples. Extensive experiments demonstrated the superiority
of our proposed framework over existing state-of-the-art instance segmentation
methods
Smart Shopping Assistant: A Multimedia and Social Media Augmented System with Mobile Devices to Enhance Customers’ Experience and Interaction
Multimedia, social media content, and interaction are common means to attract customers in shopping. However these features are not always fully available for customers when they go shopping in physical shopping centers. The authors propose Smart Shopping Assistant, a multimedia and social media augmented system on mobile devices to enhance users’ experience and interaction in shopping. Smart Shopping turns a regular mobile device into a special prism so that a customer can enjoy multimedia, get useful social media related to a product, give feedbacks or make actions on a product during shopping. The system is specified as a flexible framework to take advantages of different visual descriptors and web information extraction modules. Experimental results show that Smart Shopping can process and provide augmented data in a realtime-manner. Smart Shopping can be used to attract more customers and to build an online social community of customers to share their interests in shopping
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