Fast response fringe‐field switching mode liquid crystal development for shutter glass 3D

A type of fast response time (RT) liquid crystal (LC) was developed to adopt in fringe‐field switching (FFS) mode for shutter glass 3D display device, the average gray to gray level (GTG) RT can reduce from 7.43 ms to 4.48 ms at a refreshing rate of 60 Hz, and the transmittance can increase by 15.4% compared with conventional LC based on the same cell gap. Meanwhile, the correlation among optical efficiency, correlative color temperature (CCT) of module and retardation of LC for FFS mode was studied. Transmittance is proportional to the retardation of LC; however, the CCT is inversely proportional to the retardation. The fast response LC can keep almost the original optical efficiency and CCT.     

Personal driving diary: Automated recognition of driving events from first-person videos

In this paper, we introduce the concept of personal driving diary. A personal driving diary is a multimedia archive of a person’s daily driving experience, describing important driving events of the user with annotated videos. This paper presents an automated system that constructs such multimedia diary by analyzing videos obtained from a vehicle-mounted camera. The proposed system recognizes important interactions between the driving vehicle and the other actors in videos (e.g., accident, overtaking, etc.), and labels them together with its contextual knowledge on the vehicle (e.g., mean velocity) to construct an event log. A decision tree based activity recognizer is designed, detecting driving events of vehicles and pedestrians from the first-person view videos by analyzing their trajectories and spatio-temporal relationships. The constructed diary enables efficient searching and event-based browsing of video clips, which helps the users when retrieving videos of dangerous situations. Our experiment confirms that the proposed system reliably generates driving diaries by annotating the vehicle events learned from training examples.     

EM-GPA: Generalized Procrustes analysis with hidden variables for 3D shape modeling

Aligning shapes is essential in many computer vision problems and generalized Procrustes analysis (GPA) is one of the most popular algorithms to align shapes. However, if some of the shape data are missing, GPA cannot be applied. In this paper, we propose EM-GPA, which extends GPA to handle shapes with hidden (missing) variables by using the expectation-maximization (EM) algorithm. For example, 2D shapes can be considered as 3D shapes with missing depth information due to the projection of 3D shapes into the image plane. For a set of 2D shapes, EM-GPA finds scales, rotations and 3D shapes along with their mean and covariance matrix for 3D shape modeling. A distinctive characteristic of EM-GPA is that it does not enforce any rank constraint often appeared in other work and instead uses GPA constraints to resolve the ambiguity in finding scales, rotations, and 3D shapes. The experimental results show that EM-GPA can recover depth information accurately even when the noise level is high and there are a large number of missing variables. By using the images from the FRGC database, we show that EM-GPA can successfully align 2D shapes by taking the missing information into consideration. We also demonstrate that the 3D mean shape and its covariance matrix are accurately estimated. As an application of EM-GPA, we construct a 2D + 3D AAM (active appearance model) using the 3D shapes obtained by EM-GPA, and it gives a similar success rate in model fitting compared to the method using real 3D shapes. EM-GPA is not limited to the case of missing depth information, but it can be easily extended to more general cases.     

Extraction of enhanced evoked potentials using wavelet filtering

In order to remove physiological artefacts and gain the improved evoked potentials, we propose a filtering method using the multi-resolution wavelet transform. The wavelet transform is repeatedly performed until all resolution levels are obtained. It decomposes the measured evoked potentials into scale coefficients corresponding to low frequency components and wavelet coefficients corresponding to high frequency components. In the wavelet domain, artefacts are dispersed mainly at the wavelet coefficients rather than the scaling coefficients. Thus, when the inverse wavelet transform is performed, this method shrinks the wavelet coefficients to reduce artefacts with shrinkage functions. By repeatedly performing the inverse wavelet transform, an evoked potential having the reduced artefacts and background noise is obtained. In this study, quantitative evaluation with simulation data and actual clinical data were conducted. As a result, characteristic peaks of evoked potential could be gained removing background EEG and artefacts using suggested shrinkage function. It was improved more than 0.2–1.6Db compared to the conventional averaging method. Also, the system for measuring and analyzing evoked potentials using DSP is implemented.     

An enhanced motion estimation approach using a genetic trail bounded approximation for H.264/AVC codecs

Genetic algorithm-based motion estimation schemes play a significant role in improving the results of H.264/AVC standardization efforts when addressing conversational and non-conversational video applications. In this paper, we present a robust motion estimation scheme that uses a noble genetic trail bounded approximation (GTBA) approach to speed up the encoding process of H.264/AVC video compression and to reduce the number of bits required to code frame. The proposed algorithm is utilized to enhance the fitness function strength by integrating trail information of motion vector and sum of absolute difference (SAD) information into a fitness function. Experimental results reveal that the proposed GTBA resolves conflict obstacles with respect to both the number of bits required to code frames and the execution time for estimation.     

A hybrid user authentication protocol for mobile IPTV service

IPTV, a technological convergence that combines communication and broadcasting technologies, delivers customized, interactive TV content and other multimedia information over wired and wireless connections. Providing secure access to IPTV services calls for authentication, without proper and secure authentication mechanisms, an individual impersonating a subscriber could steal a service. This paper proposes a new authentication protocol to authenticate IPTV users. The authors based the proposed protocol, a hybrid authentication protocol providing lightweight, personalized user authentication, on RFID (radio-frequency identification) and USIM (Universal Subscriber Identity Module) technologies. In the proposed protocol, USIM performs highly personalized authentication, and the authenticated subscriber’s RFID tags can have a temporary authority to execute authentication. These RFID tags become Agent Tags authorized to authenticate subscribers. Agent Tags identify and authenticate themselves to RFID readers in the set-top box, thus, simplifying the authentication process.     

Color laser printer forensic based on noisy feature and support vector machine classifier

Digital forensics in the ubiquitous era can enhance and protect the reliability of multimedia content where this content is accessed, manipulated, and distributed using high quality computer devices. Color laser printer forensics is a kind of digital forensics which identifies the printing source of color printed materials such as fine arts, money, and document and helps to catch a criminal. This paper present a new color laser printer forensic algorithm based on noisy texture analysis and support vector machine classifier that can detect which color laser printer was used to print the unknown images. Since each printer vender uses their own printing process, printed documents from different venders have a little invisible difference looks like noise. In our identification scheme, the invisible noises are estimated with the wiener-filter and the 2D Discrete Wavelet Transform (DWT) filter. Then, a gray level co-occurrence matrix (GLCM) is calculated to analyze the texture of the noise. From the GLCM, 384 statistical features are extracted and applied to train and test the support vector machine classifier for identifying the color laser printers. In the experiment, a total of 4,800 images from 8 color laser printer models were used, where half of the image is for training and the other half is for classification. Results prove that the presented algorithm performs well by achieving 99.3%, 97.4% and 88.7% accuracy for the brand, toner and model identification respectively.     

An ontology-based personalized target advertisement system on interactive TV

We design and implement a personalized target advertisement (PTA) system in IPTV using ontology-based semantic relations between IPTV program and advertisement. In the PTA system, we focus on the development of an ontology reasoning technique of improving advertisement recommendation ability and exploiting efficient information reuse. For this purpose, we first build the IPTV program ontology and viewer profiling ontology based on the IPTV program and viewers’ content consumption behavior, using OWL (Web Ontology Language) standardized by W3C. We then classify each viewer into his/her corresponding reference group using a similarity metric. After that, we define the semantic relations between advertisement and shopping products consumed by the prototype through shopping sites in IPTV. Based on these semantic relations, we infer the preferred advertisements for each viewer through our reasoning process. In the experimental section, we demonstrate a prototype of our system and the algorithm of the PTA system using various cases.     

An enhanced fuzzy c-means algorithm for audio segmentation and classification

Automated audio segmentation and classification play important roles in multimedia content analysis. In this paper, we propose an enhanced approach, called the correlation intensive fuzzy c-means (CIFCM) algorithm, to audio segmentation and classification that is based on audio content analysis. While conventional methods work by considering the attributes of only the current frame or segment, the proposed CIFCM algorithm efficiently incorporates the influence of neighboring frames or segments in the audio stream. With this method, audio-cuts can be detected efficiently even when the signal contains audio effects such as fade-in, fade-out, and cross-fade. A number of audio features are analyzed in this paper to explore the differences between various types of audio data. The proposed CIFCM algorithm works by detecting the boundaries between different kinds of sounds and classifying them into clusters such as silence, speech, music, speech with music, and speech with noise. Our experimental results indicate that the proposed method outperforms the state-of-the-art FCM approach in terms of audio segmentation and classification.     

An object-based middleware supporting efficient interoperability on a smart home network

This paper proposes a novel object-based home network middleware for supporting the interoperability among home devices and smart grid devices. This middleware provides various types of abstract objects for flexible representation of heterogeneous home devices, which are classified based on their characteristics. It is also flexible enough to allow addition of new protocols and message conversion between different protocols through an abstraction layer, which are not supported by existing home network middlewares so that various protocols can be supported. As a result, it can be utilized to provide the interoperability among a variety of devices from sensors to typical home appliances as well as smart grid devices such as a home electric generator and a battery, which adopt different protocols. In order to demonstrate that home devices and smart grid devices are interoperable through the proposed middleware, we implement applications based on the middleware on a target platform consisting of embedded boards, sensors and laptops for emulation purposes of a home network. According to our emulation, this middleware can provide efficient interoperability among home devices and smart grid devices for future energy efficient home.