Computation of surface orientation and structure of objects using grid coding

In this correspondence, algorithms are introduced to infer surface orientation and structure of visible object surfaces using grid coding. We adopt the active lighting technique to spatially ``encode' the scene for analysis. The observed objects, which can have surfaces of arbitrary shape, are assumed to rest on a plane (base plane) in a scene which is ``encoded' with light cast through a grid plane. Two orthogonal grid patterns are used, where each pattern is obtained with a set of equally spaced stripes marked on a glass pane. The scene is observed through a camera and the object surface orientation is determined using the projected patterns on the object surface. If the surfaces under consideration obey certain smoothness constraints, a dense orientation map can be obtained through proper interpolation. The surface structure can then be recovered given this dense orientation map. Both planar and curved surfaces can be handled in a uniform manner. The algorithms we propose yield reasonably accurate results and are relatively tolerant to noise, especially when compared to shape-from-shading techniques. In contrast to other grid coding techniques reported which match the grid junctions for depth reconstruction under the stereopsis principle, our techniques use the direction of the projected stripes to infer local surface orientation and do not require any correspondence relationship between either the grid lines or the grid junctions to be specified. The algorithm has the ability to register images and can therefore be embedded in a system which integrates knowledge from multiple views.     

Using constancy of distance to estimate position and displacementin space

The problem of computing structure and motion from the observation of points in two distinct images of a scene is considered. The proposed method explicitly utilizes the principle of conservation of distance during rigid-body motion. The formulation is such that it separates the problem of estimating object position from that of determining motion parameters. The equations of invariance of distance for a rigid body are solved for the points' position in space. When these coordinates in space are known, motion parameters are computed in a simple and straightforward manner. Examples are given to illustrate the efficiency of the algorithm     

Neutrosophic classifier: An extension of fuzzy classifer

Fuzzy classification has become of great interest because of its ability to utilize simple linguistically interpretable rules and has overcome the limitations of symbolic or crisp rule based classifiers. This paper introduces an extension to fuzzy classifier: a neutrosophic classifier, which would utilize neutrosophic logic for its working. Neutrosophic logic is a generalized logic that is capable of effectively handling indeterminacy, stochasticity acquisition errors that fuzzy logic cannot handle. The proposed neutrosophic classifier employs neutrosophic logic for its working and is an extension of commonly used fuzzy classifier. It is compared with the commonly used fuzzy classifiers on the following parameters: nature of membership functions, number of rules and indeterminacy in the results generated. It is proved in the paper that extended fuzzy classifier: neutrosophic classifier; optimizes the said parameters in comparison to the fuzzy counterpart. Finally the paper is concluded with justifying that neutrosophic logic though in its nascent stage still holds the potential to be experimented for further exploration in different domains.     

Community Trolling: An Active Learning Approach for Topic Based Community Detection in Big Data

Community detection plays an important role in creation and transfer of information. Active learning has been employed recently to improve the performance of community detection techniques. Active learning provides a semi-automatic approach in a selective sampling of data. Based on this, a community trolling approach for topic based community detection in big data is proposed. Community trolling selectively samples the data relevant to the current context from polluted big data using active learning. Fine-tuned data is then used to study community and its sub-communities. Community trolling as a precursor to community detection leads to a reduction of the huge unreliable dataset into a reliable dataset and results in the better prediction of community elements such as important topics and important entities. Finally, the effectiveness of approach was evaluated by implementing it on a real world Tumbler dataset. The results illustrate that community trolling provides a richer dataset resulting in more appropriate communities.     

Maintaining frame rate perception in interactive environments by?exploiting audio-visual cross-modal interaction

The entertainment industry, primarily the video games industry, continues to dictate the development and performance requirements of graphics hardware and computer graphics algorithms. However, despite the enormous progress in the last few years, it is still not possible to achieve some of industry’s demands, in particular high-fidelity rendering of complex scenes in real-time, on a single desktop machine. A realisation that sound/music and other senses are important to entertainment led to an investigation of alternative methods, such as cross-modal interaction in order to try and achieve the goal of “realism in real-time”. In this paper we investigate the cross-modal interaction between vision and audition for reducing the amount of computation required to compute visuals by introducing movement related sound effects. Additionally, we look at the effect of camera movement speed on temporal visual perception. Our results indicate that slow animations are perceived as smoother than fast animations. Furthermore, introducing the sound effect of footsteps to walking animations further increased the animation smoothness perception. This has the consequence that for certain conditions, the number of frames that need to be rendered each second can be reduced, saving valuable computation time, without the viewer being aware of this reduction. The results presented are another step towards the full understanding of the auditory-visual cross-modal interaction and its importance for helping achieve “realism int real-time”.     

Generalized attitudinal Choquet integral

Attitudinal Choquet integral (ACI) extends Choquet integral (CI) through a consideration of a decision-maker's (DM's) attitudinal character. In this paper, we generalize ACI, and the resulting operator is termed as generalized ACI (GACI). GACI adds to the efficacy of the ACI in the representation of a DM's unique and complex attitudinal character. It also generates a vast range of exponential ACI operators, such as harmonic ACI, ACI, quadratic ACI, to name a few. We further present induced GACI to consider additional information that may be associated with the arguments of aggregation. The special cases of the proposed operators are investigated. The usefulness of the proposed operators in modelling human decision behavior is shown through a case study.     

Secure video communication using firefly optimization and visual cryptography

In recent years, we face an increasing interest in protecting multimedia data and copyrights due to the high exchange of information. Attackers are trying to get confidential information from various sources, which brings the importance of securing the data. Many researchers implemented techniques to hide secret information to maintain the integrity and privacy of data. In order to protect confidential data, histogram-based reversible data hiding with other cryptographic algorithms are widely used. Therefore, in the proposed work, a robust method for securing digital video is suggested. We implemented histogram bit shifting based reversible data hiding by embedding the encrypted watermark in featured video frames. Histogram bit shifting is used for hiding highly secured watermarks so that security for the watermark symbol is also being achieved. The novelty of the work is that only based on the quality threshold a few unique frames are selected, which holds the encrypted watermark symbol. The optimal value for this threshold is obtained using the Firefly Algorithm. The proposed method is capable of hiding high-capacity data in the video signal. The experimental result shows the higher capacity and video quality compared to other reversible data hiding techniques. The recovered watermark provides better identity identification against various attacks. A high value of PSNR and a low value of BER and MSE is reported from the results.

     

Redefining clustering for high-dimensional applications

Clustering problems are well-known in the database literature for their use in numerous applications, such as customer segmentation, classification, and trend analysis. High-dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Recent research results indicate that, in high-dimensional data, even the concept of proximity or clustering may not be meaningful. We introduce a very general concept of projected clustering which is able to construct clusters in arbitrarily aligned subspaces of lower dimensionality. The subspaces are specific to the clusters themselves. This definition is substantially more general and realistic than the currently available techniques which limit the method to only projections from the original set of attributes. The generalized projected clustering technique may also be viewed as a way of trying to redefine clustering for high-dimensional applications by searching for hidden subspaces with clusters which are created by interattribute correlations. We provide a new concept of using extended cluster feature vectors in order to make the algorithm scalable for very large databases. The running time and space requirements of the algorithm are adjustable and are likely to trade-off with better accuracy     

Synthesis of nanocrystalline Ni0.5Zn0.5Fe2O4 ferrite and study of its magnetic behavior at different temperatures

Ni_0.5Zn_0.5Fe₂O₄ ferrite nanocrystals with average diameter in the range of 1–2 nm have been synthesized by reverse microemulsion. X-ray diffraction (XRD), transmission electron microscopy (TEM) and vibrating sample magnetometer (VSM) are used to characterize the structural, morphological and magnetic properties. X-ray analysis showed that the nanocrystals possess cubic spinel structure. The absence of hysteresis, negligible remanence and coercivity at 300 K indicate the superparamagnetic character and single domain in the nanocrystalline Ni_0.5Zn_0.5Fe₂O₄ ferrite materials. The nanocrystalline Ni_0.5Zn_0.5Fe₂O₄ ferrite were annealed at 600 °C. As a result of heat treatment the average particle size increases from 2 nm to 5 nm and the corresponding magnetization values have increased to 21.69 emu/g at 300 K. However, at low temperature of 100 K, the annealed samples show hysteresis loop which is the characteristic of a superparamagnetic to ferromagnetic transition. In addition, a comparative study of the magnetic properties of Ni_0.5Zn_0.5Fe₂O₄ ferrite nanocrystals obtained from reverse microemulsion has been carried out with those obtained from the general chemical co-precipitation route.