Direction prediction for avoiding occlusion in visual surveillance

Occurrence of occlusion while providing visual surveillance leads to anarchy as the track of the subject under motion may be lost. This often results into the failure of the surveillance system. The approach of predicting motion of moving subjects and hence the chances of their mutual occlusion gives an upper hand to surveillance system to take in-time necessary action towards mitigation of loss of track during dynamic occlusion. Direction of motion of a moving subject plays a major role while studying its motion. Direction along with the velocity of a subject in a 3D plane completely describes the motion of any subject. This article proposes a model‘-based approach for direction prediction of a moving subject in a 3D global plane as acquired in a 2D camera plane. The proposed approach uses the eight discrete directions of motion as proposed in and models different directions. The proposed direction prediction method is experimentally verified with six different classifiers, i.e. regression analysis, simple logistic regression, MLP, k-NN, SVM and Bays classifier over existing as well as self-acquired databases. The initial simulation results are motivating as the overall accuracies achieved through different classifiers are of the range of 87–94 \(\%\), which advocates the suitability of the said approach.     

Fast periocular authentication in handheld devices with reduced phase intensive local pattern

To ensure highest security in handheld devices, biometric authentication has emerged as a reliable methodology. Deployment of mobile biometric authentication struggles due to computational complexity. For a fast response from a mobile biometric authentication method, it is desired that the feature extraction and matching should take least time. In this article, the periocular region captured through frontal camera of a mobile device is considered under investigation for its suitability to produce a reduced feature that takes least time for feature extraction and matching. A recently developed feature Phase Intensive Local Pattern (PILP) is subjected to reduction giving birth to a feature termed as Reduced PILP (R-PILP), which yields a matching time speed-up of 1.56 times while the vector is 20% reduced without much loss in authentication accuracy. The same is supported by experiment on four publicly available databases. The performance is also compared with one global feature: Phase Intensive Global Pattern, and three local features: Scale Invariant Feature Transform, Speeded-up Robust Features, and PILP. The amount of reduction can be varied with the requirement of the system. The amount of reduction and the performance of the system bears a trade-off. Proposed R-PILP attempts to make periocular suitable for mobile devices.     

CL-AGKA: certificateless authenticated group key agreement protocol for mobile networks

Wireless Networks - Wireless group communication has gained much popularity recently due to the increase in portable, lightweight devices. These devices are capable of performing group...     

Single image super resolution for texture images through neighbor embedding

This article proposes an improved learning based super resolution scheme using manifold learning for texture images. Pseudo Zernike moment (PZM) has been employed to extract features from the texture images. In order to efficiently retrieve similar patches from the training patches, feature similarity index matrix (FSIM) has been used. Subsequently, for reconstruction of the high resolution (HR) patch, a collaborative optimal weight is generated from the least square (LS) and non-negative matrix factorization (NMF) methods. The proposed method is tested on some color texture, gray texture, and some standard images. Results of the proposed method on texture images advocate its superior performance over established state-of-the-art methods.

     

Decoder side Wyner–Ziv frame estimation using Chebyshev polynomial-based FLANN technique for distributed video coding

Multidimensional Systems and Signal Processing - In this paper, a Chebyshev polynomial-based functional link artificial neural network (CFLANN) technique for Wyner–Ziv (WZ) frame estimation...     

A delay and energy aware coordination mechanism for WSAN

In this paper, a delay and energy aware coordination mechanism (DEACM) has been devised for wireless sensor–actor networks. In DEACM, a two‐level hierarchical K‐hop clustering mechanism is used to organize the sensors and actors for communication. In the first level, sensors form a K‐hop cluster using actors as cluster heads, and sink is made as the cluster head in the second level to form a cluster among actors. Sensor nodes, which are 1‐hop away from the actors, also called as relay nodes are elected as backup cluster head (BCH) based on the residual energy and node degree. BCH collects the data from sensors when an actor is away to perform actions in the affected area. The scheme is evaluated through exhaustive simulation in NS2 along with other existing schemes. Different parameters like average event waiting time, event reliability, and average energy dissipation are compared, varying the number of sensors, actors, and data transfer rate. In general, it is observed that the proposed DEACM outperforms other existing schemes. Copyright © 2016 John Wiley & Sons, Ltd.     

Decoder driven side information generation using ensemble of MLP networks for distributed video coding

Multimedia Tools and Applications - This paper proposes an ensemble of multi-layer perceptron (MLP) networks for side information (SI) generation in distributed video coding (DVC). In the proposed...     

Particle Swarm Optimization Based Support Vector Regression for Blind Image Restoration

This paper presents a swarm intelligence based parameter optimization of the support vector machine (SVM) for blind image restoration. In this work, SVM is used to solve a regression problem. Support vector regression (SVR) has been utilized to obtain a true mapping of images from the observed noisy blurred images. The parameters of SVR are optimized through particle swarm optimization (PSO) technique. The restoration error function has been utilized as the fitness function for PSO. The suggested scheme tries to adapt the SVM parameters depending on the type of blur and noise strength and the experimental results validate its effectiveness. The results show that the parameter optimization of the SVR model gives better performance than conventional SVR model as well as other competent schemes for blind image restoration.     

FLANN-based adaptive threshold selection for detection of impulsive noise in images

In this paper, a novel scheme has been suggested for removing random-valued impulsive noise from images. The proposed scheme utilizes a second-order differential impulse detection followed by a recursive median filter on the corrupted pixel locations. Adaptive threshold selection from noisy image characteristics has been emphasized in this paper. A functional link artificial neural network is used for this purpose. Comparative analysis on standard images at different noise conditions shows that the proposed scheme, in general, outperforms the existing schemes.