Predictively encoded graph convolutional network for noise-robust skeleton-based action recognition

Applied Intelligence - In skeleton-based action recognition, graph convolutional networks (GCNs), which model human body skeletons using graphical components such as nodes and connections, have...     

Classification of road surface status using a 94 GHz dual-channel polarimetric radiometer

In this article, we classify road surface statuses using a Bayesian classification method. This article uses principal component analysis (PCA) that combines a 94 GHz dual-channel polarimetric radiometer. The radiometer is used to investigate the behaviour of the brightness temperature (BT) of different road surface statuses in an open-air laboratory. The aim of this investigation is to characterize four different road surface classes (dry, wet, snowy and icy). Here, the BT (radiothermal emission) characteristics are measured at horizontal and vertical polarizations. For a given database of weather information (including BT, road surface temperature, wind speed, etc.), a PCA subspace is constructed, and the score vectors are classified by solving the Bayesian classification method. As a result, the road surface statuses were found to be well classified by the proposed method in real time.     

Multi-resolution approach to human activity recognition in video sequence based on combination of complex wavelet transform,Local Binary Pattern and Zernike moment

Human activity recognition is a challenging problem of computer vision and it has different emerging applications. The task of recognizing human activities from video sequence exhibits more challenges because of its highly variable nature and requirement of real time processing of data. This paper proposes a combination of features in a multiresolution framework for human activity recognition. We exploit multiresolution analysis through Daubechies complex wavelet transform (DCxWT). We combine Local binary pattern (LBP) with Zernike moment (ZM) at multiple resolutions of Daubechies complex wavelet decomposition. First, LBP coefficients of DCxWT coefficients of image frames are computed to extract texture features of image, then ZM of these LBP coefficients are computed to extract the shape feature from texture feature for construction of final feature vector. The Multi-class support vector machine classifier is used for classifying the recognized human activities. The proposed method has been tested on various standard publicly available datasets. The experimental results demonstrate that the proposed method works well for multiview human activities as well as performs better than some of the other state-of-the-art methods in terms of different quantitative performance measures.

     

Machine vision theory and applications for cyber-physical systems

Multimedia Tools and Applications -     

Bolstering efficient SSGAs based on an ensemble of probabilistic variable-wise crossover strategies

A crossover operator in genetic algorithms (GAs) plays an essential role as the main search operator to breed offspring by exchanging information between individuals. Although different types of crossover operators have been developed for real-coded GAs (RCGAs), there has been very little research on combining different crossover operators to build more effective and efficient RCGAs. In this work, we propose new steady-state generation alternation-based RCGAs (SSGAs) ameliorated with (i) an ensemble of different probabilistic variable-wise crossover strategies, which is realized by the corresponding parallel populations, to utilize synergetic and complementary effect with their efficient operations, and (ii) efficient operation at each evolution step to obtain further performance enhancement. To investigate the performance of this ensemble with respect to search abilities and computation time, we compare the proposed algorithms against various SSGAs when running 27 benchmark functions. Empirical studies showed that the proposed algorithms exhibit better performance than the contestant SSGAs on these functions. Moreover, a comparison with the state-of-the-art evolutionary algorithms on eight difficult benchmark functions clearly demonstrated outperformance of the proposed algorithms.     

The improved adaptive link adjustment evolutionary algorithm for the multiple container packing problem

In this paper, we propose a new version of Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA) for the network optimization problems, and apply it to the multiple container packing problem (MCPP). Because the proposed algorithm uses a different encoding method from that of the original ALA-EA, we also need different decoding methods for the new algorithm. In addition, to improve the performance of the proposed algorithm, we incorporate heuristic local improvement approaches into it. To verify the effectiveness of the proposed algorithm we compare it with the existing evolutionary approaches for several instances, which are known to be extremely difficult to them. Computational tests show that the algorithm is superior to the existing evolutionary approaches and the original ALA-EA in both of the solution quality and the computational time. Moreover, the performance seems to be not affected by an instance property.     

On the construction of stabilizer codes with an arbitrary binary matrix

This paper proposes a simple framework for constructing a stabilizer code with an arbitrary binary matrix. We define a relation between A ₁ and A ₂ of a binary check matrix A = (A ₁|A ₂) associated with stabilizer generators of a quantum error-correcting code. Given an arbitrary binary matrix, we can derive a pair of A ₁ and A ₂ by the relation. As examples, we illustrate two kinds of stabilizer codes: quantum LDPC codes and quantum convolutional codes. By the nature of the proposed framework, the stabilizer codes covered in this paper belong to general stabilizer (non-CSS) codes.     

New design of a VirtualGlove for grasping applications

Virtual gloves have received much attention in the field of virtual reality and computer simulations. They are considered to be essential tools for implementing any sort of hand‐based experiences in virtual environments. For applications with simpler motions, the accuracy of finger‐bending amount returned by these virtual gloves is not of primary interest. For grasping applications, however, inaccuracy in the bending amount results in unrealistic scenarios having discrepancies in the contact geometry for the hand and the object. In this study, we improve the measurements of bending amount by placing the bend sensors on the palm of the hand, where the objects are touched directly. Supporting evaluative data and design details are also discussed. © 2010 Wiley Periodicals, Inc.     

Despeckling of medical ultrasound images using Daubechies complex wavelet transform

The paper presents a novel despeckling method, based on Daubechies complex wavelet transform, for medical ultrasound images. Daubechies complex wavelet transform is used due to its approximate shift invariance property and extra information in imaginary plane of complex wavelet domain when compared to real wavelet domain. A wavelet shrinkage factor has been derived to estimate the noise-free wavelet coefficients. The proposed method firstly detects strong edges using imaginary component of complex scaling coefficients and then applies shrinkage on magnitude of complex wavelet coefficients in the wavelet domain at non-edge points. The proposed shrinkage depends on the statistical parameters of complex wavelet coefficients of noisy image which makes it adaptive in nature. Effectiveness of the proposed method is compared on the basis of signal to mean square error (SMSE) and signal to noise ratio (SNR). The experimental results demonstrate that the proposed method outperforms other conventional despeckling methods as well as wavelet based log transformed and non-log transformed methods on test images. Application of the proposed method on real diagnostic ultrasound images has shown a clear improvement over other methods.     

Shadow detection and removal for moving objects using Daubechies complex wavelet transform

Multimedia Tools and Applications - Shadow detection and removal is a challenging problem for several computer vision applications because shadow always makes object misclassified. A number of...