Histogram of Oriented Principal Components for Cross-View Action Recognition

Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the Histogram of Oriented Principal Components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of Spatio-Temporal Keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The Experimental results show that our techniques provide significant improvement over state-of-the-art methods

Action Classification with Locality-constrained Linear Coding

We propose an action classification algorithm which uses Locality-constrained Linear Coding (LLC) to capture discriminative information of human body variations in each spatiotemporal subsequence of a video sequence. Our proposed method divides the input video into equally spaced overlapping spatiotemporal subsequences, each of which is decomposed into blocks and then cells. We use the Histogram of Oriented Gradient (HOG3D) feature to encode the information in each cell. We justify the use of LLC for encoding the block descriptor by demonstrating its superiority over Sparse Coding (SC). Our sequence descriptor is obtained via a logistic regression classifier with L2 regularization. We evaluate and compare our algorithm with ten state-of-the-art algorithms on five benchmark datasets. Experimental results show that, on average, our algorithm gives better accuracy than these ten algorithms.Comment: ICPR 201

The homogeneous q-difference operator and the related polynomials

We create the homogeneous q-difference operator Ee(a, b; θ) as an extension of the exponential operator E(bθ). A new polynomials hn(a, b, x|q−1) are defined as an extension of the q−1-Rogers-Szegö polynomial hn(a, b|q−1). We provide an operator proof of the generating function and its extension, Rogers formula and the invers linearization formula, and Mehler’s formula for the polynomials hn(a, b|q−1). The generating function and its extension, Rogers formula and the invers linearization formula, and Mehler’s formula for the polynomials hn(a, b|q−1) are deduced by giving special values to parameters of a new polynomial hn(a, b, x|q−1).Publisher's Versio

Análisis de los ensayos argumentativos del alumno pakistaní: un enfoque multidimensional

The present study is a corpus based research using a statistical approach multi-dimensional analysis (MDA) by Biber to study linguistic patterns of learner language. MDA has gained much appreciation due to its objective and empirical nature. It not only systematically arranges common linguistic patterns but also elaborates the functional association of these patterns. The MDA is performed at two levels known as old MD and New MD. The present researchis only about second level (New MD). The results show that in Pakistani learners’ writing the prominent linguistic patterns are characteristically informational rather than argumentative. Instead of building arguments, learners are more interested in sharing information. Pakistani English has historical roots from pre-partitioned India, therefore, it has under gone through communal, traditional and dogmatic apexes. In the beginning, people started learning English as a second language that was inevitably entering in their social and cultural life.El presente estudio es una investigación basada en corpus que utiliza un enfoque estadístico de análisis multidimensional (MDA) de Biber para estudiar los patrones lingüísticos del lenguaje del alumno. MDA ha ganado mucha apreciación debido a su naturaleza objetiva y empírica. No solo organiza sistemáticamente patrones lingüísticos comunes, sino que también elabora la asociación funcional de estos patrones. El MDA se realiza en dos niveles conocidos como MD antiguo y MD nuevo. La presente investigación es solo sobre el segundo nivel (Nuevo MD). Los resultados muestran que en la escritura de los aprendices paquistaníes, los patrones lingüísticos prominentes son característicamente informativos más que argumentativos. En lugar de construir argumentos, los alumnos están más interesados ​​en compartir información. El inglés paquistaní tiene raíces históricas de la India pre-dividida, por lo tanto, ha pasado por vértices comunales, tradicionales y dogmáticos. Al principio, la gente comenzó a aprender inglés como un segundo idioma que inevitablemente entraba en su vida social y cultural

Deep Siamese Networks toward Robust Visual Tracking

Recently, Siamese neural networks have been widely used in visual object tracking to leverage the template matching mechanism. Siamese network architecture contains two parallel streams to estimate the similarity between two inputs and has the ability to learn their discriminative features. Various deep Siamese-based tracking frameworks have been proposed to estimate the similarity between the target and the search region. In this chapter, we categorize deep Siamese networks into three categories by the position of the merging layers as late merge, intermediate merge and early merge architectures. In the late merge architecture, inputs are processed as two separate streams and merged at the end of the network, while in the intermediate merge architecture, inputs are initially processed separately and merged intermediate well before the final layer. Whereas in the early merge architecture, inputs are combined at the start of the network and a unified data stream is processed by a single convolutional neural network. We evaluate the performance of deep Siamese trackers based on the merge architectures and their output such as similarity score, response map, and bounding box in various tracking challenges. This chapter will give an overview of the recent development in deep Siamese trackers and provide insights for the new developments in the tracking field