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Abstract: The MODIS-based estimates of woody, herbaceous and bare cover percentages within a 21 × 21, 11 × 11 and 0.5 × 0.5 km window around pollen sampling sites were attributed to the respective modern pollen spectra. Reconstructions of modern woody cover using the pollen–MODIS reference data set and the modern analogue approach matched well to the original MODIS-based estimates for three selected land-cover fractions. The best of all correspondence between the reconstructed and observed values was obtained for the 21 × 21 km window, including woody (r = 0.83), herbaceous (r = 0.80) and bare (r = 0.90) cover. Discrepancies in the pollen–MODIS cross-validation may be caused by long-distance pollen transport, patchy vegetation, particularly in the densely populated eastern areas and along the steep mountain slopes, under-representation of curtain woody taxa (e.g. Larix and Populus in pollen records), and possible uncertainties in the MODIS data. A series of tests performed with the reference data set including 1452 surface-pollen spectra and representing all vegetation and climatic units of China show the pollen-based reconstructions correspond well to the MODIS-based estimations of land-cover percentages within a 21 × 21 km window, supporting previously obtained results from northern Asia and North America. Results obtained for the smaller window sizes are less accurate, particularly when reconstructing woody cover. The pollen–MODIS-based modern analogue approach can reproduce present-day characteristics of land cover in China with a reasonably high degree of accuracy despite the complexity of the topography and regionally intensive land use. The generally strong correlations encourage the application of the approach to Chinese fossil pollen records. [Copyright &y& Elsevier]

Person re-identification is an important part of smart surveillance systems which match people across different scenarios. The most challenging aspect of person re-identification is to design robust features and similarity models that reduce the impact of viewpoints, lightings, background clutters, camera settings, occlusions, and pedestrian poses under different scenarios. DeepDiff is a learning method proposed in this paper which uses deep neural networks to identify the different features of various human body parts, and then evaluate the similarities between those corresponding parts. Given those two corresponding parts, we propose three subnets that show deep difference using original data, feature maps, and spatial variations. We focus on a part-based method which introduces a pyramid partition architecture through different partition granularities on human images. In order to determine whether those two parts belong to the same identity, we utilize two combinations of our three subnets. Lastly, we present a part-based integration validation method so as to achieve better performance. The DeepDiff’s effectiveness is validated on three public datasets, including: CUHK03 (labeled, detected), VIPeR and CUHK01 (100 and 486 identities settings). The experimental study shows that the proposed DeepDiff model has promising potential. [ABSTRACT FROM AUTHOR]

Abstract: Articulated hand tracking systems have been commonly used in virtual reality applications, including selection–move–release systems that use freehand as input device. However, building an effective real-time hand tracker remains a challenge. Motivated by the need for three-dimensional (3D) freehand-based human–computer interface in the selection–move–release systems, the current study aims to develop 3D freehand tracking, in which the high dimensionality of articulated hand models is one of the dominating obstacles for tracking 3D hand in real-time. This study focuses on forming the behavioral model for users in selection–move–release systems to build a natural, direct, and effective human–computer interface. First, the statistical models for users were learned. Second, the behavioral models were derived from the statistical models to form a general behavioral model database. Third, a freehand tracking algorithm for interaction between human and computer was presented based on the behavioral models. Fourth, the proposed approach was tested in several real selection–move–release systems, and the experimental results were provided. Unlike previous studies on this subject, the proposed behavioral model was composed of Correlation among Local Motion Models and cognitive information. Experimental results show that the proposed algorithm can achieve satisfactory results in relation to time cost and accuracy compared with some previous studies. [Copyright &y& Elsevier]

Abstract: Human hand shape features extraction from image frame sequences is one of the key steps in human hand 2D/3D tracking system and human hand shape recognition system. In order to satisfy the need of human hand tracking in real time, a fast and accurate method for acquirement of edge features from human hand images without consideration of hand over face is put forward in this paper. The proposed approach is composed of two steps, the coarse location phase (CLP) and the refined location phase (RLP) from coarseness to refinement. In the phase of CLP, the hand contour is approximately described by a polygon with concave and convex, an approach to obtaining hand shape polygon using locating points and locating lines is meticulously discussed. Then, a coarse location (CL) algorithm for extraction of interested hand shape features, such as contour, fingertips, roots of fingers, joints and the intersection of knuckle on different fingers, is proposed. In the phase of RLP, a multi-scale approach is introduced into our study to refine the features obtained by the CL algorithm. By means of defining the response strength of different types of features, a refined location (RL) algorithm is proposed. The major contribution of this paper is that the novel detection operators for features of hand images are presented in the above two steps, which have been successfully applied to our 3D hand shape tracking system and 2D hand shape recognition system. A number of comparative studies with real images and online videos demonstrate that the proposed method can extract the three defined human hand image features with high accuracy and high speed. [ABSTRACT FROM AUTHOR]