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Human action recognition has a wide range of applications including biometrics, surveillance, and human computer interaction. The use of multimodal sensors for human action recognition is steadily increasing. However, there are limited publicly available datasets where depth camera and inertial sensor data are captured at the same time. This paper describes(More)
Compressed-sensing reconstruction of still images and video sequences driven by multihypothesis predictions is considered. Specifically, for still images, multiple predictions drawn for an image block are made from spatially surrounding blocks within an initial non-predicted reconstruction. For video, multihypothesis predictions of the current frame are(More)
This paper presents a human action recognition method by using depth motion maps (DMMs). Each depth frame in a depth video sequence is projected onto three orthogonal Cartesian planes. Under each projection view, the absolute difference between two consecutive projected maps is accumulated through an entire depth video sequence forming a DMM. An l(More)
Parathyroid hormone-related protein (PTHrP) regulates proliferation and differentiation of osteoblastic cells via binding to the parathyroid hormone receptor (PTH-1R). The cAMP-dependent protein kinase A pathway governs the majority of these effects, but recent evidence also implicates the MAPK pathway. MC3T3-E1 subclone 4 cells (MC4) were treated with the(More)
The domestic pig (Sus scrofa), an important species in animal production industry, is a right model for studying adipogenesis and fat deposition. In order to expand the repertoire of porcine miRNAs and further explore potential regulatory miRNAs which have influence on adipogenesis, high-throughput Solexa sequencing approach was adopted to identify miRNAs(More)
In this paper, we introduce the completed local binary patterns (CLBP) operator for the first time on remote sensing land-use scene classification. To further improve the representation power of CLBP, we propose a multi-scale CLBP (MS-CLBP) descriptor to characterize the dominant texture features in multiple resolutions. Two different kinds of(More)
Spectral–spatial preprocessing using multihypothesis prediction is proposed for improving accuracy of hyperspectral image classification. Specifically, multiple spatially collocated pixel vectors are used as a hypothesis set fromwhich a prediction for each pixel vector of interest is generated. Additionally, a spectral-bandpartitioning strategy based on(More)
This paper presents a fusion approach for improving human action recognition based on two differing modality sensors consisting of a depth camera and an inertial body sensor. Computationally efficient action features are extracted from depth images provided by the depth camera and from accelerometer signals provided by the inertial body sensor. These(More)
It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and(More)
This paper presents a computationally efficient method for action recognition from depth video sequences. It employs the so called depth motion maps (DMMs) from three projection views (front, side and top) to capture motion cues and uses local binary patterns (LBPs) to gain a compact feature representation. Two types of fusion consisting of feature-level(More)