584 research outputs found
BIOMECHANICAL ANALYSIS OF PULLING PHASES IN WEIGHT LIFTING – A CASE STUDY
The purpose of this study was to analyze specific aspects of weight lifting techniques. In analyses of snatch lift, identification of the pulling phase, in order to determine the critical points is the primary task. The kinematics and kinetics parameters are closely interrelated with pulling phases. Because of the differences in definitions of phase structures, the data from previous studies couldn’t be compared with each other directly. Therefore, this study investigated the phase structures in snatch lift and established the relationship between several variables of kinematics, such as the knee angle, the barbell vertical velocity and position, etc. It is clear that in the course of analyses, identifying the phases by means of the barbell vertical velocity, is the most convenient and logical method for obtaining fast feedback. Based on the above definitions of the phases, the mechanical data of two snatch lifts by an elite have been measured and analyze
High-quality Panorama Stitching based on Asymmetric Bidirectional Optical Flow
In this paper, we propose a panorama stitching algorithm based on asymmetric
bidirectional optical flow. This algorithm expects multiple photos captured by
fisheye lens cameras as input, and then, through the proposed algorithm, these
photos can be merged into a high-quality 360-degree spherical panoramic image.
For photos taken from a distant perspective, the parallax among them is
relatively small, and the obtained panoramic image can be nearly seamless and
undistorted. For photos taken from a close perspective or with a relatively
large parallax, a seamless though partially distorted panoramic image can also
be obtained. Besides, with the help of Graphics Processing Unit (GPU), this
algorithm can complete the whole stitching process at a very fast speed:
typically, it only takes less than 30s to obtain a panoramic image of
9000-by-4000 pixels, which means our panorama stitching algorithm is of high
value in many real-time applications. Our code is available at
https://github.com/MungoMeng/Panorama-OpticalFlow.Comment: Published at the 5th International Conference on Computational
Intelligence and Applications (ICCIA 2020
CSD: Discriminance with Conic Section for Improving Reverse k Nearest Neighbors Queries
The reverse nearest neighbor (RNN) query finds all points that have
the query point as one of their nearest neighbors (NN), where the NN
query finds the closest points to its query point. Based on the
characteristics of conic section, we propose a discriminance, named CSD (Conic
Section Discriminance), to determine points whether belong to the RNN set
without issuing any queries with non-constant computational complexity. By
using CSD, we also implement an efficient RNN algorithm CSD-RNN with a
computational complexity at . The comparative
experiments are conducted between CSD-RNN and other two state-of-the-art
RkNN algorithms, SLICE and VR-RNN. The experimental results indicate that
the efficiency of CSD-RNN is significantly higher than its competitors
Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis
Fueled by deep learning, computer-aided diagnosis achieves huge advances.
However, out of controlled lab environments, algorithms could face multiple
challenges. Open set recognition (OSR), as an important one, states that
categories unseen in training could appear in testing. In medical fields, it
could derive from incompletely collected training datasets and the constantly
emerging new or rare diseases. OSR requires an algorithm to not only correctly
classify known classes, but also recognize unknown classes and forward them to
experts for further diagnosis. To tackle OSR, we assume that known classes
could densely occupy small parts of the embedding space and the remaining
sparse regions could be recognized as unknowns. Following it, we propose Open
Margin Cosine Loss (OMCL) unifying two mechanisms. The former, called Margin
Loss with Adaptive Scale (MLAS), introduces angular margin for reinforcing
intra-class compactness and inter-class separability, together with an adaptive
scaling factor to strengthen the generalization capacity. The latter, called
Open-Space Suppression (OSS), opens the classifier by recognizing sparse
embedding space as unknowns using proposed feature space descriptors. Besides,
since medical OSR is still a nascent field, two publicly available benchmark
datasets are proposed for comparison. Extensive ablation studies and feature
visualization demonstrate the effectiveness of each design. Compared with
state-of-the-art methods, MLAS achieves superior performances, measured by ACC,
AUROC, and OSCR
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