8,582 research outputs found
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning
This paper studies the joint learning of action recognition and temporal
localization in long, untrimmed videos. We employ a multi-task learning
framework that performs the three highly related steps of action proposal,
action recognition, and action localization refinement in parallel instead of
the standard sequential pipeline that performs the steps in order. We develop a
novel temporal actionness regression module that estimates what proportion of a
clip contains action. We use it for temporal localization but it could have
other applications like video retrieval, surveillance, summarization, etc. We
also introduce random shear augmentation during training to simulate viewpoint
change. We evaluate our framework on three popular video benchmarks. Results
demonstrate that our joint model is efficient in terms of storage and
computation in that we do not need to compute and cache dense trajectory
features, and that it is several times faster than its sequential ConvNets
counterpart. Yet, despite being more efficient, it outperforms state-of-the-art
methods with respect to accuracy.Comment: WACV 2017 camera ready, minor updates about test time efficienc
Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description
Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms
Automated novelty detection in the WISE survey with one-class support vector machines
Wide-angle photometric surveys of previously uncharted sky areas or
wavelength regimes will always bring in unexpected sources whose existence and
properties cannot be easily predicted from earlier observations: novelties or
even anomalies. Such objects can be efficiently sought for with novelty
detection algorithms. Here we present an application of such a method, called
one-class support vector machines (OCSVM), to search for anomalous patterns
among sources preselected from the mid-infrared AllWISE catalogue covering the
whole sky. To create a model of expected data we train the algorithm on a set
of objects with spectroscopic identifications from the SDSS DR13 database,
present also in AllWISE. OCSVM detects as anomalous those sources whose
patterns - WISE photometric measurements in this case - are inconsistent with
the model. Among the detected anomalies we find artefacts, such as objects with
spurious photometry due to blending, but most importantly also real sources of
genuine astrophysical interest. Among the latter, OCSVM has identified a sample
of heavily reddened AGN/quasar candidates distributed uniformly over the sky
and in a large part absent from other WISE-based AGN catalogues. It also
allowed us to find a specific group of sources of mixed types, mostly stars and
compact galaxies. By combining the semi-supervised OCSVM algorithm with
standard classification methods it will be possible to improve the latter by
accounting for sources which are not present in the training sample but are
otherwise well-represented in the target set. Anomaly detection adds
flexibility to automated source separation procedures and helps verify the
reliability and representativeness of the training samples. It should be thus
considered as an essential step in supervised classification schemes to ensure
completeness and purity of produced catalogues.Comment: 14 pages, 15 figure
- …