The Experts below are selected from a list of 50592 Experts worldwide ranked by ideXlab platform

Tang Kun - One of the best experts on this subject based on the ideXlab platform.

  • Voice Activity Detection in Non-stationary Noise
    The Proceedings of the Multiconference on "Computational Engineering in Systems Applications", 2006
    Co-Authors: Wang Tong, Cui Huijuan, Tang Kun
    Abstract:

    Existing voice activity Detection algorithms degraded severely in low SNR or in non-stationary noise environment. This paper proposes a new fusion method in such environment. The method is based on the fusion of the SNR for selected sub-bands of the input speech and this fusion is implemented through a specific function called SAF (sum of activation function). The Results show that the algorithm could give reliable voice activity Detection Result in low SNR and even in the presence of non-stationary noise.

  • Voice Activity Detection inNon-stationary Noise
    2006
    Co-Authors: Tang Kun
    Abstract:

    Existing voiceactivity Detection algorithms degraded severely inlowSNR orinnon-stationary noise environment. Thispaperproposes anewfusion methodin suchenvironment. Themethodisbasedonthefusion ofthe SNR forselected sub-bands oftheinputspeechandthis fusion isimplemented through aspecific function called SAF (sumofactivation function). TheResults showthatthe algorithm couldgivereliable voice activity Detection Result inlowSNRandeveninthepresence ofnon-stationary noise.

Xiangjuan Li - One of the best experts on this subject based on the ideXlab platform.

  • automatic target Detection in high resolution remote sensing images using a contour based spatial model
    IEEE Geoscience and Remote Sensing Letters, 2012
    Co-Authors: Yu Li, Hongqi Wang, Xiangjuan Li
    Abstract:

    In this letter, we propose a contour-based spatial model which can detect geospatial targets accurately in high-resolution remote sensing images. To detect the geospatial targets with complex structures, each image was partitioned into pieces as target candidate regions using multiple segmentations at first. Then, the automatic identification of target seed regions is achieved by computing the similarity of the contour information with the target template using dynamic programming. Finally, the contour-based similarity was further updated and combined with spatial relationships to figure out the missing parts. In this way, a more accurate target Detection Result can be achieved. The precision, robustness, and effectiveness of the proposed method were demonstrated by the experimental Results.

Ronger Zheng - One of the best experts on this subject based on the ideXlab platform.

  • micro spatial analysis of seashell surface using laser induced breakdown spectroscopy and raman spectroscopy
    Spectrochimica Acta Part B: Atomic Spectroscopy, 2015
    Co-Authors: Yangfan Wang, Shi Wang, Zhenmin Bao, Ronger Zheng
    Abstract:

    Abstract The seashell has been studied as a proxy for the marine researches since it is the biomineralization product recording the growth development and the ocean ecosystem evolution. In this work a hybrid of Laser Induced Breakdown Spectroscopy (LIBS) and Raman spectroscopy was introduced to the composition analysis of seashell (scallop, bivalve, Zhikong). Without any sample treatment, the compositional distribution of the shell was obtained using LIBS for the element Detection and Raman for the molecule recognition respectively. The elements Ca, K, Li, Mg, Mn and Sr were recognized by LIBS; the molecule carotene and carbonate were identified with Raman. It was found that the LIBS Detection Result was more related to the shell growth than the Detection Result of Raman. The obtained Result suggested the shell growth might be developing in both horizontal and vertical directions. It was indicated that the LIBS–Raman combination could be an alternative way for the shell researches.

Huilin Xiong - One of the best experts on this subject based on the ideXlab platform.

  • PolSAR Ship Detection Based on the Polarimetric Covariance Difference Matrix
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017
    Co-Authors: Tao Zhang, Zhen Yang, Huilin Xiong
    Abstract:

    Ship Detection using Polarimetric SAR data has attracted a lot of attention in recent years. Due to the sampling of the Doppler spectrum at finite intervals of the pulse repetition frequency, the azimuth ambiguities often appear in PolSAR images, which make the ship Detection in PolSAR images frequently generating false alarms, especially in the case of low backscattering sea environment. In order to handle the problem and improve the performance of ship Detection in PolSAR images, this paper presents a new method, which is mainly based on concentrating the polarimetric difference between ship pixels and background pixels. We first calculate a polarimetric covariance difference matrix, denoted as polarimetric covariance difference matrix (PCDM), by accumulating the elemental difference between the polarimetric covariance matrix at each pixel and the counterparts in its 3 × 3 neighbors. The SPAN detector is then applied on PCDM to obtain a coarse Detection Result. Meanwhile, we decompose the PCDM matrix to calculate a new polarimetric signature, called pedestal ship height (PSH), and use it together with the coarse Detection Result to distinguish ships from ambiguities. Extensive experiments on three real PolSAR datasets are carried out to demonstrate the effectiveness of the proposed method in comparing with other algorithms. The experimental Results show that the proposed method not only detects ships effectively, but also can remove the azimuth ambiguities and reduce the false alarms significantly.

Yu Li - One of the best experts on this subject based on the ideXlab platform.

  • automatic target Detection in high resolution remote sensing images using a contour based spatial model
    IEEE Geoscience and Remote Sensing Letters, 2012
    Co-Authors: Yu Li, Hongqi Wang, Xiangjuan Li
    Abstract:

    In this letter, we propose a contour-based spatial model which can detect geospatial targets accurately in high-resolution remote sensing images. To detect the geospatial targets with complex structures, each image was partitioned into pieces as target candidate regions using multiple segmentations at first. Then, the automatic identification of target seed regions is achieved by computing the similarity of the contour information with the target template using dynamic programming. Finally, the contour-based similarity was further updated and combined with spatial relationships to figure out the missing parts. In this way, a more accurate target Detection Result can be achieved. The precision, robustness, and effectiveness of the proposed method were demonstrated by the experimental Results.