A Deep Learning Paradigm for Detection of Harmful Algal Blooms
暂无分享,去创建一个
[1] Fahad Shahbaz Khan,et al. Discriminative Color Descriptors , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[3] B. Matsushita,et al. Distinguishing surface cyanobacterial blooms and aquatic macrophytes using Landsat/TM and ETM + shortwave infrared bands , 2015 .
[4] Jitendra Malik,et al. Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[5] M. Matthews,et al. An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters , 2012 .
[6] Pascal Fua,et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[7] Daniel Odermatt,et al. Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters , 2015 .
[8] Kaishan Song,et al. Remote sensing of freshwater cyanobacteria: An extended IOP Inversion Model of Inland Waters (IIMIW) for partitioning absorption coefficient and estimating phycocyanin , 2015 .
[9] Richard P. Stumpf,et al. Evaluation of cyanobacteria cell count detection derived from MERIS imagery across the eastern USA , 2015 .
[10] Michael Felsberg,et al. Compact color-texture description for texture classification , 2015, Pattern Recognit. Lett..
[11] Subhransu Maji,et al. Deep filter banks for texture recognition and segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] S. Wilde,et al. Avian vacuolar myelinopathy linked to exotic aquatic plants and a novel cyanobacterial species , 2005, Environmental toxicology.
[13] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[14] Stefan G. H. Simis,et al. Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water , 2005 .
[15] L. Backer,et al. Cyanobacterial Harmful Algal Blooms (CyanoHABs): Developing a Public Health Response , 2002 .
[16] Antonio Torralba,et al. Recognizing indoor scenes , 2009, CVPR.
[17] M. Bauer,et al. Factors affecting the measurement of CDOM by remote sensing of optically complex inland waters , 2015 .
[18] Edward H. Adelson,et al. Recognizing Materials Using Perceptually Inspired Features , 2013, International Journal of Computer Vision.
[19] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[20] Stewart Bernard,et al. Eutrophication and cyanobacteria in South Africa's standing water bodies: A view from space , 2015 .
[21] Mark William Matthews,et al. Eutrophication and cyanobacterial blooms in South African inland waters: 10years of MERIS observations , 2014 .
[22] Alexei A. Efros,et al. An empirical study of context in object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Andrew R. Humpage,et al. Health Risk Assessment of Cyanobacterial (Blue-green Algal) Toxins in Drinking Water , 2005, International journal of environmental research and public health.
[24] Iasonas Kokkinos,et al. Describing Textures in the Wild , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Jitendra Malik,et al. Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.
[26] Fahad Shahbaz Khan,et al. Modulating Shape Features by Color Attention for Object Recognition , 2012, International Journal of Computer Vision.
[27] ZhongPing Lee,et al. Bio-Optical Inversion in Highly Turbid and Cyanobacteria-Dominated Waters , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[28] Michael Felsberg,et al. Evaluating the Impact of Color on Texture Recognition , 2013, CAIP.
[29] Fahad Shahbaz Khan,et al. Color attributes for object detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[30] Kristen Grauman,et al. Top-down pairwise potentials for piecing together multi-class segmentation puzzles , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.
[31] Kunimitsu Kaya,et al. Cyanobacterial toxins, exposure routes and human health , 1999 .
[32] Igor Ogashawara,et al. A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters , 2013, Remote. Sens..
[33] Deepak R. Mishra,et al. A Novel Algorithm for Predicting Phycocyanin Concentrations in Cyanobacteria: A Proximal Hyperspectral Remote Sensing Approach , 2009, Remote. Sens..
[34] Stefan Carlsson,et al. CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.