PF Wu, F Xiao, C Sha, HP Huang, RC Wang, NX Xiong, Node scheduling strategies for achieving full-view area coverage in camera sensor networks. Sensors 17(6), 1303 (2017). https://doi.org/10.3390/s17061303
Article
Google Scholar
YH Wang, KL Chen, JN Yu, NX Xiong, H Leung, HL Zhou, L Zhu, Dynamic propagation characteristics estimation and tracking based on an EM-EKF algorithm in time-variant MIMO channel. Inf. Sci. 408, 70–83 (2017). https://doi.org/10.1016/j.ins.2017.04.035
Article
Google Scholar
J Gui, L Hui, NX Xiong, A game-based localized multi-objective topology control scheme in heterogeneous wireless networks. IEEE Access 5, 2396–2416 (2017). https://doi.org/10.1109/ACCESS.2017.2672561
Article
Google Scholar
NX Xiong, RW Liu, MH Liang, D Wu, Z Liu, HS Wu, Effective alternating direction optimization methods for sparsity-constrained blind image deblurring. Sensors 17(1) (2017). https://doi.org/10.3390/s17010174
H Zhang, RW Liu, D Wu, YL Liu, NN Xiong, Non-convex total generalized variation with spatially adaptive regularization parameters for edge-preserving image restoration. Journal of Internet Technology 17(7), 1391–1403 (2016). https://doi.org/10.6138/JIT.2016.17.7.20161108
Google Scholar
ZH Xia, XH Wang, XM Sun, QS Liu, NX Xiong, Steganalysis of LSB matching using differences between nonadjacent pixels. Multimedia Tools & Applications 75(4), 1947–1962 (2016). https://doi.org/10.1007/s11042-014-2381-8
Article
Google Scholar
LP Gao, FY Yu, QK Chen, NX Xiong, Consistency maintenance of do and undo/redo operations in real-time collaborative bitmap editing systems. Clust. Comput. 19(1), 255–267 (2016). https://doi.org/10.1007/s10586-015-0499-8
Article
Google Scholar
ZH Xia, NN Xiong, AV Vasilakos, XM Sun, EPCBIR, An efficient and privacy-preserving content-based image retrieval scheme in cloud computing. Inf. Sci. 387, 195–204 (2017). https://doi.org/10.1016/j.ins.2016.12.030
Article
Google Scholar
Z Lu, YR Lin, XX Huang, NX Xiong, ZJ Fang, Visual topic discovering, tracking and summarization from social media streams. Multimedia Tools & Applications. 1–25(2017). DOI: https://doi.org/10.1007/s11042-016-3877-1
L Shu, YM Fang, ZJ Fang, Y Yang, FC Fei, NX Xiong, A novel objective quality assessment for super- resolution images. International Journal of Signal Processing, Image Processing and Pattern Recognition 9(5), 297–308 (2016). https://doi.org/10.14257/ijsip.2016.9.5.27
Article
Google Scholar
WW Fang, YC Li, HJ Zhang, NX Xiong, JY Lai, AV Vasilakos, On the throughput-energy tradeoff for data transmission between cloud and mobile devices. Inf. Sci. 283, 79–93 (2014). https://doi.org/10.1016/j.ins.2014.06.022
Article
Google Scholar
NX Xiong, AV Vasilakos, LT Yang, LY Song, Y Pan, R Kannan, YS Li, Y Li, Comparative analysis of quality of service and memory usage for adaptive failure detectors in healthcare systems. IEEE Journal on Selected Areas in Communications 27(4), 495–509 (2009). https://doi.org/10.1109/JSAC.2009.090512
Article
Google Scholar
X Lu, LL Tu, XY Zhou, NX Xiong, LM Sun, ViMediaNet: an emulation system for interactive multimedia based telepresence services. J. Supercomput. 73(8), 3562–3578 (2017). https://doi.org/10.1007/s11227-016-1821-9
Article
Google Scholar
CY Zhang, D Wu, RW Liu, NX Xiong, Non-local regularized variational model for image deblurring under mixed Gaussian-impulse noise. Journal of Internet Technology 16(7), 1301–1319 (2015). https://doi.org/10.6138/JIT.2015.16.7.20151103a
Google Scholar
NX Xiong, AV Vasilakos, LT Yang, CX Wang, R Kannan, CC Chang, Y Pan, A novel self-tuning feedback controller for active queue management supporting TCP flows. Inf. Sci. 180(11), 2249–2263 (2010). https://doi.org/10.1016/j.ins.2009.12.001
Article
MathSciNet
Google Scholar
Y Yang, S Tong, S Huang, P Lin, Dual-tree complex wavelet transform and image block residual-based multi-focus image fusion in visual sensor networks. Sensors 14(12), 22408–22430 (2014). https://doi.org/10.3390/s141222408
Article
Google Scholar
YM Fang, ZJ Fang, FN Yuan, Y Yang, SY Yang, NN Xiong, Optimized multioperator image retargeting based on perceptual similarity measure. IEEE Transactions on Systems Man Cybernetics-Systems 47(11), 2956–2966 (2017). https://doi.org/10.1109/TSMC.2016.2557225
Article
Google Scholar
T Li, JP Zhang, XC Lu, Y Zhang, SDBD: A hierarchical region-of-interest detection approach in large-scale remote sensing image. IEEE Geoscience & Remote Sensing Letters. 14(5), 699–703 (2017). https://doi.org/10.1109/LGRS.2017.2672560
Article
Google Scholar
QH Luo, ZW Shi, in Proc. of 2016 IEEE International Geoscience and Remote Sensing Symposium(IGARSS). Airplane detection in remote sensing images based on Object Proposal(IEEE, Beijing, 2016), pp. 1388–1391. DOI: https://doi.org/10.1109/IGARSS.2016.7729355
A Zhao, K Fu, SY Wang, JW Zuo, YH Zhang, YF Hu, HQ Wang, Aircraft recognition based on landmark detection in remote sensing images. IEEE Geosci. Remote Sens. Lett. 14(8), 1413–1417 (2017). https://doi.org/10.1109/LGRS.2017.2715858
Article
Google Scholar
YD Lin, HJ He, HM Tai, F Chen, ZK Yin, Rotation and scale invariant target detection in optical remote sensing images based on pose-consistency voting. Multimedia Tools and Applications 76(12), 14461–14483 (2017). https://doi.org/10.1007/s11042-016-3857-5
Article
Google Scholar
JR Hai, XJ Ya, SZ Guang, Aircraft recognition using modular extreme learning machine. Neurocomputing 128(27), 166–174 (2014). https://doi.org/10.1016/j.neucom.2012.12.064
Google Scholar
RH Yang, Q Pan, YM Cheng, in proc. of 2006 IEEE International Conference on Machine Learning and Cybernetics. The Application of Wavelet Invariant Moments to Image Recognition(IEEE), Dalian, China, 2006), pp. 3243-3247. DOI: https://doi.org/10.1109/ICMLC.2006.258434
CS Lin, CL Hwang, New forms of shape invariants from elliptic fourier descriptors. Pattern Recogn. 20(5), 535–545 (1987). https://doi.org/10.1016/0031-3203(87)90080-X
Article
Google Scholar
CT Zahn, RZ Roskies, Fourier descriptors for plane closed curves. IEEE Trans. Comput. C-21(3), 269–281 (1972). https://doi.org/10.1109/TC.1972.5008949
Article
MathSciNet
MATH
Google Scholar
G Cheng, JW Han, A survey on object detection in optical remote sensing images. ISPRS J. Photogramm. Remote Sens. 117, 11–28 (2016). https://doi.org/10.1016/j.isprsjprs.2016.03.014
Article
Google Scholar
G Liu, X Sun, K Fu, HQ Wang, Aircraft recognition in high-resolution satellite images using coarse-to-fine shape prior. IEEE Geoscience Remote Sensing Letters 10(3), 573–577 (2013). https://doi.org/10.1109/LGRS.2012.2214022
Article
Google Scholar
QC Wu, H Sun, X Sun, DB Zhang, K Fu, HQ Wang, Aircraft recognition in high-resolution optical satellite remote sensing images. IEEE Geoscience Remote Sensing Letters 12(1), 112–116 (2015). https://doi.org/10.1109/LGRS.2014.2328358
Article
Google Scholar
Y Li, X Sun, HQ Wang, H Sun, XJ Li, Automatic target detection in high-resolution remote sensing images using a contour-based spatial model. IEEE Geoscience Remote Sensing Letters 9(5), 886–890 (2012). https://doi.org/10.1109/LGRS.2012.2183337
Article
Google Scholar
A Krizhevsky, I Sutskever, GE Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386
Article
Google Scholar
R Girshick, J Donahue, T Darrell, J Malik, in Proc. of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Rich feature hierarchies for accurate object detection and semantic segmentation(IEEE, Columbus, 2014), pp. 580–587. DOI: https://doi.org/10.1109/CVPR.2014.81
JRR Uijlings, KEA van de Sande, T Gevers, AWM Smeulders, Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013). https://doi.org/10.1007/s11263-013-0620-5
Article
Google Scholar
L Itti, C Koch, E Niebur, A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998). https://doi.org/10.1109/34.730558
Article
Google Scholar
R Girshick, in Proc. of 2015 IEEE International Conference on Computer Vision(ICCV). Fast R-CNN(IEEE, Chile, 2015), pp. 1440–1448. DOI: https://doi.org/10.1109/ICCV.2015.169
WJ Zhu, S Liang, YC Wei, J Sun, in Proc. of 2014 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Saliency optimization from robust background detection(IEEE, Boston, 2014), pp. 2814–2821. DOI: https://doi.org/10.1109/CVPR.2014.360
MM Cheng, NJ Mitra, XL Huang, PHS Torr, SM Hu, Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015). https://doi.org/10.1109/TPAMI.2014.2345401
Article
Google Scholar
R Achanta, S Hemami, F Estrada, S Susstrunk, in Proc. of 2009 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Frequency-tuned salient region detection(IEEE, Miami, 2009), pp. 1597–1604. DOI: https://doi.org/10.1109/CVPR.2009.5206596
S Goferman, L Zelnikmanor, A Tal, Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012). https://doi.org/10.1109/TPAMI.2011.272
Article
Google Scholar
Y Zhai, M Shah, in Proc. of 2006 ACM International Conference on Multimedia. Visual attention detection in video sequences using spatiotemporal cues(ACM, Santa Barbara, 2006), pp. 815–824. DOI: https://doi.org/10.1145/1180639.1180824
X Hou, L Zhang, in Proc. of 2007 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Saliency detection: a spectral residual approach(IEEE, Minneapolis, 2007), pp. 1–8. DOI: https://doi.org/10.1109/CVPR.2007.383267
XH Li, HC Lu, LH Zhang, X Ruan, MH Yang, in Proc. of 2013 IEEE International Conference on Computer Vision(ICCV). Saliency detection via dense and sparse reconstruction(IEEE, Sydney, 2013), pp. 2976–2983. DOI: https://doi.org/10.1109/ICCV.2013.370
N Tong, HC Lu, R Xiang, MH Yang, in Proc. of 2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Salient object detection via bootstrap learning(IEEE, Boston, 2015), pp. 1884–1892. DOI: https://doi.org/10.1109/CVPR.2015.7298798
C Koch, S Ullman, Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4(4), 219–227 (1985)
Google Scholar
B Schölkopf, J Platt, T Hofmann. Graph-based visual saliency. in Proceedings of advances in Neural Information Processing Systems (NIPS). (MIT Press, Vancouver, 2006) p.545–552.
T Liu, ZJ Yuan, JA Sun, JD Wang, NN Zheng, XO Tang, HY Shum, Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011). https://doi.org/10.1109/TPAMI.2010.70
Article
Google Scholar
S Goferman, L Zelnik-Manor, A Tal, Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012). https://doi.org/10.1109/TPAMI.2011.272
Article
Google Scholar
A Borji, L Itti, in Proc. of 2012 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Exploiting local and global patch rarities for saliency detection(IEEE, Providence, 2012), pp. 478–485. DOI: https://doi.org/10.1109/CVPR.2012.6247711
J Feng, YC Wei, LT Tao, C Zhang, J Sun, in Proc. of 2011 IEEE International Conference on Computer Vision(ICCV). Salient object detection by composition(IEEE, Barcelona, 2011), pp. 1028–1035. DOI: https://doi.org/10.1109/ICCV.2011.6126348
M Ran, A Tal, L Zelnik-Manor, in Proc. of 2013 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). What makes a patch distinct?(IEEE, Portland, 2013), pp. 1139–1146. DOI: https://doi.org/10.1109/CVPR.2013.151
F Perazzi, P Krahenbuhl, Y Pritch, A Hornung, in Proc. of 2012 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Saliency filters: contrast based filtering for salient region detection(IEEE, Providence, 2012), pp. 733–740. DOI: https://doi.org/10.1109/CVPR.2012.6247743
CL Guo, LM Zhang, A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185–198 (2010). https://doi.org/10.1109/TIP.2009.2030969
Article
MathSciNet
MATH
Google Scholar
G Li, Y Yu, Visual saliency detection based on multiscale deep CNN features. IEEE Trans. Image Process. 25(11), 5012–5024 (2016). https://doi.org/10.1109/TIP.2016.2602079
Article
MathSciNet
Google Scholar
P Zhang, T Zhuo, W Huang, K Chen, M Kankanhalli, Online object tracking based on CNN with spatial-temporal saliency guided sampling. Neurocomputing 257, 115–127 (2017). https://doi.org/10.1016/j.neucom.2016.10.073
Article
Google Scholar
JS Lim, WH Kim, Detection of multiple humans using motion information and adaboost algorithm based on harr-like features. International Journal of Hybrid Information Technology 5(2), 243–248 (2012)
Google Scholar
PY Reecha, V Senthamilarasu, K Kutty, PU Sunita, Implementation of robust HOG-SVM based pedestrian classification. International Journal of Computer Applications 114(19), 10–16 (2015). https://doi.org/10.5120/20084-2026
Article
Google Scholar
L Hou, WG Wan, KH Lee, JN Hwang, G Okopal, J Pitton, Robust human tracking based on DPM constrained multiple-kernel from a moving camera. Journal of Signal Processing Systems. 86(1), 27–39 (2017). https://doi.org/10.1007/s11265-015-1097-y
Article
Google Scholar
A Ali, MA Bayoumi, in Proc. of 2016 IEEE International Conference on Image Processing. Towards real-time DPM object detector for driver assistance(IEEE, Arizona, 2016), pp. 3842–3846. DOI: https://doi.org/10.1109/ICIP.2016.7533079
S Bell, CL Zitnick, K Bala, R Girshick, in Proc. of 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks(IEEE, Las Vegas, 2016), pp. 2874–2883. DOI: https://doi.org/10.1109/CVPR.2016.314
T Kong, A Yao, Y Chen, FC Sun, in Proc. of 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). HyperNet: towards accurate region proposal generation and joint object detection(IEEE, Las Vegas, 2016), pp. 845–853. DOI: https://doi.org/10.1109/CVPR.2016.98
F Yang, W Choi, Y Lin, in Proc. of 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers(IEEE, Las Vegas, 2016), pp. 2129–2137. DOI: https://doi.org/10.1109/CVPR.2016.234
KM He, XY Zhang, SQ Ren, J Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015). https://doi.org/10.1109/TPAMI.2015.2389824
Article
Google Scholar
SQ Ren, KM He, R Girshick, J Sun, Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence 39(6), 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031
Article
Google Scholar
JF Dai, Y Li, KM He, J Sun, R-FCN: object detection via region-based fully convolutional networks(2016), https://arxiv.org/abs/1605.06409, Accessed 21 Jun 2016.
J Redmon, S Divvala, R Girshick, A Farhadi, in Proc. of 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). You only look once: unified, real-time object detection(IEEE, Las Vegas, 2016), pp. 779–788. doi: https://doi.org/10.1109/CVPR.2016.91
W Liu, D Anguelov, D Erhan, C Szegedy, S Reed, CY Fu, AC Berg, in Proc. of 2016 the 14th European Conference on Computer Vision(ECCV). SSD: Single Shot MultiBox Detector(Springer, Amsterdam, 2016), pp. 21–37. DOI: https://doi.org/10.1007/978-3-319-46448-0_2
CL Zitnick, P Dollár, in Proc. of 2014 the 13th European Conference on Computer Vision(ECCV). Edge boxes: locating object proposals from edges(Springer, Zurich, 2014), pp. 391–405. DOI: https://doi.org/10.1007/978-3-319-10602-1_26
M Najibi, M Rastegari, LS Davis, in Proc. of 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). G-CNN: an iterative grid based object detector(IEEE, Las Vegas, 2016), pp. 2369–2377. DOI: https://doi.org/10.1109/CVPR.2016.260
J Huang, V Rathod, C Sun, ML Zhu, A Korattikara, A Fathi, I Fischer, Z Wojna, Y Song, S Guadarrama, K Murphy, in Proc. of 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Speed/accuracy trade-offs for modern convolutional object detectors (IEEE, Honolulu, 2017), pp. 3296–3297. DOI: https://doi.org/10.1109/CVPR.2017.351
Z. Cai, Q. Fan, RS. Feris, N Vasconcelos, in Proc. of 2016 the 14th European Conference on Computer Vision(ECCV). A unified multi-scale deep convolutional neural network for fast object detection(Springer, Amsterdam, 2016), pp. 354–370. DOI: https://doi.org/10.1007/978-3-319-46493-0_22
TY. Lin, P. Dollár, R. Girshick, KM He, B Hariharan, S Belongie, in Proc. of 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Feature pyramid networks for object detection(IEEE, Honolulu, 2017), pp. 936–944. DOI: https://doi.org/10.1109/CVPR.2017.106
A Shrivastava, R Sukthankar, J Malik, A Gupta, Beyond skip connections: top-down modulation for object detection (2017), https://arxiv.org/abs/1612.06851, Accessed 19 Sep 2017.
J Ren, XH Chen, JB Liu, WX Sun, JH Pang, Q Yan, YW Tai, L Xu, in Proc. of 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Accurate single stage detector using recurrent rolling convolution(IEEE, Honolulu, 2017), pp. 752–760. DOI: https://doi.org/10.1109/CVPR.2017.87
CY Fu, W Liu, A Ranga, A Tyagi, AC Berg, DSSD : deconvolutional single shot detector (2017), https://arxiv.org/abs/1701.06659, Accessed 23 Jan 2017.
KM He, G Gkioxari, P Dollár, R Girshick, Mask R-CNN(2017), https://arxiv.org/abs/1703.06870, Accessed 5 Apr 2017.
R Achanta, A Shaji, K Smith, A Lucchi, P Fua, S Susstrunk, SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2281 (2012). https://doi.org/10.1109/TPAMI.2012.120
Article
Google Scholar