1 |
CORRELL N, BEKRIS K E, BERENSON D, et al. Analysis and observations from the first amazon picking challenge. IEEE Trans. on Automation Science and Engineering, 2018, 15 (1): 172- 188.
doi: 10.1109/TASE.2016.2600527
|
2 |
FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2010, 32 (9): 1627- 1645.
doi: 10.1109/TPAMI.2009.167
|
3 |
PARK D, RAMANAN D, FOWLKES C. Multi-resolution models for object detection. Proc. of the European Conference on Computer Vision, 2010: 241-254.
|
4 |
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
|
5 |
OUYANG W, WANG X. Joint deep learning for pedestrian detection. Proc. of the IEEE International Conference on Computer Vision, 2013: 2056-2063.
|
6 |
PAISITKRIANGKRAI S, SHEN C, HENGEL A V D. Strengthening the effectiveness of pedestrian detection with spatially pooled features. Proc. of the European Conference on Computer Vision, 2014: 546-561.
|
7 |
BENENSON R, MATHIAS M, TUYTELAARS T, et al. Seeking the strongest rigid detector. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2013: 3666-3673.
|
8 |
HANNAT M, ZRIRA N, RAOUI Y, et al. A fast object recognition and categorization technique for robot grasping using the visual bag of words. Proc. of the International Conference on Multimedia Computing and Systems, 2016: 173-178.
|
9 |
NAM H, HAN B. Learning multi-domain convolutional neural networks for visual tracking. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 4293-4302.
|
10 |
KIRKEGAARD J, MOESLUND T B. Bin-picking based on harmonic shape contexts and graph-based matching. Proc. of the International Conference on Pattern Recognition, 2006: 581-584.
|
11 |
FANG J, DENG X, SUN C, et al. A vision based position system for robot picking. Proc. of the International Conference on Electrical and Control Engineering, 2010: 319-322.
|
12 |
YANG C, ZHANG L, LU H, et al. Saliency detection via graph-based manifold ranking. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2013: 3166-3173.
|
13 |
SHEN X, WU Y. A unified approach to salient object detection via low rank matrix recovery. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2012: 853-860.
|
14 |
JIANG Z, DAVIS L S. Submodular salient region detection. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2013: 2043-2050.
|
15 |
NOH H, HONG S, HAN B. Learning deconvolution network for semantic segmentation. Proc. of the IEEE International Conference on Computer Vision, 2015: 1520-1528.
|
16 |
BADRINARAYANAN V, HANDA A, CIPOLLA R. SegNet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. IEEE Trans. on Pattern Analysis & Machine Intelligence, 2017, 39 (12): 2481- 2495.
|
17 |
CHEN L C, YANG Y, WANG J, et al. Attention to scale: scaleaware semantic image segmentation. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 3640-3649.
|
18 |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
|
19 |
ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2012, 34 (11): 2274- 2282.
doi: 10.1109/TPAMI.2012.120
|
20 |
EVERINGHAM M, GOOL L V, WILLIAMS C K I, et al. The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 2010, 88 (2): 303- 338.
doi: 10.1007/s11263-009-0275-4
|
21 |
ZHU W, LIANG S, WEI Y, et al. Saliency optimization from robust background detection. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 2814-2821.
|
22 |
JIA Y Q, SHELHAMER E. Caffe: convolutional architecture for fast feature embedding. 2014, arXiv: 1408.5093.
|
23 |
LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2017, 39 (4): 640- 651.
doi: 10.1109/TPAMI.2016.2572683
|
24 |
PERAZZI F, KRÄHENBÜHL P, PRITCH Y, et al. Saliency filters: contrast based filtering for salient region detection. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2012: 733-740.
|
25 |
WEI Y, WEN F, ZHU W, et al. Geodesic saliency using background priors. Proc. of the European Conference on Computer Vision, 2017: 29-42.
|
26 |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
|