Hand-Crafted Features or Machine Learnt Features? Together They Improve RGB-D Object Recognition

By Lu Jin, Shenghua Gao, Zechao Li, Jinhui Tang. 2014

47 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Yingli Tian

In Computer Vision and Pattern Recognition

1. Chenyang Zhang
2. Zhi Liu
3. Yingli Tian
4. Wanqing Li
5. Pichao Wang
6. Hossein Rahmani
7. Philip O. Ogunbona
8. Zhimin Gao

Citations in the corpus

• RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, neutral): “%) can reach 90.3 (RGB), 85.9 (Depth) and 92.3 (RGB-D) in [43].”
• 3D-based Deep Convolutional Neural Network for action recognition with depth sequences (2016, background): “[41] did very interesting work to combine hand-crafted features and machine-learned features for RGB-D object recognition.”