arXiv subjects

Di Chang

Publications and source records attributed to Di Chang.

21 records · Page 2Linked to original sources

arXiv2021

CDNet is all you need: Cascade DCN based underwater object detection RCNN

Object detection is a very important basic research direction in the field of computer vision and a basic method for other advanced tasks in the field of computer vision. It has been widely used in practical applications such as object tracking, video behavior recognition and underwater robotics vision. The Cascade-RCNN and Deformable Convolution Network are both classical and excellent object detection algorithms. In this report, we evaluate our Cascade-DCN based method on underwater optical image and acoustics image datasets with different engineering tricks and augumentation.

cs.CV↗

arXiv2018

Optical nanofiber temperature monitoring via double heterodyne detection

Tapered optical fibers (nanofibers) whose diameters are smaller than the optical wavelength are very fragile and can be easily destroyed if excessively heated by energy dissipated from the transmitted light. We present a technique for monitoring the nanofiber temperature using two-stage heterodyne detection. The phase of the heterodyne output signal is determined by that of the transmitted optical field, which, in turn, depends on the temperature through the refractive index. From the phase data, by numerically solving the heat exchange equations, the temperature distribution along the nanofiber is determined. The technique is applied to the controlled heating of the nanofiber by a laser in order to remove rubidium atoms adsorbed on its surface that substantially degrade its transmission. Almost 90% of the nanofiber's original transmission is recovered.

physics.optics↗

arXiv2018

Efficient Learning of Optimal Markov Network Topology with k-Tree Modeling

The seminal work of Chow and Liu (1968) shows that approximation of a finite probabilistic system by Markov trees can achieve the minimum information loss with the topology of a maximum spanning tree. Our current paper generalizes the result to Markov networks of tree width $\leq k$, for every fixed $k\geq 2$. In particular, we prove that approximation of a finite probabilistic system with such Markov networks has the minimum information loss when the network topology is achieved with a maximum spanning $k$-tree. While constructing a maximum spanning $k$-tree is intractable for even $k=2$, we show that polynomial algorithms can be ensured by a sufficient condition accommodated by many meaningful applications. In particular, we prove an efficient algorithm for learning the optimal topology of higher order correlations among random variables that belong to an underlying linear structure.

cs.DS↗