Depth estimation based on light field imaging is a new methodology that has succeeded the traditional binocular stereo matching and depth from monocular images. Significant progress has been made in light-field depth estimation. Nevertheless, the balance between computational time and the accuracy of depth estimation is still worth exploring. The geometry in light field imaging is the basis of depth estimation, and the abundant light-field data provides convenience for applying deep learning algorithms. The Epipolar Plane Image (EPI) generated from the light-field data has a line texture containing geometric information. The slope of the line is proportional to the depth of the corresponding object. Considering the light field depth estimation as a spatial density prediction task, we design a convolutional neural network (ESTNet) to estimate the accurate depth quickly. Inspired by the strong image feature extraction ability of convolutional neural networks, especially for texture images, we propose to generate EPI synthetic images from light field data as the input of ESTNet to improve the effect of feature extraction and depth estimation. The architecture of ESTNet is characterized by three input streams, encoding-decoding structure, and skipconnections. The three input streams receive horizontal EPI synthetic image (EPIh), vertical EPI synthetic image (EPIv), and central view image (CV), respectively. EPIh and EPIv contain rich texture and depth cues, while CV provides pixel position association information. ESTNet consists of two stages: encoding and decoding. The encoding stage includes several convolution modules, and correspondingly, the decoding stage embodies some transposed convolution modules. In addition to the forward propagation of the network ESTNet, some skip-connections are added between the convolution module and the corresponding transposed convolution module to fuse the shallow local and deep semantic features. ESTNet is trained on one part of a synthetic light-field dataset and then tested on another part of the synthetic light-field dataset and real light-field dataset. Ablation experiments show that our ESTNet structure is reasonable. Experiments on the synthetic light-field dataset and real light-field dataset show that our ESTNet can balance the accuracy of depth estimation and computational time.

Depth estimation for light field (LF) images is the cornerstone of many applications of light field cameras, such as 3D reconstruction, defects inspection, face liveness detection, and so forth. In recent years, convolutional neural network (CNN) has dominated the primary workhorse for depth estimation. However, the interpretability of the network and the accuracy of the depth estimation results still need to be improved. This paper uses the conditional random field (CRF) theory to explain and model the LF depth estimation. Further, from the perspective of sequence analysis, we extract the sequence features of epipolar plane image (EPI) patches with recurrent neural network (RNN) and serve as the unary term of the energy function in the CRF. Then, a unified neural network (called as LFRNN) is designed to solve the CRF and get the depth map. Our LFRNN builds upon two-stage architecture, involving a local depth estimation and a depth refinement. In the first part, we design an RNN to analyze the vector sequences in EPI patches and obtain local depth values. There are two thinking behind the design of this part. The first is the general principle that the slope of the straight line in the EPI is inversely proportional to the depth; the second is our unique observation that those straight lines are distributed in vector sequences. In the second part, continuous CRF is used to optimize the output of the first part. We train LFRNN on a synthetic LF dataset and test it on both synthetic and real-world LF datasets. Quantitative and qualitative results validate the superior performance of our LFRNN over the state-of-the-art methods.

The vibrations of chip and tool tip during thermosonic flip chip bonding were studied. By measure the vibration of the chip and tool tip with a Doppler laser vibration measure system, the "suddenly velocity decrease" of chip was elucited. It is noticed that this means the bonding strength formed in the bonding interface. By analyzing the vibration displacement and frequency of the chip and comparing with the vibration displacement of the tool tip, it is noticed that the chip vibrates in a "stick-slip" state after the "suddenly velocity decrease", and the continued ultrasonic vibration may effect the formed bonding area, and suggest about the ultrasonic power supply pattern was obtained