다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The PASCAL Visual Object Classes Challenge

Theory of fuzzy integrals and its applications

Finding paths in difficult regions of C-space, such as narrow passages and configuration obstacle boundaries, is a rather challenging problem for path planning in changing environments. When obstacles move in W-space, these regions in C-space will change their edge points from free to collision or on the contrary, for which a “Capacitor” Bridge Builder (CBB) is proposed in this paper to identify their changing characteristics. Specifically, a “Capacitor” bridge is built between positive and negative toggled points in C-Space, which looks like capacitors stuck between narrow passages or boundary regions. Through CBB, the back boundary of an obstacle, which is less likely to be occupied immediately, is marked as a temporary safe region. Furthermore, a Half Bridge Strategy (HBS) is novelly proposed to boost samples inside these regions. Eventually, highly safe paths are revealed by predicting moving directions of obstacles, then replanning times and total planning times will be decreased significantly. Effectiveness of the proposed method has been verified by experiments with two manipulators in difficult changing environments.

A "capacitor" bridge builder based safe path planner for difficult regions identification in changing environments

RGB-Infrared person re-identification (RGB-IR Re-ID) aims to matching persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in the surveillance system under poor light conditions. Facing great challenges in complex variances including conventional single-modality and additional inter-modality discrepancies, most of the existing RGB-IR Re-ID methods propose to impose constraints in image level, feature level or a hybrid of both. Despite better performance of hybrid constraints, they are usually implemented with heavy network architecture. As a matter of fact, previous efforts contribute more as pioneering works in new cross-modal Re-ID area while leaving large space for improvement. This can be mainly attributed to: (a) lack of abundant person image pairs from different modalities for training, and (b) scarcity of salient modality-invariant features especially on coarse representations for effective matching. To address these issues, a novel Multi-Scale Part-Aware Cascading framework (MSPAC) is formulated by aggregating multi-scale fine-grained features from part to global in a cascading manner, which results in a unified representation containing rich and enhanced semantic features. Furthermore, a marginal exponential center (MeCen) loss is introduced to jointly eliminate mixed variances from intra- and inter-modal examples. Cross-modality correlations can thus be efficiently explored on salient features for distinctive modality-invariant feature learning. Extensive experiments are conducted to demonstrate that the proposed method outperforms all the state-of-the-art by a large margin.

Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification

Lip-reading methods and fusion strategy are crucial for audio-visual speech recognition. In recent years, most approaches involve two separate audio and visual streams with early or late fusion strategies. Such a single-stage fusion method may fail to guarantee the integrity and representativeness of fusion information simultaneously. This paper extends a traditional single-stage fusion network to a two-step feature fusion network by adding an audio-visual early feature fusion (AV-EFF) stream to the baseline model. This method can learn the fusion information of different stages, preserving the original features as much as possible and ensuring the independence of different features. Besides, to capture long-range dependencies of video information, a non-local block is added to the feature extraction part of the visual stream (NL-Visual) to obtain the long-term spatio-temporal features. Experimental results on the two largest public datasets in English (LRW) and Mandarin (LRW-1000) demonstrate our method is superior to other state-of-the-art methods.

Audio-Visual Speech Recognition Using A Two-Step Feature Fusion Strategy

Defects detection is an extremely important part in the production of printed circuit board (PCB) to guarantee the quality and reliability. A widely used and researched method for this task is referential comparison method. However, this method gets poor performance on the situation that PCB moves on the conveyor, due to the lack of ability to register test images with referential images. Therefore, accurate image registration which estimates the affine transformation between the captured test images and the referential images becomes an urgent problem to be solved. In this study, a spatial transformer network is proposed. In particular, a recurrent image registration strategy is introduced to step-by-step register the images in a recurrent progress. Furthermore, a referential part is designed to help training the network. Besides, in order to simulate the real moving PCB images and train the proposed model, a factitious dataset is generated by applying random affine transformations to real PCB images. Experiments show that recurrent spatial transformer network can achieve pixel-level accurate image registration. The defects detection precision of referential comparison method has a great improvement by using the registration algorithm.

Recurrent spatial transformer network for high-accuracy image registration in moving PCB defect detection

The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial-temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.

https://www.mdpi.com/1424-8220/21/8/2673/pdf

Hong Liu

Papers

A "capacitor" bridge builder based safe path planner for difficult regions identification in changing environments

Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification

Audio-Visual Speech Recognition Using A Two-Step Feature Fusion Strategy

Recurrent spatial transformer network for high-accuracy image registration in moving PCB defect detection

Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints