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

Most binaural speech source localization models perform poorly in unprecedentedly noisy and reverberant situations. Here, this issue is approached by modelling a multiscale dilated convolutional neural network (CNN). The time-related crosscorrelation function (CCF) and energy-related interaural level differences (ILD) are preprocessed in separate branches of dilated convolutional network. The multiscale dilated CNN can encode discriminative representations for CCF and ILD, respectively. After encoding, the individual interaural representations are fused to map source direction. Furthermore, in order to improve the parameter adaptation, a novel semiadaptive entropy is proposed to train the network under directional constraints. Experimental results show the proposed method can adaptively locate speech sources in simulated noisy and reverberant environments.

/pdf/an-adaptive-method-based-on-multiscale-dilated-convolutional-10ud95e39s.pdf

An Adaptive Method Based on Multiscale Dilated Convolutional Network for Binaural Speech Source Localization

http://www.yongxu.org/paper/Noise_free_representation_based_classification_and_face_recognitionex_periments.pdf

Noise-free representation based classification and face recognition experiments

Humanoid robots have abilities of stepping over or onto obstacles, which is different from wheeled robots. However it may be difficult to apply the ordinary motion planning methods such as Rapidly-exploring Random Trees (RRT) to humanoid robots directly. Because these kinds of methods only consider to circumvent obstacles and ignore the constraint of balance. Aiming at dealing with these problems in one frame, a novel approach based on hierarchical RRT is used to plan the footstep for humanoid robots. It is designed according to three basic constraints: a transition model based gait generator, an inverted pendulum based balance controller and a collision detection based path planner. First, a set of layered transition model is utilized to revise the footstep according to the terrain condition, which is able to take full use of the motion ability as well as improve the efficiency. Then, a hierarchical strategy is exploited to select the feasible foot location to be added in the random tree based on the results of collision checking and balance control. Finally, a dynamic RRT method is introduced in our work to revise paths in changing environments. Different experiments are given to verify the feasibility and performance of the proposed approach in complicated environments with both dynamic and static obstacles.

Hierarchical RRT for humanoid robot footstep planning with multiple constraints in complex environments

This work proposes a method of determining human head poses from a sequence of images. The main idea is to use some features in a 3D head model to generate a virtual fronto-parallel projection that satisfies conditions of affine approximation. Then the affine parameters between the virtual projection and input view are calculated. After that, rotation and translation parameters of the head are roughly estimated by a circle-ellipse correspondence technique based on the affine parameters. Finally, an iterative optimization algorithm is utilized further to refine the results. The accuracy is maintained by estimating reliability of the 2D-33D feature correspondences an weighting each factor of the optimization objective function. The system performance is also improved by applying a modified KLT technique to speed up the convergence during the face feature tracking process. Experimental results show that our method can accurately recover head poses in a wide range of head motion.

Affine correspondence based head pose estimation for a sequence of images by using a 3D model

In many eastern countries, a large number of seal images need to be identified every day. The system compares an input seal image with its reference seal and validates the authenticity of it. However, the reference seal is usually found manually. As each reference seal has quite a long corresponding ID, inputting ID to get the needed seal one by one costs quite a lot of time and this manual stage has become the bottleneck of automatic seal identification. To make the seal verification system more automatic, a new retrieval method based on Chinese characters' shape features will be introduced in this paper. Firstly, the main characters region is obtained by transforming the round seal image to a rectangle one and choosing the main part for each seal. Secondly, every single character is segmented using correlation method, and the number of characters in a seal can be got. Thirdly, four horizontal and four vertical features are extracted for each character in a seal and an eigenvector called position code is defined. Finally, the retrieval strategy is mainly based on transform from position code to a weight to decide which two seal images have the most similarity. Experiments on a database of 1000 testing seal images provide the retrieval ability for the proposed approach. The correct rate goes to 95.3%.

Hong Liu

Papers

An Adaptive Method Based on Multiscale Dilated Convolutional Network for Binaural Speech Source Localization

Noise-free representation based classification and face recognition experiments

Hierarchical RRT for humanoid robot footstep planning with multiple constraints in complex environments

Affine correspondence based head pose estimation for a sequence of images by using a 3D model

Automatic seal image retrieval method by using shape features of Chinese characters