Samsung Electronics Co., Ltd.

China Academy of Railway Sciences Corporation Limited

Beijing Huatie Information Technology Corporation Ltd.

Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Ltd.

Beijing Ruichi Guotie Its Eng. & Tech. Ltd.

Jiali Zhao

Seokcheol Kee

Haibing Ren

Dejun Wang

Fei Wang

Haitao Wang

Hui Zhang

Gang Li

Lin Wang

Wonjun Hwang

Yong Yang

Gang Zhao

Hua Zhang

Yuan Ma

Seok-cheol Kee

Ran Zhao

Qiang Gao

Hao Chang

Shi Yan

Zhenzhen Liu

Xianfeng Luan

Yazhou Kou

Zibiao Fu

Deji Fu

Yingying Cui

Bingyue Lin

Qichang Li

Chaoping Zhu

Zhiduo Xie

Qizheng Hu

The invention discloses a blockchain-based intrusion detection system for railway signal, which is built on a blockchain's distributed chain structure, without a central, trusted control center. This design mitigates the vulnerabilities associated with centralized intrusion detection centers. Additionally, by utilizing a blockchain-based distributed structure, it also eliminates the risk of a single point of failure for the intrusion detection center. Moreover, the data within the blockchain-based intrusion detection system is highly resistant to malicious tampering. This is achieved by leveraging the consensus mechanism inherent in the blockchain, which can achieve consensus among intrusion detection nodes. The current invention solves the internal evil attacks and avoid the inability to reach consensus between nodes due to internal evil, thereby affecting the intrusion detection performance; at the same time, the intrusion detection model can resist external network attacks.

Blockchain-based intrusion detection system for railway signals

Provided are an object verification apparatus and method. The object verification apparatus includes a matching unit performing a plurality of different matching algorithms on a query image and generating a plurality of scores; a score normalization unit normalizing each of the generated scores to be adaptive to the query image; a weight estimation unit estimating weights of the normalized scores based on the respective matching algorithms applied; and a score fusion unit fusing the normalized scores by respectively applying the weights estimated by the weight estimation unit to the normalized scores.

Object verification apparatus and method

An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. A set of reference images is also divided into sub-regions and subjected to the SadaGamma correction or SHE. A comparison is made between the each corrected sub-region and each corresponding sub-region of the reference images. Based upon the comparisons made individually for the sub-regions of the facial image, one of the stored reference images having the greatest correspondence is chosen. While usable individually, using the MIFE and/or SadaGamma correction or SHE together achieves a lower error ratio in face recognition under different expressions, illuminations and occlusions.

Apparatus for and method of feature extraction for image recognition

There is provided a method of estimating disparity for 3D object recognition. The method includes obtaining a plurality of images having different resolutions for the stereo image, estimating a disparity map for a lowest-resolution level image, estimating a coarse disparity map for an upper resolution level image by using the disparity, obtaining a fine disparity map for the upper resolution level image by using the coarse disparity, and outputting the fine disparity map as a final disparity map for the stereo image if the upper resolution level image has a resolution of a highest level.

Method of estimating disparity for 3D object recognition

A face detection method and apparatus using a level-set method. The method includes: laying a first initial shape model over a face image; evolving shape curves of the first initial shape model using the level-set method; detecting first through M-th neighboring boundary points that neighbor first through M-th initial boundary points of the first initial shape model, and that neighbor the evolved shape curves, M being a positive integer larger than 1; laying a modified shape model rendered using the detected first through M-th neighboring boundary points over the face image; detecting a rotation angle of the modified shape model; determining whether the detected rotation angle of the modified shape model is within a predetermined angle range; and selecting one of a second initial shape model, which is rotated to the left, or a third initial shape model, which is rotated to the right, according to the detected rotation angle, and laying the selected initial shape model over the face image, and returning to the detecting of the first through M-th neighboring boundary points, when the detected rotation angle of the modified shape model is outside the predetermined angle range.

Face detection method and apparatus using level-set method

A image verification method, medium, and apparatus using a local binary pattern (LBP) discriminant technique. The verification method includes generating a kernel fisher discriminant analysis (KFDA) basis vector by using the LBP feature of an input image, obtaining a Chi square inner product by using the LBP feature of an image registered in advance and a kernel LBP feature and projecting to a KFDA basis vector, obtaining a Chi square inner product by using the LBP feature of a query image and a kernel LBP feature and projecting to a KFDA basis vector, and obtaining the similarity degree of the target image and the query image that are obtained as Chi square inner product results, and projected to the KFDA basis vector. According to the method, medium, and apparatus, the KFDA based LBP shows superior performance over conventional LBP, KFDA, and biometric experimentation environment (BEE) baseline algorithms.

Image verification method, medium, and apparatus using a kernel based discriminant analysis with a local binary pattern (LBP)

A global localization apparatus, medium, and method, with the global localization method including selecting one from a plurality of samples and shifting the selected sample according to a movement of a robot, generating a new sample within a predetermined range of the shifted sample, determining either the shifted sample or the new sample as a next sample at a next time step according to a predetermined condition for the shifted sample and the next sample, repeating for all the samples, and estimating a next position of the robot according to positions of the next samples when the number of next samples is equal to or larger than the maximum number of samples.

Method, medium, and apparatus for global localization

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Beijing, China

Jiali Zhao