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39results about How to "Have the ability to identify" patented technology

Human face verification method based on bilinear united CNN

The invention discloses a human face verification method based on bilinear united convolutional nerve network. The human face verification method comprises steps of 1) using a human face image which is prepared in advance to perform convolutional nerve network (CNN) training, 2) using the human face image which is in a training set to perform bilinear CNN fine tuning, 3) inputting a human face image to be verified, segmenting the two images, extracting united characteristics outputted by the bilinear CNN, and 4) making an obtained vector go through self-encoding network training to obtain a final verification result. The human face verification method is based on the bilinear CNN, replaces two repeated inputs of an original bilinear nerve network with different human face verification input images, and brings forward a human face verification description factor. The human face description factor has robustness to illumination, shielding and posture change. Furthermore, the characteristic extracted by the bilinear CNN has a smaller dimensionality than the characteristic dimensionality of a common CNN fully connected layer, which reduces number of parameters, makes follow-up deep belief network training simple and improves accuracy of human face verification.
Owner:SYSU CMU SHUNDE INT JOINT RES INST +1

Satellite image ship model recognition method under small sample condition

The invention discloses a satellite image ship model recognition method under a small sample condition, and the method comprises the steps of obtaining a to-be-recognized ship remote sensing image of a target region, and carrying out the preprocessing of the to-be-recognized ship remote sensing image; inputting the preprocessed ship remote sensing image into a trained few-sample learning classifier and C SVM classifiers; and outputting a ship model corresponding to the ship remote sensing image subjected to multi-model fusion through an integration strategy, wherein the few-sample learning classifier and the C SVM classifiers measure distance information between the to-be-identified image and different types of targets in the small sample support set by using metric learning on the basis of a metric learning technology, so that the data feature extraction and expression capability under a small sample condition is improved, and the target identification precision under the small sample condition is improved. And on the other hand, an ensemble learning method is adopted, a classic machine learning technology is combined, an ensemble strategy is designed, ship target model stable recognition under the small sample condition is achieved, and the ship target model recognition capability is achieved.
Owner:NO 15 INST OF CHINA ELECTRONICS TECH GRP

Gesture data set acquisition method for YOLO network, and gesture recognition method and device

The invention discloses a gesture data set acquisition method for a YOLO network, and a gesture recognition method and device. The acquisition method comprises the steps that the frame including the training gesture is acquired, the image acquisition window frame is saved as the training gesture image, the training gesture image background is filtered by using a simple threshold skin color segmentation algorithm based on the YCbCr color space so as to obtain the training gesture image of diverse backgrounds, and the training gesture image of diverse backgrounds is applied to form the gesture data set. The gesture recognition method comprises the steps that YOLO network training and gesture recognition are performed by using the obtained gesture data set. The device comprises a memory and aprocessor. The YOLO network is enabled to have the recognition model for the gesture in the image or the frame, and the recognition process has high noise suppression capacity for the face, the skinlike wall and other complex backgrounds existing in the image. The gesture data set acquisition method for the YOLO network, and the gesture recognition method and device are applied to the technicalfield of image recognition processing.
Owner:深圳市智能机器人研究院

Remote sensing image target extraction system and method based on deep learning

The invention discloses a remote sensing image target extraction system and method based on deep learning, and the system comprises: a backbone network module which is used for carrying out the downsampling of an original image for many times, and obtaining a first low-level feature, a second low-level feature, a third low-level feature and a fourth low-level feature; the discrimination context perception feature extraction module that is used for obtaining a multi-scale context feature difference fusion result according to the fourth low-level feature; the first up-sampling module that is used for obtaining a first advanced feature according to a multi-scale context feature difference fusion result; the first refining decoder module that is used for fusing and up-sampling the third low-level feature and the first high-level feature to obtain a second high-level feature; and the second refining decoder module that is used for fusing and up-sampling the second low-level feature and thesecond high-level feature result to obtain a third high-level feature. The remote sensing image target extraction system and the remote sensing image target extraction method can enhance the identification capability of background and target features, and have good target extraction capability.
Owner:XIDIAN UNIV

Equipment for recognizing composite material quality and interface by electric vortex flow

The invention provides an eddy current recognition composite material and equipment on the interface thereof, belonging to the technical field of metal material recognition. An adjustable signal generation module generates electric signals with frequency being adjustable continuously; furthermore, the signals are respectively transmitted to the eddy current probe of a standard part and the eddy current probe of a part to be measured, and the eddy current probe of the standard part and the eddy current probe of the part to be measured are connected with a probe lifting mechanism; two probes convert the input electric signals into magnetic signals, emits the magnetic signals to the surface of the composite lamellar material and receives the returned signals; a signal processing module respectively receives the signals output by the two probes; a subsequent execution mechanism is controlled by two signals; an optical-coupling switch detects the position of the workpiece; a micro-processing module controls the adjustable signal generation module to send signals to the eddy current probe of the standard part and the eddy current probe of the part to be measured and controls the execution mechanism to start operations. The composite material and the equipment have recognition function for the composite lamellar material and can position for the recognition of the different material interfaces of the composite material; the operation is simple and the application is wide.
Owner:SHANGHAI JIAO TONG UNIV

Automatic charging control system and control method of intelligent household robot

The invention provides an automatic charging control system and control method of an intelligent household robot. The automatic charging control system, through a structural design, by use of a simpler infrared ray orientation technology, enables the intelligent household robot to have a charging orientation identification capability, and at the same time, provides stable charging connection by use of a lower-cost contact type wired charging connection structure, for solving the problems large research and development difficulty, high cost and the like of the automatic charging function design of the intelligent household robot in the prior art and providing favorable system realization technical conditions for automatic charging control of the intelligent household robot. The automatic charging control method can effectively guide the intelligent household robot to approach the position of a charging pedestal, is not misled easily, at the same time, carries out charging by direct use of a mature wired charging technology, is high in charging efficiency and effectively ensures the feasibility and reliability of automatic charging control between the intelligent household robot and the charging pedestal.
Owner:重庆世纪精信实业(集团)有限公司

Equipment for recognizing composite material quality and interface by electric vortex flow

The invention provides an eddy current recognition composite material and equipment on the interface thereof, belonging to the technical field of metal material recognition. An adjustable signal generation module generates electric signals with frequency being adjustable continuously; furthermore, the signals are respectively transmitted to the eddy current probe of a standard part and the eddy current probe of a part to be measured, and the eddy current probe of the standard part and the eddy current probe of the part to be measured are connected with a probe lifting mechanism; two probes convert the input electric signals into magnetic signals, emits the magnetic signals to the surface of the composite lamellar material and receives the returned signals; a signal processing module respectively receives the signals output by the two probes; a subsequent execution mechanism is controlled by two signals; an optical-coupling switch detects the position of the workpiece; a micro-processing module controls the adjustable signal generation module to send signals to the eddy current probe of the standard part and the eddy current probe of the part to be measured and controls the execution mechanism to start operations. The composite material and the equipment have recognition function for the composite lamellar material and can position for the recognition of the different material interfaces of the composite material; the operation is simple and the application is wide.
Owner:SHANGHAI JIAOTONG UNIV

Video monitoring equipment information acquisition control implementation method

The invention relates to a video monitoring equipment information acquisition control implementation method which comprises the following steps: detecting the survivability of an IP address, and judging whether the IP address has survivability or not; otherwise, exiting the step; analyzing protocol flow through a PCAP technology and a deep packet inspection technology, and identifying asset information to obtain dpi_data; sending a simulated SSDP request to the IP address, and extracting a specified key character string for describing equipment parameters by analyzing the xml file to obtain upnp_data; carrying out model data matching through the cgi_info script and the hiki_info script; and obtaining the type of the video monitoring equipment through the obtained information so as to obtain technical parameter information. By adopting the video monitoring equipment information acquisition control implementation method, through the technical scheme of data communication protocol analysis and an active and passive mode, the identification accuracy and the identification comprehensiveness of the technical parameter information are improved. Through a collision algorithm and model fitting, a digital model is established for unknown equipment, and complete dependence of a traditional method on a fingerprint database is eliminated.
Owner:THE THIRD RES INST OF MIN OF PUBLIC SECURITY +1

A lighting system based on Internet of things visual capture and its control method

The invention belongs to the technical field of dynamic lighting, in particular to a vision capture illuminating system based on the internet of things and a control method of the illuminating system. The vision capture illuminating system is formed by connecting multiple illuminating devices. Each illuminating device comprises a frog eye bionic vision capturing unit, an internet of things coordinating unit and an illuminating device control unit, wherein the frog eye bionic vision capturing unit is connected with the internet of things coordinating unit, and the internet of things coordinating unit is connected with the illuminating device control unit. The multiple internet of things coordinating units are connected through a wireless multi-hop networking protocol, so that an intelligent coordination center is formed. The control method comprises the steps that the size, the movement direction, the movement speed and the acceleration of a moving object in a view field of the system are measured by the capturing units and sent to the internet of things coordinating units, the internet of things coordinating units send corresponding instructions to the control units of all the illuminating devices, in this way, it is guaranteed that dynamic illuminating support effect is achieved in the advancing direction of the moving object, and a sufficient view field range and sufficient light brightness of the illuminating system and the response time in emergency are guaranteed.
Owner:杨鸿宇
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