Patents
Literature
Patsnap Copilot is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Patsnap Copilot

148 results about "Visual space" patented technology

Visual space is the experience of space by an aware observer. It is the subjective counterpart of the space of physical objects. There is a long history in philosophy, and later psychology of writings describing visual space, and its relationship to the space of physical objects. A partial list would include René Descartes, Immanuel Kant, Hermann von Helmholtz, William James, to name just a few.

System and method for clustering gene expression data based on manifold learning

InactiveCN102184349AAccurately discover co-regulatory relationshipsDiscovery of co-regulatory relationshipsSpecial data processing applicationsVisual spaceCluster algorithm
The invention discloses a method for clustering gene expression data based on manifold learning, and the method provided by the invention comprises the following steps: acquiring a gene expression data matrix A through an acquisition system, and preprocessing the gene expression data matrix A by using a local linear smoothing algorithm; introducing the preprocessed data matrix A, and constructing a weighted neighborhood figure G in a three-dimensional space; taking the shortest path between two points as the approximate geodesic distance between two points; calculating a two-dimensional embedded coordinate by using an MDS (minimum discernible signal), and mapping the three-dimensional data matrix A to a two-dimensional visual space; and carrying out clustering on the two-dimensional visual space subjected to mapping by using a k-mean clustering algorithm so as to obtain the clustering result. The clustering method has the characteristics of low calculating cost, capability of eliminating high-order redundancies, suitability for pattern classification tasks, and the like; and by using the method disclosed by the invention, the current states of cells, the effectiveness of medicaments to malignant cells, and the like can be discriminated effectively according to the clustering result. The invention also provides a system for clustering gene expression data based on manifold learning.
Owner:HOHAI UNIV

Neutral-point voltage balance and common-mode voltage suppression method for three-level inverter

The invention relates to a neutral-point voltage balance and common-mode voltage suppression method for a three-level inverter, and mainly provides a novel visual space vector modulation (VSVM) method. The method comprises the following steps: defining a new visual zero vector to just include zero vector 000; defining a novel visual small vector to be composed of an original negative small vector and two negative small vectors adjacent to the original negative small vector; and defining a new visual medium vector to be composed of an original medium vector and two medium vectors adjacent to the original medium vector. By using the space vector definition method, each composite vector cannot cause neutral-point voltage to be fluctuated, and basic vectors forming the novel visual space vector do not include positive small vectors and zero vector PPP and NNN, so that the output common-mode voltage is relatively small. When external nonlinear factors enable the neutral-point voltage to be fluctuated, on the basis of a new visual space vector modulation strategy, the action time distribution factors for each composite vector are appropriately adjusted by comparing the size of three-phase load current, so that neutral-point voltage balance is controlled.
Owner:丹阳博亚新材料技术服务有限公司

Auxiliary positioning method, positioning device and system for joint replacement

The embodiment of the invention provides auxiliary positioning method, positioning device and system for joint replacement. The method comprises the following steps: acquiring the mapping relation between a three-dimensional model coordinate system of a joint and a visual space coordinate system of the positioning device; acquiring the pose of the joint site in the visual space coordinate system,and acquiring the pose of the tail end of a surgical instrument in the visual space coordinate system; based on the mapping relation, converting the poses of the tail end of the surgical instrument and the joint site in the visual space coordinate system into the three-dimensional model coordinate system; and displaying the joint site and the tail end of the surgical instrument in a virtual imageof the three-dimensional model coordinate system, thereby performing surgery auxiliary positioning based on the display result of the virtual image. Because a doctor can visually observe the relativeposition relation between the joint site and the tail end of the surgical instrument during surgical operation, the osteotomy accuracy is high, operating risk is decreased, and further the accuracy and matching degree of the mounting position of a prosthesis are improved.
Owner:BEIJING YAKEBOT TECH CO LTD

Method for identifying monocular visual spaces in terrestrial gravitational field environments

The invention discloses a method for identifying monocular visual spaces in terrestrial gravitational field environments. The method is characterized by comprising steps of firstly, dividing ultra-pixels of images on the basis of CIELAB color space values L, a and b of pixels and coordinate values x and y of the pixels to generate ultra-pixel images; secondly, reducing dimensions of the divided and formed ultra-pixel images by a general clustering algorithm on the basis of vector distances from color characteristics to feature characteristics of the ultra-pixels and adjacency relations, and generating large image blocks; thirdly, respectively multiplying pixels of the obtained large image blocks by fuzzy distribution density functions of gravitational fields and solving expected values of the large image blocks so as to initially classify the sky, the ground and vertical objects; fourthly, extracting classified images of the sky, the ground and the vertical objects by the aid of single-layer wavelet sampling and characteristics of the Manhattan direction; fifthly, generating spatial depth perception images on the basis of wavelet imaging models and ground linear perspective information. The fuzzy distribution density functions of the gravitational fields represent the sky, the ground and the vertical objects. The method has the advantages of simplicity, feasibility, high resolution and wide application range.
Owner:NANJING YUANJUE INFORMATION & TECH CO NANJING

Object loading method and device, storage medium and electronic device

ActiveCN109523621ASolve technical problems that occupy more hardware resourcesReduce occupancyImage memory managementVideo gamesVisual spaceComputer terminal
The invention discloses an object loading method and device, a storage medium and an electronic device. The method comprises the steps of determining a visual space located in an acquisition range ofan image acquisition device in a virtual scene provided by a target application, the image acquisition device being an acquisition device currently located at a first position in the virtual scene; Determining a target subspace in the visual space within a visual distance threshold indicated by a target type in a plurality of types based on the first position, wherein each type in the plurality oftypes is used for indicating one visual distance threshold of an object in the subspace of the virtual scene; Obtaining an object of which the visual distance is not greater than a visual distance threshold indicated by the target type in the target subspace as a to-be-rendered object; And loading the to-be-rendered object in the storage resource of the user terminal installed with the target application, wherein the user terminal is used for rendering the image of the virtual scene. According to the method and the device, the technical problem that more hardware resources are occupied for rendering the object in the virtual scene in the related art is solved.
Owner:TENCENT TECH (SHENZHEN) CO LTD

Head surgery navigation method based on three-dimensional scanning

InactiveCN109498156AHigh precisionConvenient preoperative CT scanSurgical navigation systemsVisual spaceVisual perception
The invention relates to the field of medicine, in particular to a head surgery navigation method based on three-dimensional scanning. The method includes the following steps: (1) performing preoperative CT scanning on a patient's head; (2) mounting calibration reference frames, reference reference frames and stereo vision equipment; (3) acquiring three-dimensional models of the patient's head, the reference reference frames and all correction reference frames by adopting the three-dimensional scanning equipment, and fitting characteristic points; (4) acquiring spatial coordinates of the reference reference frames and all correction reference frames by adopting stereo vision; (5) correcting the visual space error through an error correction model; (6) performing point cloud registration ona CT model of the patient's head and a head model constructed by the three-dimensional scanning equipment; (7) acquiring coordinate systems of the target reference frames through stereo vision in real time, and displaying the position of the equipment in CT space in real time in navigation software. The method can avoid navigation deviation caused by mark deviation before operation and during operation, so that the accuracy of operation navigation is improved.
Owner:北京大华旺达科技有限公司

Zero sample image classification method and system based on a convolutional neural network and a factor space

The invention provides a zero sample image classification method and system based on a convolutional neural network and a factor space, and the method comprises the steps: building a unified zero-sample classification neural network: firstly, extracting image features in a data set through a classical convolutional neural network, and enabling the image features to serve as the input of the neuralnetwork; the dimensionality of known factors is reduced by using a factor pressure reduction technology, and the known factors and potential factors are embedded into a network to serve as an intermediate layer to jointly determine a final classification result; the network enables image input to final category output. And training a zero sample classification network, and iteratively determiningnetwork model parameters. And identifying the image by using the zero sample classification neural network to finish classification of the zero sample image. According to the method, a convolutionalneural network model is used for uniformly processing the relationship among the visual space, the factor space and the category space, the problem that the generalization ability of specific linear or nonlinear function expression is not high is solved, and the factors serving as auxiliary knowledge are embedded into the network and are easy to understand, train and use.
Owner:CHINA ACAD OF LAUNCH VEHICLE TECH

Zero sample classification method based on dual-triple deep metric learning network

The invention relates to a zero sample classification method based on a double-triple deep metric learning network, which comprises the following steps of: inputting semantic features of samples intoa mapping network, and outputting the semantic features to a visual space; in a visual space, selecting a pair of semantic features and visual features belonging to the same category to form a positive sample pair, then selecting a semantic feature different from the positive sample pair to form a triple, and inputting the triple into a semantic-guided triple network; selecting a pair of semanticfeatures and visual features belonging to the same category are selected to form a positive sample pair, then a visual feature different from the positive sample pair in category to form a triple, andinputting the triple into a visual guidance triple network; inputting the output of the semantic-guided triple network and the output of the visual-guided triple network into a double-triple loss function for calculation; and finally, classifying the test samples by using a nearest neighbor classifier. The structure is easy to realize, the training method is simpler, the training parameters are fewer, and training can still be carried out under the condition that computer hardware equipment is poorer.
Owner:TIANJIN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products