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646 results about "Spatial transformation" patented technology

Spatial Transformation. Abstract. A spatial transformation of an image is an alteration that changes the image’s orientation or ‘layout’ in the spatial domain. Spatial transformations change the position of intensity information but, at least ideally, do not change the actual information content.

Automatic mask design and registration and feature detection for computer-aided skin analysis

ActiveUS20090196475A1Avoiding skin regions not useful or amenableCharacter and pattern recognitionDiagnostic recording/measuringDiagnostic Radiology ModalityNose
Methods and systems for automatically generating a mask delineating a region of interest (ROI) within an image containing skin are disclosed. The image may be of an anatomical area containing skin, such as the face, neck, chest, shoulders, arms or hands, among others, or may be of portions of such areas, such as the cheek, forehead, or nose, among others. The mask that is generated is based on the locations of anatomical features or landmarks in the image, such as the eyes, nose, eyebrows and lips, which can vary from subject to subject and image to image. As such, masks can be adapted to individual subjects and to different images of the same subjects, while delineating anatomically standardized ROIs, thereby facilitating standardized, reproducible skin analysis over multiple subjects and/or over multiple images of each subject. Moreover, the masks can be limited to skin regions that include uniformly illuminated portions of skin while excluding skin regions in shadow or hot-spot areas that would otherwise provide erroneous feature analysis results. Methods and systems are also disclosed for automatically registering a skin mask delineating a skin ROI in a first image captured in one imaging modality (e.g., standard white light, UV light, polarized light, multi-spectral absorption or fluorescence imaging, etc.) onto a second image of the ROI captured in the same or another imaging modality. Such registration can be done using linear as well as non-linear spatial transformation techniques.
Owner:CANFIELD SCI

Methods for visualizing transformations among related series of graphs

A method for displaying in a coherent manner the changes over time of a web site's structure, usage, and content is disclosed. Time tubes are generated by a method of displaying a related series of graphs. Time tubes illustrate changes in a graph that undergoes one or more transformations from one state to another. The transformations are displayed using the length of the cylindrical tube, filling the length of the time tube with planar slices which represent the data at various stages of the transformations. Time tubes may encode several dimensions of the transformations simultaneously by altering the representation of size, color, and layout among the planar slices. Temporal transformations occur when web pages are added or deleted over time. Value-based transformations include node colors, which may be used to encode a specific page's usage parameter. Spatial transformations include the scaling of physical dimension as graphs expand or contract in size. The states of a graph at various times are represented as a series of related graphs. In a preferred embodiment, an inventory of all existing nodes is performed so as to generate a list of all nodes that have existed at any time. This inventory is used to produce a layout template in which each unique node is assigned a unique layout position. To produce each planar slice, the specific nodes which exist in the slice are placed at their respective positions assigned in the layout template. In another aspect, corresponding nodes in planar slices are linked, such as with translucent streamlines, in response to a user selecting a node in a planar slice by placing his cursor over the selected node, or to show clustering of two or more nodes in one planar slice into a single node in an adjacent planar slice.
Owner:GOOGLE LLC

Method for coding two-directional predictive video object planes and decoding device

Temporal and spatial scaling of video images including video object planes (VOPs) (117, 118, 119, 405, 415, 420, 430, 520, 522, 524, 526, 532, 542, 705, 730, 750, 760, 780, 790, 805, 815, 820, 830, 850, 860, 880, 890) in an input digital video sequence is provided. Coding efficiency is improved by adaptively compressing scaled field mode video. Upsampled VOPs (450, 490, 522, 542, 750, 790) in the enhancement layer are reordered to provide a greater correlation with the input video sequence based on a linear criteria. The resulting residue is coded using a spatial transformation such as the DCT. A motion compensation scheme is used for coding enhancement layer VOPs (450, 460, 480, 490, 522, 524, 526, 542, 750, 760, 780, 790, 850, 860, 880, 890) by scaling motion vectors which have already been determined for the base layer VOPs (405, 415, 420, 430, 520, 532, 705, 730, 805, 815, 820, 830). A reduced search area whose center is defined by the scaled motion vectors is provided. The motion compensation scheme is suitable for use with scaled frame mode or field mode video. Various processor configurations achieve particular scaleable coding results. Applications of scaleable coding include stereoscopic video, picture-in-picture, preview access channels, and ATM communications.
Owner:GOOGLE TECH HLDG LLC

A rolling bearing fault diagnosis method under variable working conditions based on deep features and transfer learning

ActiveCN109902393AMitigate the effects of differences in the distribution of different vibration characteristicsSolve the problem of difficult multi-state deep feature extractionMachine bearings testingSpecial data processing applicationsLearning basedFeature extraction
The invention discloses a deep feature and transfer learning-based rolling bearing fault diagnosis method under variable working conditions, relates to the technical field of fault diagnosis, and aimsto solve the problem of low state identification accuracy of different fault positions and different performance degradation degrees of a rolling bearing under the variable working conditions. The method comprises the following steps: firstly, carrying out feature extraction on the vibration signal frequency domain amplitude of the rolling bearing by adopting SDAE to obtain vibration signal deepfeatures, and forming a source domain feature sample set and a target domain feature sample set; then, adopting the JGSA to carry out domain adaptation processing on the source domain feature sample and the target domain feature sample, the purpose of reducing distribution offset and subspace transformation difference of feature samples between domains is achieved, and domain offset between different types of feature samples is reduced. And finally, completing rolling bearing multi-state classification under variable working conditions through a K nearest neighbor algorithm. Compared with other methods, the method disclosed by the invention shows better feature extraction capability under the variable working condition of the rolling bearing, the sample feature visualization effect of therolling bearing is optimal, and the fault diagnosis accuracy of the rolling bearing under the variable working condition is high.
Owner:HARBIN UNIV OF SCI & TECH

Deep learning-based question classification model training method and apparatus, and question classification method and apparatus

The invention discloses a deep learning-based question classification model training method and apparatus, and a question classification method and apparatus. The question classification model training method comprises the steps of extracting feature information samples in question text samples, and generating corresponding first eigenvector samples; performing spatial transformation on the first eigenvector samples to obtain second eigenvector samples; inputting the second eigenvector samples to a plurality of convolutional layers and a plurality of pooling layers in a multilayer convolutional neural network, and by superposing convolution operation and pooling operation, obtaining first fusion eigenvector samples; inputting the first fusion eigenvector samples to a full connection layer in the multilayer convolutional neural network to obtain global eigenvector samples; and training a Softmax classifier according to the global eigenvector samples to obtain a question classification model. The method can avoid a large amount of overheads of manual design of features; and through the question classification model, a more accurate classification result can be obtained, so that locating of standard question and answer is improved.
Owner:BEIJING UNIV OF POSTS & TELECOMM

Deep learning method for ship detection in high-resolution visible remote sensing images

The invention provides a deep learning method for ship detection in high-resolution visible remote sensing images, which comprises the following steps: firstly, reading and preprocessing image data; secondly, extracting the features of the whole image; thirdly, screening out target candidate regions after extracting the abstract features of the image in the convolution layer; fourthly, cutting outthe feature blocks of each target candidate region on the feature map corresponding to the whole image, and using the pooling layer in the region of interest to normalize the sizes of the feature blocks; fifthly, sending the features to the full connection layer to get spatial transformation parameters, and sending the spatial transformation parameters and the features to the spatial transformation layer to get the features after deformation correction; and sixthly, carrying out classification and position correction again on the target candidate regions according to the corrected features. The robustness of the detection method to target rotation and other deformation is enhanced, and the detection effect of ship targets in high-resolution visible remote sensing images is improved. The method can be applied to the detection of ship targets in high-resolution visible remote sensing images, and has broad application prospects and values.
Owner:BEIHANG UNIV +1
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