827 results about "Heat map" patented technology

Methods of generating heat maps of assets using clustering of assets are disclosed. Some methods include receiving a list of assets, assigning the assets to one or more heat range categories based on the status of the assets, assigning assets operating within a zone to a zone cluster, assigning the assets of the zone cluster to category clusters based on the heat range categories assigned to the assets. The positions of the clusters may be calculated for mapping, and may be displayed on a map. Some embodiments of these methods allow a user to quickly detect and locate non-standard assets on a map while standard assets are consolidated to clusters that are less prominent to the user. This leads to minimizing the time required to form responses to de-load hotspots in an electrical grid, minimizing the cost of assets by reducing the need for hardware redundancy, and minimized equipment outages.

Systems, methods and apparatus may be configured to implement automatic semantic classification of a detected object(s) disposed in a region of an environment external to an autonomous vehicle. The automatic semantic classification may include analyzing over a time period, patterns in a predicted behavior of the detected object(s) to infer a semantic classification of the detected object(s). Analysis may include processing of sensor data from the autonomous vehicle to generate heat maps indicative of a location of the detected object(s) in the region during the time period. Probabilistic statistical analysis may be applied to the sensor data to determine a confidence level in the inferred semantic classification. The inferred semantic classification may be applied to the detected object(s) when the confidence level exceeds a predetermined threshold value (e.g., greater than 50%).

The present invention extends to methods, systems, and computer program products for integrating external and cluster heat map data. Embodiments of the invention include a proxy service that manages (e.g., asynchronous) communication with cloud nodes. The proxy service simulates packets to on-premise services to simplify the integration with an existing heat map infrastructure. The proxy maintains a cache of performance counter mappings and timestamps on the on-premise head node to minimize the impact of latency into heat map infrastructure. In addition, data transfer is minimized by mapping a fixed set of resource based performance counters into a variable set of performance counters compatible with the on premise heat map infrastructure.

Methods and apparatuses are presented for generating and updating heat maps for terrestrial wireless transceivers located in an area or venue. According to some embodiments, a method may include obtaining, by a mobile device, first signal data for a terrestrial transceiver and generating, by the mobile device, a transceiver heat map using the first signal data. The method may further include displaying, by the mobile device, a request to gather additional signal data for the terrestrial transceiver and obtaining, by the mobile device, second signal data for the terrestrial transceiver. The method may further include generating, by the mobile device, an updated transceiver heat map using the second signal data and displaying, by the mobile device, the updated transceiver heat map.

A method of displaying the probabilities associated with a Markov model as a series of interactive plots is disclosed. Three dimensions of probability data associated with a Markov model are presented to a user in a concise manner. Heat maps are used to display probability data from a Markov model so that the user can identify and understand hidden patterns and information in the data such as state and emission transitions which are not possible to see with conventional state diagram plots.

A method and system for displaying predictions on a spatial map includes using a data analyzer for analyzing heterogeneous data having a spatial component to find utilizable data and using machine learning to automatically extract relationships from the utilizable data. The extracted relationships are used to make a prediction about at least one location on the spatial map and present that prediction in an oblique or perspective view. An interface presents the prediction on the spatial map in the form of a heat map overlying a 3-D topographical map. Although the 3-D map can be shown as any form of graphical projection including an oblique projection or orthographic projection, preferably a perspective view is used. It is also preferred that the graphical projection be interactive. The heat map may be 2-D or 3-D and be selectively displayed depending on the preference of a user.

The invention enables multimodal cognitive communications, collaboration, consultation and instruction between and among heterogeneous networked teams of persons, machines, devices, neural networks, robots and algorithms during various stages of medical disease management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting. The invention enables both synchronous and asynchronous multiparty collaboration with multichannel, multiplexed streaming imagery data, including interactive curation, multisensory annotation and metadata tagging, as well as multi-formatted encapsulation, saving and sharing of collaborated imagery data as packetized augmented intelligence. The invention acquires both live stream and archived medical modality imagery from network-connected medical devices, cameras, signals and sensors, as well as multiomic data [phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic and radiogenomic] maps and clinical data sets from structured reports and clinical documents, including biometric maps and movies, hapmaps, heat maps and data stream visualizations. The invention also acquires both medical and non-medical streaming imagery data from image data repositories, documents and structured reports, workstations and mobile devices, as well as from wearable computing, signals and sensors. The invention enables networked teams to interactively communicate, concurrently collaborate and bi-directionally exchange multichannel multiplexed imagery data streams, singly or together, in real time or asynchronously, generally by curating, annotating and tagging imagery information objects. The invention encapsulates and saves collaborated imagery data, together with multisensory annotations and metadata tags, in standard file formats as packetized augmented intelligence. The invention enables recursive cognitive enrichment of clinical cognitive vismemes, and saves packetized imagery information objects, multisensory annotations and metadata tags in native file formats [PDF, MPEG, JPEG, XML, XMPP, QR,TIFF, RDF, RDF / XML, SVG and DAE] as well as in formats compliant with standards for digital communications in medicine [DICOM]. The invention enables live stream multicasting of multimodal cognitive instruction and collaborative knowledge exchange with multisensory [visual, auditory, haptic] annotation of streaming imagery data, as well as secure, encrypted transmission of streaming augmented intelligence across file sharing data networks for informatics-enabled learning, specialist skills acquisition and accelerated knowledge exchange.

A visualization system comprising a persistent memory, storing a dataset, and a non-persistent memory implements a pattern visualizing method. The dataset contains discrete attribute values for each first entity in a plurality of first entities for each second entity in a plurality of second entities. The dataset is compressed by blocked compression and represents discrete attribute values in both compressed sparse row and column formats. The discrete attribute values are clustered to assign each second entity to a cluster in a plurality of clusters. Differences in the discrete attribute values for the first entity across the second entities of a given cluster relative to the discrete attribute value for the same first entity across the other clusters are computed thereby deriving differential values. A heat map of these differential values for each first entity for each cluster is displayed to reveal the pattern in the dataset.

This invention relates to a real-time calibration method and a device for infrared temperature test, which sets an infrared camera head used in collecting the infrared radiation signals of a tested object, sets a constant temperature source same to the infrared radiation rate of the tested object, in which, the source is on the same data collection surface with the tested object, the infrared radiation signals of the object and the constant temperature source are input into the computer to be processed to form a heat picture, at the same time, the real temperature value of the source is collected to get the temperature deviation value to calibrate the heat picture temperature data value of the tested object.

Embodiments provide methods and apparatus for effectively using signal strength and other data from a robot to optimize robot operation. In one embodiment, the cleaning robot can interact with other home controllers over a network to optimize the operation of the cleaning robot. In one embodiment, the cleaning robot measures a variety of data as it travels through a space, and generates a map (e.g., a heat map). The data is provided in different layers for easy display and selection by a user. In one embodiment, the cleaning robot can act as a hub for communicating with other home controllers and coordinating actions.

The invention discloses an image semantic segmentation method of guiding feature fusion based on attention mechanism. The image semantic segmentation method comprises the following steps: (10) constructing an encoder basic network: using an improved ResNet-101 to generate a series of features changing from high resolution low semantics to low resolution high semantics; (20) constructing a decoderfeature fusion module: extracting high-level semantics with strong consistency constraint by adopting a pyramid structure module based on three-layer convolution operation, and performing weighted fusion on low-level stage features layer by layer to obtain a preliminary segmentation heat map; and (30) constructing an auxiliary loss function: adding auxiliary supervision to each fusion output in the decoding stage, superposing the auxiliary supervision with main supervision loss after the up-sampling of the heat map, and strengthening the hierarchical training of the model to obtain a semanticsegmentation map. The image semantic segmentation method of guiding feature fusion based on attention mechanism is high in accuracy and clear in boundary contour.

The invention discloses a human posture estimation method based on deformation convolution, which solves the technical problem of estimating human posture from images. The method comprises the following steps: obtaining a training image; making a joint heat map; constructing a deformation convolution forward propagation module; constructing residual blocks and constructing multi-scale hourglass networks with deformed residual blocks; training and stacking of multi-scale hourglass networks with deformed residual block network structures; acquring that result of the human posture estimation. Theinvention uses deformation convolution and improves the internal connection mode of the hourglass network, a stacked multi-scale hourglass network with deformed residual blocks is constructed, For asingle natural color image, the human pose can be estimated more accurately by extracting and organizing the image features in the complex scene of distorted limbs or special posture, refraction or reflection of light, large variation of human scale, and occlusion. Used for human-computer interaction in multiple scenarios.

A method and system for displaying predictions on a spatial map includes using a data analyzer for analyzing heterogeneous data having a spatial component to find utilizable data and using machine learning to automatically extract relationships from the utilizable data. The extracted relationships are used to make a prediction about at least one location on the spatial map and present that prediction in an oblique or perspective view. An interface presents the prediction on the spatial map in the form of a heat map overlying a 3-D topographical map. Although the 3-D map can be shown as any form of graphical projection including an oblique projection or orthographic projection, preferably a perspective view is used. It is also preferred that the graphical projection be interactive. The heat map may be 2-D or 3-D and be selectively displayed depending on the preference of a user.

System and method for displaying information regarding a business process. A diagram of the business process may be displayed on a display. The diagram may include a plurality of icons connected by lines, where each of the icons represents a respective step in the business process and the lines indicate flow paths between the steps. Historical data regarding the business process may be received. The historical data may be analyzed to determine information regarding steps and / or flow paths in the business process. Graphical indications associated with one or more icons and / or lines in the diagram may be displayed. The graphical indications may visually indicate characteristics of corresponding steps and / or flow paths in the business process. For example, the graphical indications may indicate lengths of time, costs, or other characteristics associated with various ones of the steps. The graphical indications may indicate path traversals of ones of the flow paths.

A method for processing a virtual reality video (“VRV”) by a virtual reality (“VR”) computing device comprises identifying objects in the VRV for interactive product placement, generating interactive objects for the VRV based on the identified objects, embedding the VRV with the generated interactive objects, and storing the embedded VRV. Creation of interactive product placements is provided in a monoscopic or stereoscopic virtual reality video. Users' gaze directions are recorded and analyzed via heat maps to further inform and refine creation of interactive content for the virtual reality video.

The invention discloses a ship collision risk analysis method based on AIS (automatic identification system) data. Based on historical AIS data and standard ship selection and conversion, a density clustering algorithm is used for establishing a heat map of a ship collision risk to realize spatiotemporal visualization of the ship collision risk; based on real-time AIS data and the ship position field, the course direction field, and the navigational speed field, a regional ship collision risk assessment model is constructed, and a Gaussian kernel function kernel density estimation algorithm isused for proposing a dynamic ship collision risk visualization method to realize areal-time update of the regional ship collision risk. The ship collision risk analysis method is based on the AISdata,the spatiotemporal visualization of the ship collision risk is realized,the visual image after the complex abstract ship traffic flow multi-attribute information is effectively dug and fused is realized, so that the risk level of the environment of the location of the ship can be intuitively and conveniently obtained by a driver or an operator, thusself-alertness is improved, the reasonable control measures are taken, and the safe operation of the ship is ensured.

A dataset is obtained comprising data blocks, each representing a different characteristic, for a plurality of cells across a plurality of bins, each bin representing a different portion of a reference sequence. Cells are clustered on one such characteristic across the bins thereby forming a tree that includes root, intermediate, and terminal nodes, where the cells are terminal nodes and intermediate nodes have daughter nodes, themselves being intermediate nodes or a cell. A subset of the tree is displayed that includes the root and leaves, each leaf representing an intermediate node or a cell. A heat map of the characteristic is also displayed, the map including a segment for each leaf, across the bins. When a segment represents an intermediate node, it is an average of the characteristic across daughters of the node. Graphs of characteristics for the root across the bins are also displayed.

The invention provides an image alignment method based on a novel end-to-end face super-resolution network. In the image alignment method, a face super-resolution network and a face super-resolution generative adversarial network are mainly involved; the image alignment method comprises the following process: constructing a coarse-grained super-resolution (SR) network for recovering a coarse-grained high-resolution (HR) image; sending the coarse-grained HR image to a fine-grained SR encoder and a prior information estimating network respectively, wherein the fine-grained SR encoder is used forextracting image features and the prior information estimating network is used for estimating a critical point heat map; restoring the HR image by using the image features and prior information in adecoder, wherein in order to further generate a realistic face, the face super-resolution generative adversarial network is provided and adversarial loss is brought into the face super-resolution network. A network consisting of the face super-resolution network and the face super-resolution generative adversarial network is used for image alignment, so that the imaging quality can be effectivelyimproved and the image alignment method is simplified.

The present invention provides a target detection method and apparatus based on a neural network, and a neural network training method and apparatus for target detection. The target detection method comprises: acquiring a target-containing image to be detected; acquiring the first feature information of the image to be detected by employing a first pre-trained neural network, and acquiring the second feature information of the image to be detected by employing a second pre-trained neural network; combining the first feature information and the second feature information to acquire combined feature information; and acquiring a target detection result based on the combined feature information by employing the second neural network, wherein the number of layers of the second neural network is larger than the number of layers of the first neural network, and the first feature information is heat map feature information, and the second feature information is picture feature information.

Systems and methods for image modification to increase contrast between text and non-text pixels within the image. In one embodiment, an original document image is scaled to a predetermined size for processing by a convolutional neural network. The convolutional neural network identifies a probability that each pixel in the scaled is text and generates a heat map of these probabilities. The heat map is then scaled back to the size of the original document image, and the probabilities in the heat map are used to adjust the intensities of the text and non-text pixels. For positive text, intensities of text pixels are reduced and intensities of non-text pixels are increased in order to increase the contrast of the text against the background of the image. Optical character recognition may then be performed on the contrast-adjusted image.

A method and system that utilizes OLAP and supporting data structures for making predictions about business locations. The method includes providing a spatial map and analyzing heterogeneous data having a spatial component to find utilizable data. Relationships are automatically extracted from the utilizable data by employing machine learning. The step of automatically extracting relationships includes generating a composite indicator, which correlates spatial data with unstructured data. The extracted relationships are presented on a spatial map to make a prediction about at least one business location. Preferably, the predictions are presented as a rank-ordered list on the spatial map and a heat map overlays the spatial map to indicate predictions about particular regions.

System and method for displaying information regarding a business process. A diagram of the business process may be displayed on a display. The diagram may include a plurality of icons connected by lines, where each of the icons represents a respective step in the business process and the lines indicate flow paths between the steps. Historical data regarding the business process may be received. The historical data may be analyzed to determine information regarding steps and / or flow paths in the business process. Graphical indications associated with one or more icons and / or lines in the diagram may be displayed. The graphical indications may visually indicate characteristics of corresponding steps and / or flow paths in the business process. For example, the graphical indications may indicate lengths of time, costs, or other characteristics associated with various ones of the steps. The graphical indications may indicate path traversals of ones of the flow paths.

Provided herein are methods for measuring molecular flux rates of molecules of interest in a tissue sample in spatially-organized manner and generating output (e.g., an image, a heat map, a contour map, a table or a database) representing the molecular flux rates of each spatially-defined location of the sample. Provided herein are also the output, as well as systems and computer-readable medium with computer-executable instructions for determining molecular flux rates of molecules of interest in the sample.

The invention discloses an infrared nondestructive testing method based on thermal image time sequence characteristics. The new method with pixel as a unit is used for the infrared nondestructive test of flaws and is based on thermal image time sequence characteristics and PNN (probabilistic neural network). The infrared nondestructive testing method comprises the following steps: firstly, obtaining an infrared time sequence thermal image of a heating test piece in a cooling process by an infrared thermal imager; secondly, extracting the gray values of normal and abnormal areas in the time sequence thermal image, establishing the relationship between the gray values of different areas and time and then obtaining the corresponding initial time sequence characteristics; thirdly, reducing the dimensions of the initial characteristics by adopting main constituent analysis so as to obtain the time sequence characteristics; and finally establishing the probabilistic neural network with the time sequence characteristics as training samples.

Techniques are described for aligning heat map information more efficiently to an actual geometry of an area of interest. A sequence is generated corresponding to consecutive runs of included and / or excluded grid points. The sequence is provided to a device in addition to heat map information corresponding to a number of grid points. The provided heat map information may be compressed and / or may be assigned to grid points in a reoriented area overlaying the area of interest. The device uses the sequence to map the heat map information to grid points in an area. In one technique, information corresponding to inaccessible locations are excluded from the heat map.

An object detection network can be trained with training images to identify and classify objects in images from a sensor system disposed on a maritime vessel. The objects in the images can be identified, classified, and heat maps can be generated. Instructions can be sent regarding operation of the maritime vessel. For some training images, water conditions, sky conditions, and / or light conditions in the image can be changed to generate a second image.

For training data pairs comprising training text (a radiological report) and training images (radiological images associated with the radiological report), a first encoder network determines word embeddings for the training text. A concept is generated from the operation of layers of the first encoder network, which is regularized by a first loss between the generated concept and a labeled concept for the training text. A second encoder network determines features for the training image. A heatmap is generated from the operation of layers of the second encoder network, which is regularized by a second loss between the generated heatmap and a labeled heatmap for the training image. A categorical cross entropy loss is calculated between a diagnostic quality category (classified by an error encoder) and a labeled diagnostic quality category for the training data pair. A total loss function comprising the first, second, and categorical cross entropy losses is minimized.

The invention discloses a papillary thyroid cancer pathological image classification method based on deep learning, and mainly solves the problem of poor classification effect on papillary thyroid cancer pathological images in the prior art. According to the scheme, the method comprises the following steps: 1) reading a papillary thyroid cancer pathological section image with an amplification factor of 20, and inputting the papillary thyroid cancer pathological section image into an improved VGG-f convolutional neural network to obtain an attention heat map; 2) normalizing the attention diagram to obtain a discrimination force region position; reading a 40-time amplified thyroid cancer pathological image and obtaining an image block according to the position of the discrimination area; 3)inputting the image blocks into an original VGG-f network, constructing a loss function, and performing supervised training on the network; 4) extracting trained VGG-f network convolution features andperforming classification processing to obtain categories of the image blocks, and 5) judging the categories of the thyroid cancer pathological images according to the categories of the image blocks.The classification accuracy is high, and the method can be used for classifying the thyroid cancer papillary cancer pathological images by a computer.

The invention relates to the technical field of computer vision recognition, and provides a key feature area matching face recognition method based on a stacked hourglass network, which comprises thefollowing steps: collecting a training set, and preprocessing the training set; Preprocessing the input face image; Inputting the picture into a stacked hourglass network for feature extraction, and outputting a face key point heat map and key point position information; Cutting a key area of the original picture, and selecting a triple from the training set; Performing feature extraction on the key area to obtain a feature map F; Inputting the feature map F into an embedded layer to obtain a label E; Calculating a ternary loss function according to the L2 norm of the feature map, and repeating the above steps until the ternary loss function converges; And inputting the to-be-identified face image into the trained stacked hourglass network and face identification module, and outputting anidentified tag E. According to the method, the stacked hourglass network is introduced for face recognition, the influence of non-key areas is eliminated, the face recognition effect is effectively improved, and high robustness is achieved.

The invention provides a heat map providing method. The method comprises steps that a request of a user for playing a heat map of a preset event in a preset geographical area at the preset time segment is received; the data of the preset event generated at the preset geographical area at the preset time segment is acquired; according to the acquired data, a corresponding heat map is drafted by employing a webpage development language-heat map (WebGL-heatmap) drafting method; the draft heat map is displayed on a generated map base map, and the draft heat map is transmitted to a user. Through the method, the heat map can be rapidly generated and provided for the user, so various demands of the user can be satisfied, and user experience can be improved.