551 results about "Re identification" patented technology

A system for processing patient health information (PHI) protects the confidentiality of PHI to achieve regulatory compliance. The PHI contains patient medical data and associated patient identification data. A de-identification agent extracts patient medical data and separates from all identification data to create de-identified patient data. A key is generated that allows subsequent reassociation of the patient medical data and the patient identification data. The de-identified patient data base may be queried for patient screening purposes. Patient queries are processed only if the study or patient screening has been authorized by appropriate authorities, such as an internal review board. Patients whose medical characteristics conform with the patient query are selected for possible use in a study. If re-identification of the selected patients is necessary, and authorized, the key may be used to provide the necessary reassociation. A data log records all access to patient data.

The invention discloses a cross-camera pedestrian detection tracking method based on depth learning, which comprises the steps of: by training a pedestrian detection network, carrying out pedestrian detection on an input monitoring video sequence; initializing tracking targets by a target box obtained by pedestrian detection, extracting shallow layer features and deep layer features of a region corresponding to a candidate box in the pedestrian detection network, and implementing tracking; when the targets disappear, carrying out pedestrian re-identification which comprises the process of: after target disappearance information is obtained, finding images with the highest matching degrees with the disappearing targets from candidate images obtained by the pedestrian detection network and continuously tracking; and when tracking is ended, outputting motion tracks of the pedestrian targets under multiple cameras. The features extracted by the method can overcome influence of illuminationvariations and viewing angle variations; moreover, for both the tracking and pedestrian re-identification parts, the features are extracted from the pedestrian detection network; pedestrian detection, multi-target tracking and pedestrian re-identification are organically fused; and accurate cross-camera pedestrian detection and tracking in a large-range scene are implemented.

A de-identification method and an apparatus for performing same on electronic datasets are described. The method and system processes input datasets or databases that contain records relating to individual entities to produce a resulting output dataset that contains as much information as possible while minimizing the risk that any individual in the input dataset could be re-identified from that output dataset. Individual entities may include patients in a hospital or served by an insurance carrier, as well as voters, subscribers, customers, companies, or any other organization of discrete records. Criteria for preventing re-identification can be selected based on intended use of the output data and can be adjusted based on the content of reference databases. The method and system can also be associated with data acquisition equipment, such as a biologic data sampling device, to prevent de-identification of patient or other confidential data acquired by the equipment.

A method and system for extracting segments from a broadcast television signal uses a mean display attribute value of pixels of each successive frame of a video component of the signal to identify segment transitions. Elapsed times between the identified segment transitions are compared with expected durations of segments, in order to detect intervals that are likely to constitute segments. Groups of the detected intervals are subjected to tests to see if they are temporally arranged in an expected distribution typical of segments in broadcast signals. The mean display attribute values concurrent with each detected segment are then used to form a statistical profile for the segment. These statistical profiles may be used to identify re-broadcast segments. Further a stream of mean audio amplitudes is preferably used to supply further segment transitions, to improve segment detection, and to provide more statistical values.

The invention discloses a pedestrian re-identification method based on multi-attribute and multi-strategy fusion learning. The method of the invention includes the steps of in an offline training phase, firstly selecting pedestrian attributes which are easy to be judged and have a sufficient distinguishing degree, training a pedestrian attribute identifier on an attribute data set, then labeling attribute tags for a pedestrian re-identification data set by using the attribute identifier, and next, by combining the attributes and pedestrian identity tags, training a pedestrian re-identification model by using a strategy fused with pedestrian classification and novel constraint comparison verification; and in an online query phase, extracting features of a query image and images in a database by using the pedestrian re-identification model, and calculating the Euclidean distance between the feature of the query image and the feature of each image in the database to obtain the image with the shortest distance, which is considered as the result of pedestrian re-identification. In terms of performance, the features in the invention are distinguishable and high accuracy is obtained; and in terms of efficiency, the method of the invention can quickly search for the pedestrian indicated by the query image from the pedestrian image database.

The invention discloses a multi-feature fusion vehicle re-identification method based on deep learning. The method comprises five parts of model training, license plate identification, vehicle identification, similarity measurement and visualization. The method comprises the steps that a large-scale vehicle data set is used for model training, and a multi-loss function-phased joint training policyis used for training; license plate identification is carried out on each vehicle image, and a license plate identification feature vector is generated according to the license plate recognition condition; a trained model is used to extract the vehicle descriptive features and vehicle attribute features of an image to be analyzed and an image in a query library, and the vehicle descriptive features and the license plate identification vector are combined with the unique re-identification feature vector of each vehicle image; in the stage of similarity measurement, similarity measurement is carried out on the image to be analyzed and the re-identification feature vector of the image in the query library; and a search result which meets requirements is locked and visualized.

The present technique provides for the analysis of a data series to identify sequences of interest within the series. Specifically, in accordance with one embodiment of the present technique, a method is provided comprising generating a data structure that stores characteristics about a plurality of sequences present in a data series. One or more sequences are identified based upon the contents of the data structure. In accordance with other aspects of the invention, more than one heuristic is calculated for each sequence under review. The plurality of heuristics associated with each sequence are evaluated to identify a sequence of interest.

The invention relates to a pedestrian re-identification method based on global features and local features of an attention mechanism. The pedestrian re-identification method comprises the steps of respectively extracting the global features and the local features of pedestrians; in the global feature branch, taking the whole pedestrian feature image as input, sending the pedestrian feature image into a space attention mechanism module and a channel attention mechanism module, and fusing the feature representations of the two modules; in the local feature branch, horizontally and averagely dividing the pedestrian feature map into three parts, and inputting the three divided parts into a channel attention mechanism module to obtain the local feature of each part; sending the global feature and the local feature into a feature vector extraction module to obtain a feature vector for pedestrian prediction; and training the whole network to obtain a pedestrian re-identification model. According to the method, the global features and the local features of the pedestrian images are fully utilized, the attention mechanism is effectively fused, the pedestrian features have better discrimination ability, a good pedestrian re-identification result is obtained, and the model matching accuracy is improved.

Alphabetic filtering of the speech recognition of words uses a key press to indicate a desired character in an alphabetic filter string, where each key press represents two or more letters. The key presses can be disambiguated by recognizing a key-disambiguation utterance in association with a given key press. A user can select a desired recognition candidate from a choice list produced by such filtered word recognition. Ambiguous alphabetic filtering can be performed iteratively in response to the addition of successive ambiguous key presses. A user can select to re-recognize the utterance using filtering based on ambiguous key input after seeing the results of recognition without such filtering. Unambiguous alphabetic filtering can be performed by using multiple presses of an ambiguous key to disambiguate which letter is intended. A user can select between entering text by either large vocabulary speech recognition or by spelling text by pressing phone keys.

A computer method and apparatus for adaptive model predictive control (MPC) of multivariable processes is disclosed. The adaptive MPC system can perform automatic implementation for a new MPC controller, and, for an existing MPC controller, it can perform automatic maintenance when necessary. The adaptive MPC system consists of three modules: an MPC control module, an online identification module and a control monitor module. In MPC implementation, the online identification module and the MPC control module work together and perform various steps in MPC implementation automatically and efficiently. When the MPC controller is online, the control monitor module continuously monitors the MPC performance and model quality. When control performance becomes poor and considerable model degradation is detected, monitor module will start the maintenance by activating the online identification module. The identification module will re-identify the model and replace the old model. For strongly nonlinear process units, multiple models are identified and used in control.

The invention discloses a pedestrian re-identification method and device based on unsupervised learning and a medium, and the method comprises the steps: obtaining a target image and a comparison image, and identifying whether a pedestrian exists in the target image in the comparison image through a pedestrian re-identification model based on unsupervised learning; outputting a recognition result; establishing a pedestrian re-identification model: carrying out initial training on the visual classifier according to the labeled source data set to obtain a visual classifier; learning the label-free target data set by using the vision classifier after initial training to obtain a matching probability and space-time information; obtaining a Bayesian fusion model according to the matching probability and the space-time information; carrying out similarity matching on pedestrian images in the unlabeled target data set by the Bayesian fusion model according to the comparison target pedestrian images to obtain a similarity score; sorting the similarity scores according to a preset threshold value to obtain a sorting result; when it is detected that the current model training optimization frequency is smaller than or equal to a preset optimization threshold value, performing parameter updating on the visual classifier.

The invention discloses a pedestrian re-identification method based on a generative adversarial network image super-resolution technology. The method includes the following steps: utilizing a Laplace pyramid generative adversarial network to generate a group of clear images, utilizing local maximum event expression and a Dense Correspondences algorithm to separately extract HSV color features, texture features and LAB color features of the images, carrying out fusion on the features, utilizing a cross vision quadratic discriminant analysis algorithm to conduct metric learning on the features, utilizing a Manhattan distance to calculate the distance between a probe set and a gallery set, and finally utilizing a multi-shot mode to carry out 1:N and N:N evaluation. A LAPGAN network is utilized to generate high-resolution image, and then a conventional method is utilized to obtain the features of the images and carry out corresponding matching. Deeping learning and the conventional method are combined to solve the problem of low image resolution caused by illumination, angle and other reasons, and an image matching rate is increased.

A method for modeling a vehicle, includes: receiving an image that includes a vehicle; and constructing a three-dimensional (3D) model of the vehicle, wherein the 3D model is constructed by: (a) taking a predetermined set of base shapes that are extracted from a subset of vehicles; (b) multiplying each of the base shapes by a parameter; (c) adding the resultant of each multiplication to form a vector that represents the vehicle's shape; (d) fitting the vector to the vehicle in the image; and (e) repeating steps (a)-(d) by modifying the parameters until a difference between a fit vector and the vehicle in the image is minimized.

Certain embodiments of the present invention provide systems and methods for retrieving and re-identifying patient data. Patient data may be re-identified by retrieving an encrypted or abstracted patient identifier. The identifier is used to retrieve a patient identifier associated with patient identification information. Patient identification information may be inserted into a file, such as a report or other document, at a local computer or web portal for access by an authorized user. A system includes a data storage storing patient data. The patient data includes an encoded patient identifier and an unencoded patient identifier. The system includes a workstation or other processor having a viewing application capable of viewing patient data in a file including the encoded patient identifier. The workstation invokes a procedure to replace the encoded patient identifier in the file at the workstation with the corresponding unencoded patient identifier using the data storage.

The invention relates to a pedestrian re-identification method based on multi-region feature extraction and fusion, including the following steps: using a residual network to extract global features,and adding a pedestrian identity classification module for global feature extraction and optimization in the training stage; constructing a multi-region feature extraction sub-network for local feature extraction, and carrying out weighted fusion of local features; setting a loss function including the loss of the classification module and the loss of a feature fusion module; training the networkto obtain a model, and extracting the feature vectors of a query set and a test set; and in the measuring stage, re-measuring the feature distance by using a cross-nearest neighbor method. The methodis reasonable in design. Global and local feature are effectively combined. The distance measurement method is optimized. A good pedestrian re-identification result is obtained. The overall matching accuracy of the system is greatly improved.

The invention provides a pedestrian re-identification method based on transfer learning and deep feature fusion. The pedestrian re-identification method comprises the following steps of pre-training,human body posture correction and segmentation, feature vector, deep feature fusion, model training, model testing and result identification. According to the invention, global and local features of pedestrians are extracted by using a deep convolutional neural network; deep fusion is carried out on the two features to obtain final pedestrian feature representation; then, in the training process of the deep convolutional neural network, a pedestrian re-identification network model with a better effect is obtained by adopting a transfer learning mode, and finally, features extracted by the pedestrian re-identification network model have stronger resolution capability, so that the purpose of improving the pedestrian re-identification accuracy is achieved.

The invention discloses a pedestrian re-identification method based on multi-channel attention characteristics, which comprises the following steps: 1) constructing a convolutional neural network model based on channel attention, and pre-training a trunk network; 2) extracting output characteristics of the pedestrian picture in the trunk network, and calculating channel weighted vectors of the characteristics after global pooling; 3) multiplying the weighted vector by the output characteristic of the main network, and adding the multiplied weighted vector to obtain a channel attention characteristic; 4) repeatedly extracting a plurality of attention characteristics, and performing characteristic diversity regularization by adopting a Hailinger distance; 5) inputting the attention characteristics into a full connection layer and a classifier, and performing training to minimize cross entropy loss and metric loss; and 6) inputting the test set pictures into the trained model to extract features, and realizing pedestrian re-identification through metric sorting. According to the pedestrian re-identification method based on the attention mechanism, discriminative features of pedestrians are extracted based on the attention mechanism, repeated extraction of similar attention features is limited, and the accuracy and robustness of pedestrian re-identification are effectively improved.

The invention provides a method for generating and expanding the pedestrian re-identification data based on a generative network. The method includes the steps of generating a new pedestrian video frame sample by utilizing a video prediction network; generating the end-to-end pedestrian background transformation data by means of a deep generative adversarial network; expanding the breadth and therichness of the pedestrian data set by using different data generation methods; and sending the expanded data set to the feature extraction network, extracting the features and evaluating the performance through the Euclidean distance. According to the method, intra-class and inter-class data expansion of pedestrians is taken into consideration as well, more abundant samples can be generated by combining different generative networks, the expanded data set has good diversity and robustness, the problem of performance loss caused by insufficient number of samples and background interference canbe well solved, and the method has general applicability, and the expanded data set can achieve better performance and efficiency in the next step of pedestrian recognition.

The invention discloses a multi-scale generative adversarial network-based shielded pedestrian re-identification method, and relates to a computer vision technology. The method comprises the followingsteps of preparing a pedestrian image training set; designing and training a multi-scale generative adversarial network, with the network comprising a multi-scale generator and a discriminator, withthe multi-scale generator being capable of performing a de-occlusion operation on the random occlusion area to generate a high-quality reconstructed graph; enabling a discriminator to distinguish whether the input image is a real image or a generated image; generating an expanded pedestrian image training set by using the trained multi-scale generator; designing and training a classification recognition network, wherein the network is used for performing identity classification on the input pedestrian images; and extracting features of the pedestrian images by using the trained classificationand recognition network and carrying out similarity matching.