公开(公告)号：US20180139377A1

公开(公告)日：2018-05-17

申请号：US15573325

申请日：2016-01-19

Applicant: SRI International

Inventor： David Chao ZHANG ,  John Benjamin SOUTHALL ,  Michael Anthony ISNARDI ,  Michael Raymond PIACENTINO ,  David Christopher BERENDS ,  Girish ACHARYA ,  Douglas A. BERCOW ,  Aaron SPAULDING ,  Sek CHAI

IPC: H04N5/232 ,  G06K9/62 ,  G06T7/11 ,  G06T5/20 ,  A61B5/00

CPC classification number: H04N5/23212 ,  A61B5/0077 ,  A61B5/442 ,  G06K9/00228 ,  G06K9/036 ,  G06K9/6212 ,  G06T5/20 ,  G06T7/0012 ,  G06T7/11 ,  G06T2207/20016 ,  G06T2207/30088 ,  H04N5/23229 ,  H04N5/23293 ,  H04N5/2356

Abstract: Device logic in a mobile device configures a processor to capture a series of images, such as a video, using a consumer-grade camera, and to analyze the images to determine the best-focused image, of the series of images, that captures a region of interest. The images may be of a textured surface, such as facial skin of a mobile device user. The processor sets a focal length of the camera to a fixed position for collecting the images. The processor may guide the user to position the mobile device for capturing the images, using audible cues. For each image, the processor crops the image to the region of interest, extracts luminance information, and determines one or more energy levels of the luminance via a Laplacian pyramid. The energy levels may be filtered, and then are compared to energy levels of the other images to determine the best-focused image.

公开(公告)号：US20240185591A1

公开(公告)日：2024-06-06

申请号：US18282049

申请日：2022-03-24

Applicant: SRI INTERNATIONAL

Inventor： Aswin NADAMUNI RAGHAVAN ,  Michael R. PIACENTINO ,  Michael A. ISNARDI ,  Indhumathi KANDASWAMY ,  Saurabh FARKYA ,  David Chao ZHANG ,  Gooitzen S. VAN DER WAL ,  Zachary DANIELS ,  Yuzheng ZHANG

IPC: G06V10/82 ,  G06N3/0442 ,  G06V10/764

CPC classification number: G06V10/82 ,  G06N3/0442 ,  G06V10/764

Abstract: Method and apparatus for processing data using a reconfigurable, hyperdimensional neural network architecture comprising a feature extractor and a classifier. The feature extractor comprises a neural network for encoding input information into hyperdimensional (HD) vectors and extracting at least one particular HD vector representing at least one feature within the input information, wherein the neural network comprises no more than one multiply and accumulate operator. The classifier is coupled to the feature extractor for classifying the at least one particular HD vector to produce an indicium of classification for the at least one particular HD vector and wherein the classifier does not comprise any multiply and accumulate operators.

公开(公告)号：US20250156762A1

公开(公告)日：2025-05-15

申请号：US18944744

申请日：2024-11-12

Applicant: SRI International

Inventor： Aswin NADAMUNI RAGHAVAN ,  David Chao ZHANG ,  Saurabh FARKYA ,  Zachary Alan DANIELS ,  Michael PIACENTINO ,  Gooitzen S. VAN DER WAL ,  Philip MILLER ,  Michael Richard LOMNITZ ,  Abrar Abdullah RAHMAN ,  Edison MUCLLARI ,  Muhammad Shahir RAHMAN

IPC: G06N20/00

Abstract: A method, apparatus, and system for efficient machine learning with query-based knowledge assistance includes determining a state of data captured by a sensor in communication with a first edge device to determine if the captured data includes data that is out of distribution based on a trained inference model of the first edge device, if it is identified that an amount of out of distribution data in the captured data is preventing the trained inference model from making an accurate prediction, communicating a request for resources to a second edge device or a server to elicit a response from the second edge device or the server including resources required to update the trained inference model, receiving the requested resources, updating the trained inference model using the received resources, and making a prediction for the received captured data using the updated, trained inference model.

公开(公告)号：US20230394637A1

公开(公告)日：2023-12-07

申请号：US18206001

申请日：2023-06-05

Applicant: SRI International

Inventor： David Chao ZHANG ,  Michael A. ISNARDI ,  Michael R. PIACENTINO

IPC: G06T5/10 ,  G06V20/52

CPC classification number: G06T5/10 ,  G06V20/52 ,  G06V2201/07

Abstract: A method, apparatus and system for image privacy protection and actionable response includes distorting an analog image captured using an image capture device in a residential, industrial or commercial environment using a transform filter, digitizing the distorted analog image, analyzing the distorted, digitized image using a trained machine learning process to identify at least one of an individual or an object in the distorted, digitized image, the machine learning process having been trained to identify individuals and objections in the distorted image, and upon identification of at least one of an individual or an object in the distorted image for which action is to be taken, communicating an indication to at least one device in the residential, commercial or industrial environment to cause the device to perform a predetermined action.

公开(公告)号：US20230260152A1

公开(公告)日：2023-08-17

申请号：US17992006

申请日：2022-11-22

Applicant: SRI International

Inventor： David Chao ZHANG ,  Michael R. PIACENTINO ,  Aswin NADAMUNI RAGHAVAN

IPC: G06T7/73 ,  G06V10/764

CPC classification number: G06T7/74 ,  G06V10/764 ,  G06T2207/10016 ,  G06T2207/20084

Abstract: Method and apparatus of processing a sequence of video frames comprising generating at least one video frame and using an analog neural network to select, within the at least one video frame, at least one patch of pixels and process the at least one pixel patch to produce a patch feature for each of the at least one pixel patches. The method digitizes the patch feature, identifies objects within the digitized patch feature, and tracks the objects to generate control information that is used by the analog neural network to select and process the pixel patches.

公开(公告)号：US20220198782A1

公开(公告)日：2022-06-23

申请号：US17553239

申请日：2021-12-16

Applicant: SRI International

Inventor： David Chao ZHANG ,  Michael R. PIACENTINO ,  Aswin NADAMUNI RAGHAVAN

IPC: G06V10/774 ,  G06N3/04 ,  G06N3/08

Abstract: An edge device comprising a feature extractor and a reconfigurator. The feature extractor comprises a first neural network for encoding input information into data vectors and extracting particular data vectors representing features within the input information, wherein the first neural network comprises at least one encoder layer and at least one adaptor layer. The reconfigurator is coupled to the feature extractor and comprises a second neural network for classifying the particular data vectors and wherein, upon requiring additional features to be extracted, the reconfigurator adapts at least one layer in the first neural network, second neural network or both by performing at least one of: (1) altering weights, (2) adding layers, (3) deleting layers, (4) reordering layers to improve classification of particular data vector. The first neural network, the second neural network or both are trained using gradient-free training.