公开(公告)号：US10706349B2

公开(公告)日：2020-07-07

申请号：US15730316

申请日：2017-10-11

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Mihir Narendra Mody ,  Veeramanikandan Raju ,  Chaitanya Ghone ,  Deepak Poddar

IPC: G06N3/04 ,  G06N3/08 ,  H04L29/06

Abstract: A Convolutional Neural Network (CNN) based-signal processing includes receiving of an encrypted output from a first layer of a multi-layer CNN data. The received encrypted output is subsequently decrypted to form a decrypted input to a second layer of the multi-layer CNN data. A convolution of the decrypted input with a corresponding decrypted weight may generate a second layer output, which may be encrypted and used as an encrypted input to a third layer of the multi-layer CNN data.

公开(公告)号：US20190005375A1

公开(公告)日：2019-01-03

申请号：US15730316

申请日：2017-10-11

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Mihir Narendra Mody ,  Veeramanikandan Raju ,  Chaitanya Ghone ,  Deepak Poddar

IPC: G06N3/04 ,  H04L29/06 ,  G06N3/08

Abstract: A CNN based-signal processing includes receiving of an encrypted output from a first layer of a multi-layer CNN data. The received encrypted output is subsequently decrypted to form a decrypted input to a second layer of the multi-layer CNN data. A convolution of the decrypted input with a corresponding decrypted weight may generate a second layer output, which may be encrypted and used as an encrypted input to a third layer of the multi-layer CNN data.

公开(公告)号：US12243300B2

公开(公告)日：2025-03-04

申请号：US17512049

申请日：2021-10-27

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Soyeb Nagori ,  Deepak Poddar

IPC: G06V10/25 ,  G06N3/048 ,  G06V10/94

Abstract: Various embodiments of the present technology relate to using neural networks to detect objects in images. More specifically, some embodiments relate to the reduction of computational analysis regarding object detection via neural networks. In an embodiment, a method of performing object detection is provided. The method comprises determining, via a convolution neural network, at least a classification of an image, wherein the classification corresponds to an object in the image and comprises location vectors corresponding to pixels of the image. The method also comprises, for at least a location vector of the location vectors, obtaining a confidence level, wherein the confidence level represents a probability of the object being present at the location vector, and calculating an upper-bound score based at least on the confidence level. The method further comprises, for at least an upper-bound score based at least on the confidence level, performing an activation function on the upper-bound score, and classifying, via a detection layer, the object in the image.

公开(公告)号：US20240386602A1

公开(公告)日：2024-11-21

申请号：US18785164

申请日：2024-07-26

Applicant: Texas Instruments Incorporated

Inventor： Hrushikesh Tukaram Garud ,  Deepak Poddar ,  Soyeb Noormohammed Nagori

IPC: G06T7/73 ,  G01C21/30 ,  G06V10/98 ,  G06V20/56 ,  G06V20/64

Abstract: Techniques for localizing a vehicle include obtaining an image from a camera, identifying a set of image feature points in the image, obtaining an approximate location of the vehicle, determining a set of sub-volumes (SVs) of a map to access based on the approximate location, obtaining map feature points and associated map feature descriptors associated with the set of SVs, determining a set of candidate matches between the set of image feature points and the obtained map feature points, determining a set of potential poses of the camera from candidate matches from the set of candidate matches and an associated reprojection error estimated for remaining points to select a first pose of the set of potential poses having a lowest associated reprojection error, determining the first pose is within a threshold value of an expected vehicle location, and outputting a vehicle location based on the first pose.

公开(公告)号：US12080025B2

公开(公告)日：2024-09-03

申请号：US17887580

申请日：2022-08-15

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Hrushikesh Tukaram Garud ,  Deepak Poddar ,  Soyeb Noormohammed Nagori

IPC: G06T7/00 ,  G01C21/30 ,  G06T7/73 ,  G06V10/98 ,  G06V20/56 ,  G06V20/64

CPC classification number: G06T7/73 ,  G01C21/30 ,  G06T7/75 ,  G06V10/993 ,  G06V20/56 ,  G06V20/647 ,  B60R2300/302 ,  G06T2207/30252

Abstract: Techniques for localizing a vehicle include obtaining an image from a camera, identifying a set of image feature points in the image, obtaining an approximate location of the vehicle, determining a set of sub-volumes (SVs) of a map to access based on the approximate location, obtaining map feature points and associated map feature descriptors associated with the set of SVs, determining a set of candidate matches between the set of image feature points and the obtained map feature points, determining a set of potential poses of the camera from candidate matches from the set of candidate matches and an associated reprojection error estimated for remaining points to select a first pose of the set of potential poses having a lowest associated reprojection error, determining the first pose is within a threshold value of an expected vehicle location, and outputting a vehicle location based on the first pose.

公开(公告)号：US11853857B2

公开(公告)日：2023-12-26

申请号：US16889853

申请日：2020-06-02

Applicant: Texas Instruments Incorporated

Inventor： Mihir Narendra Mody ,  Veeramanikandan Raju ,  Chaitanya Ghone ,  Deepak Poddar

IPC: G06N3/04 ,  G06N3/088 ,  H04L9/40 ,  G06N3/045

CPC classification number: G06N3/04 ,  G06N3/045 ,  G06N3/088 ,  H04L63/0435 ,  H04L63/0464

Abstract: A convolutional neural network (CNN)-based signal processing includes receiving of an encrypted output from a first layer of a multi-layer CNN data. The received encrypted output is subsequently decrypted to form a decrypted input to a second layer of the multi-layer CNN data. A convolution of the decrypted input with a corresponding decrypted weight may generate a second layer output, which may be encrypted and used as an encrypted input to a third layer of the multi-layer CNN data.

公开(公告)号：US11763575B2

公开(公告)日：2023-09-19

申请号：US17678411

申请日：2022-02-23

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Deepak Poddar ,  Soyeb Nagori ,  Manu Mathew ,  Debapriya Maji

IPC: G06T7/70 ,  G06V20/58 ,  G06T11/20

CPC classification number: G06V20/586 ,  G06T7/70 ,  G06T11/203 ,  G06T2207/20081 ,  G06T2207/30264

Abstract: Techniques including receiving a distorted image from a camera disposed about a vehicle, detecting, in the distorted image, corner points associated with a target object, mapping the corner points to a distortion corrected domain based on one or more camera parameters, mapping the corner points and lines between the corner points back to a distorted domain based on the camera parameters, interpolating one or more intermediate points to generate lines between the corner points in the distortion corrected domain mapping the corner points and the lines between the corner points back to a distorted domain based on the camera parameters, and adjusting a direction of travel of the vehicle based on the located target object.

公开(公告)号：US20230202524A1

公开(公告)日：2023-06-29

申请号：US18172495

申请日：2023-02-22

Applicant: Texas Instruments Incorporated

Inventor： Soyeb Noormohammed Nagori ,  Deepak Poddar ,  Hrushikesh Tukaram Garud

IPC: B60W60/00 ,  G05D1/02 ,  G06V20/58 ,  G06F18/21 ,  G06V10/764 ,  G06V10/82

CPC classification number: B60W60/0015 ,  G05D1/0278 ,  G05D1/0246 ,  G05D1/0223 ,  G06V20/58 ,  G06F18/2163 ,  G06V10/764 ,  G06V10/82 ,  G05D2201/0213

Abstract: An example driver assistance system includes an object detection (OD) network, a semantic segmentation network, a processor, and a memory. In an example method, an image is received and stored in the memory. An object detection (OD) polygon is generated for each object detected in the image, and each OD polygon encompasses at least a portion of the corresponding object detected in the image. A region of interest (ROI) is associated with each OD polygon. Such method may further comprise generating a mask for each ROI, each mask configured as a bitmap approximating a size of the corresponding ROI; generating at least one boundary polygon for each mask based on the corresponding mask, each boundary polygon having multiple vertices and enclosing the corresponding mask; and reducing a number of vertices of the boundary polygons based on a comparison between points of the boundary polygons and respective points on the bitmaps.