Qualcomm Incorporated

Apple Inc.

Onur Canturk Hamsici

Chong U Lee

John Hyunchul Hong

Sundeep Vaddadi

Yuriy Reznik

Thorsten Gernoth

Srikrishna Sridhar

Shawn R Scully

Carlos E Guestrin

Atulit Kumar

Shreyas V Joshi

Gustav M Larsson

Leon A Gatys

Karla P Vega

Kory R Watson

Joerg A Liebelt

Vijayalakshmi Rajasundaram Raveendran

Lochan Verma

Murali Chari

Haitao Guo

Pawan Kumar Baheti

Ashwin Deepak Swaminathan

Feng Tang

Kelsey Yukio Ho

Devender Akira Yamakawa

Mina Ayman Saleh Yanni Makar

Jose Ricardo Dos Santos

Ian R Fasel

Andrew Moore Ziegler

Shuo Feng

Joseph Huang

Mark K Murphy

Donghoon Lee

Jesus Manuel Dominguez

Anuraag Sridhar

Murali Ramaswamy Chari

Chong Uk Lee

Devender A Yamakawa

José R Dos Santos

Devices, systems, and methods are disclosed for partial point cloud registration. In some implementations, a method includes obtaining a first set of three-dimensional (3D) points corresponding to an object in a physical environment, the first set of 3D points having locations in a first coordinate system, obtaining a second set of 3D points corresponding to the object in the physical environment, the second set of 3D points having locations in a second coordinate system, predicting, via a machine learning model, locations of the first set of 3D points in the second coordinate system, and determining transform parameters relating the first set of 3D points and the second set of 3D points based on the predicted location of the first set of 3D points in the second coordinate system.

Deep partial point cloud registration of objects

A device implementing a system for providing predicted RGB images includes at least one processor configured to obtain an infrared image of a subject, and to obtain a reference RGB image of the subject. The at least one processor is further configured to provide the infrared image and the reference RGB image to a machine learning model, the machine learning model having been trained to output predicted RGB images of subjects based on infrared images and reference RGB images of the subjects. The at least one processor is further configured to provide a predicted RGB image of the subject based on output by the machine learning model.

Machine learning assisted image prediction

An image captured using a camera on a device (e.g., a mobile device) may be operated on by one or more processes to determine properties of a user's face in the image. A first process may determine one or more first properties of the user's face in the image. A second process operating downstream from the first process may determine at least one second property of the user's face in the image. The second process may use at least one of the first properties from the first process to determine the second property.

Network performance by including attributes

Detection of a user paying attention to a device may be used to enable or support biometric security (e.g., facial recognition) enabled features on the device. Images captured by a camera on the device may be used to determine if the user is paying attention to the device. Facial features of the user's face in the images may be assessed to determine if the user is paying attention to the device. Facial features may be assessed through comparison of feature vectors generated from the captured image feature to a set of known feature vectors. The known feature vectors for attention may be generated using a machine learning process.

Attention detection

A single network encodes and decodes an image captured using a camera on a device. The single network detects if a face is in the image. If a face is detected in the image, the single network determines properties of the face in the image and outputs the properties along with the face detection output. Properties of the face may be determined by sharing the task for face detection. Properties of the face that are output along with the face detection output include the location of the face, the pose of the face, and/or the distance of the face from the camera.

Face detection, pose estimation, and distance from a camera estimation using a single network

In one example, a system and method includes acquiring, by a wireless dockee (WD), an image, initiating a wireless connection between the wireless dockee (WD) and a wireless docking center (WDC) having one or more peripherals, determining attributes associated with the acquired image, wherein the attributes include feature descriptors, receiving, at the WD and from the WDC, information representative of matching items stored on one or more of the peripherals, wherein the matching items include images with attributes similar to the attributes associated with the acquired image, selecting, at the WD and as a function of the information representative of the matching items, one or more of the matching items, and receiving, from the one or more of the peripherals, the selected matching items.

System and method for accessing images with a captured query image

A local feature descriptor for a point in an image is generated over multiple levels of an image scale space. The image is gradually smoothened to obtain a plurality of scale spaces. A point may be identified as the point of interest within a first scale space from the plurality of scale spaces. A plurality of image derivatives is obtained for each of the plurality of scale spaces. A plurality of orientation maps is obtained (from the plurality of image derivatives) for each scale space in the plurality of scale spaces. Each of the plurality of orientation maps is then smoothened (e.g., convolved) to obtain a corresponding plurality of smoothed orientation maps. Therefore, a local feature descriptor for the point may be generated by sparsely sampling a plurality of smoothed orientation maps corresponding to two or more scale spaces from the plurality of scale spaces.

Efficient descriptor extraction over multiple levels of an image scale space

Methods, devices, and computer program products for robust estimation of color-dependent measurements are described herein. In one aspect, a method for generating a reference color grid that may be placed beside a color-dependent measuring device is disclosed. The reference color grid may contain a number of colors which enable a mapping from the color space of a testing device to a reference color space. This mapping may allow a function that is able to determine an estimate of a color-dependent measurement based on a color in the reference color space to be used. In another aspect, a method for robust estimation of color-dependent measurement using a reference color guide is disclosed.

System and method for robust estimation of color dependent measurements

Techniques for segmentation of three-dimensional (3D) point clouds are described herein. An example of a method for user-assisted segmentation of a 3D point cloud described herein includes obtaining a 3D point cloud of a scene containing a target object; receiving a seed input indicative of a location of the target object within the scene; and generating a segmented point cloud corresponding to the target object by pruning the 3D point cloud based on the seed input.

Segmentation of 3D point clouds for dense 3D modeling

In general, techniques are described for performing a vocabulary-based visual search using multi-resolution feature descriptors. A device may comprise one or more processors configured to perform the techniques. The processors may generate a hierarchically arranged data structure to be used when classifying objects included within a query image based on multi-resolution query feature descriptor extracted from the query image at a first scale space resolution and a second scale space resolution. The hierarchically arranged data structure may represent a first query feature descriptor of the multi-resolution feature descriptor extracted at the first scale space resolution and a second corresponding query feature descriptor of the multi-resolution feature descriptor extracted at the second scale space resolution hierarchically arranged according to the first scale space resolution and the second scale space resolution. The processors may then perform a visual search based on the generated data structure.

Performing vocabulary-based visual search using multi-resolution feature descriptors

In general, techniques are described for performing a vocabulary-based visual search using multi-resolution feature descriptors. A device may comprise one or more processors configured to perform the techniques. The one or more processors may to apply a partitioning algorithm to a first subset of target feature descriptors to determine a first classifying data structure to be used when performing a visual search with respect to a query feature descriptor. The one or more processors may then apply the partitioning algorithm to a second subset of the target feature descriptors to determine a second classifying data structure to be used when performing the visual search with respect to the same query feature descriptor.

Techniques are disclosed for performing robust feature matching for visual search. An apparatus comprising an interface and a feature matching unit may implement these techniques. The interface receives a query feature descriptor. The feature matching unit then computes a distance between a query feature descriptor and reference feature descriptors and determines a first group of the computed distances and a second group of the computed distances in accordance with a clustering algorithm, where this second group of computed distances comprises two or more of the computed distances. The feature matching unit then determines whether the query feature descriptor matches one of the reference feature descriptors associated with a smallest one of the computed distances based on the determined first group and second group of the computed distances.

Robust feature matching for visual search

A method for generating a feature descriptor is provided. A set of pre-generated sparse projection vectors is obtained. A scale space for an image is also obtained, where the scale space having a plurality scale levels. A descriptor for a keypoint in the scale space is then generated based on a combination of the sparse projection vectors and sparsely sampled pixel information for a plurality of pixels across the plurality of scale levels.

Fast subspace projection of descriptor patches for image recognition

In a particular embodiment, a method includes applying a first feature detector to a portion of an image to detect a first set of features. The first set of features is used to locate a region of interest, and a boundary corresponding to the region of interest is determined. The method also includes displaying the boundary at a display. In response to receiving user input to accept the displayed boundary, a second feature detector is applied to an area of the image encapsulated by the boundary.

Growing community of inventors

San Diego, CA, United States of America

Onur Canturk Hamsici