Qualcomm Incorporated

Softeye, Inc.

The Salk Institute for Biological Studies

Softmax, Inc.

University of California

Te-Won Lee

Taesu Kim

Edwin Chongwoo Park

Kisun You

Terrence J Sejnowski

Erik Visser

Hyun-Mook Cho

Duck Hoon Kim

Minho Jin

Hyung-Il Koo

Duck-hoon Kim

Kyuwoong Hwang

Thomas Wachtler

Michael S Lewicki

Rajarshi Gupta

Saumitra Mohan Das

Ayman Fawzy Naguib

Dhananjay Ashok Gore

Ki-Hyun Kim

Khaled Helmi El-Maleh

Ananthapadmanabhan Arasanipalai Kandhadai

Pawan Kumar Baheti

Samir Kumar Gupta

Kyu Woong Hwang

Ren Li

Hyun Jin Park

Nayeem Islam

Pengjun Huang

Hui-Ya Liao Nelson

Jeremy Patrick Toman

Jongwon Shin

Young-Ki Baik

Seungwoo Yoo

Sungwoong Kim

Heejong Yoo

Yong-Hui Lee

Duck-Hoon Kim

Methods and apparatus for scalable processing. Conventional image sensors read image data in a sequential row-by-row manner. However, image data may be more efficiently processed at different scales. For example, computer vision processing at a first scale may be used to determine whether subsequent processing with more resolution is helpful. Various embodiments of the present disclosure readout image data according to different scales; scaled readouts may be processed using scale specific computer vision algorithms to determine next steps. In addition to scaled readouts of image data, some variants may also provide commonly used data and/or implement pre-processing steps.

Methods and apparatus for scalable processing

Systems, apparatus, and methods for a gesture-based augmented reality and/or extended reality (AR/XR) user interface. Conventional image processing scales quadratically based on image resolution. Processing complexity directly corresponds to memory size, power consumption, and heat dissipation. As a result, existing smart glasses solutions have short run-times (<1 hr) and may have battery weight and heat dissipation issues that are uncomfortable for continuous wear. The disclosed solution provides a system and method for low-power image processing via the use of scalable processing. In one specific implementation, gesture detection is divided into multiple stages. Each stage conditionally enables subsequent stages for more complex processing. By scaling processing complexity at each stage, high complexity processing can be performed on an 'as-needed' basis.

Systems, apparatus, and methods for gesture-based augmented reality, extended reality

A method, performed by an electronic device, for verifying a user to allow access to the electronic device is disclosed. In this method, sensor data may be received from a plurality of sensors including at least an image sensor and a sound sensor. Context information of the electronic device may be determined based on the sensor data and at least one verification unit may be selected from a plurality of verification units based on the context information. Based on the sensor data from at least one of the image sensor or the sound sensor, the at least one selected verification unit may calculate at least one verification value. The method may determine whether to allow the user to access the electronic device based on the at least one verification value and the context information.

Context-based access verification

A method, which is performed by an electronic device, for adjusting at least one image capturing parameter in a preview mode is disclosed. The method may include capturing a preview image of a scene including at least one text object based on a set of image capturing parameters. The method may also identify a plurality of text regions in the preview image. From the plurality of text regions, a target focus region may be selected. Based on the target focus region, the at least one image capturing parameter may be adjusted.

Image capturing parameter adjustment in preview mode

A method for responding in an augmented reality (AR) application of a mobile device to an external sound is disclosed. The mobile device detects a target. A virtual object is initiated in the AR application. Further, the external sound is received, by at least one sound sensor of the mobile device, from a sound source. Geometric information between the sound source and the target is determined, and at least one response for the virtual object to perform in the AR application is generated based on the geometric information.

Augmented reality with sound and geometric analysis

The various aspects are directed to automatic device-to-device connection control. An aspect extracts a first sound signature, wherein the extracting the first sound signature comprises extracting a sound signature from a sound signal emanating from a certain direction, receives a second sound signature from a peer device, compares the first sound signature to the second sound signature, and pairs with the peer device. An aspect extracts a first sound signature, wherein the extracting the first sound signature comprises extracting a sound signature from a sound signal emanating from a certain direction, sends the first sound signature to a peer device, and pairs with the peer device. An aspect detects a beacon sound signal, wherein the beacon sound signal is detected from a certain direction, extracts a code embedded in the beacon sound signal, and pairs with a peer device.

Automatic device-to-device connection control by environmental information

Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for position estimation via one or more proximate fingerprints for use in or with a mobile communication device.

Position estimation via proximate fingerprints

Active noise cancellation is combined with spectrum modification of a reproduced audio signal to enhance intelligibility.

Systems, methods, apparatus, and computer-readable media for automatic control of active noise cancellation

A method for identifying mobile devices in a similar sound environment is disclosed. Each of at least two mobile devices captures an input sound and extracts a sound signature from the input sound. Further, the mobile device extracts a sound feature from the input sound and determines a reliability value based on the sound feature. The reliability value may refer to a probability of a normal sound class given the sound feature. A server receives a packet including the sound signatures and reliability values from the mobile devices. A similarity value between sound signatures from a pair of the mobile devices is determined based on corresponding reliability values from the pair of mobile devices. Specifically, the sound signatures are weighted by the corresponding reliability values. The server identifies mobile devices in a similar sound environment based on the similarity values.

Method and apparatus for identifying mobile devices in similar sound environment

A mobile device that is capable of automatically starting and ending the recording of an audio signal captured by at least one microphone is presented. The mobile device is capable of adjusting a number of parameters related with audio logging based on the context information of the audio input signal.

System and method of smart audio logging for mobile devices

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San Diego, CA, United States of America

Te-Won Lee