Analog Devices,inc.

Analog Devices International Unlimited Company

Nicolas Le Dortz

Jonathan Ephraim David Hurwitz

Charles Mathy

Sefa Demirtas

Tao Yu

Erik D Barnes

Atulya Yellepeddi

Brian C Donnelly

Javier Calpe Maravilla

Eoin Edward English

Junhua Shen

Bin Huo

Kaushal Sanghai

Johannes Traa

Krystian Balicki

Jonathan Ari Goldberg

Jiannan Huang

John Robert McCarty

Kevin Joseph Mcandrew

Dhruvesh Gajaria

Frank M Yaul

Richard Haltmaier

James Noraky

A depth estimation system can use a guided filter to enhance depth estimation using brightness image. Light is projected onto an object. The object reflects at least a portion of the projected light. The reflected light is at least partially captured by an image sensor. The depth estimation system may generate a depth image based on a phase shift between the captured light and the projected light and generate a brightness image based on brightness of the captured light. The depth estimation system may use the guided filter to identify a pixel that represents at least a portion of a boundary of the object. The guided filter can determine a depth value of the pixel based on the value of the corresponding pixel in the brightness image. The depth estimation system can assign the depth value to the pixel and generates an enhanced depth image.

Using guided filter to enhance depth estimation with brightness image

A depth estimation system can enhance depth estimation using brightness image. Light is projected onto an object. The object reflects at least a portion of the projected light. The reflected light is at least partially captured by an image sensor, which generates image data. The depth estimation system may use the image data to generate both a depth image and a brightness image. The image sensor includes a plurality of pixels, each of which is associated with two ADCs. The ADCs receive different analog signals from the pixel and outputs different digital signals. The depth estimation system may use the different digital signals to determine the brightness value of a brightness pixel of the brightness image. The ADCs may be associated with one or more other pixels. The pixel and the one or more other pixels may be arranged in a same column in an array of the image sensor.

Enhancing depth estimation with brightness image

An image processing system having on-the-fly calibration uses the placement of the imaging sensor and the light source for calibration. The placement of the imaging sensor and light source with respect to each other affect the amount of signal received by a pixel as a function of distance to a selected object. For example, an obstruction can block the light emitter, and as the obstruction is positioned an increasing distance away from the light emitter, the signal level increases as light rays leave the light emitters, bounce off the obstruction and are received by the imaging sensor. The system includes a light source configured to emit light, and an image sensor to collect incoming signals including reflected light, and a processor to determine a distance measurement at each of the pixels and calibrate the system.

Built-in calibration of time-of-flight depth imaging systems

Far field devices typically rely on audio only for enabling user interaction and involve only audio processing. Adding a vision-based modality can greatly improve the user interface of far field devices to make them more natural to the user. For instance, users can look at the device to interact with it rather than having to repeatedly utter a wakeword. Vision can also be used to assist audio processing, such as to improve the beamformer. For instance, vision can be used for direction of arrival estimation. Combining vision and audio can greatly enhance the user interface and performance of far field devices.

Multi-modal far field user interfaces and vision-assisted audio processing

A laser emitter circuit comprises a laser diode; a driver circuit configured to generate a drive signal; and a resonant circuit coupled to the driver circuit and the laser diode, wherein the resonant circuit is configured to use the drive signal of the driver circuit to generate a continuous wave sinusoidal drive signal to drive the laser diode.

Resonant laser driver

An image processing system for time-of-flight depth imaging includes a processor for determining depth measurements using different modes of operation. The processor determines depth measurements in a first set of frames using a second set of frames. The first mode is a continuous wave modulation mode without depth linearization and the second mode is a continuous wave modulation mode with depth linearization. The depth estimates collected in the second mode using depth linearization are used to correct the depth estimates collected in the first mode.

Depth non-linearity compensation in time-of-flight imaging

Time of Flight (ToF) image processing methods include collecting correlation samples to calculate a phase estimate. Systems and methods are provided for collecting correlation samples from multiple pixels. An image processing system for continuous waves includes a light source configured to emit light, an image sensor having a plurality of pixels, and a processor configured to collect correlation samples from a subset of the plurality of pixels in the image sensor.

Spatial correlation sampling in time-of-flight imaging

Time of Flight (ToF) image processing systems and methods for proximity detection are disclosed. In particular, use of a separate proximity detector can be eliminated by using the time of flight image processing system as disclosed herein. In particular, the time of flight image processing system has two modes: a low resolution proximity detection mode and a high resolution imaging mode.

Time-of-flight camera and proximity detector

Time of Flight (ToF) depth image processing methods. Depth edge preserving filters are disclosed with superior performance to standard edge preserving filters applied to depth maps. In particular, depth variance is estimated and used to filter while preserving depth edges. In doing so, filter strength is calculated which can be used as an edge detector. A confidence map is generated with low confidence at pixels straddling a depth edge, and which reflects the reliability of the depth measurement at each pixel.

Time-of-flight depth image processing systems and methods

Aspects of the embodiments are directed to passive depth determination. Initially, a high power depth map of a scene can be created. An object in the scene can be identified, such as a rigid body or other object or portion of an object. A series of lower power or RGB images can be captured. The object can be located in one or more of the lower power or RGB images. A change in the position of an object, represented by a set of pixels, can be determined. From the change in position of the object, a new depth of the object can be extrapolated. The extrapolated depth of the object can be used to update the high power depth map.

Reducing power consumption for time-of-flight depth imaging

Aspects of the embodiments are directed to methods and imaging systems. The imaging systems can be configured to sense, by an light sensor of the imaging system, light received during a time period, process the light received by the light sensor, identify an available measurement period for the imaging system within the time period based on the processed light, and transmit and receive light during a corresponding measurement period in one or more subsequent time periods.

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Cambridge, MA, United States of America

Nicolas Le Dortz