Qualcomm Incorporated

Qualcomm Technologies, Inc.

Thermotrex Corporation

Trex Communications Corporation

Victor Hokkiu Chan

Jason Frank Hunzinger

Jeffrey Alexander Levin

Edwin Chongwoo Park

Scott Harris Bloom

Michael D Rivers

Eugene Izhikevich

Filip Lukasz Piekniewski

Micah Richert

Yinyin Liu

James H Menders

Vikram Makam Gupta

Evgeni Petrovich Gousev

Michael-David Nakayoshi Canoy

Michael Campos

Bardia Fallah Behabadi

Xiaopeng Zhang

Liang Shen

Ryan Michael Carey

Narayana S Ravirala

Paul E Bender

Ravindra Manohar Patwardhan

Alok Govil

Gerhard Reitmayr

Xiaoyun Jiang

Eric John Korevaar

Xin Zhong

Chinchuan Andrew Chiu

Regan Blythe Towal

Narayana Karthik Sadanandam Ravirala

Kiet Tuan Chau

Sarah Paige Gibson

Adrienne Milner

Irene Chen

Erik Christopher Malone

Thomas Zheng

Sach Hansen Sokol

Edward Liao

Dennis Liu

Amy Low

Gina Marcela Escobar Mora

Dihui Lai

Due to complexity constraints, most computer vision (CV) features are not suitable for certain applications such as low-power always-on applications. To address such issues, a contrast-adaptive normalized pixel difference (CA-NPD) is proposed. Unlike conventional NPD techniques, the CA-NPD smooths a metric surface, optimizes contrast discriminability, and enables context sensitive normalization.

Contrast-adaptive normalized pixel difference

Sensing of scene-based occurrences is disclosed. In one example, a vision sensor system comprises (1) dedicated computer vision (CV) computation hardware configured to receive sensor data from at least one sensor array and capable of computing CV features using readings from multiple neighboring sensor pixels and (2) a first processing unit communicatively coupled with the dedicated CV computation hardware. The vision sensor system is configured to, in response to processing of the one or more computed CV features indicating a presence of one or more irises in a scene captured by the at least one sensor array, generate data in support of iris-related operations to be performed by a second processing unit and send the generated data to the second processing unit.

Low power data generation for iris-related detection and authentication

Methods, systems, and devices for exposure control are described, including capturing a first and second field of view with a first and second sensor. The techniques may include identifying a brightness difference and an exposure time difference between the first and second sensor, and capturing a first image and a second image, and outputting a third image including both the first and second image. Techniques may include determining an exposure bias, identifying a hypothesis total gain for the first sensor and a peer sensor total gain for the second sensor, and selecting a total gain for each sensor based on comparing the hypothesis total gain and the peer sensor total gain, and based on a maximum brightness difference between the two sensors. The total gain for each sensor may be adjusted to satisfy the maximum brightness difference and the exposure bias, or based on a region of interest.

Low-power auto-exposure control (AEC) for multi-sensor systems

Embodiments described herein can address these and other issues by providing a CV image sensor that has a repeating pattern of two pairs of color-opponent color sensors and a luminance sensor to provide a high amount of functionality while maintaining relatively low power requirements. By using color opponency, embodiments can sidestep the need for processor-intensive color conversions required of other color sensors. Embodiments may optionally provide for surface area optimization, hyperspectral sensitivity, low-light optimization, neuromorphic light sensing, and more.

Computer vision sensor

Use of pupillary response to visible light for iris authentication is disclosed. One example involves (a) capturing an initial image of an eye including an inner circular boundary between a pupil region and an iris region and an outer circular boundary between the iris region and a sclera region, (b) determining a first size measurement indicative of the inner circular boundary, (c) responsive to at least the first size measurement, modulating one or more visible light sources to output visible light toward the eye of the user, (d) capturing a subsequent image of the eye of the user during a period of pupillary response, (e) obtaining an iris data record of the user, and (f) comparing the iris data record of the user to one or more registered iris data records to authenticate the user.

Light source modulation for iris size adjustment

Methods, apparatuses, computer-readable medium, and systems are disclosed for performing automatic exposure control (AEC). In one embodiment, a first digital image is captured while applying a first set of values to one or more exposure control parameters. At least one computer vision (CV) operation is performed using image data from the first digital image, thereby generating a first set of CV features from a set of possible CV features. A mask is obtained comprising a value for each feature of the set of possible CV features. Using the mask, a first measure of abundance is obtained of relevant CV features among the first set of CV features extracted from the first digital image. Based on the first measure of abundance of relevant CV features, an updated set of values is generated for applying to the one or more exposure control parameters for capturing a subsequent digital image of the scene.

Low complexity auto-exposure control for computer vision and imaging systems

Methods and apparatus are provided for identifying environmental stimuli in an artificial nervous system using both spiking onset and spike counting. One example method of operating an artificial nervous system generally includes receiving a stimulus; generating, at an artificial neuron, a spike train of two or more spikes based at least in part on the stimulus; identifying the stimulus based at least in part on an onset of the spike train; and checking the identified stimulus based at least in part on a rate of the spikes in the spike train. In this manner, certain aspects of the present disclosure may respond with short response latencies and may also maintain accuracy by allowing for error correction.

Simultaneous latency and rate coding for automatic error correction

Methods, systems, computer-readable media, and apparatuses for incremental object detection using a staged process and a band-pass feature extractor are presented. At each stage of the staged process, a different band of features from a plurality of bands of features in image data can be extracted using a dual-threshold local binary pattern operator, and compared with features of a target object within the band for a partial decision. The staged process exits if a rejection decision is made at any stage of the staged process. If no rejection decision is made in each stage of the staged process, the target object is detected. Features extracted at each stage may be from a different image for some applications.

Systems and methods for incremental object detection using dual-threshold local binary pattern operators

Certain aspects of the present disclosure support a method and apparatus for analog signal reconstruction and recognition via sub-threshold modulation. The analog waveform recognition in a sub-threshold region of an artificial neuron of the artificial nervous system can be performed by providing a predicted waveform in parallel to an input associated with the artificial neuron. The predicted waveform can be compared with the input and the signal can be generated based at least in part on the comparison. The signal can be a detection signal that detects matching and mismatching between the input and the predicted waveform

Analog signal reconstruction and recognition via sub-threshold modulation

Apparatus and methods for activity based plasticity in a spiking neuron network adapted to process sensory input. In one approach, the plasticity mechanism of a connection may comprise a causal potentiation portion and an anti-causal portion. The anti-causal portion, corresponding to the input into a neuron occurring after the neuron response, may be configured based on the prior activity of the neuron. When the neuron is in low activity state, the connection, when active, may be potentiated by a base amount. When the neuron activity increases due to another input, the efficacy of the connection, if active, may be reduced proportionally to the neuron activity. Such functionality may enable the network to maintain strong, albeit inactive, connections available for use for extended intervals.

Apparatus and methods for rate-modulated plasticity in a neuron network

Methods and apparatus are provided for effecting modulation using global scalar values in a spiking neural network. One example method for operating an artificial nervous system generally includes determining one or more updated values for artificial neuromodulators to be used by a plurality of entities in a neuron model and providing the updated values to the plurality of entities.

Effecting modulation by global scalar values in a spiking neural network

Methods and apparatus are provided for implementing structural plasticity in an artificial nervous system. One example method for altering a structure of an artificial nervous system generally includes determining a synapse in the artificial nervous system for reassignment, determining a first artificial neuron and a second artificial neuron for connecting via the synapse, and reassigning the synapse to connect the first artificial neuron with the second artificial neuron. Another example method for operating an artificial nervous system, generally includes determining a synapse in the artificial nervous system for assignment; determining a first artificial neuron and a second artificial neuron for connecting via the synapse, wherein at least one of the synapse or the first and second artificial neurons are determined randomly or pseudo-randomly; and assigning the synapse to connect the first artificial neuron with the second artificial neuron.

Implementing structural plasticity in an artificial nervous system

Aspects of the present disclosure support techniques for neural pattern sequence completion and neural pattern hierarchical replay. At least a portion of a pattern can be invoked for replay upon referencing the pattern and learning relational aspects between elements of the pattern and the referencing of the pattern using hierarchical levels of neurons.

Methods and apparatus for neural pattern sequence completion and neural pattern hierarchical replay by invoking replay of a referenced neural pattern

Aspects of the present disclosure support techniques for neural component memory transfer. A pattern in a plurality of afferent neuron outputs can be first referenced with one or more referencing neurons. One or more first relational aspects can be matched, with one or more first relational aspect neurons, between the referenced pattern and an output of the one or more referencing neurons. The referenced pattern can be transferred to one or more transferee neurons by inducing the plurality of afferent neurons to output a pattern substantially the same as the referenced pattern by the one or more referencing neurons.

Methods and apparatus for neural component memory transfer of a referenced pattern by including neurons to output a pattern substantially the same as the referenced pattern

Growing community of inventors

Del Mar, CA, United States of America

Victor Hokkiu Chan