Honeywell International Inc.

Gentex Corporation

Rida M Hamza

Kwong Wing Au

Darren Duane Cofer

Thea Lynn Feyereisen

Mohammed Ibrahim Mohideen

Saad J Bedros

Santosh Mathan

Michael E Bazakos

Rand P Whillock

Michael E Johnson

Terry Ahrens

Au W Kwong

Thomas A Henderson

Isaac D Cohen

Dinkar A Mylaraswamy

Sharath Venkatesha

Jan Jelinek

David W Meyers

Wesley J Hawkinson

David Pepitone

Thomas L Phinney

Steven D Martinez

Wayne A Soehren

Sara Susca

Gary Edward Determan

Vincent Clarence Jacobson

Misha Pavel

George A Kilgore

Marek Gierczak

Dinesh Ramegowda

Robert A Blaser

Deniz Erdogmus

Murray J Cooper

Kenneth Hild

A navigation system comprises an image sensor operable to obtain a first image at a first moment in time and a second image at a second moment in time; an inertial measurement unit (IMU) operable to obtain measurement data corresponding to the first and second moments in time; and a processing unit coupled to the image sensor and the IMU. The processing unit is operable to estimate motion between the first and second moments in time based on the measurement data from the IMU; calculate a plurality of transformations based on the estimated motion; apply each of the plurality of transformations to the first image to produce a plurality of predicted images; compare the second image to each of the plurality of predicted images; select the predicted image from the plurality of predicted images which most closely matches the second image; and compensate for error in the IMU measurement data based on the transformation corresponding to the selected predicted image.

Method and apparatus for vision aided navigation using image registration

A condition based method that selects an appropriate approach among various iris and ocular image recognition algorithms for matching periocular images of a probe and target as a function of quality of images to obtain robust matching even under non-ideal acquisition scenarios.

Ocular and iris processing system and method

A navigation system includes host and remote units. Host unit includes positioning device to determine absolute position/orientation of host unit; first communication device to communicate signals; first processor; and first memory. Remote unit includes second communication device to receive signals from first communication device; second processor; and second memory. First or second processor compares first aspects of known pattern with second aspects of image of captured pattern positioned on surface at either host unit or remote unit. First or second processor determines relative position/orientation of remote unit relative to host unit based on comparison of first aspects and second aspects. First or second processor determines absolute position/orientation of remote unit based on relative position/orientation of remote unit relative to host unit and absolute position/orientation of host unit. Either absolute position/orientation of host unit or absolute position/orientation of remote unit are communicated from host unit to remote unit.

Using structured light to update inertial navigation systems

A system using face and iris image capture for recognition of people. The system may have wide field-of-view, medium field-of-view and narrow field-of-view cameras to capture images of a scene of people, faces and irises for processing and recognition. Matching of the face and iris images with images of a database may be a basis for recognition and identification of a subject person.

Combined face and iris recognition system

An adaptive iris matching approach for processing images of irises having a quality not sufficient for conventional non-adaptive matching approaches. Visible regions in a radial direction on an iris, without segmenting a circumferential of the iris, may be processed. A boundary of the visible region of the iris may be estimated. An iris map may be constructed with the non-visible portion of the iris masked. The iris map may be at least partially encoded. Partial codes of the iris map may be extracted to index at least portions of a database containing iris information. Irises may be retrieved from the database with an iris code as a query.

Adaptive iris matching using database indexing

An encoding system for an iris recognition system. In particular, it presents a robust encoding method of the iris textures to compress the iris pixel information into few bits that constitute the iris barcode to be stored or matched against database templates of same form. The iris encoding system is relied on to extract key bits of information under various conditions of capture, such as illumination, obscuration or eye illuminations variations.

Expedient encoding system

An iris and ocular recognition system using trace transforms. The system may acquire an eye image and provide it to an image quality metrics determination module for a quality evaluation to indicate whether the image goes to an iris recognition module and/or a trace transform module. The trace transform may also be used as a pre-filtering mechanism to determine a small database from bigger datasets. If the quality evaluation reaches a predefined quality measure, the image may be passed immediately to the iris recognition module. If not, the image may go to the trace transform module. If the quality evaluation is too poor for the latter, the image may be rejected subject to rehabilitation or reacquisition. A processed image from the iris recognition module may result in an only best match. A processed image from the trace transform module may be lined up instead with the most probable matches.

Iris and ocular recognition system using trace transforms

A method and computer product are presented for identifying a subject by biometric analysis of an eye. First, an image of the iris of a subject to be identified is acquired. Texture enhancements may be done to the image as desired, but are not necessary. Next, the iris image is radially segmented into a selected number of radial segments, for example 200 segments, each segment representing 1.8&#xb0; of the iris scan. After segmenting, each radial segment is analyzed, and the peaks and valleys of color intensity are detected in the iris radial segment. These detected peaks and valleys are mathematically transformed into a data set used to construct a template. The template represents the subject's scanned and analyzed iris, being constructed of each transformed data set from each of the radial segments. After construction, this template may be stored in a database, or used for matching purposes if the subject is already registered in the database.

Invariant radial iris segmentation

Methods and apparatus are provided for recognizing particular objects of interest in a captured image. One or more salient features that are correlative to an object of interest are detected within a captured image. The captured image is segmented into one or more regions of interest that include a detected salient feature. A covariance appearance model is generated for each of the one or more regions of interest, and first and second comparisons are conducted. The first comparisons comprise comparing each of the generated covariance appearance models to a plurality of stored covariance appearance models, and the second comparisons comprise comparing each of the generated covariance appearance models to each of the other generated covariance appearance model. Based on the first and second comparisons, a determination is made as to whether each of the one or more detected salient features is a particular object of interest.

Determining probabilities from compared covariance appearance models to detect objects of interest in images

Systems and methods for detecting a present location of a target from a moving platform are provided. One system includes a movement module for estimating the present location of the target, a prediction module for estimating future locations of the target, and a first processor. The first processor is configured to receive present location estimates, receive future location estimates, store the future location estimates to create a first future location estimate, and determine the present location of the target based on the present location estimate and/or the first future location estimate. One method includes receiving an estimate of the present location of the moving platform, receiving an estimate of a future location of the moving platform, and determining the present location of the target based on the estimate of the present location and/or the estimate of the future location.

Systems and method for dynamic stabilization of target data detected from a moving platform

Systems and methods for recognizing a location of a target are provided. One system includes a camera configured to generate first data representing an object resembling the target, a memory storing second data representing a template of the target, and a processor. The processor is configured to receive the first data and the second data, and determine that the object is the target if the object matches the template within a predetermined percentage error. A method includes receiving first data representing an object resembling the target, receiving second data representing a template of the target, and determining that the object is the target if the object matches the template within a predetermined percentage error. Also provided are computer-readable mediums including processor instructions for executing the above method.

Systems and methods for recognizing a target from a moving platform

A system for one dimensional segmentation of an iris of an eye into a map of the iris and classification of the map into unaffected areas and affected areas. Also, the system may provide for regular shape fitting of the areas for normalization and identifying the unaffected areas as symmetric segments. Further, the system may assign weights to the unaffected areas and the affected areas of the map of the iris and an enrolled map of an iris and their corresponding bins for matching purposes.

Distance iris recognition

A system for iris recognition using a set of quality metrics, which may include eye image validation, blur assessment, offset, gazing, obscuration, visibility, and the like. These metrics may be established as quantitative measures which can automatically assess the quality of eye images before they are processed for recognition purposes. Quadrant iris analysis, histograms, map processing enhancements, and multi-band analysis may be used in aiding in the iris recognition approach.

Iris recognition system using quality metrics

Methods and apparatus are provided for detecting and tracking a target. Images are captured from a field of view by at least two cameras mounted on one or more platforms. These images are analyzed to identify landmarks with the images which can be used to track the targets position from frame to frame. The images are fused (merged) with information about the target or platform position from at least one sensor to detect and track the target. The targets position with respect to the position of the platform is displayed or the position of the platform relative to the target is displayed.

Object detection using cooperative sensors and video triangulation

Methods and apparatus are provided for dividing an image into a plurality of image chips for presentation on a display. Potential objects of interest are detected within an image by detecting features therein that correspond to objects of interest. The image is uniformly divided into a plurality of preliminary image chips. Triage image chips are generated by automatically adjusting each preliminary image chip such that the potential objects of interest detected within each preliminary image chip are at least substantially centered in each preliminary image chip.

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Inver Grove Heights, MN, United States of America

Rida M Hamza