Seiko Epson Corporation

Yang Yang

Guoyi Fu

Eunice Poon

Ivo Moravec

Lihui Chen

Irina Kezele

Ian Clarke

Jia Li

Mikhail Brusnitsyn

Arash Abadpour

Jie Wang

Hiu Lok Szeto

Mirza Tahir Ahmed

Yury Yakubovich

Tong Qiu

Dan Rico

An operation detection method of detecting an operation of a pointing element with respect to an operation surface includes converting first and second taken image obtained by imaging the operation surface into first and second converted taken image calibrated with respect to the operation surface respectively, determining whether or not the pointing element contacts with the operation surface and is in a resting state based on the first and second converted taken image, selecting the first and second converted taken image at when it was determined that the pointing element contacts and to be in the resting state as first and second reference image, calculating a first difference in position between the pointing element in the first reference image and the pointing element in the first converted taken image, calculating a second difference in position between the pointing element in the second reference image selected and the pointing element in the second converted taken image, and determining whether or not the pointing element separated from the operation surface based on the first difference and the second difference.

Two camera touch operation detection method, device, and system

A method may include the following steps: acquiring, from a camera, an image frame; acquiring, from an inertial sensor, a sensor data sequence; tracking a first pose of an object in a real scene based at least on the image frame; deriving a sensor pose of an inertial sensor based on the sensor data sequence; determining whether the first pose is lost; retrieving from one or more memories, or generating from a 3D model stored in one or more memories, a training template corresponding to a view that is based on the sensor pose obtained on or after the first pose is lost; and deriving a second pose of the object using the training template.

Object detection and tracking

A head-mounted display device includes a camera, an inertial sensor in a first spatial relationship with the camera, and a processor that: acquires an image data sequence from the camera; and acquires a sensor data sequence from the inertial sensor; derives a first pose of an object based at least on one of image frames in the image data sequence and template data created based on a 3D model corresponding to the object; derives a second spatial relationship between an object coordinate system defined on the object or the 3D model and a global coordinate system based at least on the first pose or a pose tracked from the first pose, the sensor data sequence and the first spatial relationship; and derives a second pose of the object based at least on the image data sequence, the sensor data sequence and the second spatial relationship using a non-linear estimation algorithm.

Information processing device and computer program

A head-mounted display device includes an image display section, an imaging section, an image setting section configured to set an image, an operation input section, an object specification section configured to derive a spatial relationship of a specific object included in an imaged outside scene with respect to the imaging section, and a parameter setting section. The image setting section causes the image display section to display a setting image based at least on the derived spatial relationship and a predetermined parameter group so as to allow a user to visually perceive the setting image, and the parameter setting section adjusts at least one parameter in the parameter group so as to allow the user to visually perceive a condition that at least a position and pose of the setting image and those of the specific object are substantially aligned with each other.

Controlling a display of a head-mounted display device

A head-mounted display device includes: a camera; an inertial sensor in a fixed or adjustably fixed spatial relationship with the camera; a display; and a processor. The processor is configured to derive a first position of a feature element in an image frame in an image data sequence using an image frame, and derive a second position of the feature element using a sensor data sequence and the spatial relationship. The processor is further configured to display, with the display, a display image containing: i) a first visual element at a first location corresponding to the first position, and ii) a second visual element at a second location corresponding to the second position. The processor is further configured to perform calibration when a received signal indicates that the camera and inertial sensor are not sufficiently calibrated.

Head-mounted display device configured to display a visual element at a location derived from sensor data and perform calibration

A method tracks a pose of an object in a field of view of a camera; derives movement of the display apparatus or movement in the field of view using at least one of: the sensor data and the image data; determines, using the processor, whether or not the derived movement exceeds a first threshold; if the derived movement exceeds the first threshold, continues tracking the pose of the object; and if the movement does not exceed the first threshold: (1) stops tracking the pose of the object and (2) displays, using the display, the image based on a previously derived pose of the object.

Head-mounted display device and method of operating a display apparatus tracking an object

A marker whose at least one of a position and pose with respect to a capturing unit is estimated includes: quadrilateral specifying points that specify a quadrilateral shape; a first circle group that is a group of a plurality of circles whose centers are present in a line of a first diagonal which is one of two diagonals of the specified quadrilateral shape, and which are included in the quadrilateral shape; a second circle group that is a group of a plurality of circles whose centers are present in a line of a second diagonal which is the other diagonal of the two diagonals than the first diagonal, and which are included in the quadrilateral shape; and a direction-identification point that specifies a direction of the quadrilateral shape.

Marker, method of detecting position and pose of marker, and computer program

A method of calibration for a head-mounted display device including an camera, an inertial sensor and a processor causes the processor to realize functions of: acquiring a first image and a second image that are captured by imaging a first real marker with the camera when the inertial sensor is respectively at a first pose and a second pose; acquiring a difference in pose between the first pose and the second pose based on output of the inertial sensor; and deriving a spatial relationship between the camera and the inertial sensor based at least on the first image, the second image and the difference in pose.

Program, device, and calibration method for a camera and an inertial sensor

An adequate solution for computer vision applications is arrived at more efficiently and, with more automation, enables users with limited or no special image processing and pattern recognition knowledge to create reliable vision systems for their applications. Computer rendering of CAD models is used to automate the dataset acquisition process and labeling process. In order to speed up the training data preparation while maintaining the data quality, a number of processed samples are generated from one or a few seed images.

Method and apparatus for improved training of object detecting system

The present invention uses dynamic grouping to divide up training samples to train different classification nodes. At the beginning of the training, all samples are in the same group. A clustering process is applied in the feature space of the selected feature vectors with cluster indexes accumulated. The average of all the accumulated cluster indexes is used as the threshold for splitting the samples into two groups. When the splitting criterion is met, samples are split into two groups based on their similarity in the feature space.

Method of automatically training a classifier hierarchy by dynamic grouping the training samples

A method for identifying a QR code in an image includes inputting an image, and using one or more computer processor to identify edges in the image; create an edge count image; label connected regions in the edge count image; create a candidate region list; for each individual candidate in the candidate region list: find regions that overlap with the region in the individual candidate; group regions that overlap into a group of overlapped regions; for each group: merge the overlapped regions into a merged region; add each merged region to the candidate region list to form a new candidate region list; and perform decoding of a QR code in each region in the new candidate region list.

Method and apparatus for locating bar codes including QR codes

For recognizing two-width barcodes having N bars and N spaces, a first class (cluster) A of wide bars or spaces contains n elements Array [] to Array [n], and a second class (cluster) B of narrow bars or spaces contains N&#x2212;n elements Array [n+1] to Array [N] are created. Looping n times where n=1 to N&#x2212;1, a mean value &#x3bc;to of class A and a mean value &#x3bc;of class B are calculated, a between-class difference D=&#x3bc;&#x2212;&#x3bc;is calculated, a value of n when D achieves a maximum value is recorded, and if n=M when D achieves the maximum value, then the N bars or spaces are recognized as a two-width barcode; otherwise, the N bars or spaces are determined as not a two-width barcode.

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Toronto, Canada

Yang Yang