Mitsubishi Electric Research Laboratories, Inc.

Polaroid Corporation

Other

Mitsubishi Electric Corporation

Senshin Capital, LLC

Zink Imaging, Inc.

Pgi Graphics Imaging LLC

Mitcham Global Investments Ltd.

Jay E Thornton

Daniel P Bybell

William T Vetterling

Stephen J Telfer

Suhail Shabbir Saquib

Brian David Busch

F Richard Cottrell

Anemarie DeYoung

Amit Kumar Agrawal

Chien Liu

Jayprakash C Bhatt

Dana Frederic Schuh

Mitchell R Balonon-Rosen

Ramesh Raskar

Toru Sasaki

Fatih M Porikli

Hajime Nakajima

Osamu Nasu

Oncel C Tuzel

Takeshi Musha

Yoshinao Tatei

Yuri A Ivanov

Abraham Goldsmith

Takuya Noguchi

Richard A Pineau

Bror O Hultgren, Iii

Shigenori Takeda

Haruhiko Takeyama

Yuichi Taguchi

Michael J Jones

Alain Bouchard

Srikumar Ramalingam

Christopher Richard Wren

Jeroen Van Baar

Tim Marks

Paul H Dietz

Michael S Viola

Alan Sullivan

Ankush Chakrabarty

Richard C Waters

Michael L Reisch

Hiroyuki Tsutada

Ryosuke Kawanishi

Makito Seki

Teng-Yok Lee

Izrail Gorian

Keisuke Sambongi

Yu Enomoto

Mamoru Kato

Janet A Allen

Jie Ni

Sandra B Lawrence

Bernd W Kolpatzik

William J Donovan

Izrail S Gorian

Michael J Vigneau

Dana F Schuh

F Richard Courell

Michael C Antin

Israel Meir

Felix J Lin

Changhua Wu

Ashish Shrivastava

Wenzhen Yuan

William Vetterling

Fatih Porikli

A system for generating a G-code for controlling an operation of a laser-cutting machine to cut parts from a sheet of material, upon receiving cutting data specifying a cutting order of parts and a cutting order of edges of each part, tests the parts for potential distortions and generates a G-code to avoid the potential distortion. For testing a current part, the system detects a potential distortion when the final edge of the current part is adjacent to an edge of a previously cut part scheduled for cutting before the current part according to the cutting order of parts. The system modifies the cutting order to select the modified cutting order for which the final edge is not adjacent to any edge of any previously cut part.

System and method for detecting and correcting laser-cutting distortion

Provided is an elevator shaft dimensions measurement device including: a plurality of 3-D distance image sensors which are arranged on a circumference of the same circle, facing the direction of the center of the circle and inclined at an elevation angle with respect to a horizontal plane, and which output measurement data by capturing an image of a pattern irradiated onto the inner walls of an elevator shaft that are imaging objects; and a computer which integrates the measurement data output from the plurality of 3-D distance image sensors at a plurality of height positions in the elevator shaft, generates first integrated measurement data covering 360 degrees in the horizontal direction, aligns the first integrated measurement data to create second integrated measurement data after the alignment, and calculates the dimensions of the elevator shaft on the basis of the second integrated measurement data after the alignment.

Elevator shaft dimensions measurement device and elevator shaft dimensions measurement method

A distributed machining simulation partitions a model of the workpiece to produce a set of sub-workpieces and modifies the machining instructions for each sub-workpiece to produce a set of groups of modified machining instructions different from the machining instructions. Each group of the modified machining instructions is associated with a sub-workpiece and includes effective machining instructions that move the machining tool through the sub-workpiece and at least one fake machining instruction that moves the machining tool through the model of the workpiece outside of the sub-workpiece. The distributed machining simulation remote hosts to simulate the machining of the workpiece using at least one group of the modified machining instructions to produce geometries of a machining simulation of the workpiece, each geometry is associated with a corresponding sub-workpiece and reconstructs a representation of the machined workpiece using a portion of each geometry within the volume of the corresponding sub-workpiece.

System and method for distributed machining simulation

A method and system estimates a three-dimensional (3D) pose of a sensor by first acquiring scene data of a 3D scene by the sensor. Two-dimensional (2D) lines are detected in the scene data and the 2D lines are matched to 3D lines of a 3D model of the scene to produce matching lines. Then, the 3D pose of the sensor is estimated using the matching lines.

Pose estimation using sensors

To obtain a position detection device capable of detecting highly accurate and robust position by suppressing affect of error detection of an edge, provided is a configuration for performing a validity determination of edges detected by binarizing an image signal () converted by a light-receiving element () for receiving light irradiated from a light source () toward a scale () having a code pattern, and performing position detection after removing the edges determined to be invalid as edges that have been affected by foreign matter and the like among the edges on which the validity determination has been performed.

Position detection device

A method generates a high-resolution (HR) image from a low-resolution (LR) image using regression functions. During a training stage, training HR images are downsampled to LR images. A signature is determined for each LR-HR patch pair based on a local ternary pattern (LTP). The signature is a low dimensional descriptor used as an abstraction of the patch pair features. Then, patch pairs with the same signature are clustered, and a regression function which maps the LR patches to the HR patches is determined. In some cases patch pairs of similar signatures can be combined for learning and a single regression function determined, thus decreasing the number of required regression functions. During actual upscaling, LR patches of an input image are similarly processed to obtain the signatures and from the regression functions. The LR patches can then be upscaled using the training regression functions.

Method for generating high-resolution images using regression patterns

Provided is an absolute encoder capable of detecting the absolute angle at high resolution and with high precision. An image sensor receives light in an absolute value code pattern of a scale, an edge detecting unit detects from the received light signal an edge pixel position and an edge direction, and an edge position correcting unit corrects the edge pixel position based on the edge direction. A phase detecting unit detects from the corrected edge pixel position the phase shift amount of a shift from a reference pixel position of the image sensor, and a high precision detection unit uses a rough absolute position detected by a rough detection unit and the phase shift amount detected by the phase detecting unit to detect the absolute position with high precision.

Absolute encoder

A rotary encoder includes a single read-head and a circular scale. The encoder is self-calibrated by acquiring calibration samples with the read-head for rotational angles of the circular scale, and estimating spatial frequency and spatial distortion parameters of the encoder from the calibration samples.

Self-calibrating single track absolute rotary encoder

Three-dimensional (3D) geometric boundaries are detected in images of a scene that undergoes varying lighting conditions caused by light sources in different positions, from a set of input images of the scene illuminated by at least two different lighting conditions. The images are aligned, e.g., acquired by a stationary camera, so that pixels at the same location in all of the input images correspond to the same point in the scene. For each location, a patch of corresponding pixels centered at the location is extracted from each input image. For each location, a confidence value that there is a 3D geometric boundary at the location is determined.

Method for detecting 3D geometric boundaries in images of scenes subject to varying lighting

A method denoises a noisy image by, for each pixel in the noisy image, first constructing a key from a patch, wherein the patch includes locally neighboring pixels around the pixel. A function is selected from a function library using the key. Then, the function is applied to the patch to generate a corresponding noise free pixel for the pixel.

Image denoising using a library of functions

A structured light pattern is projected onto a scene using pulses of light emitted by light emitting diodes (LEDs) of the projector, wherein during the pulses of the LEDs, the LEDs are driven at an overdrive current when the LEDS are ON. A sensor of a camera only integrates light from the scene during the pulses to acquire images of the scene.

Structured light projection and acquisition

During pre-processing, a 3D model of the object is rendered for various poses by arranging virtual point light sources around the lens of a virtual camera. The shadows are used to obtain oriented depth edges of the object illuminated from multiple directions. The oriented depth edges are stored in a database. A camera acquires images of the scene by casting shadows onto the scene from different directions. The scene can include one or more objects arranged in arbitrary poses with respect to each other. The poses of the objects are determined by comparing the oriented depth edges obtained from the acquired images to the oriented depth edges stored in the database. The comparing evaluates, at each pixel, a cost function based on chamfer matching, which can be speed up using downhill simplex optimization.

System and method for determining poses of objects

A network in an environment includes passive sensors with associated sensor identification numbers. A set of active badges with associated badge identification numbers, wherein each badge is associated with an object, and wherein a particular sensor broadcasts a packet in response to detecting a particular object, wherein the packet includes the sensor identification number and a time, and only if the particular object is associated with a particular badge then the badge appends the badge identification number to the packet and broadcasts the appended packet, and the particular sensor receives and rebroadcasts the appended packet for further processing to passively and actively monitor the environment.

Active badge localization using passive sensors

Techniques are disclosed for correcting the exposure of a digital image. An exposure predictor may be generated based on a set of images for which ground truth data are known. After identifying an optimal set of features, the exposure of the digital image may be corrected by extracting values of the selected optimal features from the image, using the predictor to predict a desired exposure correction for the image, and correcting the exposure of the image by the predicted desired amount. Exposure correction is based on a model that relates intensity of light in the world to the RGB digits of the digital image. The model comprises a gamma function that models the response of a typical monitor and a S-shaped curve that compresses the large dynamic range of the world to the small dynamic range of the RGB digit space.

Digital image exposure correction

A method identifies an unknown face in an input image using reference images of known faces. A Haar-like feature vector is extracted from each image. The vectors are compressed. An L1 norm is determined between the compressed feature vector of the input image and each compressed feature vector from the set of reference images to determine a most similar reference image. The identity of the face associated with the most similar reference image is assigned as the identity of the unknown face in the input image.

Method for identifying faces in images with improved accuracy using compressed feature vectors

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Watertown, MA, United States of America

Jay E Thornton