Imaging

USB 3.0 Cameras

Lumenera Corporation (Ottawa, Ontario, Canada) has released USB 3.0 cameras based on Sony’s EXview HAD II ICX674 sensor. Running 53 fps at full resolution, or 66 at an HDTV resolution of 1920 × 1088, the Lt365R series features memory buffer technology so frames are not lost while running the camera at the sensor’s maximum output. The Lt365R set also has a 2.8 megapixel progressive scan sensor equipped with a global shutter.

Posted in: Imaging, Products

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Embedded Video Interface

Pleora Technologies (Ottawa, Ontario, Canada) has introduced an embedded hardware product for integrating GigE Vision 2.0™-compliant video connectivity into cameras, x-ray detector panels, and imaging systems. The iPORT™ NTx-GigE Embedded Video Interface is now in trials for use in medical, military, and industrial automation applications. Other features of the 37 × 37 × 28 mm iPORT NTx-GigE include Power over Ethernet (PoE) and external power options, combined with power consumption of approximately 2 watts when streaming video at 1 Gb/s. Metadata, such as pre-processing results or the position of a quadrature encoder, can be added to each image transmitted over the GigE link. The IEEE 1588 Precision Time Protocol synchronizes multiple cameras and imaging devices to a network master clock, with up to 1 μs (microsecond) precision.

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CMOS Camera

Adimec (Stoneham, MA) has released the Q-12A65, an addition to the company’s CMOS-based QUARTZ series. The Q- 12A65 camera, based on the CMOSIS CMV12000 image sensor, delivers 12 Megapixels at 66 fps. Equipped with the Camera Link deca interface, the Q-12A65 also supports full configurations.

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Novel Hemispherical Dynamic Camera for EVAs

A novel optical design for imaging systems is able to achieve an ultra-wide field of view (UW-FOV) of up to 208°. The design uses an integrated optical design (IOD). The UW-FOV optics design reduces the wasted pixels by 49% when compared against the baseline fisheye lens. The IOD approach results in a design with superior optical performance and minimal distortion.

Posted in: Physical Sciences, Imaging, Briefs

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Reducing Drift in Stereo Visual Odometry

The drift was reduced from an uncorrected 47 cm to just 7 cm. Visual odometry (VO) refers to the estimation of vehicle motion using onboard cameras. A common mode of operation utilizes stereovision to tri angulate a set of image features, track these over time, and infer vehicle motion by computing the apparent point cloud motion with respect to the cameras. It has been observed that stereo VO is subject to drift over time.

Posted in: Imaging, Briefs

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Frame Grabber Deployed in Robomotive Humanoid Robot

Robomotive and other humanoid robots equipped with 3D vision sensors are playing an increasing role in industrial automation, including in smaller binpicking set-ups where parts are constantly changing.

Posted in: Imaging, Application Briefs

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Measuring Motion with Imaging Software

High-speed photography is as much of an engineering tool as an oscilloscope, spectrum analyzer, or logic analyzer. The photographic technique enables us to visualize and analyze motion, especially motion that is too fast for the human eye or conventional cameras to perceive.

Posted in: Imaging, Articles

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LED Illumination in Simulation and Training

Since its inception, projection has been the dominant technology for displaying large, high-resolution images. Over the years, projectors have evolved to keep pace with advances in computer graphics and video sources, following the trends toward ever higher resolutions and ever larger screens.

Posted in: Imaging, Articles

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Reducing Drift in Stereo Visual Odometry

The drift was reduced from an uncorrected 47 cm to just 7 cm. Visual odometry (VO) refers to the estimation of vehicle motion using onboard cameras. A common mode of operation utilizes stereovision to tri angulate a set of image features, track these over time, and infer vehicle motion by computing the apparent point cloud motion with respect to the cameras. It has been observed that stereo VO is subject to drift over time.

Posted in: Imaging, Briefs

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Resource-Constrained Application of Support Vector Machines to Imagery

Fast computation of the SVM decision function over an image using minimal RAM. Machine learning techniques have shown considerable promise for automating common visual inspection tasks. For example, Support Vector Machine (SVM) classifiers that have been learned from labeled training data deliver strong detection performance both for finding human faces in photographs and locating geologic landforms such as craters and volcanoes in planetary images gathered by spacecraft. However, SVMs are computationally expensive to apply to an image using the traditional spatial scanning method in which a rectangular window is slid across the image one pixel at a time and the SVM is evaluated on each patch of pixels under the window. The new software uses small fast Fourier transforms (FFTs) and the overlap- and-add technique from signal processing to quickly and efficiently compute the exact SVM decision function over an entire image using minimal random access memory (RAM).

Posted in: Imaging, Briefs

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