2000 fps real-time vision system with high-frame-rate video recording

Ishii, Idaku; Tatebe, Tetsuro; Gu, Qingyi; Moriue, Yuta; Takaki, Takeshi; Tajima, Kiyohiro

doi:10.1109/robot.2010.5509731

Cited by 144 publications

(54 citation statements)

References 7 publications

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“…The system consists of a high-speed vision platform (IDP Express) [15], a CCTV zoom lens, two piezo tilt stages (PT1M36-500S-N, Nano Control Co., Japan) with mirror surfaces, and a personal computer (PC) with an ASUSTeK P6T7 WS Supercomputer mainboard, Intel Core i7 960 3.20-GHz CPU, 6-GB memory, Windows 7 Professional 32-bit OS, and a D/A board (PEX-340416, Interface Co., Japan). Figure 4 provides an overview of the prototype system when the HTZ-11000 (Joble Co., Japan) was used as the CCTV zoom lens.…”

Section: System Configurationmentioning

confidence: 99%

“…Many high-speed vision systems operating at 1000 fps or more have been developed [14,15], and visual tracking algorithms, such as optical-flow systems [16] and face-tracking systems [17], have been implemented in high-speed vision systems. The effectiveness of real-time high-speed image processing has been verified in many types of applications, such as robot manipulation [18], flying-object tracking [19], and micro-organism tracking [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Motion-blur-free video shooting system based on frame-by-frame intermittent tracking

et al. 2017

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In this paper, a concept of frame-by-frame intermittent tracking to achieve motion-blur-free and high-brightness images when video shooting fast moving scenes is proposed. In our tracking concept, two control methods are applied alternately at hundreds of hertz, according to the open or closed shutter state of the camera. When the shutter is open, the target's speed in images is controlled at zero and visual feedback is transmitted to achieve motion blur reduction, and when the shutter is closed, the camera returns to its home position. We developed a prototype of our motion-blur-free video shooting system, which consists of our tracking method implemented on a high-speed two degrees-of-freedom tracking vision platform that controls the pan and tilt directions of the camera view by using high-speed video processing in order to reduce motion blur. Our motion-blur-free video shooting system can capture gray-level 512 × 512 images at 125 fps with frame-by-frame intermittent tracking. Its performance is verified by the experimental results for several video sequences of fast moving objects. In the experiments, without a decrease in the exposure times our method reduced image degradation caused by motion blur.

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Section: System Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Motion-blur-free video shooting system based on frame-by-frame intermittent tracking

et al. 2017

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“…In this study, we employed the IDP-Express system [21] as an FPGA-based HFR vision platform for real-time AE control of HFR images. The IDP Express system was designed to implement various types of image processing algorithms to record both images and features at a high frame rate in the memory of a personal computer (PC.…”

Section: System Configurationmentioning

confidence: 99%

A High-Frame-Rate Vision System with Automatic Exposure Control

Noman

Aoyama

et al. 2014

IEICE Trans. Inf. & Syst.

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Qingyi GU†a) , Abdullah AL NOMAN †b) , Tadayoshi AOYAMA †c) , Takeshi TAKAKI †d) , Nonmembers, and Idaku ISHII †e) , Member SUMMARYIn this paper, we present a high frame rate (HFR) vision system that can automatically control its exposure time by executing brightness histogram-based image processing in real time at a high frame rate. Our aim is to obtain high-quality HFR images for robust image processing of high-speed phenomena even under dynamically changing illumination, such as lamps flickering at 100 Hz, corresponding to an AC power supply at 50 / 60 Hz. Our vision system can simultaneously calculate a 256-bin brightness histogram for an 8-bit gray image of 512×512 pixels at 2000 fps by implementing a brightness histogram calculation circuit module as parallel hardware logic on an FPGA-based high-speed vision platform. Based on the HFR brightness histogram calculation, our method realizes automatic exposure (AE) control of 512×512 images at 2000 fps using our proposed AE algorithm. The proposed AE algorithm can maximize the number of pixels in the effective range of the brightness histogram, thus excluding much darker and brighter pixels, to improve the dynamic range of the captured image without over-and under-exposure. The effectiveness of our HFR system with AE control is evaluated according to experimental results for several scenes with illumination flickering at 100 Hz, which is too fast for the human eye to see. key words: automatic exposure control, high-frame-rate vision system, hardware implementation

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“…The centroid positions were calculated by dividing the 1st moment features with the 0th moment feature as shown in Eq. (23), and the true centroid positions were assumed as those when θ = 1, which were exactly matched with those calculated using pixel-based connected components labeling algorithms. In Fig.…”

Section: Accuracy Verificationmentioning

confidence: 99%

A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling

Takaki

Ishii

2012

IEICE Trans. Inf. & Syst.

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Qingyi GU†a) , Takeshi TAKAKI †b) , Nonmembers, and Idaku ISHII †c) , Member SUMMARYWe describe a cell-based connected component labeling algorithm to calculate the 0th and 1st moment features as the attributes for labeled regions. These can be used to indicate their sizes and positions for multi-object extraction. Based on the additivity in moment features, the cell-based labeling algorithm can label divided cells of a certain size in an image by scanning the image only once to obtain the moment features of the labeled regions with remarkably reduced computational complexity and memory consumption for labeling. Our algorithm is a simple-onetime-scan cell-based labeling algorithm, which is suitable for hardware and parallel implementation. We also compared it with conventional labeling algorithms. The experimental results showed that our algorithm is faster than conventional raster-scan labeling algorithms. key words: fast multi-object extraction, connected component labeling

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2000 fps real-time vision system with high-frame-rate video recording

Cited by 144 publications

References 7 publications

Motion-blur-free video shooting system based on frame-by-frame intermittent tracking

Motion-blur-free video shooting system based on frame-by-frame intermittent tracking

A High-Frame-Rate Vision System with Automatic Exposure Control

A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling

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