scholarly journals Projection-Mapping-Based Object Pointing Using a High-Frame-Rate Camera-Projector System

2020 ◽  
Author(s):  
Idaku Ishii ◽  
Deepak Kumar ◽  
Sushil Raut ◽  
Kohei Shimasaki ◽  
Taku Senoo

Abstract An informative object pointing method using a spatiotemporal-modulated pattern projection is proposed to recognize and localize pointed objects by using a distantly located high-frame-rate vision system. We developed a prototype for projection-mapping-based object pointing that consists of an AI-camera-enabled projection (AiCP) system used as a transmitter, for informative projection mapping, and an HFR vision system operated as a receiver. The AiCP system detects multiple objects in real time at 30 fps with a CNN-based object detector, and simultaneously encodes and projects the recognition results of the detector as 480-Hz-modulated light patterns on to the objects to be pointed. The multiple 480-fps cameras can directly recognize and track the objects pointed at by the AiCP system without camera calibration or complex recognition methods by decoding the brightness signals of pixels in the images. To demonstrate the eectiveness of our proposed method, several desktop experiments using miniature objects and scenes were conducted under various conditions.

2015 ◽  
Vol 8 (4) ◽  
pp. 265-272 ◽  
Author(s):  
Jun CHEN ◽  
Takashi YAMAMOTO ◽  
Tadayoshi AOYAMA ◽  
Takeshi TAKAKI ◽  
Idaku ISHII

2011 ◽  
Vol 23 (1) ◽  
pp. 53-65 ◽  
Author(s):  
Yao-DongWang ◽  
◽  
Idaku Ishii ◽  
Takeshi Takaki ◽  
Kenji Tajima ◽  
...  

This paper introduces a high-speed vision system called IDP Express, which can execute real-time image processing and High-Frame-Rate (HFR) video recording simultaneously. In IDP Express, 512×512 pixel images from two camera heads and the processed results on a dedicated FPGA (Field Programmable Gate Array) board are transferred to standard PC memory at a rate of 1000 fps or more. Owing to the simultaneous HFR video processing and recording, IDP Express can be used as an intelligent video logging system for long-term high-speed phenomenon analysis. In this paper, a real-time abnormal behavior detection algorithm was implemented on IDP-Express to capture HFR videos of crucial moments of unpredictable abnormal behaviors in high-speed periodic motions. Several experiments were performed for a high-speed slider machine with repetitive operation at a frequency of 15 Hz and videos of the abnormal behaviors were automatically recorded to verify the effectiveness of our intelligent HFR video logging system.


2021 ◽  
Author(s):  
Deepak Kumar ◽  
Sushil Raut ◽  
Kohei Shimasaki ◽  
Taku Senoo ◽  
Idaku Ishii

Abstract The novel approach to physical security based on visible light communication (VLC) using an informative object-pointing and simultaneous recognition by high-framerate (HFR) vision systems is presented in this study. In the proposed approach, a convolutional neural network (CNN) based object detection method is used to detect the environmental objects that assist a spatiotemporal-modulated-pattern (SMP) based imperceptible projection mapping for pointing the desired objects. The distantly located HFR vision systems that operate at hundreds of frames per second (fps) can recognize and localize the pointed objects in real-time. The prototype of an artificial intelligence-enabled camera-projector (AiCP) system is used as a transmitter that detects the multiple objects in real-time at 30~fps and simultaneously projects the detection results by means of the encoded-480-Hz-SMP masks on to the objects. The multiple 480-fps HFR vision systems as receivers can recognize the pointed objects by decoding pixel-brightness variations in HFR sequences without any camera calibration or complex recognition methods. Several experiments were conducted to demonstrate our proposed method's usefulness using miniature and real-world objects under various conditions


Author(s):  
Idaku Ishii ◽  
Tetsuro Tatebe ◽  
Qingyi Gu ◽  
Yuta Moriue ◽  
Takeshi Takaki ◽  
...  

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Deepak Kumar ◽  
Sushil Raut ◽  
Kohei Shimasaki ◽  
Taku Senoo ◽  
Idaku Ishii

AbstractThe novel approach to physical security based on visible light communication (VLC) using an informative object-pointing and simultaneous recognition by high-framerate (HFR) vision systems is presented in this study. In the proposed approach, a convolutional neural network (CNN) based object detection method is used to detect the environmental objects that assist a spatiotemporal-modulated-pattern (SMP) based imperceptible projection mapping for pointing the desired objects. The distantly located HFR vision systems that operate at hundreds of frames per second (fps) can recognize and localize the pointed objects in real-time. The prototype of an artificial intelligence-enabled camera-projector (AiCP) system is used as a transmitter that detects the multiple objects in real-time at 30 fps and simultaneously projects the detection results by means of the encoded-480-Hz-SMP masks on to the objects. The multiple 480-fps HFR vision systems as receivers can recognize the pointed objects by decoding pixel-brightness variations in HFR sequences without any camera calibration or complex recognition methods. Several experiments were conducted to demonstrate our proposed method’s usefulness using miniature and real-world objects under various conditions.


2014 ◽  
Vol E97.D (4) ◽  
pp. 936-950 ◽  
Author(s):  
Qingyi GU ◽  
Abdullah AL NOMAN ◽  
Tadayoshi AOYAMA ◽  
Takeshi TAKAKI ◽  
Idaku ISHII

2021 ◽  
Vol 15 (4) ◽  
pp. 820-833
Author(s):  
Junming Zeng ◽  
Lei Kuang ◽  
Miguel Cacho-Soblechero ◽  
Pantelis Georgiou

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5368
Author(s):  
Atul Sharma ◽  
Sushil Raut ◽  
Kohei Shimasaki ◽  
Taku Senoo ◽  
Idaku Ishii

This study develops a projector–camera-based visible light communication (VLC) system for real-time broadband video streaming, in which a high frame rate (HFR) projector can encode and project a color input video sequence into binary image patterns modulated at thousands of frames per second and an HFR vision system can capture and decode these binary patterns into the input color video sequence with real-time video processing. For maximum utilization of the high-throughput transmission ability of the HFR projector, we introduce a projector–camera VLC protocol, wherein a multi-level color video sequence is binary-modulated with a gray code for encoding and decoding instead of pure-code-based binary modulation. Gray code encoding is introduced to address the ambiguity with mismatched pixel alignments along the gradients between the projector and vision system. Our proposed VLC system consists of an HFR projector, which can project 590 × 1060 binary images at 1041 fps via HDMI streaming and a monochrome HFR camera system, which can capture and process 12-bit 512 × 512 images in real time at 3125 fps; it can simultaneously decode and reconstruct 24-bit RGB video sequences at 31 fps, including an error correction process. The effectiveness of the proposed VLC system was verified via several experiments by streaming offline and live video sequences.


2015 ◽  
Vol 27 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Qingyi Gu ◽  
◽  
Sushil Raut ◽  
Ken-ichi Okumura ◽  
Tadayoshi Aoyama ◽  
...  

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270001/02.jpg"" width=""300"" />Synthesized panoramic images</div> In this paper, we propose a real-time image mosaicing system that uses a high-frame-rate video sequence. Our proposed system can mosaic 512 × 512 color images captured at 500 fps as a single synthesized panoramic image in real time by stitching the images based on their estimated frame-to-frame changes in displacement and orientation. In the system, feature point extraction is accelerated by implementing a parallel processing circuit module for Harris corner detection, and hundreds of selected feature points in the current frame can be simultaneously corresponded with those in their neighbor ranges in the previous frame, assuming that frame-to-frame image displacement becomes smaller in high-speed vision. The efficacy of our system for improved feature-based real-time image mosaicing at 500 fps was verified by implementing it on a field-programmable gate array (FPGA)-based high-speed vision platform and conducting several experiments: (1) capturing an indoor scene using a camera mounted on a fast-moving two-degrees-of-freedom active vision, (2) capturing an outdoor scene using a hand-held camera that was rapidly moved in a periodic fashion by hand. </span>


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