scholarly journals A binary image sensor with flexible motion vector detection using block matching method

2002 ◽  
Author(s):  
T. Nezuka ◽  
T. Fujita ◽  
M. Ikeda ◽  
K. Asada
Keyword(s):  
Motion Vector ◽  
Image Sensor ◽  
Binary Image ◽  
Block Matching ◽  
Matching Method

A Binary Image Sensor for Motion Detection

2000 ◽  
Vol 12 (5) ◽  
pp. 508-514
Author(s):  
Tomohiro Nezuka ◽  
◽  
Takafumi Fujita ◽  
Makoto Ikeda ◽  
Kunihiro Asada
Keyword(s):  
Motion Detection ◽  
Motion Vector ◽  
Cmos Technology ◽  
Image Sensor ◽  
Binary Image ◽  
Block Matching ◽  
Absolute Difference ◽  
Current Frame ◽  
Previous Frame ◽  
Exclusive Or

This Paper proposes a binary image sensor for motion detection. The sensor detects motion vectors using block-matching method. Each pixel of the image sensor has a shift register. Exclusive OR(XOR) circuits in each pixel compare the shifted image of the previous frame and the image of the current frame. The outputs of the XOR circuits are converted to current value. The current-sum circuits calculate the current-sum of pixel outputs. The current-sum of a matching block represents the sum of absolute difference. The motion vector is obtained by routine of shifting image of the previous frame and comparing the sum of absolute difference. The chip was fabricated using 1.2um 2-Metal 2-Poly-Si CMOS technology and composed of 32 × 32 pixel array and peripheral circuits in a 7.3mm × 7.3mm die.

scholarly journals Mechanical and Electronic Video Stabilization Strategy of Mortars with Trajectory Correction Fuze Based on Infrared Image Sensor

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2461 ◽  
Author(s):  
Cong Zhang ◽  
Dongguang Li
Keyword(s):  
Motion Vector ◽  
Computational Cost ◽  
Infrared Image ◽  
Image Sensor ◽  
Projection Algorithm ◽  
Video Stabilization ◽  
Sensors And Actuators ◽  
Simulation And Experiment ◽  
Transformation Relation ◽  
Trajectory Correction

For a higher attack accuracy of projectiles, a novel mechanical and electronic video stabilization strategy is proposed for trajectory correction fuze. In this design, the complexity of sensors and actuators were reduced. To cope with complex combat environments, an infrared image sensor was used to provide video output. Following the introduction of the fuze’s workflow, the limitation of sensors for mechanical video stabilization on fuze was proposed. Particularly, the parameters of the infrared image sensor that strapdown with fuze were calculated. Then, the transformation relation between the projectile’s motion and the shaky video was investigated so that the electronic video stabilization method could be determined. Correspondingly, a novel method of dividing sub-blocks by adaptive global gray threshold was proposed for the image pre-processing. In addition, the gray projection algorithm was used to estimate the global motion vector by calculating the correlation between the curves of the adjacent frames. An example simulation and experiment were implemented to verify the effectiveness of this strategy. The results illustrated that the proposed algorithm significantly reduced the computational cost without affecting the accuracy of the motion estimation. This research provides theoretical and experimental basis for the intelligent application of sensor systems on fuze.

Adaptive Return Prediction Block Matching Algorithms for Video Coding

2011 ◽  
Vol 145 ◽  
pp. 277-281
Author(s):  
Vaci Istanda ◽  
Tsong Yi Chen ◽  
Wan Chun Lee ◽  
Yuan Chen Liu ◽  
Wen Yen Chen
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
Web Site ◽  
Motion Vector ◽  
Temporal Correlation ◽  
Block Matching ◽  
Asynchronous Learning ◽  
Learning And Teaching ◽  
Block Motion ◽  
Reconstruction Image

As the development of network learning, video compression is important for both data transmission and storage, especially in a digit channel. In this paper, we present the return prediction search (RPS) algorithm for block motion estimation. The proposed algorithm exploits the temporal correlation and characteristic of returning origin to obtain one or two predictive motion vector and selects one motion vector, which presents better result, to be the initial search center. In addition, we utilize the center-biased block matching algorithms to refine the final motion vector. Moreover, we used adaptive threshold technique to reduce the computational complexity in motion estimation. Experimental results show that RPS algorithm combined with 4SS, BBGDS, and UCBDS effectively improves the performance in terms of mean-square error measure with less average searching points. On the other hand, accelerated RPS (ARPS) algorithm takes only 38% of the searching computations than 3SS algorithm, and the reconstruction image quality of the ARPS algorithm is superior to 3SS algorithm about 0.30dB in average overall test sequences. In addition, we create an asynchronous learning environment which provides students and instructors flexibility in learning and teaching activities. The purpose of this web site is to teach and display our researchable results. Therefore, we believe this web site is one of the keys to help the modern student achieve mastery of complex Motion Estimation.

CMOS binary image sensor using gate/body-tied MOSFET-Type photodetector

2014 ◽  
Author(s):  
Byoung-Soo Choi ◽  
Sung-Hyun Jo ◽  
Myunghan Bae ◽  
Pyung Choi ◽  
Jang-Kyoo Shin ◽  
...  
Keyword(s):  
Image Sensor ◽  
Binary Image

scholarly journals Template Check and Block Matching Method for Automatic Defects Detection of the Back Light Unit

2006 ◽  
Vol 13B (4) ◽  
pp. 377-382
Author(s):  
Chang-Ho Han ◽  
Sang-Hee Cho ◽  
Choon-Suk Oh ◽  
Young-Kee Ryu
Keyword(s):  
Block Matching ◽  
Matching Method

scholarly journals A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771878175 ◽  
Author(s):  
Shahrukh Ashraf ◽  
Priyanka Aggarwal ◽  
Praveen Damacharla ◽  
Hong Wang ◽  
Ahmad Y Javaid ◽  
...  
Keyword(s):  
Optical Flow ◽  
Unmanned Aerial Vehicle ◽  
Global Positioning System ◽  
Motion Vector ◽  
Block Matching ◽  
Positioning System ◽  
Simulation Studies ◽  
Flow Measurements ◽  
Global Positioning ◽  
Aerial Vehicle

The ability of an autonomous unmanned aerial vehicle to navigate and fly precisely determines its utility and performance. The current navigation systems are highly dependent on the global positioning system and are prone to error because of global positioning system signal outages. However, advancements in onboard processing have enabled inertial navigation algorithms to perform well during short global positioning system outages. In this article, we propose an intelligent optical flow–based algorithm combined with Kalman filters to provide the navigation capability during global positioning system outages and global positioning system–denied environments. Traditional optical flow measurement uses block matching for motion vector calculation that makes the measurement task computationally expensive and slow. We propose the application of an artificial bee colony–based block matching technique for faster optical flow measurements. To effectively fuse optical flow data with inertial sensors output, we employ a modified form of extended Kalman filter. The modifications make the filter less noisy by utilizing the redundancy of sensors. We have achieved an accuracy of ~95% for all non-global positioning system navigation during our simulation studies. Our real-world experiments are in agreement with the simulation studies when effects of wind are taken into consideration.

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