Extremely Low and Variable Bandwidth Image Compression with Region of Interest Applied to Real Time Underwater Robotic Interventions

Purpose: Augmented reality overlay systems can be used to project a CT image directly onto a patient during procedures. They have been actively trialed for computer-guided procedures, however they have not become commonplace in practice due to restrictions of previous systems. Previous systems have not been handheld, and have had complicated calibration procedures. We put forward a handheld tablet-based system for assisting with needle interventions. Methods: The system consists of a tablet display and a 3-D printed reusable and customizable frame. A simple and accurate calibration method was designed to align the patient to the projected image. The entire system is tracked via camera, with respect to the patient, and the projected image is updated in real time as the system is moved around the region of interest. Results: The resulting system allowed for 0.99mm mean position error in the plane of the image, and a mean position error of 0.61mm out of the plane of the image. This accuracy was thought to be clinically acceptable for tool using computer-guidance in several procedures that involve musculoskeletal needle placements. Conclusion: Our calibration method was developed and tested using the designed handheld system. Our results illustrate the potential for the use of augmented reality handheld systems in computer-guided needle procedures.

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Real-time In-network Image Compression via Distributed Dictionary Learning

IEEE Transactions on Mobile Computing ◽

10.1109/tmc.2021.3072066 ◽

2021 ◽

pp. 1-1

Author(s):

Parul Pandey ◽

Mehdi Rahmati ◽

Waheed U. Bajwa ◽

Dario Pompili

Keyword(s):

Image Compression ◽

Real Time ◽

Dictionary Learning

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Remote Video Surveillance System Based on Direct Show and Image Compression

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.439 ◽

2013 ◽

Vol 756-759 ◽

pp. 439-442

Author(s):

Shang Fu Gong ◽

Li Gang Wu ◽

Yan Jun Wang

Keyword(s):

Image Compression ◽

Real Time ◽

Video Surveillance ◽

Data Transmission ◽

Video Transmission ◽

Massive Data ◽

Time Requirements ◽

Rich Information ◽

Precautionary Measures ◽

Application Requirements

Video surveillance, convenience and rich information, has been widely used in security, protection, monitoring and other occasions, and has already been one of the most important precautionary measures in commercial, residential and transportation areas. However, considering the massive data transmission needs and higher real-time requirements for video surveillance, a remote video surveillance plan has been put forward on the analysis basis of Microsoft DirectShow and Image Compression. This plan adopts the C/S structure, adapts to the requirements of real-time video transmission, with a better fluency. In addition, the picture clarity satisfies the application requirements.

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A new searchless fractal image encoding method for a real-time image compression device

2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512) ◽

10.1109/iscas.2004.1328907 ◽

2004 ◽

Cited By ~ 2

Author(s):

S. Ongwattanakul ◽

Xianwei Wu ◽

D.J. Jackson

Keyword(s):

Image Compression ◽

Real Time ◽

Image Encoding ◽

Compression Device ◽

Fractal Image ◽

Real Time Image ◽

Encoding Method ◽

Time Image

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SMART SENSOR: AN ON-BOARD IMAGE PROCESSING SYSTEM FOR REAL-TIME REMOTE SENSING

International Journal of Image and Graphics ◽

10.1142/s0219467802000718 ◽

2002 ◽

Vol 02 (03) ◽

pp. 481-499

Author(s):

JANE YOU ◽

DAVID ZHANG

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Image Compression ◽

Real Time ◽

Processing System ◽

Smart Sensor ◽

Pipeline Architecture ◽

New Approach ◽

Hardware Processing ◽

Sensor System Design

This paper presents a new approach to smart sensor system design for real-time remote sensing. A combination of techniques for image analysis and image compression is investigated. The proposed algorithms include: (1) a fractional discrimination function for image analysis, (2) a comparison of effective algorithms for image compression, (3) a pipeline architecture for parallel image classification and compression on-board satellites, and (4) a task control strategy for mapping image computing models to hardware processing elements. The efficiency and accuracy of the proposed techniques are demonstrated throughout system simulation.

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