Improved camera-tracking method by combining motion prediction and image registration for bronchoscope navigation system

2004 ◽  
Vol 1268 ◽  
pp. 1261
Author(s):  
Kensaku Mori ◽  
Tsutomu Enjoji ◽  
Daisuke Deguchi ◽  
Takayuki Kitasaka ◽  
Yasuhito Suenaga ◽  
...  
Keyword(s):  
Image Registration ◽  
Navigation System ◽  
Motion Prediction ◽  
Camera Tracking ◽  
Tracking Method

Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management

2014 ◽  
Author(s):  
W.S. Lee ◽  
Victor Alchanatis ◽  
Asher Levi
Keyword(s):  
Image Registration ◽  
Navigation System ◽  
Thermal Imaging ◽  
Imaging System ◽  
Citrus Fruit ◽  
Mobile Platform ◽  
Yield Estimation ◽  
Yield Mapping ◽  
Thermal Images ◽  
Mapping System

Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved. 

Research on Underwater Target Tracking Based on Contour Detection

2011 ◽  
Vol 317-319 ◽  
pp. 890-896
Author(s):  
Ming Jun Zhang ◽  
Yuan Yuan Wan ◽  
Zhen Zhong Chu
Keyword(s):  
Prediction Model ◽  
Target Tracking ◽  
Tracking System ◽  
Contour Detection ◽  
Motion Prediction ◽  
Moving Target ◽  
Contour Extraction ◽  
Tracking Accuracy ◽  
Tracking Method ◽  
Underwater Target

The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.

Image-guided vascular neurosurgery based on three-dimensional rotational angiography

2007 ◽  
Vol 106 (3) ◽  
pp. 501-506 ◽  
Author(s):  
Peter W. A. Willems ◽  
Theo Van Walsum ◽  
Peter A. Woerdeman ◽  
Everine B. Van De Kraats ◽  
Gerard A. P. De Kort ◽  
...  
Keyword(s):  
Image Registration ◽  
Navigation System ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Small Region ◽  
Rotational Angiography ◽  
Vascular Tree ◽  
Data Set ◽  
3D Rotational Angiography ◽  
Vascular Neurosurgery

✓Three-dimensional rotational angiography is capable of exquisite visualization of cerebral blood vessels and their pathophysiology. Unfortunately, images obtained using this modality typically show a small region of interest without exterior landmarks to allow patient-to-image registration, precluding their use for neuronavigation purposes. The aim of this study was to find an alternative technique to enable 3D rotational angiography–guided vascular neurosurgery. Three-dimensional rotational angiograms were obtained in an angiographic suite with direct navigation capabilities. After image acquisition, a navigated pointer was used to touch fiducial positions on the patient's head. These positions were located outside the image volume but could nevertheless be transformed into image coordinates and stored in the navigation system. Prior to surgery, the data set was transferred to the navigation system in the operating room, and the same fiducial positions were touched again to complete the patient-to-image registration. This technique was tested on a Perspex phantom representing the cerebral vascular tree and on two patients with an intracranial aneurysm. In both the phantom and patients, the neuronavigation system provided 3D images representing the vascular tree in its correct orientation, that is, the orientation seen by the neurosurgeon through the microscope. In one patient, tissue shift was clearly observed without significant changes in the orientation of the structures. Results in this study demonstrate the feasibility of using 3D rotational angiography data sets for neuronavigation purposes. Determining the benefit of this type of navigation should be the subject of future studies.

scholarly journals A Multi-Agent Motion Prediction and Tracking Method Based on Non-Cooperative Equilibrium

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 164
Author(s):  
Yan Li ◽  
Mengyu Zhao ◽  
Huazhi Zhang ◽  
Yuanyuan Qu ◽  
Suyu Wang
Keyword(s):  
Motion Prediction ◽  
Random Mutation ◽  
Prediction Ability ◽  
Tracking Method ◽  
Cooperative Equilibrium ◽  
Predictive Learning ◽  
Difference Function ◽  
Tracking Ability ◽  
Multi Agent ◽  
Agent Intelligence

A Multi-Agent Motion Prediction and Tracking method based on non-cooperative equilibrium (MPT-NCE) is proposed according to the fact that some multi-agent intelligent evolution methods, like the MADDPG, lack adaptability facing unfamiliar environments, and are unable to achieve multi-agent motion prediction and tracking, although they own advantages in multi-agent intelligence. Featured by a performance discrimination module using the time difference function together with a random mutation module applying predictive learning, the MPT-NCE is capable of improving the prediction and tracking ability of the agents in the intelligent game confrontation. Two groups of multi-agent prediction and tracking experiments are conducted and the results show that compared with the MADDPG method, in the aspect of prediction ability, the MPT-NCE achieves a prediction rate at more than 90%, which is 23.52% higher and increases the whole evolution efficiency by 16.89%; in the aspect of tracking ability, the MPT-NCE promotes the convergent speed by 11.76% while facilitating the target tracking by 25.85%. The proposed MPT-NCE method shows impressive environmental adaptability and prediction and tracking ability.

scholarly journals The Tracking Method of the Position of a People which is Used in the Navigation System for the Visually Impaired

2000 ◽  
Vol 36 (1) ◽  
pp. 32-37
Author(s):  
Kazushige MAGATANI ◽  
Hirohito IWAYA ◽  
Hironori YAMASAKI ◽  
Kenji YANASHIMA
Keyword(s):  
Navigation System ◽  
Visually Impaired ◽  
Tracking Method
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