A system for measuring cell division patterns of earlyCaenorhabditis elegans embryos by using image processing and object tracking

2007 ◽  
Vol 38 (11) ◽  
pp. 12-24 ◽  
Author(s):  
Shugo Hamahashi ◽  
Hiroaki Kitano ◽  
Shuichi Onami
Keyword(s):  
Image Processing ◽  
Cell Division ◽  
Object Tracking ◽  
Measuring Cell

scholarly journals Real-time object tracking system based on field-programmable gate array and convolution neural network

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141668270 ◽  
Author(s):  
Congyi Lyu ◽  
Haoyao Chen ◽  
Xin Jiang ◽  
Peng Li ◽  
Yunhui Liu
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Object Tracking ◽  
Real Time ◽  
Field Programmable Gate Array ◽  
Tracking System ◽  
Convolution Neural Network ◽  
Processing Unit ◽  
Field Programmable ◽  
Gate Array

Vision-based object tracking has lots of applications in robotics, like surveillance, navigation, motion capturing, and so on. However, the existing object tracking systems still suffer from the challenging problem of high computation consumption in the image processing algorithms. The problem can prevent current systems from being used in many robotic applications which have limitations of payload and power, for example, micro air vehicles. In these applications, the central processing unit- or graphics processing unit-based computers are not good choices due to the high weight and power consumption. To address the problem, this article proposed a real-time object tracking system based on field-programmable gate array, convolution neural network, and visual servo technology. The time-consuming image processing algorithms, such as distortion correction, color space convertor, and Sobel edge, Harris corner features detector, and convolution neural network were redesigned using the programmable gates in field-programmable gate array. Based on the field-programmable gate array-based image processing, an image-based visual servo controller was designed to drive a two degree of freedom manipulator to track the target in real time. Finally, experiments on the proposed system were performed to illustrate the effectiveness of the real-time object tracking system.

scholarly journals The Implementation of a Red Object Tracking Algorithm with a Single, Static Camera

2021 ◽  
Vol 12 (1) ◽  
pp. 40
Author(s):  
Ali Arshad ◽  
Saman Cheema ◽  
Umair Ahsan
Keyword(s):  
Image Processing ◽  
Object Tracking ◽  
Activity Recognition ◽  
Moving Objects ◽  
Processing Algorithm ◽  
Location Information ◽  
Surveillance Systems ◽  
Image Processing Algorithm ◽  
Adaptive Image Processing ◽  
Increasing Demand

In recent years, activity recognition and object tracking are receiving extensive attention due to the increasing demand for adaptable surveillance systems. Activity recognition is guided by the parameters such as the shape, size, and color of the object. This article purposes an examination of the performance of existing color-based object detection and tracking algorithms using thermal/visual camera-based video steaming in MATLAB. A framework is developed to detect and track red moving objects in real time. Detection is carried out based on the location information acquired from an adaptive image processing algorithm. Coordinate extraction is followed by tracking and locking the object with the help of a laser barrel. The movement of the laser barrel is controlled with the help of an 8051 microcontroller. Location information is communicated from the image-processing algorithm to the microcontroller serially. During implementation, a single static camera is used that provides 30 frames per second. For each frame, 88 ms are required to complete all three steps from detection to tracking, to locking, so a processing speed of 12 frames per second is implemented. This repetition makes the setup adaptive to the environment despite the presence of a single static camera. This setup can handle multiple objects with shades of red and has demonstrated equally good results in varying outdoor conditions. Currently, the setup can lock only single targets, but the capacity of the system can be increased with the installation of multiple cameras and laser barrels.

scholarly journals Development of an Automatic Tracking Camera System Integrating Image Processing and Machine Learning

2021 ◽  
Vol 33 (6) ◽  
pp. 1303-1314
Author(s):  
Masato Fujitake ◽  
Makito Inoue ◽  
Takashi Yoshimi ◽  
◽  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Object Detection ◽  
Object Tracking ◽  
Tracking System ◽  
Construction Technology ◽  
Camera System ◽  
Automatic Tracking ◽  
Machine Operators ◽  
Surveillance Cameras

This paper describes the development of a robust object tracking system that combines detection methods based on image processing and machine learning for automatic construction machine tracking cameras at unmanned construction sites. In recent years, unmanned construction technology has been developed to prevent secondary disasters from harming workers in hazardous areas. There are surveillance cameras on disaster sites that monitor the environment and movements of construction machines. By watching footage from the surveillance cameras, machine operators can control the construction machines from a safe remote site. However, to control surveillance cameras to follow the target machines, camera operators are also required to work next to machine operators. To improve efficiency, an automatic tracking camera system for construction machines is required. We propose a robust and scalable object tracking system and robust object detection algorithm, and present an accurate and robust tracking system for construction machines by integrating these two methods. Our proposed image-processing algorithm is able to continue tracking for a longer period than previous methods, and the proposed object detection method using machine learning detects machines robustly by focusing on their component parts of the target objects. Evaluations in real-world field scenarios demonstrate that our methods are more accurate and robust than existing off-the-shelf object tracking algorithms while maintaining practical real-time processing performance.

Selection of Proper Cells Using Connected Components Tracking Algorithms

2012 ◽  
Vol 245 ◽  
pp. 90-96
Author(s):  
Daniel Penchev
Keyword(s):  
Image Processing ◽  
Object Tracking ◽  
Connected Components ◽  
Egg Cell ◽  
Sperm Cells ◽  
Cell Injection ◽  
Tracking Algorithms ◽  
Male Sperm ◽  
Selection Of

This paper describes an approach of automation selecting the proper male sperm cells to be used for female egg cell injection. Connected components analyze is used for image processing and micro object tracking.

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