Optical flow of vesicles: computer vision approach for endocytosis of nanoparticles in a living cell

2020 ◽  
Author(s):  
Seohyun Lee ◽  
Hyuno Kim ◽  
Hideo Higuchi ◽  
Masatoshi Ishikawa
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Living Cell

scholarly journals Toward a Sociomaterial Understanding of Writing Experiences Incorporating Digital Technology in an Early Childhood Classroom

2017 ◽  
Vol 66 (1) ◽  
pp. 183-197 ◽  
Author(s):  
Lisa Kervin ◽  
Barbara Comber ◽  
Annette Woods
Keyword(s):  
Computer Vision ◽  
Early Childhood ◽  
Optical Flow ◽  
Digital Technology ◽  
Early Childhood Classroom ◽  
Use Of Space ◽  
Independent Writing ◽  
Classroom Space ◽  
Multimodal Text ◽  
Text Construction

This article examines the resources, tools, and opportunities children enact as they engage with teacher-devised writing experiences within their classroom space. We begin with discussion about classroom writing time from the perspective of both the teacher and children of one Grade 1/2 composite class. We also reveal resources within the classroom space to consider the expertise available during writing times. We then examine a 5-week unit that focused on multimodal text construction. Using optical flow computer vision analysis to examine the movement of children during four video-recorded independent writing instances, we provide commentary about how the classroom writing experiences have been interpreted as the use of space, resources, and interactions come to the forefront. In taking this approach, this article will explore learning to write from a sociomaterial perspective, as we investigate the operation of the classroom.

WED 112 Seeing invisible parkinsonian tremor with a smartphone camera

2018 ◽  
Vol 89 (10) ◽  
pp. A12.1-A12
Author(s):  
O’Gorman Paschal ◽  
Williams Stefan ◽  
Fang Hui ◽  
Qahwaji Rami ◽  
Patel Parisa ◽  
...  
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Small Amplitude ◽  
Computer Processing ◽  
Advanced Technology ◽  
Computing Technique ◽  
Parkinsonian Tremor ◽  
Video Recordings ◽  
Third Law ◽  
Computer Processor

Arthur C Clarke’s ‘third law’ states that any sufficiently advanced technology is indistinguishable from magic. Computer vision is the processing of images or video by computer to extract useful information. A technique termed ‘Eulerian magnification’ involves amplification of tiny movements from video recordings, so that very small motions can become visible to the human eye. This has the potential to detect tremor that is of such small amplitude it cannot otherwise be seen. Crucially, the only hardware required is a camera and computer processor, items that are ubiquitous. There is only one previous report of Eulerian magnification applied to a simple video of a Parkinson’s patient, but Parkinsonian signs could clearly be seen in the pre-processing video, and no control video was shown. We present remarkable video in which no tremor is seen in either patient or control before processing, and yet a Parkinsonian tremor is revealed in patient but not control after amplification. Blinded clinician ratings detect a greater number of Parkinsonian tremors after computer processing. Furthermore, we report a method using an ‘optical flow’ computing technique that records pixel motion vectors, and enables the computer to measure the direction and relative amplitude of this amplified movement.

scholarly journals Comparative Analysis of Tracking Objects Using Optical Flow and Background Estimation on Silent Camera

2018 ◽  
pp. 153-162
Author(s):  
Wahyu Supriyatin ◽  
Winda Widya Ariestya ◽  
Ida Astuti
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Object Tracking ◽  
Estimation Method ◽  
Flow Estimation ◽  
Background Estimation ◽  
Existing Problems ◽  
Parameter Values ◽  
Tracking Objects ◽  
Better Than

Tracking and object is one of the utilizations on the field of the computer vision application. Object tracking utilization as a computer vision in this study is used to identify objects which exist within a frame and calculate the number of objects passing within a frame. The utilization of computer vision in various fields of application can be used to solve the existing problems. The method used in object tracking is by comparison between optical flow estimation method with background method. The test is conducted by using a still camera for both methods by making changes to the parameter values used as a reference. The results of the tests, conducted on the three video objects by comparing the two methods show a Total Recorded Time better than those of the background estimation method, being smaller than 100 seconds. Testing both methods successfully identifies the object tracking and calculates the number of passing cars.

scholarly journals Tracking and Size Estimation of Objects in Motion based on Median of Localized Thresholding

2018 ◽  
Vol 7 (4.6) ◽  
pp. 78
Author(s):  
Sagar Gujjunoori ◽  
Madhu Oruganti ◽  
N. Aparna ◽  
M. Srija ◽  
Chaitrali Dangare
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Motion Detection ◽  
Moving Objects ◽  
Bilateral Filtering ◽  
Size Estimation ◽  
Detection And Tracking ◽  
Object Based ◽  
Local Thresholding ◽  
And Robotics

Motion detection and tracking play an important role in Computer vision and Robotics. Optical flow based methods to estimate the motion are widely explored during the last decade. The motion information retrieved from these techniques has enormous applications. Video analysis based on the size, speed, and directions of objects have wider applications in computer vision, robotics and watermarking. Segmentation of moving objects based on the optical flow is very challenging. In this paper, we present a model to estimate the size of a moving object based on the optical flow technique and present localized thresholding technique. Over segmentation is reduced by the proposed local thresholding technique and use of bilateral filtering. We compare our results with Sagar et al. scheme.  

scholarly journals A novel multi-stream method for violent interaction detection using deep learning

2020 ◽  
Vol 53 (5-6) ◽  
pp. 796-806
Author(s):  
Hongchang Li ◽  
Jing Wang ◽  
Jianjun Han ◽  
Jinmin Zhang ◽  
Yushan Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Optical Flow ◽  
High Probability ◽  
Recognition Performance ◽  
Surveillance Video ◽  
Interaction Detection ◽  
Temporal Features ◽  
Violent Action ◽  
Learning In Computer Vision

Violent interaction detection is a hot topic in computer vision. However, the recent research works on violent interaction detection mainly focus on the traditional hand-craft features, and does not make full use of the research results of deep learning in computer vision. In this paper, we propose a new robust violent interaction detection framework based on multi-stream deep learning in surveillance scene. The proposed approach enhances the recognition performance of violent action in video by fusing three different streams: attention-based spatial RGB stream, temporal stream, and local spatial stream. The attention-based spatial RGB stream learns the spatial attention regions of persons that have high probability to be action region through soft-attention mechanism. The temporal stream employs optical flow as input to extract temporal features. The local spatial stream learns spatial local features using block images as input. Experimental results demonstrate the effectiveness and reliability of the proposed method on three violent interactive datasets: hockey fights, movies, violent interaction. We also verify the proposed method on our own elevator surveillance video dataset and the performance of the proposed method is satisfied.

scholarly journals Parallel Processor for 3D Recovery from Optical Flow

2009 ◽  
Vol 2009 ◽  
pp. 1-11
Author(s):  
Jose Hugo Barron-Zambrano ◽  
Fernando Martin del Campo-Ramirez ◽  
Miguel Arias-Estrada
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Parallel Processor ◽  
Vision Systems ◽  
Software Implementations ◽  
Computer Vision Systems ◽  
Memory Accesses

3D recovery from motion has received a major effort in computer vision systems in the recent years. The main problem lies in the number of operations and memory accesses to be performed by the majority of the existing techniques when translated to hardware or software implementations. This paper proposes a parallel processor for 3D recovery from optical flow. Its main feature is the maximum reuse of data and the low number of clock cycles to calculate the optical flow, along with the precision with which 3D recovery is achieved. The results of the proposed architecture as well as those from processor synthesis are presented.

Object Tracking Based on Dual-View Stereo System

2013 ◽  
Vol 850-851 ◽  
pp. 780-783
Author(s):  
Jian De Fan ◽  
Jiang Bo Zhu
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Object Tracking ◽  
Moving Objects ◽  
Current Method ◽  
Research Area ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Feature Points ◽  
Motion Trajectories

Tracking moving objects in dual-view stereo system is becoming a hot research area in computer vision. To capture the moving objects pixels more accurately, we proposed a new object tracking algorithm which first compute moving objects feature points and then match these points, finally connect the matching feature points and get objects motion trajectories. The algorithm was tested in the video sequences with resolution 640×480 and 768×576 individually. The results show that the algorithm is more robust and the trajectories of the moving objects tracked with our method are more accurate compared with current method of L-K optical flow.

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