Estimating the focus of expansion in a video sequence using the trajectories of interest points

2016 ◽  
Vol 50 ◽  
pp. 14-26 ◽  
Author(s):  
Pedro Gil-Jiménez ◽  
Hilario Gómez-Moreno ◽  
Roberto J. López-Sastre ◽  
Alberto Bermejillo-Martín-Romo
Keyword(s):  
Video Sequence ◽  
Interest Points ◽  
Focus Of Expansion

Fast Mosaicking Panoramic Images with Parallax Scene from a PTZ Camera

2012 ◽  
Vol 468-471 ◽  
pp. 1775-1780
Author(s):  
Yu Shuang Zhang ◽  
Shu Xiao Li ◽  
Hong Xing Chang
Keyword(s):  
Video Sequence ◽  
Image Mosaic ◽  
Feature Points ◽  
Interest Points ◽  
New Approach ◽  
Ptz Camera ◽  
Panoramic Images ◽  
Transform Matrix ◽  
The Cost

This paper presents a fast panoramic mosaic algorithm from a video sequence with parallax scene taken by a PTZ camera. A new approach that uses a four-step automatic imaging mosaic, based on interest points, is proposed. The four steps are extraction of interest points, finding corresponding points in the stitching images, deriving the spatial transform matrix then image mosaic. In order to reduce the cost of searching best match of feature points, we employ SIFT-16 descriptor and the LMPs descriptor as index . Our method preserves the efficiency and accuracy of image mosaic.

Human Behavior Recognition Based on Motion Decomposition

2014 ◽  
Vol 945-949 ◽  
pp. 1780-1783
Author(s):  
Shao Ping Zhu ◽  
Yu Hua Chen
Keyword(s):  
Image Processing ◽  
Human Behavior ◽  
Video Sequence ◽  
Image Sequences ◽  
Research Field ◽  
Interest Points ◽  
Behavior Recognition ◽  
Novel Method ◽  
Active Research ◽  
Human Behavior Recognition

Human behavior recognition is an active research field in computer vision and image processing. A novel method is proposed for human behavior recognition in video image sequences. First of all, a video sequence is represented by extracting space-time interest points. Then Human behavior is represented by activities through Motion Decomposition. The activity comprises labeled bags that are composed of unlabeled instances comprising to action. Final labeled activities are used to train a strong classifier which is used to predict the labels of unseen behavior bags. Experimental results show the effectiveness of the proposed method in comparison with other related works in the literature and can also tolerate noise and interference conditions.

An Efficient Multiple Description Coding for Multi-View Video Based on the Correlation of Spatial Polyphase Transformed Subsequences

2019 ◽  
Vol 63 (5) ◽  
pp. 50401-1-50401-7 ◽  
Author(s):  
Jing Chen ◽  
Jie Liao ◽  
Huanqiang Zeng ◽  
Canhui Cai ◽  
Kai-Kuang Ma
Keyword(s):  
Video Transmission ◽  
Video Sequence ◽  
Three Dimensional ◽  
The Other ◽  
Multiple Description Coding ◽  
Multiple Description ◽  
Prediction Mode ◽  
Gradient Based ◽  
Directional Interpolation ◽  
Description Coding

Abstract For a robust three-dimensional video transmission through error prone channels, an efficient multiple description coding for multi-view video based on the correlation of spatial polyphase transformed subsequences (CSPT_MDC_MVC) is proposed in this article. The input multi-view video sequence is first separated into four subsequences by spatial polyphase transform and then grouped into two descriptions. With the correlation of macroblocks in corresponding subsequence positions, these subsequences should not be coded in completely the same way. In each description, one subsequence is directly coded by the Joint Multi-view Video Coding (JMVC) encoder and the other subsequence is classified into four sets. According to the classification, the indirectly coding subsequence selectively employed the prediction mode and the prediction vector of the counter directly coding subsequence, which reduces the bitrate consumption and the coding complexity of multiple description coding for multi-view video. On the decoder side, the gradient-based directional interpolation is employed to improve the side reconstructed quality. The effectiveness and robustness of the proposed algorithm is verified by experiments in the JMVC coding platform.

scholarly journals Understanding the clinical reasoning processes involved in the management of multimorbidity in an ambulatory setting: study protocol of a stimulated recall research

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
M.-C. Audétat ◽  
S. Cairo Notari ◽  
J. Sader ◽  
C. Ritz ◽  
T. Fassier ◽  
...  
Keyword(s):  
Primary Care ◽  
Clinical Reasoning ◽  
Primary Care Physicians ◽  
Video Sequence ◽  
Structured Interview ◽  
Ambulatory Setting ◽  
Structured Interviews ◽  
Stimulated Recall ◽  
Study Results

Abstract Background Primary care physicians are at the very heart of managing patients suffering from multimorbidity. However, several studies have highlighted that some physicians feel ill-equipped to manage these kinds of complex clinical situations. Few studies are available on the clinical reasoning processes at play during the long-term management and follow-up of patients suffering from multimorbidity. This study aims to contribute to a better understanding on how the clinical reasoning of primary care physicians is affected during follow-up consultations with these patients. Methods A qualitative research project based on semi-structured interviews with primary care physicians in an ambulatory setting will be carried out, using the video stimulated recall interview method. Participants will be filmed in their work environment during a standard consultation with a patient suffering from multimorbidity using a “button camera” (small camera) which will be pinned to their white coat. The recording will be used in a following semi-structured interview with physicians and the research team to instigate a stimulated recall. Stimulated recall is a research method that allows the investigation of cognitive processes by inviting participants to recall their concurrent thinking during an event when prompted by a video sequence recall. During this interview, participants will be prompted by different video sequence and asked to discuss them; the aim will be to encourage them to make their clinical reasoning processes explicit. Fifteen to twenty interviews are planned to reach data saturation. The interviews will be transcribed verbatim and data will be analysed according to a standard content analysis, using deductive and inductive approaches. Conclusion Study results will contribute to the scientific community’s overall understanding of clinical reasoning. This will subsequently allow future generation of primary care physicians to have access to more adequate trainings to manage patients suffering from multimorbidity in their practice. As a result, this will improve the quality of the patient’s care and treatments.

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