Video segmentation based on watershed algorithm and optical flow motion estimation

1999 ◽  
Author(s):  
Wei-Lien Hsu ◽  
Chen-Wei Shih
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Video Segmentation ◽  
Watershed Algorithm ◽  
Flow Motion

scholarly journals Optical flow analysis reveals that Kinesin-mediated advection impacts the orientation of microtubules in the Drosophila oocyte

2020 ◽  
Vol 31 (12) ◽  
pp. 1246-1258 ◽  
Author(s):  
Maik Drechsler ◽  
Lukas F. Lang ◽  
Layla Al-Khatib ◽  
Hendrik Dirks ◽  
Martin Burger ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Cytoplasmic Streaming ◽  
Flow Analysis ◽  
Flow Motion ◽  
A Cell

Here we introduce an optical flow motion estimation approach to study microtubule (MT) orientation in the Drosophila oocyte, a cell displaying substantial cytoplasmic streaming. We show that MT polarity is affected by the regime of these flows and, furthermore, that the presence of flows is necessary for MTs to adopt their proper polarity.

A robust optical flow motion estimation and correction method for IRT imaging in brain surgery

2020 ◽  
pp. 1-26
Author(s):  
Yahya Moshaei-Nezhad ◽  
Juliane Müller ◽  
Christian Schnabel ◽  
Matthias Kirsch ◽  
Ronald Tetzlaff
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Correction Method ◽  
Brain Surgery ◽  
Flow Motion ◽  
Estimation And Correction

scholarly journals Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow

2020 ◽  
Vol 34 (07) ◽  
pp. 10713-10720
Author(s):  
Mingyu Ding ◽  
Zhe Wang ◽  
Bolei Zhou ◽  
Jianping Shi ◽  
Zhiwu Lu ◽  
...  
Keyword(s):  
Optical Flow ◽  
Video Segmentation ◽  
Video Clip ◽  
Semantic Segmentation ◽  
Temporal Consistency ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Optical Flows ◽  
Benchmark Datasets ◽  
Spatio Temporal

A major challenge for video semantic segmentation is the lack of labeled data. In most benchmark datasets, only one frame of a video clip is annotated, which makes most supervised methods fail to utilize information from the rest of the frames. To exploit the spatio-temporal information in videos, many previous works use pre-computed optical flows, which encode the temporal consistency to improve the video segmentation. However, the video segmentation and optical flow estimation are still considered as two separate tasks. In this paper, we propose a novel framework for joint video semantic segmentation and optical flow estimation. Semantic segmentation brings semantic information to handle occlusion for more robust optical flow estimation, while the non-occluded optical flow provides accurate pixel-level temporal correspondences to guarantee the temporal consistency of the segmentation. Moreover, our framework is able to utilize both labeled and unlabeled frames in the video through joint training, while no additional calculation is required in inference. Extensive experiments show that the proposed model makes the video semantic segmentation and optical flow estimation benefit from each other and outperforms existing methods under the same settings in both tasks.

scholarly journals Ego-Motion Estimation Using Recurrent Convolutional Neural Networks through Optical Flow Learning

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 222
Author(s):  
Baigan Zhao ◽  
Yingping Huang ◽  
Hongjian Wei ◽  
Xing Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Estimation ◽  
Optical Flow ◽  
Feature Representation ◽  
Sequential Learning ◽  
Navigation Systems ◽  
Motion State ◽  
Training Schemes ◽  
Sequential Images

Visual odometry (VO) refers to incremental estimation of the motion state of an agent (e.g., vehicle and robot) by using image information, and is a key component of modern localization and navigation systems. Addressing the monocular VO problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent subspace of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in a self-encoding manner. A Recurrent Neural Network is then followed to examine the OF changes, i.e., to conduct sequential learning. The extracted sequential OF subspace is used to compute the regression of the 6-dimensional pose vector. We derive three models with different network structures and different training schemes: LS-CNN-VO, LS-AE-VO, and LS-RCNN-VO. Particularly, we separately train the encoder in an unsupervised manner. By this means, we avoid non-convergence during the training of the whole network and allow more generalized and effective feature representation. Substantial experiments have been conducted on KITTI and Malaga datasets, and the results demonstrate that our LS-RCNN-VO outperforms the existing learning-based VO approaches.

An Improved Object-Based Video Segmentation Method

2012 ◽  
Vol 220-223 ◽  
pp. 2445-2449
Author(s):  
Wen Dan Xu ◽  
Xin Quan Lai ◽  
Dong Lai Xu
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
High Performance ◽  
Video Segmentation ◽  
Error Function ◽  
Least Square ◽  
Video Frame ◽  
Merging Process ◽  
Initial Segmentation ◽  
Two Stages

This paper presents an improved video segmentation scheme, which consists of two stages: initial segmentation and motion estimation. In the initial segmentation, the watershed transformation followed by a region adjacency graph guided region merging process is used to partition the first video frame into spatial homogenous regions. Then the motion of changed region is estimated. Based on the highly efficient quadratic motion model, the motion estimation is undertaken using Gauss-Newton Levenberg-Marquardt method to minimize the least-square error function. Experimental results show the proposed scheme provides high performance in terms of segmentation accuracy and video compression ratio.

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