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CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRF [article]

Linchao Bao, Baoyuan Wu, Wei Liu
2018 arXiv   pre-print
As a result, higher-order, richer dependencies among pixels in the set can be implicitly modeled by the CNN.  ...  This paper addresses the problem of video object segmentation, where the initial object mask is given in the first frame of an input video.  ...  Conclusions In this paper, we proposed a novel spatio-temporal MRF model for video object segmentation.  ... 
arXiv:1803.09453v1 fatcat:q7gepbiy5fg4bf5vkzk63hoodu

CNN in MRF: Video Object Segmentation via Inference in a CNN-Based Higher-Order Spatio-Temporal MRF

Linchao Bao, Baoyuan Wu, Wei Liu
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
As a result, higher-order, richer dependencies among pixels in the set can be implicitly modeled by the CNN.  ...  With temporal dependencies established by optical flow, the resulting MRF model combines both spatial and temporal cues for tackling video object segmentation.  ...  Conclusions In this paper, we proposed a novel spatio-temporal MRF model for video object segmentation.  ... 
doi:10.1109/cvpr.2018.00626 dblp:conf/cvpr/BaoW018 fatcat:xneli3oh2fek7phgiouhbi2a2m

Video Object Segmentation and Tracking: A Survey [article]

Rui Yao, Guosheng Lin, Shixiong Xia, Jiaqi Zhao, Yong Zhou
2019 arXiv   pre-print
Object segmentation and object tracking are fundamental research area in the computer vision community.  ...  First, we provide a hierarchical categorization existing approaches, including unsupervised VOS, semi-supervised VOS, interactive VOS, weakly supervised VOS, and segmentation-based tracking methods.  ...  [10] propose a VOS method via inference in CNN-based spatio-temporal MRF. Hu et al. [69] employ active contour on optical ow to segment moving object.  ... 
arXiv:1904.09172v3 fatcat:nm3zptbidvgxfkxezqjekwdpdi

An End-to-end 3D Convolutional Neural Network for Action Detection and Segmentation in Videos [article]

Rui Hou, Chen Chen, Mubarak Shah
2017 arXiv   pre-print
In this paper, we propose an end-to-end 3D CNN for action detection and segmentation in videos.  ...  A video is first divided into equal length clips and next for each clip a set of tube proposals are generated based on 3D CNN features.  ...  ST-CNN for video object segmentation Since ST-CNN already includes a segmentation loss, it can be easily applied to the video object segmentation task, i.e. segmenting the primary foreground object in  ... 
arXiv:1712.01111v1 fatcat:nmaal54fgna7bkxl3vcgyvyh7a

Survey on Semantic Segmentation using Deep Learning Techniques

Fahad Lateef, Yassine Ruichek
2019 Neurocomputing  
Semantic segmentation is a challenging task in computer vision systems.  ...  Moreover, we focus on some of the methods and look closely at their architectures in order to find out how they have achieved their reported performances.  ...  ACKNOWLEDGMENT The authors express their gratitude to University Technology Belfort-Montbeliard and Higher Education Commission of Pakistan for providing the support and necessary requirement for completion  ... 
doi:10.1016/j.neucom.2019.02.003 fatcat:aelsfl7unvdw5j2rtyqhtgqrsm

Deep Full-Body HPE for Activity Recognition from RGB Frames Only

Sameh Neili Boualia, Najoua Essoukri Ben Amara
2021 Informatics  
Human Pose Estimation (HPE) is defined as the problem of human joints' localization (also known as keypoints: elbows, wrists, etc.) in images or videos.  ...  In this context, we present in this paper a Deep Full-Body-HPE (DFB-HPE) approach from RGB images only.  ...  Thus, a CNN with 3D spatio-temporal convolutions addresses this issue and provides a natural extension of a 2D CNN to video.  ... 
doi:10.3390/informatics8010002 fatcat:eipb3jxy5vhrldyepwxgur56ze

Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies [article]

Dandan Ding, Zhan Ma, Di Chen, Qingshuang Chen, Zoe Liu, Fengqing Zhu
2021 arXiv   pre-print
On pre-processing, we show a switchable texture-based video coding example that leverages DNN-based scene understanding to extract semantic areas for the improvement of subsequent video coder.  ...  In this article, we review extensively recent technical advances in video compression system, with an emphasis on deep neural network (DNN)-based approaches; and then present three comprehensive case studies  ...  On the other hand, DNNs have demonstrated a powerful capacity for video spatio-temporal feature representation for vision tasks, such as object segmentation, tracking, etc.  ... 
arXiv:2101.06341v1 fatcat:63vikavtpnb3dilixbakkcbwnq

A Survey on Deep Learning Technique for Video Segmentation [article]

Wenguan Wang, Tianfei Zhou, Fatih Porikli, David Crandall, Luc Van Gool
2021 arXiv   pre-print
Video segmentation, i.e., partitioning video frames into multiple segments or objects, plays a critical role in a broad range of practical applications, from enhancing visual effects in movie, to understanding  ...  In this survey, we comprehensively review two basic lines of research - generic object segmentation (of unknown categories) in videos and video semantic segmentation - by introducing their respective task  ...  Later, [216] proposes to jointly learn CNN based per-frame segmentation and CRF based spatio-temporal reasoning in an end-to-end manner.  ... 
arXiv:2107.01153v3 fatcat:nry4yjhq7zhtzbfh53wf7ie3um

A Survey of Human Action Recognition and Posture Prediction

Nan Ma, Zhixuan Wu, Yiu-ming Cheung, Yuchen Guo, Yue Gao, Jiahong Li, Beijyan Jiang
2022 Tsinghua Science and Technology  
Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in videos.  ...  Hence, it is necessary to make a comprehensive review of recent developments. In this paper, firstly, we attempt to present the background, and then discuss research progresses.  ...  The output of the CNNs was passed through the LSTM in chronological order, so that the video data are finally characterized in the spatial and temporal dimensions.  ... 
doi:10.26599/tst.2021.9010068 fatcat:lygnvsm3unddnngyd7s3wkchjy

Learning Ensembles of Potential Functions for Structured Prediction with Latent Variables

Hossein Hajimirsadeghi, Greg Mori
2015 2015 IEEE International Conference on Computer Vision (ICCV)  
As a result, a complex and flexible ensemble method is achieved for structured prediction which can be successfully used in a variety of applications.  ...  This paper presents HCRF-Boost, a novel and general framework for learning HCRFs in functional space.  ...  These methods jointly train an MRF and a CNN by maximizing likelihood via back-propagation and stochastic gradient descent.  ... 
doi:10.1109/iccv.2015.462 dblp:conf/iccv/HajimirsadeghiM15 fatcat:z3yegkffybcfrhd6pe5zvtnh3y

Deep Image Deblurring: A Survey [article]

Kaihao Zhang, Wenqi Ren, Wenhan Luo, Wei-Sheng Lai, Bjorn Stenger, Ming-Hsuan Yang, Hongdong Li
2022 arXiv   pre-print
Next, we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review and comparison.  ...  Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image.  ...  Acknowledgment This research was funded in part by the NSF CA-REER Grant #1149783, ARC-Discovery grant projects (DP 190102261 and DP220100800), and a Ford Alliance URP grant.  ... 
arXiv:2201.10700v2 fatcat:a3ckgjsgdrbufctsnneesmeyp4

Comprehensive Analysis Of Crowd Behavior Techniques: A Thorough Exploration

Safvan Vahora, Krupa Galiya, Harsh Sapariya, Srijyaa Varshney
2022 International Journal of Computing and Digital Systems  
In recent years, one of the most important issues for public security is "Automated analysis of a crowd behavior" using surveillance videos.  ...  PCA, HOG, SIFT, optical flow, GMM, spatiotemporal filter, etc. and the self-learned feature descriptor based approaches which use deep learning models like CNN, RNN, GD-GAN, etc.  ...  Wang et al. suggested a method for Spatio-temporal recognition of crowd anomalies [21] .  ... 
doi:10.12785/ijcds/110181 fatcat:v2va6j7g2jes7d6jc7eobktfoa

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

Asim Zaman, Ling Yu, Nasir Ud Din, Kifayat Ullah, Qaisar Hayat
2022 Journal of Engineering Research and Reports  
Discrete Wavelet Transform (DWT) algorithm is considered in the extraction features, and their classification is executed by a convolutional neural network (CNN) and support vector machine (SVM) algorithms  ...  The Median filters are used in the pre-processing step, and morphological operation and Otsu thresholding are used to segment MR images.  ...  Liu, CNN in MRF: Video Object Segmentation via Inference in a CNN-Based Higher-Order Spatio- Temporal MRF.  ... 
doi:10.9734/jerr/2022/v22i917563 fatcat:5n63okmm3bczrfrp7bq4xy77su

Moving Objects Detection with a Moving Camera: A Comprehensive Review [article]

Marie-Neige Chapel, Thierry Bouwmans
2020 arXiv   pre-print
During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments.  ...  A reminder of methods for static cameras is provided as well as the challenges with both static and moving cameras. Publicly available datasets and evaluation metrics are also surveyed in this paper.  ...  Acknowledgments This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  ... 
arXiv:2001.05238v1 fatcat:itswnqe3g5ey7c2apy35xqjjza

Fast Online Object Tracking and Segmentation: A Unifying Approach

Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H.S. Torr
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.  ...  and the best speed for the semisupervised video object segmentation task on DAVIS-2016 and DAVIS-2017.  ...  [1] recently proposed a very accurate method that makes use of a spatio-temporal MRF in which temporal dependencies are modelled by optical flow, while spatial dependencies are expressed by a CNN.  ... 
doi:10.1109/cvpr.2019.00142 dblp:conf/cvpr/Wang0BHT19 fatcat:uuftbjelrnezxcrw4jtazmec7e
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