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https://www.sciencedirect.com/science/article/pii/S0952197606000960
3. Support vector regression for optic flow estimation. The aim of SVR approach is to estimate affine parameters, in other words, to determine the optimal hyperplane which models the data set. This hyperplane must interpolate data points with an ɛ-margin.Cited by: 4
http://www.cs.toronto.edu/pub/jepson/teaching/vision/2503/opticalFlow.pdf
Optical Flow Estimation Goal: Introduction to image motion and 2D optical flow estimation. ... Written in vector form, with : When the duration between frames is large, it is sometimes more ap- ... (or spatiotemporal) support window above. This produces five images, each of which ...
http://www.cs.toronto.edu/~jepson/csc2503/opticalFlow.pdf
Optical Flow Estimation Goal: Introduction to image motion and 2D optical flow estimation. ... Optical Flow Field – Estimate of the 2D motion field. 2503: Optical Flow Page: 2. ... (or spatiotemporal) support window g above. This produces five images, each of which ...
https://www.researchgate.net/publication/251826966_Robust_Optic_Flow_Computation_with_Support_Vector_Regression
An optical flow estimation technique is presented which is based on the least-median-of-squares (LMedS) robust regression algorithm enabling more accurate flow estimates to be computed in the ...
https://www.sciencedirect.com/science/article/pii/S0003267015005589
Jun 23, 2015 · An integrated fiber-optic probe combined with support vector regression for fast estimation of optical properties of turbid media. ... And the time needed for estimation of optical properties was about 5 s for one set of diffuse reflectance values. The speed of …Cited by: 10
http://www1.icsi.berkeley.edu/~cnieuwe/papers/andres-et-al-cvpr08.pdf
On Errors-In-Variables Regression with Arbitrary Covariance and its Application to Optical Flow Estimation Bjorn Andres, Claudia Kondermann, Daniel Kondermann,¨ Ullrich Kothe, Fred A. Hamprecht, Christoph S. Garbe¨ Interdisciplinary Center for Scientific Computing, University of Heidelberg Abstract Linear inverse problems in computer vision ...
https://www.sciencedirect.com/science/article/pii/S0167865517304415
Mar 01, 2019 · Boosting deep attribute learning via support vector regression for fast moving crowd counting. ... it extracts the super-pixel patches and optical-flow features simultaneously from scene images. So it reduces computational complexity for feature extraction stages. ... To verify proposed boosting deep attribute learning via SVR method, ...Cited by: 3
https://www.sciencedirect.com/science/article/pii/S0010448514002322
Support vector machines (SVMs) as on type of metaheuristic soft computing technique, have gained importance regarding issues related with the environment , . There are two fundamental classes of support vector machines: support vector classification (SVC) and support vector regression (SVR).Cited by: 6
https://www.sciencedirect.com/science/article/pii/S092523121930013X
Mar 21, 2019 · We take advantage of multi-level features ranging from low detailed levels to high semantic levels to exploit the powerful expressiveness of both the image branch and optical flow branch. Besides, we adopt a global appearance regression to obtain high-quality segmentation.Cited by: 1
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4000537/
II. Optical Flow Estimation. A comprehensive survey of optical flow since the pioneering papers by Horn/Schunck and Lucas/Kanade from 1981 is beyond the scope of this paper. However, the short introduction in Section II-A, should suffice to understand the …Cited by: 92
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4000537/
II. Optical Flow Estimation. A comprehensive survey of optical flow since the pioneering papers by Horn/Schunck and Lucas/Kanade from 1981 is beyond the scope of this paper. However, the short introduction in Section II-A, should suffice to understand the issues of classical optical flow …
https://www.sciencedirect.com/science/article/pii/S092523121930013X
Mar 21, 2019 · We take advantage of multi-level features ranging from low detailed levels to high semantic levels to exploit the powerful expressiveness of both the image branch and optical flow branch. Besides, we adopt a global appearance regression to obtain high-quality segmentation.
https://ieeexplore.ieee.org/document/6643718
Pedestrian Counting Based on Crowd Density Estimation and Lucas-Kanade Optical Flow ... Firstly, the crowd density estimation for regions is based on regional feature analysis and support vector regression (SVR). We extract the following features from each segmented region: the pixel ratio and block-size histogram of the foreground, the pixel ...
https://file.scirp.org/Html/88556_88556.htm
The amount of computation for detecting moving objects by the optical flow algorithm is large. The optical flow information in the smooth region cannot be detected by the optical flow algorithm, and it is susceptible to noise in a complicated environment. In this study, an optimized Horn-Schunck (HS) optical flow algorithm based on motion estimation is proposed.
https://pdfs.semanticscholar.org/930b/69c0b3b0f16f3d9d3a2c06d3da67e1c2ee78.pdf
Through optical flow estimation, motion parameters of moving objects can be obtained and at the ... tracking is done using Linear Support Vector Machine (SVM) with histograms of oriented gradients (HOG) [4] and ... for classification and regression analysis. Linear SVM is a binary classifier where positive and negati ve samples are
https://pdfs.semanticscholar.org/1ba4/b6345a624a742dae17106abf1ec527075097.pdf
Fire Detection in Video Sequences using Optical Flow Estimation A.Vidhya1, M.Karthika2 1Student, ... proposes support vector machine (SVM) which is a ... network means performing a non-linear regression in the feature space to best separate the labeled training data into
https://www.researchgate.net/publication/330767306_In-Band_OSNR_Monitoring_from_Stokes_Parameters_Using_Support_Vector_Regression
Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio estimation and ...
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004270206
Support Vector Regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with outliers and structured outliers in training data sets commonly encountered in computer vision applications. In this paper, we present a weighted version of SVM for regression. The proposed approach introduces an adaptive binary …
https://dl.acm.org/citation.cfm?id=2489672
Unfortunately, the use of the wrong transition or measurement model may lead to large estimation errors or to divergence, even when the otherwise optimal filter is deployed. In this paper on-line learning of the transition model via Support Vector Regression is proposed.
https://link.springer.com/article/10.1007%2Fs13042-012-0072-1
Twin support vector regression (TSVR) determines a pair of $$\epsilon$$ -insensitive up- and down-bound functions by solving two related support vector machine-type problems, each of which is smaller than that in a classical SVR. On the lines of TSVR, we have proposed a novel regressor for the simultaneous learning of a function and its derivatives, termed as TSVR of a Function and its ...
https://link.springer.com/chapter/10.1007/978-3-319-45886-1_32
Aug 27, 2016 · Kalman Filter Optical Flow Support Vector Regression System Availability Confidence Measurement These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
http://vision.ucsd.edu/~sagarwal/exam/ong_spann_1999.pdf
Robust Optical Flow Computation Based on Least-Median-of-Squares Regression E.P. ONG ... Abstract. An optical flow estimation technique is presented which is based on the least-median-of-squares (LMedS) robust regression algorithm enabling more accurate flow estimates to be computed in the vicinity of ... gion of support of each segmented ...
https://people.eng.unimelb.edu.au/jgl/Papers/2013CrowdDensityICACCI.pdf
Crowd Density Estimation Based on Optical Flow ... and ν-Support Vector Regression (ν-SVR) for high crowd density estimation. The four statistical measure provide a 16-feature vector. However, this approach requires to tune the ν parameter for video sequences during training. Ma et
https://link.springer.com/article/10.1023/A:1008046826441
An optical flow estimation technique is presented which is based on the least-median-of-squares (LMedS) robust regression algorithm enabling more accurate flow estimates to be computed in the vicinity of motion discontinuities. The flow is computed in a blockwise fashion using an affine model. Through the use of overlapping blocks coupled with a block shifting strategy, redundancy is ...
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