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智能優化算法 ?神經網絡預測 雷達通信? 無線傳感器

信號處理 圖像處理 路徑規劃 元胞自動機 無人機

? 內容介紹

Middlebury 光流基準測試的結果證明，光流估計算法的準確性一直在穩步提高。然而，自從 Horn 和 Schunck 的工作以來，典型的表述幾乎沒有改變。我們試圖通過對目標函數、優化方法和現代實施實踐如何影響準確性的全面分析來揭示使最近取得的進展成為可能的原因。我們發現，當與現代優化和實施技術相結合時，“經典”流動公式的表現出奇地好。此外，我們發現，雖然優化期間中間流場的中值濾波是近期性能提升的關鍵，但它會帶來更高能量的解決方案。要了解這種現象背后的原理，我們推導出一個新的目標，將中值過濾啟發式形式化。

? 部分代碼

function uvo = estimate_flow_demo(method, iSeq, seqName, varargin)

%ESTIMATE_FLOW_DEMO? ?Optical flow estimation demo program

%

% output UV is an M*N*2 matrix. UV(:,:,1) is the horizontal flow and

%? ?UV(:,:,2) is the vertical flow.

%

% Example

% -------

% uv = estimate_flow_demo; or estimate_flow_demo;

% reads the color RubberWhale sequence and uses default parameters and

% default method "Classic+NL-Fast"?

%?

% same as

% uv = estimate_flow_demo('classic+nl-fast');

%

% uv = estimate_flow_demo('classic+nl-fast', 4, 'middle-other');

%

% uv = estimate_flow_demo('classic+nl-fast', 4, 'middle-other', 'lambda', 3, 'pyramid_levels', 5);

% takes user-defined parameters?

%

% Method can be

%? ?'classic+nl-fast' (default)? 'classic+nl' 'classic+nl-full'

%? ?'classic++'? 'classic-c'? 'classic-l'/'ba' 'hs'

%

% iSeq can ben 1 to 12: selects the sequence in the following cell arrays to process

% % training data

% SeqName = 'middle-other'?

%? ? ? ? ? ?{'Venus', 'Dimetrodon',? ?'Hydrangea',? ? 'RubberWhale',...

%? ? ? ? ? ? 'Grove2', 'Grove3', 'Urban2', 'Urban3', ...

%? ? ? ? ? ? 'Walking', 'Beanbags',? ? ?'DogDance',? ? ?'MiniCooper'};

% % test data

% SeqName = 'middle-eval'?

%? ? ? ? ? ?{'Army',? 'Mequon', 'Schefflera', 'Wooden',? 'Grove', 'Urban', ...

%? ? ? ? ? ? 'Yosemite',? 'Teddy', 'Basketball',? 'Evergreen',? 'Backyard',? 'Dumptruck'};

%?

% 'lambda'? ? ? ? ? ? ? ? trade-off (regularization) parameter; larger produces smoother flow fields?

% 'sigma_d'? ? ? ? ? ? ? ?parameter of the robust penalty function for the spatial term

% 'sigma_s'? ? ? ? ? ? ? ?parameter of the robust penalty function for the data term

% 'pyramid_levels'? ? ? ? pyramid levels for the quadratic formulation; default is automatic?

% 'pyramid_spacing'? ? ? ?downsampling ratio up each pyramid level for the quadratic formulation; default is 2

% 'gnc_pyramid_levels'? ? pyramid levels for the non-quadratic formulation; default is 2

% 'gnc_pyramid_spacing'? ?downsampling ratio up each pyramid level for the non-quadratic formulation; default is 1.25

%

%

% References:

% -----------

% Sun, D.; Roth, S. & Black, M. J. "Secrets of Optical Flow Estimation and

%? ?Their Principles" IEEE Int. Conf. on Comp. Vision & Pattern Recognition, 2010??

%?

% Sun, D.; Roth, S. & Black, M. J. "A Quantitative Analysis of Current

%? ?Practices in Optical Flow Estimation and The Principles Behind Them"?

%? ?Technical Report Brown-CS-10-03, 2010? ?

%

% Authors: Deqing Sun, Department of Computer Science, Brown University

% Contact: dqsun@cs.brown.edu

% $Date: $

% $Revision: $

%

% Copyright 2007-2010, Brown University, Providence, RI. USA

%?

%? ? ? ? ? ? ? ? ? ? ? ? ? All Rights Reserved

%?

% All commercial use of this software, whether direct or indirect, is

% strictly prohibited including, without limitation, incorporation into in

% a commercial product, use in a commercial service, or production of other

% artifacts for commercial purposes.? ? ?

%

% Permission to use, copy, modify, and distribute this software and its

% documentation for research purposes is hereby granted without fee,

% provided that the above copyright notice appears in all copies and that

% both that copyright notice and this permission notice appear in

% supporting documentation, and that the name of the author and Brown

% University not be used in advertising or publicity pertaining to

% distribution of the software without specific, written prior permission.? ? ? ??

%

% For commercial uses contact the Technology Venture Office of Brown University

%?

% THE AUTHOR AND BROWN UNIVERSITY DISCLAIM ALL WARRANTIES WITH REGARD TO

% THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND

% FITNESS FOR ANY PARTICULAR PURPOSE.? IN NO EVENT SHALL THE AUTHOR OR

% BROWN UNIVERSITY BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL

% DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR

% PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS

% ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF

% THIS SOFTWARE.? ? ? ??

if (~isdeployed)

? ? addpath(genpath('utils'));

end

if nargin < 1

? ? method = 'classic+nl-fast';

? ? iSeq? ?= 4;

? ? seqName = 'middle-other';

? ? %seqName = 'middle-eval';

elseif nargin == 1

? ? iSeq? ?= 4;

? ? seqName = 'middle-other';

end;

[im1, im2, tu, tv] = read_flow_file(seqName, iSeq);

uv = estimate_flow_interface(im1, im2, method, varargin);

% Display estimated flow fields

figure; subplot(1,2,1);imshow(uint8(flowToColor(uv))); title('Middlebury color coding');

subplot(1,2,2); plotflow(uv);? ?title('Vector plot');

if sum(~isnan(tu(:))) > 1

? ? [aae stdae aepe] = flowAngErr(tu, tv, uv(:,:,1), uv(:,:,2), 0); % ignore 0 boundary pixels

? ? fprintf('\nAAE %3.3f average EPE %3.3f \n', aae, aepe);

? ??

end;

% Uncomment below and change FN to save the flow fields

% if ~exist(['result/' seqName], 'file');

%? ? ?mkdir(['result/' seqName]);

% end;

% fn? = sprintf('result/%s/estimated_flow_%03d.flo', seqName, iSeq);

% writeFlowFile(uv, fn);

% Uncomment below to read the save flow field

% uv = readFlowFile(fn);

if nargout == 1

? ? uvo = uv;

end;

% Uncomment below? to remove 'utils/' to your

%? ?matlab search path

% rmpath(genpath('utils'));

? 運行結果

? 參考文獻

[1] Sun D ,? Roth S ,? Black M J . Secrets of Optical Flow Estimation and Their Principles[C]// The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13-18 June 2010. IEEE, 2010.

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