Computer Vision Best Papers

by Lord Omlette.
Created 2006-04-30, 270 BMT.
Updated 2008-10-04, 190 BMT.

If you see can fill in any of these details or if you see something wrong, for the love of Xenu, PLEASE leave a comment!

IEEE CVPR

CVPR @ dblp.

ECCV

ECCV @ dblp.

Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP)

ICVGIP @ dblp.

### RANSAC Algorithm Simulation in MATLAB Implementation The Random Sample Consensus (RANSAC) algorithm is widely utilized due to its robustness against outliers when fitting a model to data points[^1]. In MATLAB, implementing this method involves several key components including defining an appropriate error threshold, selecting random subsets from the dataset, estimating parameters using these samples, testing all other data points relative to those estimated values, and finally choosing the best fit based on consensus. A basic example demonstrating how one might implement RANSAC within MATLAB can be seen below: ```matlab function [modelParams, inliersIdx] = ransac(dataPoints, distanceFunc, ... sampleSize, maxIterations, threshold) % Initialize variables. bestModelScore = 0; [modelParams, inliersIdx] = deal([]); for i = 1:maxIterations % Select 'sampleSize' number of indices randomly without replacement. idxSampled = datasample(1:size(dataPoints, 1), sampleSize); % Estimate initial parameter set given sampled subset. paramsEstimate = estimateParameters(dataPoints(idxSampled,:)); % Compute distances between each point & current estimation. distsToCurrentFit = arrayfun(@(j)distanceFunc(paramsEstimate,... dataPoints(j,:),threshold), 1:size(dataPoints,1)); % Determine which points lie close enough according to specified tolerance level ('threshold'). currInlierIndices = find(distsToCurrentFit <= threshold); % Update global optimal solution if necessary. numCurrInliers = length(currInlierIndices); if(numCurrInliers > bestModelScore) bestModelScore = numCurrInliers; modelParams = paramsEstimate; inliersIdx = currInlierIndices; end end ``` This code snippet provides a generic framework that could serve as starting material for more specific applications such as line fitting or homography computation. Users would need to define their own `estimateParameters` function along with suitable metric functions like Euclidean distance depending upon application requirements. For further exploration into real-world usage scenarios involving RANSAC alongside detailed explanations regarding underlying principles behind it, consider consulting academic papers related specifically towards computer vision tasks where object articulation methods employ supervised learning combined with factor graphs over multiple stages processing pipelines operating across sequences captured via RGB-D sensors[^2].
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值