Large displacement optical flow algorithms are generally categorised into descriptor-based matching and pixel-based matching.Descriptor-based approaches are robust to geometric variation,however they have inherent loc...Large displacement optical flow algorithms are generally categorised into descriptor-based matching and pixel-based matching.Descriptor-based approaches are robust to geometric variation,however they have inherent localisation precision limitation due to histogram nature.This work presents a novel method called improved precision dense descriptor flow(IPDDF).The authors introduce an additional pixel-based matching cost within an existing dense Daisy descriptor framework to improve the flow estimation precision.Pixel-based features such as pixel colour and gradient are computed on top of the original descriptor in the authors’matching cost formulation.The pixel-based cost only requires a light-weight pre-computation and can be adapted seamlessly into the matching cost formulation.The framework is built based on the Daisy Filter Flow work.In the framework,Daisy descriptor and a filter-based efficient flow inference technique,as well as a randomised fast patch match search algorithm,are adopted.Given the novel matching cost formulation,the framework enables efficiently solving dense correspondence field estimation in a high-dimensional search space,which includes scale and orientation.Experiments on various challenging image pairs demonstrate the proposed algorithm enhances flow estimation accuracy as well as generate a spatially coherent yet edge-aware flow field result efficiently.展开更多
Image matching is a well-studied problem in computer vision.Conventional image matching is solved using image feature matching algorithms,and later deep learning techniques are also applied to tackle the problem.Here,...Image matching is a well-studied problem in computer vision.Conventional image matching is solved using image feature matching algorithms,and later deep learning techniques are also applied to tackle the problem.Here,a slope-modelling framework is proposed by adopting the image matching techniques.First,image pairs of a slope scene are captured and camera calibration as well as image rectification are performed.Then,PatchMatch Filter(PMF-S)and PWC-Net techniques are adapted to solve the matching of image pairs.In the proposed PatchMatch Filter-Census(PMF-Census),slanted-plane modelling,image census transform and gradient difference are employed in matching cost formulation.Later,nine matching points are manually selected from an image pair.Matching point pairs are further used in fitting a transformation matrix to relate the matching between the image pair.Then,the transformation matrix is applied to obtain a ground truth matching image for algorithm evaluation.The challenges in this matching problem are that the slope is of a homogenous region and it has a slanted-surface geometric structure.In this work,it is found out that the error rate of the proposed PMF-Census is significantly lower as compared with the PWC-Net method and is more suitable in this slope-modelling task.In addition,to show the robustness of the proposed PMF-Census against the original PMF-S,further experiments on some image pairs from Middlebury Stereo 2006 dataset are conducted.It is demonstrated that the error percentage by the proposed PMF-Census is reduced significantly especially in the low-texture and photometric distorted region,in comparison to the original PMF-S algorithm.This further verifies the suitability of the PMF-Census in modelling the outdoor low-texture slope scene.展开更多
文摘Large displacement optical flow algorithms are generally categorised into descriptor-based matching and pixel-based matching.Descriptor-based approaches are robust to geometric variation,however they have inherent localisation precision limitation due to histogram nature.This work presents a novel method called improved precision dense descriptor flow(IPDDF).The authors introduce an additional pixel-based matching cost within an existing dense Daisy descriptor framework to improve the flow estimation precision.Pixel-based features such as pixel colour and gradient are computed on top of the original descriptor in the authors’matching cost formulation.The pixel-based cost only requires a light-weight pre-computation and can be adapted seamlessly into the matching cost formulation.The framework is built based on the Daisy Filter Flow work.In the framework,Daisy descriptor and a filter-based efficient flow inference technique,as well as a randomised fast patch match search algorithm,are adopted.Given the novel matching cost formulation,the framework enables efficiently solving dense correspondence field estimation in a high-dimensional search space,which includes scale and orientation.Experiments on various challenging image pairs demonstrate the proposed algorithm enhances flow estimation accuracy as well as generate a spatially coherent yet edge-aware flow field result efficiently.
文摘Image matching is a well-studied problem in computer vision.Conventional image matching is solved using image feature matching algorithms,and later deep learning techniques are also applied to tackle the problem.Here,a slope-modelling framework is proposed by adopting the image matching techniques.First,image pairs of a slope scene are captured and camera calibration as well as image rectification are performed.Then,PatchMatch Filter(PMF-S)and PWC-Net techniques are adapted to solve the matching of image pairs.In the proposed PatchMatch Filter-Census(PMF-Census),slanted-plane modelling,image census transform and gradient difference are employed in matching cost formulation.Later,nine matching points are manually selected from an image pair.Matching point pairs are further used in fitting a transformation matrix to relate the matching between the image pair.Then,the transformation matrix is applied to obtain a ground truth matching image for algorithm evaluation.The challenges in this matching problem are that the slope is of a homogenous region and it has a slanted-surface geometric structure.In this work,it is found out that the error rate of the proposed PMF-Census is significantly lower as compared with the PWC-Net method and is more suitable in this slope-modelling task.In addition,to show the robustness of the proposed PMF-Census against the original PMF-S,further experiments on some image pairs from Middlebury Stereo 2006 dataset are conducted.It is demonstrated that the error percentage by the proposed PMF-Census is reduced significantly especially in the low-texture and photometric distorted region,in comparison to the original PMF-S algorithm.This further verifies the suitability of the PMF-Census in modelling the outdoor low-texture slope scene.
