Multidimensional directional steerable filters — Theory and application to 3D flow estimation

Alexiadis, Dimitrios S.; Mitianoudis, Nikolaos; Stathaki, Tania

doi:10.1016/j.imavis.2018.01.002

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…SF is highly useful for the task of image enhancement because this algorithm is able to distinguish the objects of interest and the surrounding background [21]. SF has been successfully employed in various fields including recognition of object tracking, road crack detection, and many computer vision problems [22][23][24][25][26]. In this current study, a linear combination of Gaussian second derivatives is used as a basic filter.…”

Section: Gaussian Filter (Gf)mentioning

confidence: 99%

An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter‐Based Feature Extraction

Hoang

2018

Advances in Civil Engineering

101

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is study establishes an artificial intelligence (AI) model for detecting pothole on asphalt pavement surface. Image processing methods including Gaussian filter, steerable filter, and integral projection are utilized for extracting features from digital images. A data set consisting of 200 image samples has been collected to train and validate the predictive performance of two machine learning algorithms including the least squares support vector machine (LS-SVM) and the artificial neural network (ANN). Experimental results obtained from a repeated subsampling process with 20 runs show that both LS-SVM and ANN are capable methods for pothole detection with classification accuracy rate larger than 85%. In addition, the LS-SVM has achieved the highest classification accuracy rate (roughly 89%) and the area under the curve (0.96). Accordingly, the proposed AI approach used with LS-SVM can be very potential to assist transportation agencies and road inspectors in the task of pavement pothole detection.

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Section: Gaussian Filter (Gf)mentioning

confidence: 99%

An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter‐Based Feature Extraction

Hoang

2018

Advances in Civil Engineering

101

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“…About the estimation of 3D optical flow in general, some works have similarities with at least one of the aspects of this article, thereby, in [34] they use a 3D model of the human body and motion captured data to synthesize flow fields and train a convolutional neural network (CNN) to estimate human flow fields from pairs of images. In [35] a steerable filter-based algorithm is formulated, in its simplest form, for estimating 3D flow in sequences of volumetric or point-cloud data.…”

Section: Introductionmentioning

confidence: 99%

3D Hermite Transform Optical Flow Estimation in Left Ventricle CT Sequences

Mira

Moya-Albor

Escalante-Ramı́rez

et al. 2020

Sensors

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Heart diseases are the most important causes of death in the world and over the years, the study of cardiac movement has been carried out mainly in two dimensions, however, it is important to consider that the deformations due to the movement of the heart occur in a three-dimensional space. The 3 D + t analysis allows to describe most of the motions of the heart, for example, the twisting motion that takes place on every beat cycle that allows us identifying abnormalities of the heart walls. Therefore, it is necessary to develop algorithms that help specialists understand the cardiac movement. In this work, we developed a new approach to determine the cardiac movement in three dimensions using a differential optical flow approach in which we use the steered Hermite transform (SHT) which allows us to decompose cardiac volumes taking advantage of it as a model of the human vision system (HVS). Our proposal was tested in complete cardiac computed tomography (CT) volumes ( 3 D + t ), as well as its respective left ventricular segmentation. The robustness to noise was tested with good results. The evaluation of the results was carried out through errors in forwarding reconstruction, from the volume at time t to time t + 1 using the optical flow obtained (interpolation errors). The parameters were tuned extensively. In the case of the 2D algorithm, the interpolation errors and normalized interpolation errors are very close and below the values reported in ground truth flows. In the case of the 3D algorithm, the results were compared with another similar method in 3D and the interpolation errors remained below 0.1. These results of interpolation errors for complete cardiac volumes and the left ventricle are shown graphically for clarity. Finally, a series of graphs are observed where the characteristic of contraction and dilation of the left ventricle is evident through the representation of the 3D optical flow.

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A hybrid framework for efficient and accurate orientation estimation based on single and multiple orientation vector fields

Legaz-Aparicio

Verdú‐Monedero

Morales‐Sánchez

et al. 2023

Expert Systems with Applications

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Multidimensional directional steerable filters — Theory and application to 3D flow estimation

Cited by 3 publications

References 23 publications

An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter‐Based Feature Extraction

An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter‐Based Feature Extraction

3D Hermite Transform Optical Flow Estimation in Left Ventricle CT Sequences

A hybrid framework for efficient and accurate orientation estimation based on single and multiple orientation vector fields

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