Image Features Detection, Description and Matching

Hassaballah, M.; Abdelmgeid, A A; Alshazly, Hammam

doi:10.1007/978-3-319-28854-3_2

Cited by 193 publications

(105 citation statements)

References 72 publications

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“…The predicted result in terms of semantic segmentation is illustrated by an error map in Figure 4b, where the colored pixels represent the errors; the result was in good agreement with the ground truth data, though the crack width was overestimated by one or two pixels. The statistical scores for this case are shown in Table 3, wherein the results for SegNet are also listed for comparison, and the overall mean value of the five classes is calculated from Equation (6). The advantage of the proposed models was not apparent although a slightly better result is foreseen.…”

Section: Application Of Proposed Models To Thick Cracksmentioning

confidence: 99%

Estimation of crack width based on shape‐sensitive kernels and semantic segmentation

Lee

Hwang

Choi

et al. 2019

Struct Control Health Monit

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Summary Cracks that develop in railway infrastructural components such as tunnel linings and track systems are not easy to detect on high‐speed rail routes, since inspection time is limited during the daytime and visibility is very poor at night. Meanwhile, cracks to structures such as those above mentioned are strictly monitored and treated to prevent possible malfunction or accident. In this regard, a track measurement vehicle is normally deployed to image track components and measure geometric information. The main goal of the present study was to detect cracks in images and to simultaneously measure the maximum crack width by means of newly introduced deep learning technology. For this, a shape‐sensitive kernel, that is, crack‐like kernel, within a semantic segmentation framework and a modified deep layer model were proposed. In addition to the conventional statistical models such as accuracy and intersection over union, the predicted results of the proposed models were verified by considering the boxplot and root mean square errors of the estimated crack widths. According to the results, the proposed shape‐sensitive kernel function was able to predict crack width more precisely by one or two pixels than the conventional semantic segmentation model. Future work will concentrate on the integration of the crack detection model with deterioration prediction of track geometry in order to enable systematic decision making for the predictive maintenance of railway systems.

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Section: Application Of Proposed Models To Thick Cracksmentioning

confidence: 99%

Estimation of crack width based on shape‐sensitive kernels and semantic segmentation

Lee

Hwang

Choi

et al. 2019

Struct Control Health Monit

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“…In this work, we considered a total of 30 candidate features, shown in Table 2. The features were chosen based on previous image classification work [21] e.g. edge based features (more edges lead to a more complex image), as well as intuition based on our motivation (Section 2.1), e.g.…”

Section: Featuresmentioning

confidence: 99%

Adaptive deep learning model selection on embedded systems

et al. 2018

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The recent ground-breaking advances in deep learning networks (DNNs) make them attractive for embedded systems.However, it can take a long time for DNNs to make an inference on resource-limited embedded devices. Offloading the computation into the cloud is often infeasible due to privacy concerns, high latency, or the lack of connectivity. As such, there is a critical need to find a way to effectively execute the DNN models locally on the devices. This paper presents an adaptive scheme to determine which DNN model to use for a given input, by considering the desired accuracy and inference time. Our approach employs machine learning to develop a predictive model to quickly select a pre-trained DNN to use for a given input and the optimization constraint. We achieve this by first training off-line a predictive model, and then use the learnt model to select a DNN model to use for new, unseen inputs. We apply our approach to the image classification task and evaluate it on a Jetson TX2 embedded deep learning platform using the ImageNet ILSVRC 2012 validation dataset. We consider a range of influential DNN models. Experimental results show that our approach achieves a 7.52% improvement in inference accuracy, and a 1.8x reduction in inference time over the most-capable single DNN model.

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“…SIFT is invariant to scale change, rotation, affine transformation and rescaling of images, but not good in case of illumination change. Whereas, the SUFT is not fully affine invariant, unstable under extreme rotation and illumination changes (Juan and Gwun, 2009, Hassaballah et al, 2016, Vedaldi and Fulkerson, 2008.…”

Section: Comparison Of Sift and Surfmentioning

confidence: 99%

“…From the last few years, image feature detectors and descriptors are most widely used techniques for such applications which includes 3D scene reconstruction, panoramic mosaicking/stitching, image classification, object recognition and robot localization etc., all are depends upon the presence of stable and representative features in an image space. Thus, the image features detection and extraction are important steps for these applications (Hassaballah et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Matching in Terrestrial Image Sequences

Abbas¹,

Ghuffar²

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:From the last decade, the feature detection, description and matching techniques are most commonly exploited in various photogrammetric and computer vision applications, which includes: 3D reconstruction of scenes, image stitching for panoramic creation, image classification, or object recognition etc. However, in terrestrial imagery of urban scenes contains various issues, which include duplicate and identical structures (i.e. repeated windows and doors) that cause the problem in feature matching phase and ultimately lead to failure of results specially in case of camera pose and scene structure estimation. In this paper, we will address the issue related to ambiguous feature matching in urban environment due to repeating patterns.

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Image Features Detection, Description and Matching

Cited by 193 publications

References 72 publications

Estimation of crack width based on shape‐sensitive kernels and semantic segmentation

Estimation of crack width based on shape‐sensitive kernels and semantic segmentation

Adaptive deep learning model selection on embedded systems

Robust Feature Matching in Terrestrial Image Sequences

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