Stereo matching for infrared images using guided filtering weighted by exponential moving average

Zhu, Chengtao; Chang, Yau-Zen

doi:10.1049/iet-ipr.2019.0144

Cited by 9 publications

(8 citation statements)

References 31 publications

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“…It can only suppress low-frequency noise and cannot deal with impulse noise well. e guided filter can filter out the noise and protect the edge information as much as possible [21] and it adopts a local linear process. e linear relationship between the filtered output of pixel i and the output image q and the guide image I is as follows:…”

Section: ⎧ ⎨ ⎩mentioning

confidence: 99%

Intelligent Transport Surveillance Memory Enhanced Method for Detection of Abnormal Behavior in Video

Zhang

2022

Journal of Advanced Transportation

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The purpose is to build a better intelligent transport platform and improve the performance of surveillance video abnormal behavior detection systems under rapid progress of science and technology, to process large-scale traffic surveillance video data. Autoencoder (AE) can detect abnormal behavior by using reconstruction error information. However, it cannot decode some abnormal codes well, so an AE based on memory needs improvement. The objective of this research is to propose a model where abnormal surveillance video can be handled. Therefore, a self-coding method based on memory enhancement is proposed. The steps are as follows: different abnormal behavior detection system algorithms are analyzed at first. The characteristics of three different methods, namely, the original autoencoder (AE), recurrent neural network, and convolutional neural network, are compared. Then, a memory module is proposed to enhance the automatic encoder to reduce the reconstruction error of normal samples and increase the reconstruction error of abnormal samples. The effect image is obtained by Laplace transform and convolution for the image with low definition, and the image with noise is processed by guided filtering. Finally, different methods are used for experimental comparison. Experiments show that, on the dataset Avenue, the frame-level result of the method proposed is about 2% higher than that of the optimal ConvLSTM in the comparison method; on the Ped1 and Ped2 datasets, it is also about 3% higher than ConvLSTM. The comparison of different methods shows that the effect of the method proposed is the best. The self-coding traffic surveillance video abnormal behavior detection system based on memory enhancement is designed with a modular structure and it uses the self-coding method based on memory enhancement. The effectiveness of the proposed method in the real scene is verified by comparing the performance of different methods in the same data set (Xia and Li, 2021).

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Section: ⎧ ⎨ ⎩mentioning

confidence: 99%

Intelligent Transport Surveillance Memory Enhanced Method for Detection of Abnormal Behavior in Video

Zhang

2022

Journal of Advanced Transportation

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“…A detailed flowchart of our algorithm is presented in Figure 1. Because of the low power of the infrared projector of R200, the reflected infrared ray in many places in the scene is quite weak, which directly leads to texture-less regions of the infrared image (Zhu and Chang, 2019). Also, as the infrared light intensity can be affected by a variety of factors, for instance, the angle of incidence and distance, there are usually some noises in the image.…”

Section: Our Proposed Algorithmmentioning

confidence: 99%

Real-Time Depth Map Estimation From Infrared Stereo Images of RGB-D Cameras

Zhong

Liao

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. RGB-D cameras are novel sensing systems that can rapidly provide accurate depth information for 3D perception, among which the type based on active stereo vision has been widely used. However, there are some problems exiting in use, such as the short measurement range and incomplete depth maps. This paper presents a robust and efficient matching algorithm based on semi-global matching to obtain more complete and accurate depth maps in real time. Considering characteristics of captured infrared speckle images, the Gaussian filter is performed firstly to restrain noise and enhance the relativity. It also adopts the idea of block matching for reliability, and a dynamic threshold selection of the block size is used to adapt to various situation. Moreover, several optimizations are applied to improve precision and reduce error. Through experiments on the Intel Realsense R200, the excellent capability of our proposed method is verified.

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“…SAS [32], SCA [33], and the proposed method are implemented with the SLIC algorithm. GF_EMA [39] employed the EWMA filter and guided filter for smoothing in the cost volume in pixel space.…”

Section: 21mentioning

confidence: 99%

An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space

Yang

Lei

Liu

et al. 2021

IET Image Processing

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Rapidly obtaining accurate dense disparity maps has been the focus of stereo matching research. At present, approaches that achieve superior disparity maps require a large amount of computation, which is not suitable for practical applications. To address this issue, this paper proposes an efficient local matching method based on an adaptive exponentially weighted moving average filter and simple linear iterative clustering segmentation algorithm. First, an effective matching cost is introduced to adaptively integrate absolute intensity difference with Census transform, which is robust against texture free and luminance variate. Following this, during the cost aggregation, the exponentially weighted moving average filter and the SLIC segmentation are combined to handle the problems of computing consumption and adaptive expansion of the cost aggregation window. Finally, the dense disparity map is obtained by a winner-takes-all approach and disparity refinement. To demonstrate its efficiency and validity, the method is quantitatively tested and compared to existing approaches on the Middlebury benchmark. The results show that it has a non-occlusion accuracy of 90.66% and an average runtime of 7.01 s on the 2014 Middlebury dataset. Compared with existing competitive methods, the proposed method achieves superior matching results with a lower time cost. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Stereo matching for infrared images using guided filtering weighted by exponential moving average

Cited by 9 publications

References 31 publications

Intelligent Transport Surveillance Memory Enhanced Method for Detection of Abnormal Behavior in Video

Intelligent Transport Surveillance Memory Enhanced Method for Detection of Abnormal Behavior in Video

Real-Time Depth Map Estimation From Infrared Stereo Images of RGB-D Cameras

An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space

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