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Adaptive enhanced affine transformation for non-rigid registration of visible and infrared images

IET image processing 2021年第5期15卷 1144-1156页

作者： Min, Chaobo Gu, Yan Li, Yingjie Yang, Feng Hohai Univ Coll Internet Things Engn Changzhou 213000 Jiangsu Peoples R China North Night Vis Technol Co Ltd Res & Dev Dept Sensor Applicat Nanjing 211106 Peoples R China North Informat Control Res Acad Grp Co Ltd Res & Dev Dept Photoelect Syst Nanjing 211153 Peoples R China

Non-rigid registration, performing well in all-weather and all-day/night conditions, directly determine the reliability of visible (VIS) and infrared (IR) image fusion. On account of non-planar scenes and differences between IR and VIS cameras, non-linear transformation models are more helpful to non-rigid image registration than the affine model. However, most of non-linear models usually used on non-rigid registration are constructed by control points at present. Aiming at the issue that the adaptiveness and generalization of the control-point-based models are limited, adaptive enhanced affine transformation (AEAT) is proposed for image registration, generalizing the affine model from linear to non-linear case. Firstly, Gaussian weighted shape context, measuring the structural similarity between multimodal images, is designed to extract putative matches from edge maps of IR and VIS images. Secondly, to implement global image registration, the optimal parameters of the AEAT modal are estimated from putative matches by a strategy of subsection optimization. Experiment results show that this approach is robust in different registration tasks and outperforms several competitive methods on registration precision and speed.

关键词： Optical, image and video signal processing computer vision and image processing techniques Patient diagnostic methods and instrumentation Biology and medical computing

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Segregation of meaningful strokes, a pre-requisite for self co-articulation removal in isolated dynamic gestures

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IET image processing 2021年第5期15卷 1166-1178页

作者： Monsley, K. Anish Yadav, Kuldeep Singh Misra, Songhita Khan, Taimoor Bhuyan, M. K. Laskar, Rabul Hussain Natl Inst Technol Silchar Dept Elect & Commun Silchar 788010 Assam India Natl Inst Technol Andhra Pradesh Dept Elect & Commun Engn Tadepalligudem Andra Pradesh India Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati Assam India

Gesture formation, a pre-processing step, has its importance when variations in patterns, scale, and speed come into play. Self co-articulations are intentional movements performed by an individual to complete a gesture, whose presence in the trajectory alters its original meaning. For recognition, most researchers have directly used the trajectory formed along with these self co-articulated strokes, with a few removing it using visible trait-like velocity. Usage of velocity has shortcomings as gesturing in air differs from gesturing over a solid surface;hence, we propose a gesture formation model, which incorporates global and local measures to remove these self co-articulations. The global measure uses Euclidean distance, instantaneous velocity, and polarity calculated from the complete gesture, while the local measure segments the gesture into stroke-level segments by using the minimum-maximum-polarity algorithm and applies the selective bypass rules. The proposed model, when experimented on gestures patterns with premeditated speed variation, has a mean error rate of 0.0069 and 7.40% self co-articulations;individuals' natural gesticulation has a mean error rate of 0.0371 and 12.07% self co-articulations. Experimentation on each gesture of NITS hand gesture databases showed a relative improvement of 40% (accuracy 97%) over the existing baseline models.

关键词： Optical, image and video signal processing image recognition computer vision and image processing techniques

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Generative adversarial network for low-light image enhancement

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IET image processing 2021年第7期15卷 1542-1552页

作者： Li, Fei Zheng, Jiangbin Zhang, Yuan-fang Northwestern Polytech Univ Sch Software Xian 710100 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710100 Shaanxi Peoples R China Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia

Low-light image enhancement is rapidly gaining research attention due to the increasing demands of extreme visual tasks in various applications. Although numerous methods exist to enhance image qualities in low light, it is still undetermined how to trade-off between the human observation and computer vision processing. In this work, an effective generative adversarial network structure is proposed comprising both the densely residual block (DRB) and the enhancing block (EB) for low-light image enhancement. Specifically, the proposed end-to-end image enhancement method, consisting of a generator and a discriminator, is trained using the hyper loss function. The DRB adopts the residual and dense skip connections to connect and enhance the features extracted from different depths in the network while the EB receives unique multi-scale features to ensure feature diversity. Additionally, increasing the feature sizes allows the discriminator to further distinguish between fake and real images from the patch levels. The merits of the loss function are also studied to recover both contextual and local details. Extensive experimental results show that our method is capable of dealing with extremely low-light scenes and the realistic feature generator outperforms several state-of-the-art methods in a number of qualitative and quantitative evaluation tests.

关键词： image recognition computer vision and image processing techniques Neural nets

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A pixel pair-based encoding pattern for stereo matching via an adaptively weighted cost

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IET image processing 2021年第4期15卷 908-917页

作者： Fu, Yuli Lai, Kaimin Chen, Weixiang Xiang, Youjun South China Univ Technol Sch Elect & Informat Engn Guangzhou Peoples R China

Stereo matching, which is a key problem in computer vision, faces the challenge of radiometric distortions. Most of the existing stereo matching methods are based on simple matching cost algorithms and appear the problem of mismatch under radiometric distortions. It is necessary to improve the robustness and accuracy of matching cost algorithms. A novel encoding pattern is proposed for stereo matching. In the proposed encoding pattern, each of the matching windows in the grey image and gradient images is divided into several isoline-like sets with different radii. Then, pixel pairs are defined in the isoline-like sets. An encoding function is used to decide the relative order between the two pixels in each pixel pair. To apply the pattern for matching cost computation and enhance the matching accuracy, an adaptively weighted cost is designed that is related to the isoline-like sets. Experiments are conducted on the Middlebury and KITTI data sets to show the validity of the proposed method under severe radiometric distortions. Also, the comparisons with some widely used methods are made in the experiments to illustrate the advantage of the proposed method.

关键词： image and video coding image recognition computer vision and image processing techniques

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A pyramid non-local enhanced residual dense network for single image de-raining

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IET image processing 2021年第8期15卷 1786-1799页

作者： Zhao, Minghua Fan, Hengrui DU, Shuangli Wang, Li Li, Peng Hu, Jing Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian Shaanxi Peoples R China Xian Univ Technol Personnel Dept Xian 710048 Peoples R China

Single image de-raining based on convolutional neural network (CNN) has made considerable progress in recent years. However, usually the de-rained result has dark artifacts and image textures tend to be over-smoothed. In this paper, a pyramid non-local enhanced residual dense network is proposed to reduce such distortion. Firstly, the down-sampled images are input into the Laplacian pyramid, which can extract the overall and partial texture clues, and subsequently a set of images of different scales are produced. Secondly, these images are fed into a non-local enhanced residual dense block, which can not only capture long-distance dependencies of feature maps, but also fully utilizes the hierarchical features in every dense block, leading to high accuracy of rain streaks extraction and better preservation of image edge detail. Finally, the de-rained image is gradually restored by Gaussian reconstruction pyramid. Experimental results on both synthetic data and real-world data show that the artifacts distortion is obviously reduced by the proposed network. And the quality of de-rained image is significantly improved compared with the state-of-the-art methods.

关键词： edge detection image recognition pyramid nonlocal enhanced residual dense network down-sampled images image edge detail computer vision and image processing techniques Optical, image and video signal processing de-rained image image texture Gaussian processes rain nonlocal enhanced residual dense block single image de-raining neural nets image reconstruction convolutional neural network

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Segmentation of osteosarcoma in MRI images by K-means clustering, Chan-Vese segmentation, and iterative Gaussian filtering

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IET image processing 2021年第6期15卷 1310-1318页

作者： Nasor, Mohamed Obaid, Walid Ajman Univ Dept Biomed Engn Ajman U Arab Emirates

Unlike other types of tumours, automated osteosarcoma segmentation in magnetic resonance images (MRI) is a challenging task due to its different and unique intensity and texture. This paper presents a technique for segmenting osteosarcoma in MRI images using a combination of image processing techniques which include K-means clustering, Chan-Vese segmentation, iterative Gaussian filtering, and Canny edge detection. In addition, the proposed technique involves iterative morphological operations and object counting. The technique was tested using 50 MRI scan images that contain osteosarcoma tumours. The proposed technique was able to segment the osteosarcoma regardless of the variations in their intensities, textures and locations. The performance of the technique was measured by calculating the values for precision, recall, specificity, Dice score coefficient, accuracy and the running time (RT) for all tested cases. The proposed technique achieved 95.96% precision, 86.15% recall, 99.51% specificity, 89.84% Dice score coefficient, 98.02% accuracy, and 191.62 s average running time. This technique can assist clinicians in making treatment plans for patients with osteosarcoma.

关键词： image recognition Biomedical magnetic resonance imaging and spectroscopy computer vision and image processing techniques Medical magnetic resonance imaging and spectroscopy Patient diagnostic methods and instrumentation Biology and medical computing

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A feature-optimized Faster regional convolutional neural network for complex background objects detection

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IET image processing 2021年第2期15卷 378-392页

作者： Wang, Kun Liu, Maozhen Civil Aviat Univ China Dept Elect Informat & Automat Tianjin Peoples R China

In recent years, convolutional neural networks are playing an increasingly important role in the field of object detection. However, the complex background of the detected image, the limited receptive field by the fixed geometry of the convolution kernel when building the model, and the positioning and pooling deviation from the region of interest are still important factors that affect the detection accuracy. In this paper, an improved algorithm is proposed for target detection based on Faster regional convolutional neural network. In the bounding box positioning phase, an improved interpolation algorithm-Newton's parabolic interpolation is proposed instead of bilinear interpolation, after ROI size normalized by extending a parallel branch of tensor to weaken the negative impact of complex background on prospects in the phase of feature extraction using neural network recently popular attention CBAM mechanism model and the deformable convolution. Without bells and whistles, a series of experiments show that our method has higher target detection accuracy on the datasets PASCAL VOC2007, VOC2012, COCO 2014 and DIOR. Hence, the method is effective for actual target recognition tasks in complex background environments. The authors hope that the method will contribute to future research. Code has been made available at: https://***/liumaozhen-lmz/Faster_R-CNN_***.

关键词： Optical, image and video signal processing image recognition Interpolation and function approximation (numerical analysis) computer vision and image processing techniques Interpolation and function approximation (numerical analysis)

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Unsupervised change detection method in SAR images based on deep belief network using an improved fuzzy C-means clustering algorithm

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IET image processing 2021年第4期15卷 974-982页

作者： Attioui, Sanae Najah, Said Univ Sidi Mohamed Ben Abdellah Dept Comp Sci Fac Sci & Technol Fes Morocco

Deep learning methods have recently displayed ground-breaking results for synthetic aperture radar image change detection problem. However, they still face the challenges of intrinsic noise and the difficulty of acquiring labeled data. To sort out these issues, we aim to develop a change detection approach specifically designed for analyzing synthetic aperture radar images based on Deep Belief Network as the deep architecture which includes unsupervised feature learning and supervised network fine-tuning. The deep neural networks can reach the final change maps directly from the two original images. A pre-classification based on Morphological Reconstruction and Membership Filtering is employed in order to minimize the effect of noise. Appropriate diversity samples are provided by a virtual sample generation method in order to mitigate overfitting raised by limited synthetic aperture radar data. Visual and quantitative analysis as well as comparisons with advanced algorithms show that our algorithm not only achieves better results but also requires less implementation time.

关键词： Algebra, set theory, and graph theory Data and information acquisition, processing, storage and dissemination in geophysics Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research image recognition Filtering methods in signal processing Radar theory Radar equipment, systems and applications Geophysical techniques and equipment computer vision and image processing techniques Combinatorial mathematics Combinatorial mathematics Geophysics computing Knowledge representation Unsupervised learning

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A method of lining seam elimination with angle adaptation and rectangular mark for road tunnel concrete lining images

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IET image processing 2021年第9期15卷 2056-2067页

作者： Qu, Zhong Zhong, Yu-Lu Liu, Ling Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol 2 Chongwen Rd Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Coll Mobile Telecommun 1 Jiari Rd Chongqing Peoples R China

Road Tunnels are an important part of the current road transportation infrastructure. As the main form of tunnel lining diseases, cracks are easy to interact with other areas, which seriously affects the safe operation of the tunnel. Due to the similarity of brightness and linearity between surface cracks and lining cracks, the existing crack detection algorithms can not extract cracks accurately and quickly. An algorithm of lining seam crack elimination with rectangular mark is proposed here. First, the line segments in the image are detected by the Line Segment Detector algorithm based on the coarse percolation detection of the crack. Second, the distribution directions are calculated, and cracks from the lining seams are distinguished by the adaptive threshold judgment method. Third, by using the distribution characteristics of pixels, the line segments are extended to form rectangular marks perpendicular to the direction of lining seams. Finally, the marking information is used to remove the lining joints and obtain the real surface cracks of tunnel lining. Experimental results show that the algorithm can quickly and effectively remove any shape distribution of lining seam. The algorithm fills in the blank of concrete tunnel lining surface crack detection technology.

关键词： Optical, image and video signal processing computer vision and image processing techniques Engineering materials Geotechnical structures Construction industry

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A night low-illumination image enhancement model based on small probability area filtering and lossless mapping enhancement

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IET image processing 2021年第13期15卷 3221-3238页

作者： He, Lei Long, Wei Liu, Shouxin Li, Yanyan Ding, Wei Sichuan Univ Sch Mech Engn Yihuan Rd Chengdu 610065 Peoples R China

A novel night-time image enhancement approach was proposed in this paper to address the problems of low contrast and poor details of low-illumination images captured at night. To begin with, the luminance component V was extracted that was irrelevant to the colour information of the image upon converting the image to the HSV space from the RGB space. Then, by converting the luminance component V of the image into the probability space, the image was divided into a small-probability grey-scale area and a normal area based on the theory of probability. Moreover, pixels were transferred from the small probability area to the normal area of the image according to the nearest attribution principle that was established. Lastly, the contrast enhancement of the image was realized thanks to lossless mapping functions without losing the number of grey levels of the image. As can be observed from experimental results, the proposed method is superior to the most advanced algorithm in visual quality and quantitative measurement.

关键词： image capture HSV space probability lossless mapping functions visual quality probability space computer vision and image processing techniques low-illumination images capture luminance component RGB space night low-illumination image enhancement model Optical, image and video signal processing image enhancement small probability area filtering image colour analysis Other topics in statistics lossless mapping enhancement small-probability grey-scale area

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