检索结果-内蒙古大学图书馆

Mathernatical-morphology-based edge detectors for detection of thin edges in low-contrast regions

IET image processing 2007年第3期1卷 269-277页

作者： Jiang, J.-A. Chuang, C.-L. Lu, Y.-L. Fahn, C.-S. Natl Taiwan Univ Dept Bioind Mechatron Engn Taipei 106 Taiwan Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei 106 Taiwan Natl Taiwan Univ Inst Biomed Engn Taipei 106 Taiwan

A new edge detector based on mathematical morphology to preserve thin edge features in low-contrast regions as well as other apparent edges is proposed. A quad-decomposition edge enhancement process, a thresholding process, and a mask-based noise filtering process were developed and used to enhance thin edge features, extract edge points and filter out some meaningless noise points, respectively. Moreover, five bipolar oriented edge masks were also designed to remove most of the incorrectly detected edge features. Many experiments were conducted to evaluate and compare the performance of the proposed algorithm and several conventional ones. Pratt's figure of merit achieved by the proposed algorithm was as high as 92.5. The comprehensive experimental results show that the proposed algorithm is capable of preserving thin edge details successfully in low-contrast regions and is robust against noise.

关键词： filtering theory edge detection computer vision and image processing techniques mask-based noise filtering process quad-decomposition edge enhancement process mathematical-morphology-based edge detector Pratt figure mathematical morphology bipolar oriented edge mask Filtering methods in signal processing image enhancement low-contrast region thresholding process Optical, image and video signal processing image segmentation

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Accuracy and numerical stability of high-order polar harmonic transforms

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IET image processing 2012年第6期6卷 617-626页

作者： Singh, C. Upneja, R. Punjabi Univ Dept Comp Sci Patiala Punjab India

Invariant moments and transforms are a special kind of statistical measure designed to remain constant after some transformations, such as object rotation, scaling, translation or image illumination changes, in order to improve the reliability of a pattern recognition system. Polar harmonic transforms (PHTs) are one of such image descriptors based on a set of orthogonal projection bases, which are better rated in comparison with other moments and transforms because they possess less redundant information. PHTs suffer from geometric error and numerical integration error. Geometric error is caused when a square image is mapped into a unit circular disk, which cannot exactly match the square domain. Numerical integration error is caused when the integration is approximated by zeroth-order summation. We propose a computational framework based on numerical integration approach that reduces the geometric error and the numerical integration error simultaneously. The effect of the improved accuracy in the PHTs is analysed for rotation invariance, scale invariance and image noise. Various numerical experiments demonstrate that the effect of these errors is much more prominent in small images and, therefore the proposed method is suitable for applications involving small images, such as in optical character recognition and template-matching applications.

关键词： integration numerical integration error statistical analysis optical character recognition scale invariance PHT statistical measure unit circular disk computer vision and image processing techniques image processing template-matching invariant moment geometric error object rotation zeroth-order summation Optical, image and video signal processing Integral transforms numerical stability Numerical integration and differentiation image noise pattern recognition Integral transforms in numerical analysis object scaling image illumination transforms orthogonal projection bases rotation invariance high-order polar harmonic transform image descriptor

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Multi-focus image fusion evaluation based on jointly sparse representation and atom focus measure

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IET image processing 2021年第5期15卷 1032-1041页

作者： Hu, Yanxiang Zhang, Bo Zhang, Juntong Gao, Qian Tianjin Normal Univ Coll Comp & Informat Engn Tianjin Peoples R China

Multi-focus image fusion (MFIF) tries to combine images with different in-focus regions and get a composite image that is in focus everywhere. Although many new MFIF algorithms based on various new representation models have been proposed in recent years, the performance evaluation of MFIF is still a challenging issue. In this study, a novel MFIF objective evaluation metric based on jointly sparse representation and atom focus measure is proposed. It not only provides a more reliable alternative for MFIF quality measurement but also supplies a unique MFIF performance analysis method at the same time. In the measurement, the sources and their fusion results are decomposed jointly sparse with an over-complete learning dictionary to extract the atom remnants of the source images. Meanwhile, in order to emphasise the fusion effect of in-focus atoms, the sum-modified-Laplacian model is used to measure the atom focus degree. Then, the atom remnants weighted by their focus measures are used to measure MFIF quality. In the experiments, nine recently proposed fusion algorithms were tested to contrast the proposed metrics with other four widely used objective metrics. The experimental results demonstrated the rationality and accuracy of our method. Moreover, it was also proved quantificationally that the fusion degree of atoms is directly related to their in-focus degree, the high in-focus degree atoms usually have poor fusion effect.

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

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Robust watermarking using fractional wavelet packet transform

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IET image processing 2012年第4期6卷 386-397页

作者： Bhatnagar, G. Raman, B. Wu, Q. M. J. Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Indian Inst Technol Dept Math Roorkee Uttar Pradesh India

In this study, the concept of fractional wavelet packet transform is explored with its application in digital watermarking. The core idea of the proposed watermarking scheme is to decompose an image via fractional wavelet packet transform and then a reference image is created by changing the positions of all frequency sub-bands at each level with respect to some rule which is secret and only known to the owner/creator. For embedding, the reference image is segmented into non-overlapping blocks and modify its singular values with the watermark singular values. Finally, a reliable watermark extraction algorithm is developed for the extraction of watermark from the distorted image. The feasibility of this method and its robustness against different kind of attacks are verified by computer simulations.

关键词： computer vision and image processing techniques image watermarking image and video coding fractional wavelet packet transform digital watermarking Data security wavelet transforms watermark extraction algorithm Integral transforms image segmentation

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Fall detection based on OpenPose and MobileNetV2 network

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IET image processing 2023年第3期17卷 722-732页

作者： Gao, Mengqi Li, Jiangjiao Zhou, Dazheng Zhi, Yumin Zhang, Mingliang Li, Bin Qilu Univ Technol Shandong Acad Sci Sch Math & Stat Jinan Peoples R China

The proposed fall detection approach is aimed at building a support system for the elders. In this work, a method based on human pose estimation and lightweight neural network is used to detect falls. First, the OpenPose is used to extract human keypoints and label them in the images. After that, the modified MobileNetV2 network is used to detect falls by integrating both human keypoint information and pose information in the original images. The above operation can use the original image information to correct the deviation in the keypoint labeling process. Through experiments, the accuracy of the proposed method is 98.6% and 99.75% on the UR and Le2i datasets, which is higher than the listed comparison methods.

关键词： Biology and medical computing feature extraction video signal processing fall detection approach original images human keypoints Neural nets modified MobileNetV2 network OpenPose geriatrics Optical, image and video signal processing computer vision and image processing techniques human keypoint information object detection image recognition listed comparison methods fall detection pose estimation Video signal processing lightweight neural network elders keypoint labeling process original image information

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Geodesic Gramian denoising applied to the images contaminated with noise sampled from diverse probability distributions

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IET image processing 2023年第1期17卷 144-156页

作者： Gajamannage, Kelum Park, Yonggi Sadovski, Alexey Texas A&M Univ Dept Math & Stat 6300 Ocean Dr Corpus Christi TX 78412 USA

As quotidian use of sophisticated cameras surges, people in modern society are more interested in capturing fine-quality images. However, the quality of the images might be inferior to people's expectations due to the noise contamination in the images. Thus, filtering out the noise while preserving vital image features is an essential requirement. Existing denoising methods have assumptions, on the probability distribution in which the contaminated noise is sampled, for the method to attain its expected denoising performance. In this paper, the recent Gramian-based filtering scheme to remove noise sampled from five prominent probability distributions from selected images is utilized. This method preserves image smoothness by adopting patches partitioned from the image, rather than pixels, and retains vital image features by performing denoising on the manifold underlying the patch space rather than in the image domain. Its denoising performance is validated, using six benchmark computer vision test images applied to two state-of-the-art denoising methods, namely BM3D and K-SVD.

关键词： Filtering methods in signal processing prominent probability distributions patch space image domain differential geometry expected denoising performance diverse probability distributions sophisticated cameras selected images benchmark computer vision test images BM3D denoising method Gramian-based filtering scheme Optical, image and video signal processing computer vision and image processing techniques geodesic Gramian denoising image denoising noise contamination image smoothness probability fine-quality images vital image features contaminated noise K-SVD denoising method computer vision Other topics in statistics image filtering

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Pre-training of gated convolution neural network for remote sensing image super-resolution

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IET image processing 2021年第5期15卷 1179-1188页

作者： Peng, Yali Wang, Xuning Zhang, Junwei Liu, Shigang Minist Educ Key Lab Modern Teaching Technol Xian 710062 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

Many very deep neural networks are proposed to obtain accurate super-resolution reconstruction of remote sensing images. However, the deeper the network for image SR is, the more difficult it is to train. The low-resolution inputs and features contain abundant low-frequency information and noise, which are treated equally as the high-frequency information to across the network. To solve these problems, a novel single-image super-resolution algorithm named pre-training of gated convolution neural network (PGCNN) is proposed for remote sensing images. The proposed PGCNN consists of several residual blocks with long skip connections. Each residual block contains an additional well-designed gated convolution unit, which provides different weights to high-frequency information and low-frequency information to control the transmission of information, making the main network focus on learning high-frequency information. Compared with several state-of-the-art methods, experimental results on the remote sensing datasets (SIRI-WHU, NWPU-RESISC45, RSSCN7 and UC-Merced-Land-Use) show that the proposed PGCNN has the accuracy and visual improvements.

关键词： Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Optical, image and video signal processing Geophysical techniques and equipment computer vision and image processing techniques Geophysics computing Neural nets

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image zooming using directional cubic convolution interpolation

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IET image processing 2012年第6期6卷 627-634页

作者： Zhou, D. Shen, X. Dong, W. N China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China CAS Inst Automat LIAMA NLPR Beijing Peoples R China

image-zooming is a technique of producing a high-resolution image from its low-resolution counterpart. It is also called image interpolation because it is usually implemented by interpolation. Keys' cubic convolution ( CC) interpolation method has become a standard in the image interpolation field, but CC interpolates indiscriminately the missing pixels in the horizontal or vertical direction and typically incurs blurring, blocking, ringing or other artefacts. In this study, the authors propose a novel edge-directed CC interpolation scheme which can adapt to the varying edge structures of images. The authors also give an estimation method of the strong edge for a missing pixel location, which guides the interpolation for the missing pixel. The authors' method can preserve the sharp edges and details of images with notable suppression of the artefacts that usually occur with CC interpolation. The experiment results demonstrate that the authors' method outperforms significantly CC interpolation in terms of both subjective and objective measures.

关键词： image resolution high resolution image artefact suppression edge directed CC interpolation image zooming computer vision and image processing techniques image blurring missing pixel location image blocking Interpolation and function approximation (numerical analysis) objective measures subjective measures Optical, image and video signal processing directional cubic convolution interpolation interpolation

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Iris segmentation methodology for non-cooperative recognition

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IEE PROCEEDINGS-vision image AND SIGNAL processing 2006年第2期153卷 199-205页

作者： Proenca, H. Alexandre, L. A. Univ Beira Interior Dept Informat IT Networks & Multimedia Grp P-6200001 Covilha Portugal

An overview of the iris image segmentation methodologies for biometric purposes is presented. The main focus is on the analysis of the ability of segmentation algorithms to process images with heterogeneous characteristics, simulating the dynamics of a non-cooperative environment. The accuracy of the four selected methodologies on the UBIRIS database is tested and, having concluded about their weak robustness when dealing with non-optimal images regarding focus, reflections, brightness or eyelid obstruction, the authors introduce a new and more robust iris image segmentation methodology. This new methodology could contribute to the aim of non-cooperative biometric iris recognition, where the ability to process this type of image is required.

关键词： iris segmentation methodology biometric purposes image segmentation image recognition biometrics (access control) eye noncooperative recognition computer vision and image processing techniques image recognition

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Design and realisation of a microwave three-dimensional imaging system with application to breast-cancer detection

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IET MICROWAVES ANTENNAS & PROPAGATION 2010年第12期4卷 2200-2211页

作者： Zhurbenko, V. Rubaek, T. Krozer, V. Meincke, P. Tech Univ Denmark DK-2800 Lyngby Denmark Chalmers Univ Technol Dept Signals & Syst SE-412 Gothenburg Sweden Goethe Univ Frankfurt D-60438 Frankfurt Germany TICRA DK-1201 Copenhagen K Denmark

An active microwave-imaging system for non-invasive detection of breast cancer based on dedicated hardware is described. Thirty-two transceiving channels are used to measure the amplitude and phase of the scattered fields in the three-dimensional (3D) imaging domain using electronic scanning. The 3D inverse electromagnetic scattering problem is then solved in order to reconstruct the distribution of the complex permittivity in the imaging domain. The dedicated hardware is based on an array architecture allowing for a short acquisition time while maintaining a high sensitivity, which is important for measurement accuracy and reproducibility as well as for patient comfort. The dedicated hardware achieves a receiver noise figure of 2.3 dB at a gain of 97 dB. The operating frequency range is from 0.3 to 3 GHz. The image acquisition time at one frequency is approximately 50 s and an image is created within 2 h using the single-frequency reconstruction algorithm. The performance of the system is illustrated by an analysis of the standard deviations in amplitude and phase of a series of measurements as well as by a simple image reconstruction example.

关键词： computer vision and image processing techniques noninvasive breast-cancer detection Microwaves and other electromagnetic waves (medical uses) Biology and medical computing medical image processing Optical, image and video signal processing image reconstruction transceiving channels noise figure 2.3 dB Patient diagnostic methods and instrumentation single-frequency reconstruction algorithm electromagnetic wave scattering microwave imaging electronic scanning 3D inverse electromagnetic scattering problem cancer Microwaves and other electromagnetic waves (biomedical imaging/measurement) frequency 0.3 GHz to 3 GHz Electromagnetic wave propagation object detection microwave three-dimensional imaging system gain 97 dB

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