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Multilevel Quantum Inspired Fractional Order Ant Colony Optimization for Automatic Clustering of Hyperspectral images

Multilevel Quantum Inspired Fractional Order Ant Colony Opti...

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ieee Congress on Evolutionary Computation (CEC) as part of the ieee World Congress on computational intelligence (ieee WCCI)

作者： Bhattacharyya, Siddhartha Dutta, Tulika Dey, Sandip CHRIST Deemed Univ Dept Comp Sci & Engn Bangalore Karnataka India Burdwan Univ Univ Inst Technol Dept Comp Sci & Engn Burdwan W Bengal India Sukanta Mahavidyalaya Dhupguri Dept Comp Sci Jalpaiguri W Bengal India

ISBN: (纸本)9781728169293

Hyperspectral images contain a wide variety of information, varying from relatively large regions to smaller man-made buildings, roads and others. Automatic clustering of various regions in such images is a tedious task. A multilevel quantum inspired fractional order ant colony optimization algorithm is proposed in this paper for automatic clustering of hyperspectral images. Application of fractional order pheromone updation technique in the proposed algorithm produces more accurate results. Moreover, the quantum inspired version of the algorithm produces results faster than its classical counterpart. A new band fusion technique, applying principal component analysis and adaptive subspace decomposition, is successfully proposed for the pre-processing of hyperspectral images. Score Function is used as the fitness function and K-Harmonic Means is used to determine the clusters. The proposed algorithm is implemented on the Xuzhou HYSPEX dataset and compared with classical Ant Colony Optimization and fractional order Ant Colony Optimization algorithms. Furthermore, the performance of each method is validated by peak signal-to-noise ratio which clearly indicates better segmentation in the proposed algorithm. The Kruskal-Wallis test is also conducted along with box plot, which establishes that the proposed algorithm performs better when compared with other algorithms.

关键词： Hyperspectral image Segmentation Peak signal-to-noise ratio Fractional Order Ant Colony Optimization Qutrits Kruskal-Wallis test

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Lightweight Non-Local Network for image Super-Resolution

Lightweight Non-Local Network for Image Super-Resolution

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ieee International Conference on Acoustics, Speech and signal processing

作者： Risheng Wang Tao Lei Wenzheng Zhou Qi Wang Hongying Meng Asoke K. Nandi School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi’an P. R. China Shaanxi University of Science and Technology Xi’an P. R. China School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi’an P. R. China School of Computer Science and the Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an China Brunel University London Uxbridge Middlesex United Kingdom

The popular deep convolutional networks used for image super-resolution (SR) reconstruction often increase the network depth and employ attention mechanism to improve image reconstruction effect. However, these networks suffer from two problems. The first is the deeper network easily causes higher computational cost and more GPU memory usage. The second is traditional attention mechanism often misses the spatial information of images leading the loss of image detail information. To address these issues, we propose a lightweight non-local network (LNLN) for image super resolution in this paper. The proposed network makes two contributions. First, we use non-local module instead of normal attention module to obtain larger receptive field and extract more comprehensive feature information, which is helpful for improving image SR reconstruction results. Secondly, we use the depthwise separable convolution (DSC) instead of the vanilla convolution to reconstruct the residual block, which greatly reduces the number of parameters and computational cost. The proposed LNLN and comparative networks are evaluated on five commonly public datasets, and experiments demonstrate that the proposed LNLN is superior to state-of-the-art networks in terms of reconstruction performance, the number of parameters and storage space.

关键词： deep learning image super-resolution (SR) non-local module depthwise separable convolution (DSC)

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Local Unsupervised Wheat Head Segmentation

Local Unsupervised Wheat Head Segmentation

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ieee International Conference on Intelligent Computer Communication and processing (ICCP)

作者： Elias Ennadifi Sébastien Dandrifosse Mohammed El Amine Mokhtari Alexis Carlier Sohaib Laraba Benoît Mercatoris Bernard Gosselin Information Signal and Artificial Intelligence Lab Faculty of Engineering University of Mons Mons Belgium Biosystems Dynamics and Exchanges TERRA Teaching and Research Centre Gembloux Agro-Bio Tech University of Liège Liège Belgium

ISBN: (纸本)9781665464383

Traditional wheat head detection and segmentation methods based on machine learning algorithms suffer from issues such as low efficiency and poor accuracy, resulting in the algorithms’ inability to generalize. The recent advances in deep learning, specifically in object detection methods, as well as computer development, have enabled the development of robust wheat head detection and segmentation methods. However, while international datasets of box labels are available for head detection, mask labels for segmentation are missing, and collecting them on a large scale is prohibitively expensive, time-consuming, and difficult. In this paper, we propose an unsupervised approach for segmenting wheat heads based only on box labels. Multiple state-of-the-art object detection methods have been trained on reference datasets and our collected data in order to find the best model to extract head bounding boxes. The obtained boxes were used as input of an unsupervised segmentation model named DeepMAC, which predicts the head mask in each box. Then, those masks are exploited to train several state-of-the-art supervised segmentation models. These models showed promising results on the collected dataset, covering all the wheat development stages. The average F1 score of head bounding box detection is 0.93 and the average F1 score of segmentation is 0.86.

关键词： Deep learning image segmentation Head Machine learning algorithms computational modeling Object detection Predictive models

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Optimizing Homomorphic Encryption based Secure image Analytics

Optimizing Homomorphic Encryption based Secure Image Analyti...

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ieee Workshop on Multimedia signal processing

作者： Nayna Jain Karthik Nandakumar Nalini Ratha Sharath Pankanti Uttam Kumar International Institute of Information Technology Bangalore & IBM US USA Mohamed Bin Zayed University of Artificial Intelligence UAE University at Buffalo USA Microsoft USA International Institute of Information Technology Bangalore India

ISBN: (纸本)9781665432894

Data privacy is a growing concern as more cloud-based solutions for extracting insights from images are available. Fully Homomorphic Encryption (FHE) is one of the state-of-the-art techniques to enable privacy preserving machine learning. However, encrypted deep neural network-based inference methods face the fundamental challenge of optimizing computational depth and complexity to achieve an acceptable trade-off between accuracy and practical feasibility. Existing works only report high level implementations without providing rigorous analysis of the intricacies involved in the FHE implementation of generic primitive operators of a convolutional neural network (CNN). In this paper, we use the CKKS encryption scheme available in the open-source HElib library to run encrypted inference experiments on the MNIST dataset. The experiments indicate that efficient ciphertext packing schemes, model optimization and multi-threading strategies play a critical role in determining the throughput and latency of the inference process. We also show that operational parameters of the chosen FHE scheme such as the degree of the cyclotomic polynomial, depth limitations of the underlying leveled HE scheme, and the computational precision parameters result in significant trade-offs between accuracy, security level and computational time of the machine learning model. The key contribution of the paper is the analysis and recommendation of optimization techniques for efficient encrypted CNN inference.

关键词： Privacy computational modeling Machine learning signal processing Throughput Libraries Convolutional neural networks

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Coherence Factor-Based Delay-Multiply-And-Sum image Reconstruction Algorithm

Coherence Factor-Based Delay-Multiply-And-Sum Image Reconstr...

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ieee International symposium on Antennas and Propagation

作者： Sisi Hao Jianying Li Xujing Yu Yao Feng School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728146713

Microwave imaging is one of the most promising imaging modalities for the early detection of breast cancer. Many image reconstruction algorithms have been developed. In this paper, we propose a fast Delay-Multiply-And-Sum (DMAS) beamforming algorithm with an additional weighting factor. Simulation results show that the side lobes are suppressed by using the coherence of received signals. Moreover, the computational load order is lower than DMAS.

关键词： Array signal processing Simulation Meetings Phantoms Coherence Microwave theory and techniques Microwave antenna arrays

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Research on Radar Echo signal Noise processing and Adaptive RLS Noise Reduction Algorithm 12

Research on Radar Echo Signal Noise Processing and Adaptive ...

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12th International symposium on computational intelligence and Design (ISCID)

作者： Luo, Jingxin Wen, Hui Univ Elect Sci & Technol China Res Inst Elect Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9781728146522

In the process of signal processing, noise will affect the accuracy of data processing and research. The accuracy obtained by different algorithms is different[1]. In this paper, a variety of radar signal denoising algorithms are discussed and simulated. Finally, the self-adaptive noise processing method[2] is described and optimized. The self-adaptive noise cancellation method is obtained. The comparison between the simulated data and the actual measurement data is obtained. It is proved that the self-adaptive RLS noise reduction effect studied in this paper is better, thus reducing the influence of noise.

关键词： signal processing noise noise reduction self-adaptive RLS

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Variational Inference of Finite Generalized Gaussian Mixture Models

Variational Inference of Finite Generalized Gaussian Mixture...

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ieee symposium Series on computational intelligence (SSCI)

作者： Amudala, Srikanth Ali, Samr Najar, Fatma Bouguila, Nizar Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ Canada Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9781728124858

This paper presents a variational learning framework to analyze finite g eneralized G aussian m ixture models (GGMM). The model incorporates several mixtures that are widely used in signal and image processing applications. The motivation behind this work is the shape flexibility characteristics of the generalized Gaussian distribution (GGD) because of which it can be applied to different types of data. We present a method to evaluate the posterior distribution and Bayes estimators using the variational expectation-maximization algorithm. The effective number of components of the GGMM is determined automatically. The test results show the adequacy of the proposed algorithm by applying it to medical, astrological, and image segmentation applications;while comparing it with various other approaches.

关键词： generalized gaussian mixture model variational inference taylor approximation binary classification image segmentation

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Biomedical Imaging and Analysis in the Age of Big Data and Deep Learning

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PROCEEDINGS OF THE ieee 2020年第1期108卷 3-10页

作者： Duncan, James S. Insana, Michael F. Ayache, Nicholas Yale Univ Radiol & Elect Engn New Haven CT 06520 USA NIH Bldg 10 Bethesda MD 20892 USA Natl Sci Fdn 4201 Wilson Blvd Arlington VA 22230 USA AIMBE Washington DC 20005 USA Med Image Comp & Comp Assisted Intervent MICCAI S Washington DC USA MICCAI Soc Washington DC USA UIUC Bioengn Urbana IL USA UIUC Engn Joint Appointments Elect & Comp Engn Urbana IL USA UIUC Beckman Inst Adv Sci & Technol Urbana IL USA Inst Phys IoP London England Acoust Soc Amer Melville NY USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Cambridge MA 02138 USA Brigham & Womens Hosp 75 Francis St Boston MA 02115 USA Coll France Paris France IHU Strasbourg France INRIA Sophia Antipolis France Inst 3IA Cote Azur New Artificial Intelligence AI Nice France ERC Brussels Belgium French Acad Sci Paris France EAMBES Heverlee Belgium

Imaging of the human body using a number of different modalities has revolutionized the field of medicine over the past several decades and continues to grow at a rapid pace [2]. More than ever, previously unknown information about biology and disease is being unveiled at a range of spatiotemporal scales. Although results and clinical adoption of strategies related to the computational and quantitative analysis of the images have lagged behind development of image acquisition approaches, there has been a noticeable increase of effort and interest in these areas in recent years [6]. This special issue aims to define and highlight some of the "hot" newer ideas that are in biomedical imaging and analysis, intending to shine a light on where the field might move in the next several decades, and focuses on emphasizing where electrical engineers have been involved and could potentially have the most impact. These areas include image acquisition physics, image/signal processing, and image analysis, including pattern recognition and machine learning. This issue focuses on two themes common in much of this effort: first, engineers and computer scientists have found that the information contained in medical images, when viewed through image-based vector spaces, is generally quite sparse. This observation has been transformative in many ways and is quite pervasive in the articles we include here. Second, medical imaging is one of the largest producers of "big data," and, data-driven machinelearning techniques (e.g., deep learning) are gaining significant attention because improved performance over previous approaches. Thus, data-driven techniques, e.g., formation via image reconstruction [11] and image analysis via deep learning [8], [9], are gaining momentum in their development.

关键词： Special issues and sections Deep learning Machine learning Biomedical imaging Medical treatment Artificial intelligence

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Dense Feature Pyramid Grids Network for Single image Deraining

Dense Feature Pyramid Grids Network for Single Image Deraini...

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ieee International Conference on Acoustics, Speech and signal processing

作者： Zhen Wang Cong Wang Zhixun Su Junyang Chen Dalian University of Technology The Hong Kong Polytechnic University Guilin University of Electronic Technology Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province Shenzhen University

Rainy images degrade the visional performance that may bring down the accuracy of various applications. In this paper, we propose a novel densely connected network with Dense Feature Pyramid Grids Modules, called DFPGN, to solve the rain removal task. Specifically, in the proposed DFPG, there are five operations from different layers with various pathways and scales as the input of the current layer so that each layer can fuse various features from shallower and deeper ones to improve the deraining ability of the network. Extensive experiments on real and synthetic rainy images are conducted to demonstrate the proposed method achieves superior rain removal performance over state-of-the-art approaches.

关键词： Rain Fuses Conferences signal processing Acoustics Task analysis Speech processing

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Small Infrared Target Detection via Iteratively-Reweighted-Nuclear-Norm

Small Infrared Target Detection via Iteratively-Reweighted-N...

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International symposium on Artificial intelligence and Robotics

作者： Gao, Haoran Ren, Jinlei He, Chunming Deng, Lizhen Nanjing Univ Posts & Tele Commun Coll Telecommun & Informat Engn Nanjing Peoples R China China Acad Launch Vehicle Technol CALT Beijing Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China Nanjing Univ Posts & Telecommun Bell Honor Sch Nanjing Peoples R China

ISBN: (数字)9781510639690

ISBN: (纸本)9781510639690

The process of infrared images via computer-based algorithms for better application is a frontier field integrating physical technology with computer science. One of the key techniques in infrared image processing is the detection of infrared targets. This technique is extensively applied in security and defense systems and search and tracking systems. However, due to their small size, dim light and lack of texture, the detection of infrared targets is a technical problem. One strategy to address this problem is to transform the detection work into a non-convex optimization problem of recovering a low-rank matrix (background) and a sparse matrix (target) from a patch-image matrix (original image) based on IPI (infrared patch-image) model. When targets are clear and recognizable, the APG (accelerated proximal gradient) algorithm works effectively to solve it. However, when targets become much dimmer and are screened by the intricate texture of background, the experimental detection results degrade dramatically. In order to solve this problem, a novel method via IRNN (iteratively reweighted nuclear norm) is proposed in this paper. Experimental results show that under different complicated backgrounds, targets with higher SCRG (signal-to-clutter ratio gain) values and BSF (background suppression factor) values can be acquired through IRNN algorithm compared with the APG algorithm, which means that our method performs better.

关键词： small target detection Iteratively reweighted nuclear norm infrared patch-image model

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