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2020 ieee International symposium on Sustainable Energy, signal processing and Cyber Security, iSSSC 2020

作者： Samantaray, Aswini K. Rahulkar, Amol D. National Institute of Technology Goa Department of EEE Goa403401 India

ISBN: (纸本)9781728188805

In content based retrieval system, the reliability of image retrieval outcomes relies much upon the image features utilised for measurement of image similarity. In this work, we present retrieval of medical images based on lifting scheme wavelet transform. Low computational complexity in terms of integer-to-integer transform is the main advantage of lifting scheme over classical wavelet transforms which are implemented using floating point operation. In this work, the features are extracted from the medical images using lifting scheme. The feature vector, used to measure similarity, is determined by evaluating the standard deviation and energy from the lifting based wavelet filtered coefficients. The retrieved image is the pertinence between the query image and the image from database, the ranking of the similarity relevance is as per the closest similarity determined by Manhattan distance. The proposed work performance is assessed on three different publicly available medical image databases namely NEMA, OASIS and EXACT09. The effectiveness of the proposed method is compared with other well known existing methods in terms of average retrieval precision (ARP) and average retrieval rate (ARR). © 2020 ieee.

关键词： Wavelet transforms

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A Pioneering Approach of Hyperspectral image Classification Employing the Cooperative Efforts of 3D, 2D and Depthwise Separable-1D Convolutions

A Pioneering Approach of Hyperspectral Image Classification ...

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Sustainable Energy, signal processing and Cyber Security (iSSSC), ieee International symposium on

作者： Sandeep Kumar Ladi G. K. Panda Ratnakar Dash Pradeep Kumar Ladi Dept. of ECE BPUT Odisha India Dept. of EECE GST GITAM Deemed to be University Visakhapatnam Andhra Pradesh India Dept. of CSE MITS Rayagada Rayagada Odisha India Dept. of CSE NIT Rourkela Rourkela Odisha India Dept. of CSE School of Engineering and Technology GIET University Gunupur Odisha India

ISBN: (纸本)9781665490573

Due to its promising uses in diverse sectors including agriculture, food technology, land cover mapping, etc., hyperspectral image (HI) analysis plays an important role. HI analysis using deep learning (DL) has made enormous strides in recent years. The literature makes it clear that employing simply 2-D-CNN or 3-D-CNN included several limitations, such as inadequate channel association details or mandating very complex models, respectively. The most important justification is that HIs are volumetric data with several spectral bands. This paper proposes a synergetic effort by 3D, 2D, and Depthwise separable-1D Convolutional layers in the proposed Lightweight Deep Learning model for enhancing HI classification accuracy. The spectral dimension of the input HI is reduced by utilizing traditional Principal Component Analysis (PCA). The 3D CNN expedites the synthesis of simultaneous spatial-spectral characteristics out of a pile of spectral channels. The 2D CNN also picks up the higher-level spatial interpretation in addition to the 3D CNN. Finally, the Depthwise separable-1D Convolutional layer that works on depth and space is incorporated into the model. The introduction of the Depthwise separable-1D Convolutional layer, the quantity of the channels, and the kernel sizes resulted in a sharp reduction in computation. Strenuous classification trials are conducted over the Indian Pines, University of Pavia, and Salinas Scene HI data to assess the viability of this proposed approach. The findings are juxtaposed with cutting-edge hand-crafted and sophisticated deep learning-based methods. The suggested DL architecture for HI classification performs quite effectively with fewer training samples and less trainable parameters.

关键词： Training Deep learning Solid modeling Three-dimensional displays Convolution computational modeling Convolutional neural networks

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DEEP OPTICS: LEARNING CAMERAS AND OPTICAL COMPUTING SYSTEMS 54

DEEP OPTICS: LEARNING CAMERAS AND OPTICAL COMPUTING SYSTEMS

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54th Asilomar Conference on signals, Systems and Computers

作者： Wetzstein, Gordon Ikoma, Hayato Metzler, Christopher Peng, Yifan Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9780738131269

Neural networks and other advanced image processing algorithms excel in a wide variety of computer vision and imaging applications, but their high performance also comes at a high computational cost and their success is sometimes limited. Here, we review recent hybrid optical-digital strategies to computational imaging that outsource parts of the algorithm into the optical domain. Using such a co-design of optics and image processing, we can facilitate application-domain-specific cameras or compute parts of a convolutional neural network in optics. Optical computing happens at the speed of light and without any memory or power requirements, thereby opening new directions for intelligent imaging systems.

关键词： computational optics computational imaging optical neural networks

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Lace Fabric image Retrieval Using Siamese Neural Network

Lace Fabric Image Retrieval Using Siamese Neural Network

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ieee International Conference on signal and image processing (ICSIP)

作者： DongDong Xu Yueyang Li HaiChi Luo Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China College of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (纸本)9781665446006

An efficient lace fabric image retrieval method based on DCNN learning features is proposed in this paper. Fine-tuning with Siamese Neural Network is used to learn effective feature of lace fabric image. During the process of training the Siamese Neural Network, hard negative pairs are selective to achieve fast convergence and good performance. The DCNN learning features are combined with the unique shape feature to enable accurate and efficient retrieval of massive image data. Experimental results demonstrate the effectiveness of retrieval performance of the proposed algorithm and possible practical application of the retrieval system in lace fabric industry to improve management efficiency.

关键词： Training Industries Shape image processing Conferences Neural networks image retrieval

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Research on Calculation of Feeding Frequency of Fish for Precise Feeding Based on Deep Learning

Research on Calculation of Feeding Frequency of Fish for Pre...

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ieee International Conference on Information Communication and signal processing (ICICSP)

作者： Yufeng Zhang Junchao Yang Lulu Jia Jinhui Yang School of artificial intelligence Chongqing Technology and Business University Chongqing China School of environment and resources Chongqing Technology and Business University Chongqing China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China

In Recirculating Aquaculture System (RAS), feeding frequency is an important factor affecting fish growth, and precise feeding according to the state of fish is the key to improving the aquaculture efficiency. The current problems in detecting the feeding frequency of fish include low efficiency, high technical requirements, and the influenced by the aquaculture environment. In this paper, we adopt a computer vision method to calculate feeding frequency to achieve objective with high accuracy. Firstly, we process the video of the fish according to the inter-frame difference method to obtain the image of the feeding state of the fish. Then we propose a modified VGG16 model to determine the feeding state of the fish, transform it into a 0–1 classification problem and calculate the feeding frequency of the fish. The feeding frequency and the growth status of the fish are then used to develop an intelligent feeding strategy to improve the growth rate of the fish and the conversion rate of the bait. Tests have shown that the accuracy of identifying feeding state by the modified VGG16 model can reach 92.4%. The method has a positive effect on the development of recirculating aquaculture system.

关键词： Technical requirements Computer vision Time-frequency analysis computational modeling Transforms signal processing Fish

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A Parallel Change Detection Method for Spatiotemporally Multi-Temporal SAR image Based On Enhance Learning and Wavelet 13

A Parallel Change Detection Method for Spatiotemporally Mult...

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13th International symposium on computational intelligence and Design (ISCID)

作者： Peng, Jinxi Su, Yuanqi Xue, Xiaorong Li, Yi Liu, Bin Xue, Xiaoyong Wu, Aihua Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian 710049 Shaanxi Peoples R China Liaoning Univ Technol LUT Sch Elect & Informat Engn Jinzhou 121001 Liaoning Peoples R China Anyang Normal Univ Sch Comp & Informat Engn Anyang 455000 Henan Peoples R China Guangzhou Univ South China Inst Software Engn Dept AI & Software Engn Guangzhou 510990 Guangdong Peoples R China Intelligent & Connected Vehicles Beijing Res Inst Beijing 100176 Peoples R China Middle Sch Luonan Cty Shangluo 726100 Shaanxi Peoples R China

ISBN: (纸本)9781728184463

To take the advantages of a variety of remote sensing data, the application of remote sensing image change detection is a very important choice. Remote sensing image change detection is large in computing capacity and time-consuming, and with the development of modern remote sensing technology, however, the amount of various remote sensing data obtained is getting larger and larger to find a change detection in the Synthetic Aperture Radar(SAR) images accurately for effective computing power. As a basis for parallelization which is a parallel change detection methods of multi- temporal SAR image based on wavelet transform is proposed. In the methods, platform based on parallel computing enhance learning. According to the statistical characteristics of SAR images and the semantics of Convolutional Neural Networks (CNN) analysis, an efficient Change detection methods based on Enhance Learning Semantic Analysis and wavelet transform is proposed to achieve precision of change detection, the probability and Statistics Characteristics of conjugate multi-distribution function which the pixel and task were pre-handled for parallel computing. Then, it's superior to traditional change detection results that is obtained Multi-sample training set. To establish Bellman equation model, and iterate the threshold-change area to calculate statistical characteristics. The experiments show that the improving methods and computing power on parallel change detection platform will achieve better results,and it is in remote sensing applications with enhance learning of behavior science methods to solve the image threshold area change detection in enhance learning for image processing for hybrid parallel computing.

关键词： Semantic Analysis SAR image change detection multi-temporal SAR image wavelet transform parallel computing Enhance Learning CNN

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Feature Extraction of Citrus Juice During Storage for Electronic Nose Based on Cellular Neural Network

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ieee SENSORS JOURNAL 2020年第7期20卷 3803-3812页

作者： Cao, Huaisheng Jia, Pengfei Xu, Duo Jiang, Yuanjing Qiao, Siqi Southwest Univ Brain Inspired Comp & Intelligent Control Chongqi Chongqing 400715 Peoples R China Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Neijiang Normal Univ Data Recovery Key Lab Sichuan Prov Neijiang 641100 Peoples R China Southwest Univ Westa Coll Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

Aroma is one of the most important factors affecting the quality of citrus fruit and its processed products. We use electronic nose (E-nose) to detect and analyze volatile components in citrus. An E-nose is an artificial intelligence system with strong independence and fast detection speed. It combines an array of gas sensors and intelligent algorithms designed to analyze gas. Moreover, it has the ability to detect and analyze volatile components. Feature extraction is the first step of sensor signal processing and plays an important role in subsequent pattern recognition. Cellular neural network (CNN) is a real-time high-speed parallel array processor and a locally connected network, which has mature applications in the field of image processing. Previous researches have shown that CNN has an outstanding impact on image feature extractio. In this paper, the traditional CNN is improved and a template for dynamic feature extraction of the E-nose response curve is proposed. In addition, we provide users with single-template and multi-template solutions which can be applied in different environments. To free up the computational power of occupancy, the effect of the single-template version of CNN is not as effective as the multi-template version, but it still has good feature extraction ability. These two solutions prove that CNN is sensitive to dynamic features. In order to make the results more representative, we choose several traditional feature extraction methods for comparison.

关键词： Electronic nose cellular neural network dynamic feature extraction citrus juice storage template design

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VIRTUAL STAINING FOR MITOSIS DETECTION IN BREAST HISTOPATHOLOGY 17

VIRTUAL STAINING FOR MITOSIS DETECTION IN BREAST HISTOPATHOL...

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ieee 17th International symposium on Biomedical Imaging (ISBI)

作者： Mercan, C. Mooij, G. C. A. M. Tellez, D. Lotz, J. Weiss, N. van Gerven, M. Ciompi, E. Radboudumc Diagnost Image Anal Grp Nijmegen Netherlands Radboud Univ Nijmegen Dept Artificial Intelligence Nijmegen Netherlands Fraunhofer MeVis Lubeck Germany

ISBN: (纸本)9781538693308

We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures, a strong prognostic biomarker used in routine breast cancer diagnosis and grading. We propose several scenarios, in which CNN trained with synthetically generated histopathology images perform on par with or even better than the same baseline model trained with real images. We discuss the potential of this application to scale the number of training samples without the need for manual annotations.

关键词： computational pathology Generative Adversarial Networks Mitosis detection Breast cancer

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PhlatCam: Designed Phase-Mask Based Thin Lensless Camera

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE intelligence 2020年第7期42卷 1618-1629页

作者： Boominathan, Vivek Adams, Jesse K. Robinson, Jacob T. Veeraraghavan, Ashok Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA Rice Univ Appl Phys Program Houston TX 77005 USA

We demonstrate a versatile thin lensless camera with a designed phase-mask placed at sub-2 mm from an imaging CMOS sensor. Using wave optics and phase retrieval methods, we present a general-purpose framework to create phase-masks that achieve desired sharp point-spread-functions (PSFs) for desired camera thicknesses. From a single 2D encoded measurement, we show the reconstruction of high-resolution 2D images, computational refocusing, and 3D imaging. This ability is made possible by our proposed high-performance contour-based PSF. The heuristic contour-based PSF is designed using concepts in signal processing to achieve maximal information transfer to a bit-depth limited sensor. Due to the efficient coding, we can use fast linear methods for high-quality image reconstructions and switch to iterative nonlinear methods for higher fidelity reconstructions and 3D imaging.

关键词： Cameras image reconstruction Two dimensional displays Lenses Three-dimensional displays lensless imaging diffractive masks phase retrieval refocusing 3D imagin

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COUPLING PRINCIPLED REFINEMENT WITH BI-DIRECTIONAL DEEP ESTIMATION FOR ROBUST DEFORMABLE 3D MEDICAL image REGISTRATION 17

COUPLING PRINCIPLED REFINEMENT WITH BI-DIRECTIONAL DEEP ESTI...

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ieee 17th International symposium on Biomedical Imaging (ISBI)

作者： Zhang, Yuxi Liu, Risheng Li, Zi Liu, Zhu Fan, Xin Luo, Zhongxuan Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China Dalian Univ Technol Sch Math Sci Dalian Peoples R China Guilin Univ Elect Technol Inst Artificial Intelligence Dalian Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781538693308

Deformable 3D medical image registration is challenging due to the complicated transformations between image pairs. Traditional approaches estimate deformation fields by optimizing a task-guided energy embedded with physical priors, achieving high accuracy while suffering from expensive computational loads for the iterative optimization. Recently, deep networks, encoding the information underlying data examples, render fast predictions but severely dependent on training data and have limited flexibility. In this study, we develop a paradigm integrating the principled prior into a bidirectional deep estimation process. Inheriting from the merits of both domain knowledge and deep representation, our approach achieves a more efficient and stable estimation of deformation fields than the state-of-the-art, especially when the testing pairs exhibit great variations with the training.

关键词： Medical image registration optimization deep learning 3D neural network

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