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Multi-face detection based on downsampling and modified subtractive clustering for color images

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第1期8卷 72-78页

作者： KONG Wan-zeng ZHU Shan-an School of Electrical Engineering Zhejiang University Hangzhou 310027 China

This paper presents a multi-face detection method for color images. The method is based on the assumption that faces are well separated from the background by skin color detection. These faces can be located by the proposed method which modifies the subtractive clustering. The modified clustering algorithm proposes a new definition of distance for multi-face detection, and its key parameters can be predetermined adaptively by statistical information of face objects in the image. downsampling is employed to reduce the computation of clustering and speed up the process of the proposed method. The effectiveness of the proposed method is illustrated by three experiments.

关键词： Multi-face detection Skin color Modified subtractive clustering downsampling

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Effect of the Pixel Interpolation Method for downsampling Medical Images on Deep Learning Accuracy

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Journal of Computer and Communications 2021年第11期9卷 150-156页

作者： Daisuke Hirahara Eichi Takaya Mizuki Kadowaki Yasuyuki Kobayashi Takuya Ueda Department of AI Research Lab Harada Academy Kagoshima Japan Department of Clinical Imaging Graduate School of Medicine Tohoku University Sendai Japan Department of Advanced Biomedical Imaging Informatics St. Marianna University School of Medicine Kawasaki Japan Tohoku University Hospital AI Lab Sendai Japan

Background: High-resolution medical images often need to be downsampled because of the memory limitations of the hardware used for machine learning. Although various image interpolation methods are applicable to downsampling, the effect of data preprocessing on the learning performance of convolutional neural networks (CNNs) has not been fully investigated. Methods: In this study, five different pixel interpolation algorithms (nearest neighbor, bilinear, Hamming window, bicubic, and Lanczos interpolation) were used for image downsampling to investigate their effects on the prediction accuracy of a CNN. Chest X-ray images from the NIH public dataset were examined by downsampling 10 patterns. Results: The accuracy improved with a decreasing image size, and the best accuracy was achieved at 64 × 64 pixels. Among the interpolation methods, bicubic interpolation obtained the highest accuracy, followed by the Hamming window.

关键词： downsampling Interpolation Deep Learning Convolutional Neural Networks Medical Images Nearest Neighbor Bilinear Hamming Window Bicubic Lanczos

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An Improved Convolutional Neural Network Model for DNA Classification

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Computers, Materials & Continua 2022年第3期70卷 5907-5927页

作者： Naglaa.F.Soliman Samia M.Abd-Alhalem Walid El-Shafai Salah Eldin S.E.Abdulrahman N.Ismaiel El-Sayed M.El-Rabaie Abeer D.Algarni Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)*** any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature *** downsampling is an improved form of the existing pooling layer to obtain better classification *** two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for *** convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature ***,there are parameters which directly affect how a CNN model is *** this paper,some issues concerned with the training of CNNs have been *** CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of *** and assessment of the performance of CNNs are carried out on 16S rRNA bacterial *** results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

关键词： DNA classification CNN downsampling hyperparameters DL 2D DT 2D RP

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A Downsampled SAR-BM3D Despeckling Approach for Single-Look SAR Images in High Resolution

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Journal of Computer and Communications 2016年第15期4卷 126-131页

作者： Wuchao Wang Xiaolin Liu Wenlong Zhang National University of Defense Technology Changsha China

SAR-BM3D is one of the state of the art despeckling algorithms for SAR images. However, when tackling with high resolution SAR images, it often has an unsatisfying despeckling performance in the homogeneous smooth regions, together with a high time complexity. In this paper, a novel downsampled SAR-BM3D despeckling approach combined with edge compensation is proposed. The proposed algorithm consists of two steps. First, despeckle the image which is a downsampled version of original image with SAR-BM3D. Then, compensate edges in each level when upsampling. This approach not only utilizes the good ability of feature preservation, but also improves performance of smoothing homogenous regions. When it comes to high resolution SAR images, the efficiency can be raised by six to seven times, compared to original SAR-BM3D. Experiments on simulated and real SAR images show that the proposed method reaches a high level in terms of visual quality and act more efficiently.

关键词： Despeckling SAR-BM3D downsampling High Resolution Synthetic Aperture Radar (SAR)

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AN ENHANCEMENT TO MRME TECHNIQUES FOR VIDEO COMPRESSION

AN ENHANCEMENT TO MRME TECHNIQUES FOR VIDEO COMPRESSION

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Electronic Communication Systems--The 2000 IEEE Asia-Pacific Conference on Circuits and Systems

作者： Changqing Wu Zhizhong Wang Department of Biomedical Engineering Shanghai Jiaotong University

ISBN: (纸本)0780362535

＜正＞The wavelet transform is a valuable tool in video compression because of its *** and Zafar proposed a video compression algorithm based on the wavelet transform and multiresolution motion esti-mation （MRME）,but MRME doesn’t consider the aliasing effects caused by downsampling in the *** this article, we use a special interpolation filter to remove those effects. The combined MRME techniques greatly improve the coding *** also show subjectively pleasing reconstructed pictures.

关键词： video coding data compression wavelet transforms motion estimation image reconstruction MRME techniques video compression wavelet transform multiresolution motion estimation aliasing effects downsampling interpolation filter coding performance reconstructed pictures

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