Deep Learning in Image Processing and Pattern Recognition

doi:10.3390/books978-3-7258-4372-5

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https://mdpi.com/books/pdfview/book/11120

Contributor(s)

Iwahori, Yuji (editor)

Wang, Aili (editor)

Wu, Haibin (editor)

Language

English

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Abstract

This Reprint aims to provide readers with an extensive insight into the latest developments in image processing technology. As images are the main method by which humans acquire and exchange information, the application of image processing is inevitably involved in all aspects of human life. Currently, image processing technology holds a prominent role in the aerospace, public security, biomedicine, industrial engineering, and business communication fields. Recent years have seen the rapid development of image processing, especially with the application of deep learning, enabling it to become the most successfully applied intelligent technology. Pattern recognition is an important research field in image processing and includes image preprocessing, feature extraction and selection, classifier design, and classification decisions. Focusing on these elements, this Reprint covers advancements in thirteen research directions, including image preprocessing, features and selection of images, pattern recognition in image processing technology, image processing in intelligent transportation, hyperspectral image processing, biomedical image processing, image processing in intelligent monitoring, deep learning for image processing, deep learning for image processing.

URI

https://directory.doabooks.org/handle/20.500.12854/165701

Keywords

modulation recognition; modulation classification; support vector machine; K-means; lightweight network structure; hybrid feature pyramid; BLSTM network; acceptance domain; attention mechanism; coverage hole; hybrid heterogeneous wireless sensor network (WSN); priority mechanism; patching; image enhancement; Retinex algorithm; ECA net module; connected attention; low brightness background; scene text image super-resolution; multi-task learning; scene text recognition; transformer; furniture image classification; deep learning; VGG16; group convolution; depthwise over-parameterized convolution; high dynamic range; tone mapping; piecewise linear mapping; local contrast; the least squares method; weights; HDR image; Retinex; ensemble empirical pattern decomposition (EEMD); multifractal; detrended fluctuations analysis (DFA); support vector machines (SVM); circuit fault diagnosis; mathematical morphology fractal dimension; kernel principal component analysis; variational modal decomposition; feature extraction; cognitive radio (CR); spectrum prediction; long short-term memory network (LSTM); long sequence time-series forecasting (LSTF); signal-dependent noise; noise parameter estimation; convolutional neural network; image denoising; fall detection; YOLOv7; dual illumination estimation; CNN; Deep SORT; sparse signal reconstruction; L-BFGS quasi-Newton method; two-loop recursion algorithm; reconstruction rate; adaptive logarithmic transformation; preprocessing; ReInForM routing protocol; energy selection; erasure correcting coding fault-tolerant machine; node residual energy ranking; intelligent retail; anomaly detection; graph convolutional networks; action recognition; semantic segmentation; speech emotion recognition; mel-spectrogram; DRSN; BiGRU; facial expression recognition (FER); DenseNet; depthwise separable convolution (DSC); posture normalization; generative adversarial network (GAN); crowd counting; density map estimation; spatial pyramid pooling (SPP); multi-scale feature extraction; dilated convolution; X-ray security image; YOLOv4; deformable convolution; path aggregation network; Soft-NMS; head position estimation; viewpoint tracking; unscented Kalman filter; Kinect; text emotion recognition; XLNET; attention; BERT; quality of life; walking scene; walkability; object detection; deep convolutional neural networks; knowledge distillation; gaze estimation algorithm; face feature extractor; feature information; PSA attention mechanism; hyperspectral image (HSI) classification; neural architecture search; differentiable architecture search (DARTS); multi-scale attention mechanism; data augmentation; class imbalance; YOLOv3-tiny; convolutional autoencoder; smoke detection; image quality enhancement; deep learning models; level set models; Phosphor in Glass; industrial detection; degradation model; scene text image; super resolution; single-image super-resolution; blueprint-separable convolution; efficient transformer; spatial attention; channel attention; electronic speckle pattern interferometry (ESPI); phase extraction; fringe pattern; wrapped phase maps; small target detection; E-ELAN network; human pose estimation; character recognition; neural network; n/a