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Over the past 40 years, neurobiology and computational neuroscience has proved that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems. One of the main difficulties that arises when designing automatic vision systems is developing a mechanism that can recognize - or simply find - an object when faced with all the possible variations that may occur in a natural scene, with the ease of the primate visual system. The area of the brain in primates that is dedicated at analyzing visual information is the visual cortex. The visual cortex performs a wide variety of complex tasks by means of simple operations. These seemingly simple operations are applied to several layers of neurons organized into a hierarchy, the layers representing increasingly complex, abstract intermediate processing stages. In this Research Topic we propose to bring together current efforts in neurophysiology and computer vision in order 1) To understand how the visual cortex encodes an object from a starting point where neurons respond to lines, bars or edges to the representation of an object at the top of the hierarchy that is invariant to illumination, size, location, viewpoint, rotation and robust to occlusions and clutter; and 2) How the design of automatic vision systems benefit from that knowledge to get closer to human accuracy, efficiency and robustness to variations.

object recognition --- Neuronal modeling --- shape --- Neuromorphic --- Computational neuroscence --- Attention --- Visual Cortex --- Computer Vision

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Computer vision --- Vision par ordinateur --- Periodicals. --- Périodiques --- Pattern recognition systems --- Computer vision. --- Pattern recognition systems. --- Engineering --- Civil Engineering --- Périodiques --- EBSCOASP-E EBSCOBSP-E EJINFOR EJINGEN EPUB-ALPHA-I EPUB-PER-FT IET-E --- computer vision --- object recognition --- image understanding --- feature extraction --- artificial intelligence --- pattern recognition --- Reconnaissance des formes (Informatique) --- Applied Physics --- Pattern classification systems --- Pattern recognition computers --- Pattern perception --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Computer vision - Periodicals

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The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.

Information technology industries --- facial image analysis --- facial nerve paralysis --- deep convolutional neural networks --- image classification --- Chinese text classification --- long short-term memory --- convolutional neural network --- Arabic named entity recognition --- bidirectional recurrent neural network --- GRU --- LSTM --- natural language processing --- word embedding --- CNN --- object detection network --- attention mechanism --- feature fusion --- LSTM-CRF model --- elements recognition --- linguistic features --- POS syntactic rules --- action recognition --- fused features --- 3D convolution neural network --- motion map --- long short-term-memory --- tooth-marked tongue --- gradient-weighted class activation maps --- ship identification --- fully convolutional network --- embedded deep learning --- scalability --- gesture recognition --- human computer interaction --- alternative fusion neural network --- deep learning --- sentiment attention mechanism --- bidirectional gated recurrent unit --- Internet of Things --- convolutional neural networks --- graph partitioning --- distributed systems --- resource-efficient inference --- pedestrian attribute recognition --- graph convolutional network --- multi-label learning --- autoencoders --- long-short-term memory networks --- convolution neural Networks --- object recognition --- sentiment analysis --- text recognition --- IoT (Internet of Thing) systems --- medical applications

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This book includes 23 published papers on Special issues of "Image and Video Processing and Recognition Based on Artificial Intelligence" in the journal Sensors. The purpose of this Special Issue was to invite high-quality and state-of-the-art academic papers on challenging issues in the field of AI-based image and video processing and recognition.

Technology: general issues --- emotion recognition --- brain computer interface --- bag of deep features --- continuous wavelet transform --- face image analysis --- deep learning --- face parsing --- facial attributes classification --- building extraction --- convolutional neural networks --- mask R-CNN --- high-resolution remote sensing image --- autoencoders --- semi-supervised learning --- computer vision --- pathology --- epidermis --- skin --- image processing --- generative models --- generative adversarial net --- depth map --- super-resolution --- guidance --- residual network --- channel interaction --- pose estimation --- body orientation --- multi-person --- multi-task --- surface defect detection --- active learning --- generative adversarial network --- presentation attack detection --- artificial image generation --- presentation attack face images --- ultrasound image --- malignant thyroid nodule --- artificial intelligence --- weighted binary cross-entropy loss --- infrared circumferential scanning system --- target recognition --- deep convolutional neural networks --- data augmentation --- transfer learning --- bounding box regression --- loss function --- medical image fusion --- convolutional neural network --- image pyramid --- multi-scale decomposition --- armature --- surface inspection --- action recognition --- social robotics --- common spatial patterns --- vehicle recognition --- multi resolution network --- optimization --- semantic segmentation --- global context --- local context --- fully convolutional networks --- image-to-image conversion --- image de-raining --- label to photos --- edges to photos --- generative adversarial network (GAN) --- remote sensing --- helicopter footage --- crowd counting --- multitask learning --- normalized cross-correlation --- Marr wavelets --- entropy and response --- graph matching --- RANSAC --- GC–LSTM model --- typhoon --- satellite image --- prediction system --- monocular depth estimation --- feature distillation --- joint attention --- finger-vein recognition --- camera position --- finger position --- lighting --- unobserved database --- heterogeneous database --- domain adaptation --- cycle-consistent adversarial networks --- SDUMLA-HMT-DB --- HKPolyU-DB --- biometrics --- face recognition --- single-sample face recognition --- binarized statistical image features --- K-nearest neighbors --- sparse coding --- fast approximation --- homotopy iterative hard thresholding --- object recognition --- character recognition --- orthogonal polynomials --- orthogonal moments --- Krawtchouk polynomials --- Tchebichef polynomials --- support vector machine