HVS-based rate-control scheme for object-based embedded image coding by Dong-jie LI, Zhong-jie ZHU, Yu-er WANG

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Scalable video coding algorithm based on 3D hybrid tree and visual characteristics by Ming-zhe FU, Xiang-hai WANG, Chuan-ming SONG

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Sparse Representation-Based LDCT Image Quality Assessment Using the JND Model by Mo Shen, Rongrong Sun, Wen Ye

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RETRACTED: An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring by Yuanyuan Chen, Huiqian Wang, Yu Pang, Jinhui Han, En Mou, Enling Cao

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Scalable territorial identity – Designing the communication of complexity by Luca Casarotto, Monica Oddone

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Performance evaluation of underwater vision systems by V.Y. Kolyuchkin, N.M. Kostylev, Y.S. Gulina

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Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches by Haiyang Zhang, Shuo Wu, Lehan Yang, Chengjing Fan, Huifang Chen, Hui Wang, Tianyi Zhu, Yinwei Li, Jing Sun, Xuefei Song, Huifang Zhou, Terry J Smith, Xianqun Fan

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Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition. by Pranjul Gupta, Katharina Dobs

“…The human visual system possesses a remarkable ability to detect and process faces across diverse contexts, including the phenomenon of face pareidolia--seeing faces in inanimate objects. …”
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Research on the robustness of convolutional neural networks in image recognition by Dian LIN, Li PAN, Ping YI

“…Convolutional neural network is one of the key technologies in the application of image recognition and processing in artificial intelligence.Its wide application makes researches on its robustness more and more important.Previous researches on robustness of neural networks were too sweeping and most of them focused on adversarial robustness, which causes difficulty in further study in the mechanism of neural network robustness.The related researches of neuroscience were introduced and the concept of visual robustness was put forward.By studying the similarity and difference between neural network models and human visual system, the internal mechanism and faults of neural network robustness were revealed.The researches of neural network robustness in recent years were reviewed, and the reasons for the lack of robustness of neural network models were analyzed.The lack of robustness of neural networks is reflected in their sensitivity to small perturbations.The reason is that neural networks tend to learn high-frequency information for calculation and inference, which is difficult for humans to recognize.High-frequency information is easily affected by perturbations, and eventually causes mistakes of models.Previous researches on robustness mostly focused on mathematical properties of models, and were limited in the natural faults of neural networks.Visual robustness extends the traditional concept of robustness.The traditional concept of robustness measures the discrimination ability of models for distorted image examples.Distorted examples and clean examples can get correct outputs through robust models.Visual robustness measures the consistency between models and humans in discrimination ability.Visual robustness combines the research methods and achievements of neuroscience and psychology with artificial intelligence.The development of neuroscience in the field of vision were reviewed, and the application of research methods of cognitive psychology in neural network robustness were discussed.Human visual system has advantages in learning and abstract ability, whill neural network models have better performance in calculation speed and memory.The difference between the physiological structure of human brain and the logical structure of neural network models is the key factor leading to the problem of robustness of neural networks.The research of visual robustness requires deeper understanding of human visual system.Revealing the differences in cognitive mechanism between human visual system and neural network models and effectively improving the algorithm are the development trends of neural network robustness and even artificial intelligence.…”
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Compressive Sensing Network Deeply Induced by Visual Mechanism by Mingkun Feng, Xiaole Han, Kai Zheng

“…To address these challenges, we propose a Full Visual Mechanism-based Compressive Sensing Network (FVM-CSNet) inspired by the human visual system’s process of perceiving and understanding images. …”
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Increased brightness assimilation in rod vision by Pablo A. Barrionuevo, Alexander C. Schütz, Karl R. Gegenfurtner

“…Summary: Our visual system uses contextual cues to estimate the brightness of surfaces: brightness can shift toward (assimilation) or away from (contrast) the brightness of the surroundings. …”
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The impact of playing tennis on the organ of vision - threats, benefits and prevention of the organ of vision by Dominik Rabstein, Piotr Gościniewicz, Hanna Porwolik, Agata Porwolik, Patrycja Pabis, Agnieszka Porwolik, Magdalena Bodera, Radosław Szydłowski

“… This paper examines the impact of playing tennis on the visual system, providing an in-depth analysis of physiological, adaptive, and risk-related aspects associated with the sport. …”
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Total Variation Based Perceptual Image Quality Assessment Modeling by Yadong Wu, Hongying Zhang, Ran Duan

“…In this paper, based on the assumption that human visual system is sensitive to image structures (edges) and image local luminance (light stimulation), we propose a new perceptual image quality assessment (PIQA) measure based on total variation (TV) model (TVPIQA) in spatial domain. …”
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Lesion classification and diabetic retinopathy grading by integrating softmax and pooling operators into vision transformer by Chong Liu, Weiguang Wang, Jian Lian, Wanzhen Jiao

“…In practice, this task mainly relies on manual inspection using human visual system. However, the human visual system-based screening process is labor-intensive, time-consuming, and error-prone. …”
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Multimedia Motion of Picking Robot Based on Cooperative Relationship of Matching Gradient Algorithm by Jinghai Han

“…In view of the existing limitations, this study combines the gradient matching algorithm, by using a high-precision motion matching for the visual system, improves the automation precision, and, at the same time, picking fruit image smoothing and enhancement, achieves fruit edge feature extraction; the simulation experiments proved that matching the gradient algorithm is effective and can realize the visual and actions match together and support picking robot movement.…”
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Long term adult visual plasticity after the developmental critical period in genetically determined peripheral visual loss by Otília C. d’Almeida, Joana M. Sampaio, Sónia Ferreira, Eduardo D. Silva, Miguel Castelo-Branco

“…Adult neural plasticity within the visual system remains controversial. Starkly opposing views still remain on the ability of the visual system to reorganize in adulthood. …”
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ESTIMATION OF THERMAL IMAGER MINIMUM RESOLVABLE TEMPERATURE DIFFERENCE by Ahmed M.L. Al-Mzirawi, V. G. Kolobrodov, V. I. Mykytenko

“…An MRTD calculating algorithm, which is based on a more reliable approximation of the visual system modulation transfer function by Schultz, is considered. …”
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Image quality evaluation method base on digital wavelet transform and vision weighted by JIANG Ming1, MA Zhao-feng1, XIN Yu3, NIU Xin-xin1, YANG Yi-xian1

“…A new objective method WVWPSNR（wavelet and vision weighted peak signal noise ratio） was presented.In which the method was based on DWT,and the sensitivity of HVS（human visual system） was weighted using sub-band division.In this method,the assessment valued of the new method accord with MOS（mean opinion score）.In order to demonstrate the effectiveness and to verify the reliability of WVWPSNR method,a series of experiment for JPEG、JPEG2000、White Noise、Gaussian Blur and FastFading images were made and tested.The results suggest that the proposed method can provide accurate measures of image quality under JPEG compression.…”
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