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Related Concept Videos

Vision01:24

Vision

59.4K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Gestalt Principles of Perception01:21

Gestalt Principles of Perception

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Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Visual perception based deep learning transformers for classifying paintings and photographs through feature

Liu Yu1

  • 1School of Design and Fine Arts, Qingdao Huanghai University, Qingdao, 266555, Shandong, China. LiuYu6464632@163.com.

Scientific Reports
|January 16, 2026
PubMed
Summary
This summary is machine-generated.

Computer vision models can now distinguish paintings from photos with 95% accuracy. The Vision Transformer (ViT) model excels at identifying artistic features, offering a reliable solution for automated artwork classification.

Keywords:
Artistic style recognitionAttention mechanismDeep learningFeature extractionHuman-Created paintingsImage classificationTransformer blockVision transformerVisual art analysis

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Digital Image Analysis

Background:

  • Computer vision and deep learning are increasingly applied to analyze digital images across various domains, including artwork.
  • Analyzing textures, color, and lighting patterns in both artistic and real-world imagery is crucial for image classification tasks.

Purpose of the Study:

  • To develop and evaluate a deep learning model for classifying images as either human artwork (paintings) or captured photographs.
  • To assess the performance of the Vision Transformer (ViT) architecture against established models like DenseNet, CNN, and VGG19 for artwork classification.

Main Methods:

  • Utilized the Vision Transformer (ViT) architecture for image classification.
  • Benchmarked ViT against DenseNet, Convolutional Neural Networks (CNN), and Visual Geometry Group (VGG19) on a standard dataset.
  • Employed Grad-CAM for model interpretability to identify key visual features influencing classification decisions.

Main Results:

  • Achieved a classification accuracy of 95% using the ViT model, outperforming existing methods in the literature.
  • ViT demonstrated superior performance in capturing complex visual features, including texture variations and compositional details.
  • Grad-CAM analysis confirmed ViT's ability to identify meaningful artistic attributes like brushstrokes and illumination gradients.

Conclusions:

  • The Vision Transformer (ViT) architecture provides a highly accurate and explainable solution for automated artwork classification.
  • ViT's effectiveness in analyzing intricate artistic features surpasses traditional deep learning models.
  • The combination of high performance and transparency makes ViT a reliable tool for distinguishing artwork from photographs.