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Visual System

Visual System

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...

Vision

Vision

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.

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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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Related Experiment Video

Updated: Jul 23, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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DenseTextPVT: Pyramid Vision Transformer with Deep Multi-Scale Feature Refinement Network for Dense Text Detection.

My-Tham Dinh¹, Deok-Jai Choi¹, Guee-Sang Lee¹

¹Department of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Gwangju 500-757, Republic of Korea.

Sensors (Basel, Switzerland)

|July 14, 2023

Summary

We developed DenseTextPVT, an efficient method for detecting dense text in complex scene images. This approach improves text detection accuracy and reduces overlapping regions, outperforming existing methods on benchmark datasets.

Keywords:

dense adjacent text pyramid vision transformer scene text detection

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Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Dense text detection in scene images is challenging due to high variability, complexity, and overlapping text.
Existing methods struggle with accurately identifying and segmenting densely packed text instances.

Purpose of the Study:

To propose an efficient and accurate method for detecting dense text in scene images.
To enhance feature representation for improved detection of texts with diverse characteristics.
To reduce overlapping text regions in post-processing for better precision.

Main Methods:

Generated high-resolution features at multiple levels for accurate dense text detection.
Designed the Deep Multi-scale Feature Refinement Network (DMFRN) to enhance feature representation.
Utilized Pixel Aggregation (PA) similarity vector algorithms for clustering text pixels into kernels.

Main Results:

The proposed DenseTextPVT method demonstrates effectiveness in detecting dense text.
Achieved improved precision and reduced overlapping text regions in natural images.
Outperformed existing methods on TotalText, CTW1500, and ICDAR-2015 benchmark datasets.

Conclusions:

DenseTextPVT offers an efficient solution for dense text detection in challenging scene images.
The method's ability to handle varying text scales, shapes, and fonts, including small texts, is significant.
The approach effectively addresses the limitations of current methods in dense text scenarios.