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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.

Focusing of Light in the Eye

Focusing of Light in the Eye

Light rays enter the eye through the cornea, a transparent dome-shaped tissue that is the eye's outermost layer. The cornea bends or refracts, light rays traveling to the pupil. The shape of the cornea determines how much of the light is bent and whether the image will be focused correctly on the retina at the back of the eye. Once the light has passed through both refraction layers, it converges into a single focal point onto a small area. This is where photoreceptors start transforming...

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...

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Related Experiment Video

Updated: May 28, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Image captioning in Bengali language using visual attention.

Adiba Masud^1,2, Md Biplob Hosen¹, Md Habibullah¹

¹Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh.

|February 13, 2025

Summary

This summary is machine-generated.

Researchers developed an AI model for Bengali image captioning, creating a new dataset and achieving state-of-the-art results. This advances artificial intelligence (AI) in natural language processing and computer vision for under-resourced languages.

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Area of Science:

Artificial Intelligence
Computer Vision
Natural Language Processing

Background:

Automatic image captioning is a complex AI task, especially for intricate languages like Bengali.
There is a significant lack of Bengali-captioned image datasets, hindering research and development.

Purpose of the Study:

To curate a human-annotated Bengali image caption dataset.
To develop an innovative end-to-end deep learning architecture for Bengali image captioning.

Main Methods:

A new, meticulously human-annotated Bengali image caption dataset was created.
An attention-driven decoder architecture was employed, integrating Gated Recurrent Units (GRUs) for visual features.
An attention mechanism facilitated the interrelation between visual and linguistic representations.

Main Results:

The model achieved a BLEU-4 score of 43%, METEOR score of 39%, and ROUGE score of 47%.
These scores represent the highest performance to date for Bengali image captioning tasks.

Conclusions:

The developed model and dataset significantly advance the field of Bengali image captioning.
The attention-based architecture effectively generates coherent Bengali image descriptions.