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Visual System01:26

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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.
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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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Neuromorphic computing spiking neural network edge detection model for content based image retrieval.

Ambuj1, Rajendra Machavaram1

  • 1Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.

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|May 6, 2024
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Summary

This study introduces a bioinspired Spiking Neural Network (SNN) for edge detection in content-based image retrieval (CBIR). The novel SNN approach enhances CBIR performance, improving mean precision by over 3% with reduced computational cost.

Keywords:
Content-based image retrieval (CBIR)edge detectionfeature extractionhuman visual system (HVS)image analysisspiking neural network (SNN)

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Content-based image retrieval (CBIR) commonly uses linear edge detection methods.
  • Existing CBIR techniques often rely on traditional gradient-based and derivative-based edge detection.
  • There is a need for more efficient and effective edge detection in CBIR.

Purpose of the Study:

  • To integrate bioinspired Spiking Neural Network (SNN) based edge detection into CBIR systems.
  • To develop a computationally efficient SNN approach for CBIR applications.
  • To evaluate the performance improvement of CBIR using the proposed SNN-based edge detection.

Main Methods:

  • Developed a novel, computationally efficient Spiking Neural Network (SNN) for edge detection.
  • Integrated the proposed SNN-based edge detection into three conventional CBIR techniques (Sobel, Canny, image derivatives).
  • Evaluated the approach using the Corel-10k and crop weed datasets.

Main Results:

  • The proposed SNN approach reduced computational overhead by 2.5 times compared to existing SNN models.
  • CBIR methodologies integrating the SNN-based edge detection showed an average increase in mean precision values exceeding 3%.
  • The SNN-based edge detection optimized feature extraction for edge-centric CBIR.

Conclusions:

  • The proposed SNN-based edge detection is a viable and effective method for enhancing CBIR systems.
  • This bioinspired approach offers significant improvements in efficiency and performance for image retrieval.
  • The study highlights the potential of SNNs in advancing edge-centric CBIR methodologies.