Analyzing the Impact of Integrated Signal and Image Processing Techniques on Pattern Recognition and Decision Accuracy in Complex Systems

Authors

  • Shinnu Jangra Assistant Professor, Department of Artificial Intelligence and machine learning, Manipal University Jaipur, Jaipur, Rajasthan 303007
  • C. J. Jacintha Cathrin Assistant Professor, Department of Electronics and communication Engineering, Specialization in Communication system & Transmission line, Arunai Engineering College Autonomous , Tamil Nadu, Tiruvannamalai Dst, 605757
  • Dr. Poornima Raikar Assistant Professor, Department of Computer Science and Engineering (AI&ML), Specialization in Image processing, Artificial Intelligence, KLS VDIT Haliyal, Pin code: 581329
  • Harish Chandra Sharma Associate Professor, SGRR University, Dehradun
  • Dr. M. Mary Adline Priya Associate Professor, Specialization in Image Processing, Department of Medical Electronics, Saveetha Engineering College, Pin code: 600056, Chennai, India
  • Deepak Murlidhar Sonje Associate professor, Department of Electrical engineering, R H Sapat College of Engineering Management Studies and Research Nashik, Savitribai Phule Pune University, Pune
  • Nyan Lin Satt Master In Information Technology, Inti International University, Malaysia

DOI:

https://doi.org/10.29284/rqhrys14

Keywords:

machine learning, computer vision, data fusion, underwater robotics, pattern recognition

Abstract

Integrated signal and image processing is being used more to enhance pattern recognition and decision quality in complex sensing conditions, but the quality of benefit of combining underwater sonar and camera data to ensure continuous localization is context-dependent. This work quantifies the effect of feature-level fusion of multibeam sonar and monocular camera imagery on diver localization accuracy. A publicly available underwater dataset was
synchronized to produce 1,193 paired samples across four experimental sets. Each frame was converted to grayscale, resized to 64×64 pixels, and represented using global intensity statistics and regional mean features. Three Random Forest regression models were trained under identical conditions: sonar-only, camera-only, and fused features. Localization error was evaluated using Euclidean distance and standard regression metrics. Sonar-only processing produced the highest error, camera-only significantly reduced error, and multimodal fusion achieved the lowest overall RMSE and MAE. The greatest improvements from fusion were observed in scenarios with higher spatial variability, while in visually favorable conditions, camera-only performance remained competitive. These findings indicate that integrating acoustic and visual features enhances overall localization robustness, though the magnitude of improvement depends on environmental characteristics. Adaptive fusion strategies may further improve stability in real-world underwater systems.

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Published

2026-02-10

Issue

Vol. 12 No. 2s (2026)

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite

[1]
S. Jangra, “Analyzing the Impact of Integrated Signal and Image Processing Techniques on Pattern Recognition and Decision Accuracy in Complex Systems”, IJASIS, pp. 2043–2051, Feb. 2026, doi: 10.29284/rqhrys14.

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