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This study introduces a new method for automatic target recognition (ATR) in infrared images. The novel Soft Concave-Convex Orthogonal Combination of Robust Local Ternary Patterns (SCC_OC_RLTP) improves accuracy and efficiency in detecting targets.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Automatic Target Recognition (ATR) in infrared imagery faces challenges with illumination variations.
  • Existing Local Ternary Patterns (LTP) lack robustness to illumination changes.
  • Feature dimensionality reduction is crucial for efficient ATR systems.

Purpose of the Study:

  • To develop an improved feature descriptor for ATR in infrared imagery.
  • To enhance the invariance of LTP to illumination transformations.
  • To improve the discriminability and efficiency of ATR algorithms.

Main Methods:

  • Proposed a Robust Local Ternary Pattern (RLTP) for illumination invariance.
  • Introduced a Soft Concave-Convex Partition (SCCP) for increased flexibility.
  • Utilized Orthogonal Combination of LTP (OC_LTP) for dimensionality reduction.
  • Developed the Soft Concave-Convex Orthogonal Combination of Robust LTP (SCC_OC_RLTP) operator.
  • Applied a blocking schedule and feature selection for improved ATR performance.

Main Results:

  • The proposed SCC_OC_RLTP operator demonstrates improved performance in ATR.
  • Experimental results show competitive accuracy compared to state-of-the-art methods.
  • The feature selection technique enhances the efficiency of the ATR system.

Conclusions:

  • The SCC_OC_RLTP operator is a promising advancement for ATR in infrared imagery.
  • The method effectively addresses illumination variations and reduces feature dimensionality.
  • The proposed approach offers a robust and efficient solution for target recognition.