Application Evaluation of Virtual Image Visual Recognition Algorithm in Two-dimensional Digital Art Animation Synthesis Technology

This research project presents an evaluation framework for virtual image visual recognition algorithms integrated within two-dimensional digital art animation synthesis systems. In modern digital animation, the seamless blending of synthesized elements requires precise visual recognition and spatial alignment. The study focuses on the application of digital signal and image processing methodologies, specifically leveraging Fast Fourier Transform (FFT) algorithms and frequency-domain analysis, to optimize the detection and tracking of virtual image boundaries. By analyzing the spectral characteristics of 2D animation frames, the proposed methodology enhances the computational efficiency of visual recognition models under varying illumination and scale constraints. The performance evaluation framework assesses key metrics, including recognition accuracy, processing latency, and

synthesis fidelity, comparing the FFT-based approach against conventional spatial-domain techniques. Implementation support and research direction are provided to guide the development of robust simulation models. Ultimately, this research aims to establish a structured methodology for evaluating visual recognition algorithms, offering critical insights into their integration within automated digital art synthesis pipelines and contributing to the advancement of real-time image processing in computer-generated animation.

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