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Программная обработка терагерцовых тепловых изображений лица для выявления стабильных зон активации потовых желез под воздействием стресса
This paper presents a software tool developed for processing terahertz thermal images of the human face acquired during a controlled psychophysiological experiment. In the experiment, a 500 GHz single-pixel detector combined with a two-mirror scanning system forms a sequence of low-resolution thermal frames (700×51 pixels) of the subject’s face while the person undergoes relaxation and stress-inducing cognitive and physical loads. The goal of the processing is automatic detection and interframe grouping of anomalous zones characterized by vertical low-frequency ripple, which are most frequently observed in the central forehead region above the eyebrows. The working hypothesis is that helical sweat ducts may alter the sub-terahertz response of skin under sympathetic activation; it motivates the choice of frequency band and the search for stable spatial zones. The program implements a practical heuristic multi-stage algorithm comprising dynamic-range normalization via histogram matching to a reference frame, construction of an inner face mask, bandpass filtering in the Fourier domain to enhance the characteristic ripple, morphological peak enhancement, connected-component detection of bright spots, and robust grouping of spots across frames into stable logical zones based on center proximity and area consistency. As a result, for each subject a compact set of non-overlapping logical zones is obtained and visualized as chronological collages containing both the original normalized thermal fragments and the corresponding ripple-enhanced patches. The results demonstrate robust extraction of stable spatial zones with the characteristic ripple (the central forehead zone is present in up to 100 % of frames) and provide a convenient visual tool for subsequent quantitative analysis.