SSVEP-Enhanced Threat Detection and Its Impact on Image Segmentation

Abstract

Selective attention, essential in discerning visual stimuli, enables the identification of threats such as snakes—a prime evolutionary influence on the human visual system. This phenomenon is encapsulated in snake detection theory (SDT), which posits that our ancestors' need to recognize these predators led to specialized perceptual abilities. This investigation utilizes steady-state visual evoked potentials (SSVEP) alongside the random image structure evolution technique, which systematically increases visual clarity through the interpolation of random noise, to probe the neural mechanisms underpinning selective attention, with a focus on serpentine forms. These findings underscore snakes' unique perceptual impact due to their curved forms and scaled textures, crucial for quick recognition—advancing image semantic segmentation and recognition tech.. This is particularly relevant for security and wildlife management, showcasing the evolutionary progression and cognitive prowess of the human visual apparatus.

Semantic-Based Optimization of Deep Learning for Efficient Real-Time Medical Image Segmentation

“…The creation of efficient, real-time image analysis systems is crucial in the digital era, such innovations can transform diagnostics and treatment planning to make advanced medical services widely accessible. (Gao et al, 2024;Srivastava et al, 2022). These systems transform diagnostics and treatment planning, making advanced medical services accessible to remote areas.…”