Biologically-inspired Vision Models - Visual Computing: Investigating biologically-inspired vision models for developing computer vision systems that mimic the human visual system
Keywords:
Biologically-inspired, Vision Models, Visual ComputingAbstract
Biologically-inspired vision models offer promising avenues for advancing computer vision systems, aiming to replicate the remarkable capabilities of the human visual system. This paper explores the state-of-the-art in biologically-inspired vision models and their application in visual computing. We examine key concepts from neuroscience and psychology that inform these models, such as hierarchical processing, attention mechanisms, and motion perception. Additionally, we discuss various computational models, including Convolutional Neural Networks (CNNs), Sparse Coding, and Dynamic Vision Models, highlighting their strengths and limitations. Furthermore, we explore how these models are utilized in practical applications, such as image recognition, object detection, and scene understanding. Through this analysis, we provide insights into the current landscape of biologically-inspired vision models and their potential for shaping the future of computer vision.
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