• Edge and boundary cues underpin early scene interpretation, with explicit focus on detecting lines, curves, and object boundaries to drive segmentation and real-world scene understanding [3], [4], [6], [17], [18].

• Texture representation and local features underpin image classification and scene analysis, illustrating a shift from global to feature-based characterization of content [1], [2], [11].

• Dominant exploration of binocular depth cues and stereoscopic processing reveals how 3D structure is inferred from disparity, guiding both perception studies and computational models [8], [15].

• Biological and cognitive studies on human recognition, eye movements, and pathological cases shape machine-vision benchmarks, emphasizing human-like inference and robust recognition under variation [10], [12], [14], [16].