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About

Shih-Fu Chang is a pioneering researcher whose work spans computer vision, robotics, and affective computing, with a focus on understanding and manipulating the physical and perceptual world. His key research areas include image forensics, robotic manipulation of deformable objects, and mid-level concept representation for social media analysis. Chang’s major contributions include developing physics-based features for detecting recaptured images, a critical advancement in digital image forensics that has garnered 65 citations. In robotics, he introduced predictive thin shell modeling for regrasping and unfolding garments, enabling two-arm robots to efficiently track and manipulate highly unstructured deformable objects—a breakthrough cited 60 times. He also created the Assistive Image Comment Robot, a novel framework using mid-level concept representations to predict viewer affective responses in social media, earning 21 citations. Additionally, his model-driven feed-forward prediction methods for deformable object manipulation further advance robotic dexterity. Chang’s work is notable for bridging theoretical models with practical applications, from forensic image analysis to assistive robotics, demonstrating a profound impact on both academic research and real-world systems.