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Intelligent Perceptual Interfaces Trevor Darrell Eric Grimson MIT Artificial Intelligence Laboratory — Research Directions Perceptual User Interfaces • • • • Interactivity- be aware of viewer! People have natural interface modes Watch, listen, learn signals from user Key concepts: – Transparency - embedded interfaces – Expressiveness - balance interface I/O bandwidth • Key technologies: – Computer Vision – Machine Learning – Spoken Language Understanding MIT Artificial Intelligence Laboratory — Research Directions A Face Responsive Display • Faces are natural interfaces! – Ubiquitous, fast, expressive, general. – Want machines to generate and perceive faces. • A Face Responsive Display... – – – – – Knows when it’s being observed Recognizes returning observers Tracks head pose Recognizes speech without attached microphone Robust to changing lighting, moving backgrounds… MIT Artificial Intelligence Laboratory — Research Directions Head Pose Estimation • Estimate gaze angle of user’s head • Rigid body model: 6 DOF QuickTime™ and a decompressor are needed to see this picture. QuickTime™ and a decompressor are needed to see this picture. QuickTime™ and a decompressor are needed to see this picture. MIT Artificial Intelligence Laboratory — Research Directions Fast and Efficient to Solve! QuickTime™ and a decompressor are needed to see this picture. MIT Artificial Intelligence Laboratory — Research Directions Lip Contour Tracking Conventional Intelligent shape tracking MIT Artificial Intelligence Laboratory — Research Directions Lip Contour Tracking - Video QuickTime™ and a Cinepak decompressor are needed to see this picture. MIT Artificial Intelligence Laboratory — Research Directions Untethered Audio-Visual Interface • Current audio interfaces often require attached microphone — future systems need wireless interface • Common approaches – Beam-forming microphone – Active narrow-field microphone • New idea — exploit joint statistics of audio and visual information • Correlation / mutual information between audio and image pixels can separate sources MIT Artificial Intelligence Laboratory — Research Directions Audio-based Image Localization Can we locate visual sources given audio information? Original Sequence MIT Artificial Intelligence Laboratory — Research Directions Audio-based Image Localization Image variance (ignoring audio) will find all motion in the sequence: Image Variance MIT Artificial Intelligence Laboratory — Research Directions Audio-based Image Localization Examine Mutual Information (Correlation in simplest case) between image and audio: Pixels with high mutual information with audio track MIT Artificial Intelligence Laboratory — Research Directions Learning an Informative Subspace Learned Subspace video projection audio projection Find a projection of both the video data and the audio data to a lowdimensional space so that MI is maximized. MIT Artificial Intelligence Laboratory — Research Directions
