VUB Artificial Intelligence Lab

1. VUB Artificial Intelligence Lab • Director: Prof. Dr. Luc Steels • Expertise in • Complex Dynamical systems research • Artificial Life • Behavior-based robotics • Evolution of natural language • Cognitive modelling

2. Evolution of language • Language might be the key to intelligent behaviour. • Research questions: • What is needed in order to learn and produce linguistic utterances. • How do humans conceptualise the world. • How does language evolve. • Language (and its components) is a complex dynamical system emerging from the interactions between individual language users.

3. Cognitive modelling • In order to investigate these issues, we use agent-based cognitive modelling • For example • Evolution of phonemes: vocal tract and auditory perception is faithfully modelled, together with a phonetic memory which behaves as observed human behavior.

4. Talking Heads • Large scale experiment started in 1999 to study the emergence and evolution of lexicons. • Agent based • Combined perception and language • Lacked action and syntax.

5. Extending the Talking Heads • Talking Heads lacked two important components • Compositionality • Action • New experiment to solve • Stereo colour vision. • Learning in action space. • Associating perception, action, conceptualisation and language.

6. VUB Electronics & Information Processing Lab • Director: Prof. Dr. Jan Cornelis • Expertise in • Compression of Images and Video – Multimedia Applications • Remote Sensing – Satellite Image Processing • Pattern Recognition – Classifier Models and Evaluation/Applications • Medical Image Processing - Registration, Segmentation, Analysis, Tele-Medicine • Applied Numerical Analysis and Inverse Problems – Theory and Applications • Computer Vision– Computational Vision,Robotics and Applications • Landmine Detection – Remote Sensing, Subsurface Imaging

7. Frank Sabine Multimedia Applications • Image Compression • JPEG, JPEG2000 • Segmentation-based Coding • Volumetric Wavelet Coding (Medical, Remote Sensing) • Video Compression • MPEG-x, H.26x • In-band Wavelet Video Coding • Video Segmentation and Key Frame Extraction • MPEG-7 • Interactive Television • Synthetic/Natural Hybrid Coding • MPEG-4: Facial Animation, Advanced Animation Framework (AFX), Mesh-Coding • Memory Efficient HW/SW Implementations of Multimedia Systems

8. Applied Numerical Analysis& Inverse Problems • Applied Numerical Analysis • Subspace Algorithms • Nonlinear Optimization • Radial Basis Functions (RBF) Techniques • Inverse Problems • Focus on Numerical Aspects (instead of more classical functional analysis approach) • Multi-level Regularization • Application Domains • Electrical Impedance Tomography (EIT) … (General Medical, Dental Diagnosis, Subsurface Imaging) • Tomographic Ground Penetrating Radar (GPR) Imaging … (Landmine Detection) • Intra-Oral Digital Subtraction Radiography … (Hidden Caries Phenomenon)

9. Computer Vision • Computational Vision • Inverse Problems for Scene Reconstruction • Differential Equation Models in Vision • Scale Space Theory for Image Analysis • Model based image analysis/understanding • Perception for Robotics • Active Visual Perception • Visual Feedback for Control and Navigation

10. Computer Vision • Applications • Measurement and interpretation of visual motion • Motion and 3D shape from image sequences • Segmentation of Image/Motion • Perceptual Grouping • Face detection, tracking and animation • Visual tracking (surveillance) • Visual guidance/navigation of mobile robots

11. Vision Problems • Reconstruction • estimate parameters of external 3D world. • Segmentation • partition I(x,y,t) into subsets of separate objects. • Visual Control • visually guided locomotion and manipulation • Recognition • classes: face vs. non-face, • activities: gesture, expression.

12. Reconstruction • Computer vision address the inverse problem: given an image/multiple images, reconstruct the scene geometry, motion parameters, … • Single images adequate given knowledge of object class • Multiple images make the problem easier, but not trivial as corresponding points must be identified.

13. knowledge of object class Building detection MRF labeling Line segment detection Perceptual grouping Original image Detected line segments MRF labeling Selected rooftops All possible rooftop hypotheses

14. Structure from Motion Problem Statement: 3D line orientation (r) estimation from motion Objective functional PDE Model: vector-valued, reaction-diffusion Properties diffusion system coupled through the reaction term from 2D motion constraint, three processes evolve simultaneously; L-curve technique for estimating regularization parameter. Experimental Results

15. Diffusion for Motion Estimation • Variational Formulation • Governing PDEs • Multigrid Framework • Experimental Results

16. Challenges in Reconstruction • Finding correspondences automatically • Optimal estimation of structure from n views under perspective projection • Models of reflectance and texture for natural materials and objects

17. Image Segmentation • Boundaries of image regions defined by a number of attributes Brightness/color Texture Motion depth • Approach • Multiscale region-based segmentation

18. Multiscale region-based segmentation • Motivation Partitioning of multivalued images into meaningful objects. • Main issues • Generation of a multiscale tower using non-linear diffusion filtering.. • Segmentation using gradient-driven watersheds: • Methods • Hierarchical Segmentation Using Dynamics of Contours of Multiscale Color Gradient Watersheds: fine-to-coarse region merging using a saliency measure • Hierarchical Labeling of Contours: Introduction of a causal Bayesian model to the scale space hierarchy of watersheds. Coarse-to-fine labeling using a MAP criterion based on a contour saliency measure and transition probabilities. • Segmentation examples

19. Temporal Segmentation: Tracking

20. Challenges in Segmentation • Interaction of multiple cues • Local measurements to global percepts • Interplay of image-driven and object model driven processing

21. Control • Visual feedback signal for control • for tasks such as grasping and moving • Visual feedback for guiding locomotion • Obstacle avoidance for a moving robot • Lateral and longitudinal control of driving

22. Challenges in control • Delay in feedback loop due to visual processing • Hierarchies in sensory motor control • Open loop or closed loop • Discrete planning or continuous control