Computer Vision Group Computer Vision Projects (:,5) Vincenzo Caglioti Giacomo Boracchi, Simone Gasparini, Alessandro Giusti, Pierluigi Taddei

Computer Vision Group Computer Vision Group Team  Vincenzo Caglioti  Giacomo Boracchi, Simone Gasparini, Alessandro Giusti, Pierluigi Taddei

Computer Vision Group Reconstructing Canal Surfaces, trajectories and spin of moving balls We can reconstruct circular-cross section canal surfaces from a single image (main algorithms are already implemented in well-packaged Java methods).

Computer Vision Group Project proposals  Explore possibilities for a 3D input device (maybe real-time?) with a webcam + flexible tube. Develop demo application (OpenGL or Java3D). Example: virtual flexible stick-figure. Example: 3D modeling of flexible objects. Be creative! :) Requisites: learn principles of 3D visualization  Quantitatively evaluate performance of current algorithms, and test possible improvements. Implement camera autocalibration. Prerequisite: knowledge of camera calibration and projective geometry (Image Analysis and Synthesis classes).

Computer Vision Group Other applications With the same algorithm we can reconstruct the trajectory of a moving ball from a long-exposure photograph (no frame rate issues, can handle very fast games, any lighting condition, inexpensive equipment).  Reconstruct the trajectory of a real moving ball. Evaluate reconstruction accuracy. Can we also measure nonparabolic trajectories (e.g. Pirlo’s penalty kick, spinning table tennis shots, volleyball “floater” serves…)? Mostly implementation work, some interesting possible optimizations.  Implement an automatic refereeing system for table tennis.  Implement a system for detecting the exact bounce position of a fast-moving ball from its blurred trail (“in or out?”).  Augment low-frame rate videos of table tennis matches (think of the new Shell’s TV advertisement). For example: Draw ball “shadow” on the table (3D reconstruction) Draw ball velocity vector Predict the remaining trajectory portion Manipulate the ball trail opacity and/or color (image processing) Estimate ball speed Synthethize a 100 fps slow motion replay from a 20 fps video…

Computer Vision Group Ball spin from a single image (ongoing research) If the ball surface is textured: analyze the trail and find the ball spin (axis and rotational speed) – ongoing research 3D geometry issues Image processing issues  Find ball spin axis and speed from traces left by dots on the ball surface (long-exposure).  Find ball spin axis and speed from blur of the ball’s surface features (short-exposure) -- joint work with Giacomo Boracchi.  Find ball spin speed from orthographic images (long-exposure)  look for periodic color patterns.

Computer Vision Group Tram transit detection and notification  A webcam will be placed on the DEI building pointing at Via Edoardo Bassini.  The video stream will be analized in order to detect the transit of any ATM tram, identifying its number and registering the transit time  A web framework will be then implemented in order to predict the next tram transit  The system will be exploited by the department employees in order to leave the office at the last usefull moment Next predicted transits: heading to Duomo: 5’ 23’’ heading to Lambrate: 2’12’’

Computer Vision Group Structure from Motion  The aim is to reconstruct the 3D structure of a scene and the camera motion using as input only the video sequence caputred

Computer Vision Group Project 1: Surface fitting  Given the 3D points and the initial image frames identify reliable surface patches onto which the initial images can be mapped Coordinators: Caglioti, Taddei

Computer Vision Group Project 2: Feature Tracking for Structure from Motion  The features tracked may disappear due to occlusion or to wrong matches between images  A good tracking algorithm should compensate for these effects and extract as many features as possible Coordinators: Caglioti, Taddei

Computer Vision Group Project 3: Paper Like surfaces  The object recorded is assumed to be a paper-like surface that is represented by a particular family: developable surfaces  The project will be aimed to build a framework to generate sintetic datas to test the alghoritms Coordinators: Caglioti, Taddei

Computer Vision Group Motion Estimation from a Single Blurred Image  Application: 3D reconstruction from a single image Local motion extraction from blurred details (Corners/Texture) Exploit Global Camera Movement

Computer Vision Group Motion Estimation from a Single Blurred Image  Image Restoration: De-Blurring Build a “Blur Map” Adapt Existing De-blurring Techniques to real blurred images

Computer Vision Group  Objective: automatically detect holdup situations (“Hands Up!”) from video-surveillance sequences on a dsp-equipped camera. Robbery detection Background subtraction Detection of “hands up” pose Color-based skin segmentation

Computer Vision Group Robbery detection Face and hands detection

Computer Vision Group Robbery detection - Available projects  Pose recognition Develop an approach based on silhouette extraction and pose recognition  Face detection Improve performance and robustness of face detector, test on a larger training set  Hands detection Develop a new detection algorithm (similar to the face detection one) and test performance (hit rate and computational speed)  Skin segmentation Improve performance of the skin detector using a voting system involving three color spaces RGB, YCbCR, HUV. Coordinators: Caglioti, Boracchi, Gasparini, Giusti, Taddei

Computer Vision Group Rectification of perspective images  Objective: removing perspective effect from images Perspective ImageRectified Image

Computer Vision Group Natural image recognition through compression level analysis  Natural image recognition Recognition of natural image (e.g. leaves, flowers) by compressing the contour image and matching the compression levels Coordinator: Caglioti Original imageEdge image

Computer Vision Group Rectification of perspective images  Available project: 3D reconstruction of urban scene from uncalibrated images for virtual tour Coordinator: Caglioti

Computer Vision Group Calibration of catadioptric camera  Catadioptric camera: a perspective camera placed in front of a curved mirror Catadioptric cameraCatadioptric images Mirror Camera

Computer Vision Group Calibration of catadioptric camera  Calibration procedure Develop a new calibration procedure for catadioptric cameras from single image exploiting the silhouette of the mirror and the alignment constraints deriving from the image of straight lines. Coordinator: Caglioti, Gasparini, Taddei

Computer Vision Group License Plate Recognition  Objective: automatically detect and recognize license plate from video sequences on a dsp-equipped camera. YZH 4025

Computer Vision Group License Plate Recognition – Available Projects  New Starting Project  Probably Strict Deadlines  ONLY THE BRAVES!  License Plate Detection Module Develop a module that detect the license plate in image according to color and shape  License Plate Recognition Module Given the license plate image, develop a module that recognize characters (e.g. using a neural network) and provide the license number  Both projects require good C-programming skills  Coordinators: Caglioti, Gasparini, Taddei

Computer Vision Group Robot mapping  Objective: built a map of the environment collecting laser scans while robot is moving  Scanning while moving algorithm Implement and test on a real robot (Mo.Ro 2) the mapping algorithm − Coordinators: Caglioti, Gasparini Map built by collecting scans Laser scanner

Computer Vision Group Thank you… Further informations avaialble at {caglioti | boracchi | gasparini | giusti |