Technologies of Vision: activities and research projects
Physical objects are the units in the real world to which people
associate basic knowledge about the environment. The ultimate goal
of semantic image labelling is to detect and classify all the
objects present in an image. For its importance and challenge,
automatic semantic labelling of images is an area that attracts
many research interests.
In the past we have developed algorithms to describe an image in terms of low level features such as color, edges, texture. The results have been used to index image archives for a subsequent retrieval by similarity. Analogous algorithms are typically tailored to segment images in multiple regions: this often is the first step towards the detection of objects in specific applications. An orthogonal approach relies on the well known template matching techniques. In both cases a model of the object should be provided for a proper recognition.
activities and projects
- MEMORI - MEMory-based Object Recognition in Images (memory means a database collecting all the objects of interest depicted from different point of views which is exploited for the labelling of the scene).
- TIS Text In Scene - Localization and segmentation of text in generic images plays an important role in the image content understanding.
- Template Matching Techniques in Computer Vision: Theory and Practice. Look inside through Google Book - Template Matching Techniques in Computer Vision: the Code Companion [here]
- Visual Environmental Monitoring (marmota project) - Mountains et al. labelling by making a correspondence between peak profiles and 3D model of the Earth, providing augmented reality scenes.
- COPILOSK - COntent Processing by Integrating LOgical and Statistical Knowledge (FBK joint research project) - In the framework of semantic image labelling the idea is to combine image analysis with knowledge-assisted techniques that use ontologies and domain knowledge (in collaboration with DKM research unit) to aggregate regions in the typically oversegmented image and to assign semantic labels to these regions.
Understanding a dynamic visual scene is the core problem of computer vision. Its goal is the detection and recognition of spatio-temporal patterns in video steams. For this reason modeling events is one of the key issue to describe the dynamic structure of a visual scene. Spatial and temporal knowledge along with specific knowledge and measured parameters of a changing environment must be considered.
activities and projects
- SmarTrack - a SmarT people Tracker. It is an adaptive system for real-time tracking of multiple people in a monitored scene. It provides accurate information about the spatial location of people through multiple persistent occlusions in cluttered environment using a number of timestamped image streams.
- PERE - People re-identification in video surveillance camera network.
- TRAVEL - Traffic Road Analysis by Visual Event Labelling. Since several years TeV is involved in automatic analysis of traffic video sequences from static or moving cameras for traffic monitoring and road safety. A flexible system for counting, classifying and tracking vehicles in road intersections is SCOCA.
- PuMALab - attentive Perception with a Unified approach to Multi-modality, Adaptation and Learning (FBK joint research project). The goal is to establish a unified approach to machine perception and cognition supporting multi-modality, adaptation, and learning. See some results in Adaptive Learning research page.
- VENTURI - immersiVe ENhancemenT of User-woRld Interactions (EU Project). The goal is to create a pervasive user-centered Augmented Reality paradigm. VENTURI is funded within the 7th Framework Program - ICT Theme (FP7-288238) and runs from October 2011 to September 2014.
activity and projects in the past
For information please contact
Oswald Lanz | FBK-irst, Povo, via Sommarive 18, I-38123 Trento, Italy | e-mail l a n z (at) fbk . eu