Out-of-Distribution learning is concerned with developing methods where the distribution of the data processed at test time may be different from the distribution of the data used during the training of a model.
Recent advanecs in augmented, mixed, and virtual reality, coupled with the need to perform analysis and decision-making on large-scale collections of volumetric images stimulate the research in immersive analytics.
Video understanding is concerned with the parsing of the image data flow for the semantic understanding of the objects in the scene, but also their actions and interactions defining their behavior.
Recognizing human interactions from video is an important step forward towards the long-term goal of performing scene understanding fully automatically. Recent years have seen a concentration of works revolving around the problem of recognizing single person actions, as well as group activities, while the area of modeling the interactions between two people is still relatively unexplored.
Recent successes in the use of sparse coding for many computer vision applications have triggered the attention towards the problem of how an over-complete dictionary should be learned from data.
Long-duration tracking of individuals across large sites remains an almost untouched research area. Trucks of individuals acquired in disjoint fields of view have to be connected despite the fact that the same person will appear in a different pose, from a different viewpoint, and under different illumination conditions.
Exemplar-based Object Layout
Recognizing the presence of object classes in an image, or image classification, has become an increasingly important topic of interest. Equally important, however, is also the capability to locate these object classes in the image. The combined problem, usually referred to as object layout, is approached with models that require intense training.
Transfer Learning from Multiple Sources
Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. The effectiveness of the transfer is affected by the relationship between source and target.
Increasingly, large networks of surveillance cameras are employed to monitor public and private facilities. This continuous collection of imagery has the potential for tremendous impact on public safety and security. Unfortunately, this potential is often unrealized since manual monitoring of growing numbers of video feeds is not feasible.
Aerial Video Analysis
In aerial video moving objects of interest are typically very small, and being able to detect them is key to enable tracking. There are detection methods that learn the background and distinguish when a foreground object is present.
Face Modeling and Tracking
Active Appearance Models (AAMs) represent facial images with generative models for both shape and appearance of the face. Despite their success, they enjoy limited performance when used on faces that were not part of the training set. Moreover, training them with a lot of examples degrades their effectiveness.
People detection and tracking in video are fundamental Computer Vision capabilities that still constitute a research challenge. Important difficulties are due to the partial occlusions of the objects of interest (people), the dynamic background (possibly due to the motion of the observer), and the foreground clutter (due to non-person objects in motion).
One of the most important elements of modern Computer Vision is the concept of image texture, or simply texture. Depending on the task at hand (e.g. image-based rendering, recognition, or segmentation, just to mention a few broad areas), several texture models have been proposed in the literature.
Dynamic Texture Editing
The operation that by processing video data allows producing new video data in Computer Graphics is known as Video-Based Rendering (VBR). Developing new VBR techniques is important because they allow to quickly synthesize new photo-realistic scenes (because of the origin of the data) without having to develop and simulate a synthetic model of the scene.
Dynamic Texture Segmentation
Segmenting the image plane of video sequences is often one of the first steps towards the analysis of video. A lot of effort has been spent on developing image segmentation techniques based on cues such as color, or texture. Similarly, there are several methods for segmenting image motion based on optical flow, or motion features.
Dynamic Texture Recognition
Recognition of objects based on their images is one of the central problems in modern Computer Vision. Objects can be characterized by their geometric, photometric, and dynamic properties. While a vast literature exists on recognition based on geometry and photometry, much less has been said about recognizing scenes based upon their dynamics.
3D Object Modeling
Building 3D models is an important problem in several areas, such as forensic applications, medical applications, industrial inspection, or virtual visits of 3D synthetic environments. One way to build models is by acquiring range data images, by means of laser scanners, and then stitching them together.