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A large part of computer vision research is devoted to building models
and algorithms aimed at understanding human appearance and behaviour
from images and videos. Ultimately, we want to build automated systems
that are at least as capable as people when i ...
Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growin ...
Feature detection and description constitute important steps of many computer vision applications such as object detection and panorama stitching. Since those steps are computationally heavy, they might occupy significant portion of the full operation. Alt ...
Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...
Polarimetric imaging techniques demonstrate enhanced capabilities in advanced object detection tasks with their capability to discriminate man-made objects from natural background surfaces. While spectral signatures carry information only about material pr ...
Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects inc ...
Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...
Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...
Automatically extracting linear structures from images is a fundamental low-level vision problem with numerous applications in different domains. Centerline detection and radial estimation are the first crucial steps in most Computer Vision pipelines aimin ...
Image matching is a fundamental problem in Computer Vision. In the context of feature-based matching, SIFT and its variants have long excelled in a wide array of applications. However, for ultra-wide baselines, as in the case of aerial images captured unde ...
