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Probabilistic and Biologically Inspired Feature Representations
Under the title "Probabilistic and Biologically Inspired Feature Representations," this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife—they can be used for image enhancement, visual obje...
Towards the Next Generation of 3D Reconstruction
Humans perceive our visual surroundings through the projection of light rays through our pupils and onto the retina. Aided by motion, we gain an understanding of our environment, as well as our location within it. The goal of image-based 3D reconstruction is to imbue machines with similar capabilities. The most prominent paradigm for image-based 3D reconstruction is called Structure-from-Motion (SfM). Traditionally, SfM has been approached through handcrafted algorithms, which are brittle when assumptions do not hold. Humans, on the other hand, understand their environment intuitively and show remarkable robustness in their ability to localize themselves in, and map the world. The main purpo...
Learning Convolution Operators for Visual Tracking
Visual tracking is one of the fundamental problems in computer vision. Its numerous applications include robotics, autonomous driving, augmented reality and 3D reconstruction. In essence, visual tracking can be described as the problem of estimating the trajectory of a target in a sequence of images. The target can be any image region or object of interest. While humans excel at this task, requiring little effort to perform accurate and robust visual tracking, it has proven difficult to automate. It has therefore remained one of the most active research topics in computer vision. In its most general form, no prior knowledge about the object of interest or environment is given, except for the...
Uncertainty-Aware Convolutional Neural Networks for Vision Tasks on Sparse Data
Early computer vision algorithms operated on dense 2D images captured using conventional monocular or color sensors. Those sensors embrace a passive nature providing limited scene representations based on light reflux, and are only able to operate under adequate lighting conditions. These limitations hindered the development of many computer vision algorithms that require some knowledge of the scene structure under varying conditions. The emergence of active sensors such as Time-of-Flight (ToF) cameras contributed to mitigating these limitations; however, they gave a rise to many novel challenges, such as data sparsity that stems from multi-path interference, and occlusion. Many approaches h...
Video Object Tracking
This book provides a thorough overview of recent progress in video object tracking, allowing researchers and industrial practitioners to gain a better understanding of the most important problems and developed technologies in the area. Video tracking is a key research area in computer vision and aims to track unique objects in a given video, which are useful for various applications such as video conference, video editing, surveillance, and autonomous driving. This book begins with an introduction to the task of video object tracking, including the most common problem settings. Given the revolution of deep learning in computer vision problems, numerous new tasks, datasets, and methods have been recently proposed in the domain of video tracking. The book includes these recent results as well as benchmarks in large-scale human-centric video analysis in complex events.
Eighth IEEE International Conference on Computer Vision
This two-volume set contains the proceedings of the July 2001 conference on computer vision. The 205 papers discuss sensors and early vision, stereo and multiple views, segmentation and matching, learning in vision, shape representation and recovery, stereo and multiple views, segmentation and matching, object recognition, tracking, video analysis, reflectance, image databases, vision systems and texture, and demo overviews. There is no subject index. The included CD-ROM contains a full version of the proceedings. c. Book News Inc.
Pattern Recognition
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Mathematical Reviews
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