No abstract

Stereoscopic movie production has been a topic in the making of films for a long time. However, it hasn't made it to the amateur sector. Commercially available stereoscopic cameras are too expensive for non professionals. When producing a stereo video with two separate standard cameras, synchronicity and spatial offset maintenance between the two views is a challenging non-trivial task. Even when this is done properly, the general lack of software-tools for stereo video post production definitely daunts any non-professional ambitions. In this work we present a tool for preprocessing stereoscopic videos. We describe how the input videos can be converted automatically to a stereo-movie, ready for displaying or further processing in standard video software.

The bag-of-features model is often deployed in content-based image retrieval to measure image similarity. In cases where the visual appearance of semantically similar images differs largely, feature histograms mismatch and the model fails. We increase the robustness of feature histograms by automatically augmenting them with features of related images. We establish image relations by image web construction and adapt a label propagation scheme from the domain of semisupervised learning for feature augmentation. While the benefit of feature augmentation has been shown before, our approach refrains from the use of semantic labels. Instead we show how to increase the performance of the bag-of-features model substantially on a completely unlabeled image corpus.

This paper presents a new hardware‐accelerated approach to the volumetric reconstruction of trees from photographs, based on the methods introduced by Reche‐Martinez et al. The system applies an adapted computed tomography (CT) procedure that uses a set of oriented photographs with known interior and exterior camera parameters for creating a 3D model of a tree, while requiring considerably fewer images than standard CT. As tomographic reconstructions are complex tasks that result in time‐consuming processes for high‐resolution volumes, the hitherto existing methods are improved and modified to allow an ideal parallelisation of the computations on graphics hardware. The paper delivers a detailed insight into the complete process of the reconstruction, from the acquisition and preparation of the input data to the implementation of the final system on graphics processing hardware.

Abstract. Amplitude spectra of natural images look surprisingly alike. Their shape is governed by the famous 1/f power law. In this work we propose a novel low parameter model for describing these spectra. The Sum-of-Superellipses conserves their common falloff behavior while simultaneously capturing the dimensions of variation-concavity, isotropy, slope, main orientation-in a small set of meaningful illustrative parameters. We demonstrate its general usefulness in standard computer vision tasks like scene recognition and image compression.