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In this paper we have compared Time-of-Flight cameras of different vendors at object-camera distances of 500 mm, 1500 mm and 2500 mm. The aim was to find the highest possible precision at the distance of 500 mm, to estimate the change of the accuracy depending on scene-reflectivity and working distance and to investigate the possibility to use the cameras as per-pixel sub-centimeter accurate me...

In this paper we consider representations for use in models of the processing that occurs between the eardrum and our conscious experience of sound. We first list “good” properties for such mid-level representations, then present a framework within which to discuss some examples. We compare in detail two popular schemes — sinusoid tracks and correlograms — and propose a new representation, weft...

A multi-class traffic scene segmentation approach based on scene flow data is presented. Opposed to many other approaches using color or texture features, our approach is purely based on dense depth and 3D motion information. Using prior knowledge on tracked objects in the scene and the pixel-wise uncertainties of the scene flow data, each pixel is assigned to either a particular moving object ...

Sound source localization and separation with permutation resolution are essential for achieving a computational auditory scene analysis system that can extract useful information from a mixture of various sounds. Because existing methods cope separately with these problems despite their mutual dependence, the overall result with these approaches can be degraded by any failure in one of these c...

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Although there exist a number of single color constancy algorithms, none of them can be considered universal. Consequently, how to select and combine existing single algorithms are two important research directions in the field of color constancy. In this paper we use ridge regression, a simple yet effective machine learning approach, to select and combine existing color constancy algorithms. T...

Scene classification is a popular research topic in computer vision, and has received much attention in the recent past. Conventionally, scene classes are considered to be mutually exclusive. However, in real-world scenarios a scene image may belong to multiple classes, depending upon different perceptions of the masses. In this paper, we propose an improved Bandler and Kohout’s sub-triangle pr...

We propose a self-supervised framework that learns to group visual entities based on their rate of co-occurrence in space and time. To model statistical dependencies between the entities, we set up a simple binary classification problem in which the goal is to predict if two visual primitives occur in the same spatial or temporal context. We apply this framework to three domains: learning patch...