Abstract-This paper illustrates how taking advantage of user studies highlighting the user requirements can lead to the selection of suitable visual features in image search systems. The results of a study to identify pertinent visual features to enhance a text-based press photo search system used by journalists are presented. A requirement was that the visual features should be intuitively understandable by the journalists. This feature selection task is approached by first determining the journalists' photo searching requirements based on a published user study. These requirements are then mapped to suitable visual features. The emphasis was on identifying suitable and intuitive low-level features, as these can be rapidly implemented in the existing text-based image search system. Results demonstrating the use of the selected features are shown.

Due to the increasing flood of digital images and the overall increase of storage capacity, large scale image databases are common these days. This work deals with the problem of finding replicas in image databases containing more than 100000 images. A clustering algorithm is developed that has linear runtime and can be carried out in parallel. We observe that with increasing size of the database, the problem of decreasing discrimination between high frequency images arises. Features of images with natural repetitive texture become similar to other images and show up in most of the search results. This problem is addressed by developing an asymmetric Hamming distance measurement for bags of visual words. It allows better discrimination power in large databases, while being robust to image transformations such as rotation, cropping, or change of resolution and size.

Commodity RGB-D sensors capture color images along with dense pixel-wise depth information in real-time. Typical RGB-D sensors are provided with a factory calibration and exhibit erratic depth readings due to coarse calibration values, ageing and thermal influence effects. This limits their applicability in computer vision and robotics. We propose a novel method to accurately calibrate depth considering spatial and thermal influences jointly. Our work is based on Gaussian Process Regression in a four dimensional Cartesian and thermal domain. We propose to leverage modern GPUs for dense depth map correction in real-time. For reproducibility we make our dataset and source code publicly available.

No abstract

Figure 1: An overview of various use cases from left to right: (1) a guide path directs workers movements, (2) the worker is made aware of non-ergonomic behaviour and changes position accordingly (3), and a restricted area (4) warns against unintended entering.