Image geo-localization is an important problem with many applications such as augmented reality and navigation. The most common ways to geo-localize an image are to use its meta-data such as GPS or to match it against a geotagged database. When neither of those is available, it is still possible to apply shadow analysis to determine the camera heading for outdoor images. This could be useful pruning the search space in geo-localization applications, for example by removing roads with incompatible orientations from a database such as Open Street Map. In this paper, we develop a novel interactive method for deducing the global heading of a query image using the shadows in it. We start by constructing a model of the sun-earth system to determine all shadows possible at a given approximate latitude, and compare shadows within the query to those possible under the model to determine the range of possible headings. We demonstrate this on 54 query images with known ground truth, and show that in 52 cases the ground truth lies in the computed range.

We present a memory scalable image localization system that uses distributed kd-trees created on overlapping geographic cells using a database of 10 million Google Street View images for an area of approximately 10,000 square kilometers in Taiwan. Given a collection of images over a region of interest (ROI), we generate a database by dynamically creating geographic cells that are optimized so that each cell contains roughly the same number of images. We then create kd-trees for each cell from SIFT features extracted from the images in that cell. When querying the system, we run traditional feature matching on each cell and pool the results for each cell to rerank with a geometric constraint. The key idea is the subdivisions of the ROI into overlapping geographic cells, allowing our system to scale to 10 million images and to efficiently utilize prior query location information when available. We evaluate our system on a test set of 29 geo-tagged images, not from Google Street View, taken throughout Taiwan with various resolutions, aspect ratios, and qualities. We also evaluate our system on a set of 97 images without geo-tag data.

Task scheduling is important for the proper functioning of parallel processor systems. The static scheduling of tasks onto networks of parallel processors is well-defined and documented in the literature. However, in many practical situations a priori information about the tasks that need to be scheduled is not available. In such situations, tasks usually arrive dynamically and the scheduling should be performed on-line or "on the fly". In this paper, we present a framework based on stochastic reinforcement learning, which is usually used to solve optimization problems in a simple and efficient way. The use of reinforcement learning reduces the dynamic scheduling problem to that of learning a stochastic approximation of an unknown average error surface. The main advantage of the proposed approach is that no prior information is required about the parallel processor system under consideration. The learning system develops an association between the best action (schedule) and the current state of the environment (parallel system). The performance of reinforcement learning is demonstrated by solving several dynamic scheduling problems. The conditions under which reinforcement learning can used to efficiently solve the dynamic scheduling problem are highlighted.