Advice giving has been long explored in artificial intelligence to build robust learning algorithms. We consider advice giving in relational domains where the noise is systematic. The advice is provided as logical statements that are then explicitly considered by the learning algorithm at every update. Our empirical evidence proves that human advice can effectively accelerate learning in noisy structured domains where so far humans have been merely used as labelers or as designers of initial structure of the model.

With increasing understanding and availability of nuclear technologies, and increasing persuasion of nuclear technologies by several new countries, it is increasingly becoming important to monitor the nuclear proliferation activities. There is a great need for developing technologies to automatically or semi-automatically detect nuclear proliferation activities using remote sensing. Images acquired from earth observation satellites is an important source of information in detecting proliferation activities. High-resolution remote sensing images are highly useful in verifying the correctness, as well as completeness of any nuclear program. DOE national laboratories are interested in detecting nuclear proliferation by developing advanced geospatial image mining algorithms. In this paper we describe the current understanding of geospatial image mining techniques and enumerate key gaps and identify future research needs in the context of nuclear proliferation.

The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.

Vector and data-flow processors are particularly strong at dense, regular computation. Sparse, irregular data layouts cause problems because their unpredictable data access patterns prevent computational pipelines from filling effectively. A number of algorithms in image processing have been proposed which are not dense, and instead apply local neighborhood operations to a sparse, irregular set of points. Sparse and irregular data transfer is difficult for modern processors because they have more processing power than memory bandwidth. However, if the computation can be expanded while not increasing the bandwidth, modern processors can be made more efficient. The application targeted in this paper is patch matching over large scenes. Given two sequential frames of video data, corresponding points between the two frames are found. Correspondences are determined by comparing small image patches around each point. By rotating and comparing patches of the image over a range of angles, it is possible to more accurately match them through the scene. Rotation and interpolation are required to produce an appropriate image to compare against. Results for CPU, FPGA, and GPU are presented, with FPGA far outperforming the GPU or CPU due to its potential for high levels of hardware parallelism as the total volume of computation increases.

We propose a run-time re-configurable parametric architecture (fabric) for local neighborhood image processing. The proposed architecture is composed of polymorphous cells where each cell accesses neighborhood data from a local cell memory, and executes a neighborhood function sequentially. The architecture is flexible since different neighborhood functions can be implemented by rewriting a cell's software micro-code. High throughput is achieved because many cells execute concurrently. We show that for a satellite image feature extraction application, our architecture, implemented on Stratix II and Virtex 2 field programmable gate arrays, achieves similar performance, hardware resource utilization, and throughput as a fully pipelined systolic array architecture, yet offers imp roved flexibility to the developer. We compare and contrast these two architectures for their usability to the image processing community

The authors propose a run-time re-configurable architecture for local neighborhood image processing. Discussion of how the new architecture can offer improved flexibility to the developer. The authors show that for a satellite image feature extraction application, our architecture, implemented on Stratix II and Virtex 2 field programmable gate arrays, achieves similar performance, hardware resource utilization, and throughput as fully pipelined systolic array architecture

Abstract — We have developed an automated feature detection/classification system, called Genie (GENetic Imagery Exploitation), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. Genie is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multi-spectral remotely-sensed images. We describe our system in detail together with experiments involving comparisons of Genie with several conventional supervised classification techniques, for a number of classification tasks using multi-spectral remotely-sensed imagery.