Learning has become an essential part of many image and video processing systems, but it is not often used as an end-to-end solution. Some of the most successful demonstrations of end-to-end learning have been with convolutional, or shared weight networks. We are interested in how this approach can scale and have developed a flexible framework for implementing and training large scale convolutional networks called Harpo. We present an overview of the Harpo framework and describe a multilevel learning strategy used to optimize convolutional networks for particular features of interest in video data streams. Harpo is designed to exploit reconfigurable hardware to accelerate massively parallel convolutional network components and achieve real-time processing speeds. In this paper, we present initial software experiments which use the system to segment exhaust plumes coming from military vehicles in unmanned aerial vehicle video data

We present Genie Pro, a new software tool for image analysis produced by the ISIS (Intelligent Search in Images and Signals) group at Los Alamos National Laboratory. Like the earlier GENIE tool produced by the same group, Genie Pro is a general purpose adaptive tool that derives automatic pixel classification algorithms for satellite/aerial imagery, from training input provided by a human expert. Genie Pro is a complete rewrite of our earlier work that incorporates many new ideas and concepts. In particular, the new software integrates spectral information; and spatial cues such as texture, local morphology and large-scale shape information; in a much more sophisticated way. In addition, attention has been paid to how the human expert interacts with the software: Genie Pro facilitates highly efficient training through an interactive and iterative "training dialog". Finally, the new software runs on both Linux and Windows platforms, increasing its versatility. We give detailed descriptions of the new techniques and ideas in Genie Pro, and summarize the results of a recent evaluation of the software.

Feature identification attempts to find algorithms that can consistently separate a feature of interest from the background in the presence of noise and uncertain conditions. This paper describes the development of a high-throughput, reconfigurable computer based, feature identification system known as POOKA. POOKA is based on a novel spatio-spectral network, which can be optimized with an evolutionary algorithm on a problem-by-problem basis. The reconfigurable computer provides speed up in two places: 1) in the training environment to accelerate the computationally intensive search for new feature identification algorithms, and 2) in the application of trained networks to accelerate content based search in large multi-spectral image databases. The network is applied to several broad area features relevant to scene classification. The results are compared to those found with traditional remote sensing techniques as well as an advanced software system known as GENIE. The hardware efficiency and performance gains compared to software are also reported.

This paper investigates the design of evolvable FPGA circuits where the design space is severely constrained to an interconnected network of meaningful high-level operators. The specific design domain is image processing, especially pattern recognition in remotely sensed images. Preliminary experiments are reported that compare neural networks with a recently introduced variant known as morphological networks. A novel network node is then presented that is particularly suited to the problem of pattern recognition in multi-spectral data sets. More specifically, the node can exploit both spectral and spatial information, and implements both feature extraction and classification components of a typical image processing pipeline. Once trained, the network can be applied to large image data sets, for at the sensor to extract features of interest with two orders of magnitude speed-up compared to software implementations

Compute performance and algorithm design are key problems of image processing and scientific computing in general. For example, imaging spectrometers are capable of producing data in hundreds of spectral bands with millions of pixels. These data sets show great promise for remote sensing applications, but require new and computationally intensive processing. The goal of the Deployable Adaptive Processing Systems (DAPS) project at Los Alamos National Laboratory is to develop advanced processing hardware and algorithms for high-bandwidth sensor applications. The project has produced electronics for processing multi- and hyper-spectral sensor data, as well as LIDAR data, while employing processing elements using a variety of technologies. The project team is currently working on reconfigurable computing technology and advanced feature extraction techniques, with an emphasis on their application to image and RF signal processing. This paper presents reconfigurable computing technology and advanced feature extraction algorithm work and their application to multi- and hyperspectral image processing. Related projects on genetic algorithms as applied to image processing will be introduced, as will the collaboration between the DAPS project and the DARPA Adaptive Computing Systems program. Further details are presented in other talks during this conference and in other conferences taking place during this symposium.

We describe the implementation and performance of a genetic algorithm which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of our analysis of the efficiency of the classic genetic operations of crossover and mutation for our application, and discuss our choice of evolutionary control parameters. We exhibit some of our evolved algorithms, and discuss possible avenues for future progress.