Neurons to Algorithms
Abstract not provided.
Publications
Modeling cortical circuits
The neocortex is perhaps the highest region of the human brain, where audio and visual perception takes place along with many important cognitive functions. An important research goal is to describe the mechanisms implemented by the neocortex. There is an apparent regularity in the structure of the neocortex [Brodmann 1909, Mountcastle 1957] which may help simplify this task. The work reported here addresses the problem of how to describe the putative repeated units ('cortical circuits') in a manner that is easily understood and manipulated, with the long-term goal of developing a mathematical and algorithmic description of their function. The approach is to reduce each algorithm to an enhanced perceptron-like structure and describe its computation using difference equations. We organize this algorithmic processing into larger structures based on physiological observations, and implement key modeling concepts in software which runs on parallel computing hardware.
Information in Living Systems
Abstract not provided.
Cognitive Science
Abstract not provided.
Embodiment
Abstract not provided.
Implementing wide baseline matching algorithms on a graphics processing unit
Wide baseline matching is the state of the art for object recognition and image registration problems in computer vision. Though effective, the computational expense of these algorithms limits their application to many real-world problems. The performance of wide baseline matching algorithms may be improved by using a graphical processing unit as a fast multithreaded co-processor. In this paper, we present an implementation of the difference of Gaussian feature extractor, based on the CUDA system of GPU programming developed by NVIDIA, and implemented on their hardware. For a 2000x2000 pixel image, the GPU-based method executes nearly thirteen times faster than a comparable CPU-based method, with no significant loss of accuracy.
SLAM using camera and IMU sensors
Visual simultaneous localization and mapping (VSLAM) is the problem of using video input to reconstruct the 3D world and the path of the camera in an 'on-line' manner. Since the data is processed in real time, one does not have access to all of the data at once. (Contrast this with structure from motion (SFM), which is usually formulated as an 'off-line' process on all the data seen, and is not time dependent.) A VSLAM solution is useful for mobile robot navigation or as an assistant for humans exploring an unknown environment. This report documents the design and implementation of a VSLAM system that consists of a small inertial measurement unit (IMU) and camera. The approach is based on a modified Extended Kalman Filter. This research was performed under a Laboratory Directed Research and Development (LDRD) effort.
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