Enhanced Image-Aided Navigation Algorithm with Automatic Calibration and Affine Distortion Prediction

This research aims at improving two key steps within the image aided navigation process: camera calibration and landmark tracking. The camera calibration step is improved by automating the point correspondence calculation within the standard camera calibration algorithm, thereby reducing the required time for calibration while maintaining the output model accuracy. The feature landmark tracking step is improved by digitally simulating affine distortions on input images in order to calculate more accurate feature descriptors for improved feature matching in high relative viewpoint change. These techniques are experimentally demonstrated in an outdoor environment with a consumer-grade inertial sensor and three imaging sensors, one of which is orthogonal to the rest. Using a tactical-grade inertial sensor coupled with GPS position data for comparison, the improved image aided navigation algorithm is shown to reduce navigation errors by 24% in position, 16% in velocity and 35% in attitude when compared to the standard image-aided navigation algorithm

Local Descriptors Optimized for Average Precision

Extraction of local feature descriptors is a vital stage in the solution pipelines for numerous computer vision tasks. Learning-based approaches improve performance in certain tasks, but still cannot replace handcrafted features in general. In this paper, we improve the learning of local feature descriptors by optimizing the performance of descriptor matching, which is a common stage that follows descriptor extraction in local feature based pipelines, and can be formulated as nearest neighbor retrieval. Specifically, we directly optimize a ranking-based retrieval performance metric, Average Precision, using deep neural networks. This general-purpose solution can also be viewed as a listwise learning to rank approach, which is advantageous compared to recent local ranking approaches. On standard benchmarks, descriptors learned with our formulation achieve state-of-the-art results in patch verification, patch retrieval, and image matching.Comment: 13 pages, 8 figures. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

Second order scattering descriptors predict fMRI activity due to visual textures

Second layer scattering descriptors are known to provide good classification performance on natural quasi-stationary processes such as visual textures due to their sensitivity to higher order moments and continuity with respect to small deformations. In a functional Magnetic Resonance Imaging (fMRI) experiment we present visual textures to subjects and evaluate the predictive power of these descriptors with respect to the predictive power of simple contour energy - the first scattering layer. We are able to conclude not only that invariant second layer scattering coefficients better encode voxel activity, but also that well predicted voxels need not necessarily lie in known retinotopic regions.Comment: 3nd International Workshop on Pattern Recognition in NeuroImaging (2013