A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking

Despite years of work, a robust, widely applicable generic “symmetry detector” that can paral-lel other kinds of computer vision/image processing tools for the more basic structural charac-teristics, such as a “edge” or “corner” detector, remains a computational challenge. A new symmetry feature detector with a descriptor is proposed in this paper, namely the Simple Robust Features (SRF) algorithm. A performance comparison is made among SRF with SRF, Speeded-up Robust Features (SURF) with SURF, Maximally Stable Extremal Regions (MSER) with SURF, Harris with Fast Retina Keypoint (FREAK), Minimum Eigenvalue with FREAK, Features from Accelerated Segment Test (FAST) with FREAK, and Binary Robust Invariant Scalable Keypoints (BRISK) with FREAK. A visual tracking dataset is used in this performance evaluation in terms of accuracy and computational cost. The results have shown that combining the SRF detector with the SRF descriptor is preferable, as it has on average the highest accuracy. Additionally, the computational cost of SRF with SRF is much lower than the others.

Feature Detector on the Moon Trek Portal

<p>The Moon Trek portal (https://trek.nasa.gov/moon) aims to provide the scientific community as well as the general public access to lunar data collected from various lunar missions. The portal also offers a suite of tools with the goal of allowing users to analyze the data for the purposes of education, mission planning, and research. Such tools include elevation profilers, crater and rock detection, lighting analysis, and slope analysis to name a few. Moon Trek is further expanding its analytic capabilities by adding feature detection to its toolset.</p> <p>The feature detector, similar to the rock and crater detection tools, seeks to detect features on the lunar surface using orbital imagery. Unlike the detection tools currently available on the Moon Trek, the feature detector is built to be generic, trainable, and able to seek out any feature when provided a training set for the feature in question. The tool currently supports detection of craters, rocks, and lunar pits.</p> <p>The feature detector takes a deep learning approach in finding features from orbital imagery. The model used in the latest detection tool is a Faster Region Based Convolutional Neural Network (Faster-RCNN) with a finetuning approach. More succinctly, the finetuning approach uses a model which has been developed and trained on a different and larger training set. The classification layer is replaced to detect features of the chosen domain (rocks, pits, craters, etc.) The model is then trained with smaller training sets.</p> <p>Currently we use panchromatic Narrow Angle Camera (NAC) images from the Lunar Reconnaissance Orbiter Camera (LROC) as input. However, the model can be trained on orbital imagery from any mission. The tool’s output includes the NAC image with bounding boxes over detected and an ascii file showing pixel coordinates of each detected feature.</p>

A SIFT-Like Feature Detector and Descriptor for Multibeam Sonar Imaging

Multibeam imaging sonar has become an increasingly important tool in the field of underwater object detection and description. In recent years, the scale-invariant feature transform (SIFT) algorithm has been widely adopted to obtain stable features of objects in sonar images but does not perform well on multibeam sonar images due to its sensitivity to speckle noise. In this paper, we introduce MBS-SIFT, a SIFT-like feature detector and descriptor for multibeam sonar images. This algorithm contains a feature detector followed by a local feature descriptor. A new gradient definition robust to speckle noise is presented to detect extrema in scale space, and then, interest points are filtered and located. It is also used to assign orientation and generate descriptors of interest points. Simulations and experiments demonstrate that the proposed method can capture features of underwater objects more accurately than existing approaches.