Spencer G. Fowers

Brigham Young University - Provo Main Campus | BYU · Department of Electrical and Computer Engineering

When Sean James, who works on data-center technology for Microsoft, suggested that the company put server farms entirely underwater, his colleagues were a bit dubious. But for James, who had earlier served on board a submarine for the U.S. Navy, submerging whole data centers beneath the waves made perfect sense. . This tactic, he argued, would not...

This paper presents the development of a new feature descriptor derived from previous work on the basis sparsecoding inspired similarity descriptor that provides smaller descriptor size, simpler computations, faster matching speed, and higher accuracy. The TreeBASIS descriptor algorithm uses a binary vocabulary tree that is computed offline using b...

This paper presents a novel feature descriptor called TreeBASIS that provides improvements in descriptor size, computation time, matching speed, and accuracy. This new descriptor uses a binary vocabulary tree that is computed using basis dictionary images and a test set of feature region images. To facilitate real-time implementation, a feature reg...

Feature point matching is an important step for many vision-based unmanned-aerial-vehicle applications. This paper presents the development of a new feature descriptor for feature point matching that is well suited for micro unmanned aerial vehicles equipped with a low-resource, compact, lightweight, low-power embedded vision sensor. The Basis Spar...

This paper presents a novel feature descriptor called TreeBASIS that provides improvements in descriptor size, computation time, matching speed, and accuracy. This new descriptor uses a binary vocabulary tree that is computed using basis dictionary images and a test set of feature region images. To facilitate real-time implementation, a feature reg...

The important task of library book inventory, or shelf-reading, requires humans to remove each book from a library shelf, open the front cover, scan a barcode, and reshelve the book. It is a labor-intensive and often error-prone process. Technologies such as 2D barcode scanning or radio frequency identification (RFID) tags have recently been prop...

Many computer vision algorithms for pattern matching, object tracking, and 3-D reconstruction, etc., begin with feature detection and matching. Common feature detectors such as Harris, Sobel, Canny, and Difference of Gaussians perform basic linear algebra operations on an image in order to identify "corners" or "edges" for matching. These detectors...

Many grayscale image processing techniques such as edge and feature detection, template matching, require the computations of image gradients and intensity difference. These computations in grayscale are very much like measuring color difference between two colors. The goal of this work is to determine an efficient method to represent color differe...

Human vision system relies on stereovision to determine object distance in the 3-D world. Human vision system achieves this by first locating the objects, then matching the corresponding objects seen by the left and right eyes, and finally using triangulation to estimate the object distance. Inspired by the same concept, this paper presents a depth...

We describe a senior design project in which teams of students design and implement the hardware and software modules that allow an off-the-shelf RC vehicle to operate autonomously using only on-board vision, sensors and computational resources. Projects involving autonomous robots are ideally suited as culminating design experiences because of the...

A feature tracker is only as good as the features found by the feature detector. Common feature detectors such as Harris, Sobel, Canny, and Difference of Gaussians convolve an image with a specific kernel in order to identify "corners" or "edges". This convolution requires, however, that the source image contain only one value (or color channel) pe...

Vision algorithms were implemented on an field programmable gate array to provide additional information to supplement the insufficient data of a standard inertial measurement unit in order to create a previously unrealized completely onboard vision system for microunmanned aerial vehicles. The onboard vision system is composed of an field programm...

An efficient algorithm to detect, correlate, and track features in a scene was implemented on an FPGA in order to obtain real-time performance. The algorithm implemented was a Harris Feature Detector combined with a correlator based on a priority queue of feature strengths that considered minimum distances between features. The remaining processin...

Micro Unmanned Air Vehicles are well suited for a wide variety of applications in agriculture, homeland security, military, search and rescue, and surveillance. In response to these opportunities, a quad-rotor micro UAV has been developed at the Robotic Vision Lab at Brigham Young University. The quad-rotor UAV uses a custom, low-power FPGA platfor...

In this paper an embedded vision system and control module is introduced that is capable of controlling an unmanned four-rotor helicopter and processing live video for various law enforcement, security, military, and civilian applications. The vision system is implemented on a newly designed compact FPGA board (Helios). The Helios board contains a...

This paper discusses a simple, inexpensive, and effective implementation of a vision-guided autonomous robot. This implementation is a second year entrance for Brigham Young University students to the Intelligent Ground Vehicle Competition. The objective of the robot was to navigate a course constructed of white boundary lines and orange obstacles...