1,098 research outputs found
Model-Based Environmental Visual Perception for Humanoid Robots
The visual perception of a robot should answer two fundamental questions: What? and Where? In order to properly and efficiently reply to these questions, it is essential to establish a bidirectional coupling between the external stimuli and the internal representations. This coupling links the physical world with the inner abstraction models by sensor transformation, recognition, matching and optimization algorithms. The objective of this PhD is to establish this sensor-model coupling
Processor, Payload, and Power Subsystem Development of the MISSat-1 CubeSat
This thesis details the development and programming of the processor subsystem, camera payload, and power subsystem of the Mississippi Imaging Space Satellite (MISSat-1). An overview of the hardware and software considerations necessary for the processor subsystem is discussed. An explanation of microcontroller uses as well as real time operating system fundamentals is also presented as it relates to MISSat-1. The subsystem deals with varieties of peripheral integration and communication standards among devices. The camera graphical user interface (GUI) was expanded with the addition of functions that improve CubeSat image handling. Additionally, image processing techniques and algorithms are considered to improve CubeSat images. This work continues the camera payload work undertaken by University of Mississippi electrical engineering students from previous years. This paper will then discuss the design and analysis completed thus far for the power subsystem of the MISSat-1. Such topics will include an in-depth solar panel investigation, which will lead to the selection of the solar panels that will be used on the MISSat-1. The solar panel selection, along with the other chosen subsystem components, will allow for the formation of the power budget, which shows the breakdown of power usage for each subsystem. The power budget will then be developed into a Matlab GUI. Finally, the power budget will be further analyzed by comparing it to other satellite projects
Non-parametric Methods for Automatic Exposure Control, Radiometric Calibration and Dynamic Range Compression
Imaging systems are essential to a wide range of modern day
applications. With the continuous advancement in imaging systems,
there is an on-going need to adapt and improve the imaging
pipeline running inside the imaging systems.
In this thesis, methods are presented to improve the imaging
pipeline of digital cameras. Here we present three methods to
improve important phases of the imaging process, which are (i)
``Automatic exposure adjustment'' (ii) ``Radiometric
calibration'' (iii) ''High dynamic range compression''. These
contributions touch the initial, intermediate and final stages of
imaging pipeline of digital cameras.
For exposure control, we propose two methods. The first makes use
of CCD-based equations to formulate the exposure control problem.
To estimate the exposure time, an initial image was acquired for
each wavelength channel to which contrast adjustment techniques
were applied. This helps to recover a reference cumulative
distribution function of image brightness at each channel. The
second method proposed for automatic exposure control is an
iterative method applicable for a broad range of imaging systems.
It uses spectral sensitivity functions such as the photopic
response functions for the generation of a spectral power image
of the captured scene. A target image is then generated using the
spectral power image by applying histogram equalization. The
exposure time is hence calculated iteratively by minimizing the
squared difference between target and the current spectral power
image. Here we further analyze the method by performing its
stability and controllability analysis using a state space
representation used in control theory. The applicability of the
proposed method for exposure time calculation was shown on real
world scenes using cameras with varying architectures.
Radiometric calibration is the estimate of the non-linear mapping
of the input radiance map to the output brightness values. The
radiometric mapping is represented by the camera response
function with which the radiance map of the scene is estimated.
Our radiometric calibration method employs an L1 cost function by
taking advantage of Weisfeld optimization scheme. The proposed
calibration works with multiple input images of the scene with
varying exposure. It can also perform calibration using a single
input with few constraints. The proposed method outperforms,
quantitatively and qualitatively, various alternative methods
found in the literature of radiometric calibration.
Finally, to realistically represent the estimated radiance maps
on low dynamic range display (LDR) devices, we propose a method
for dynamic range compression. Radiance maps generally have
higher dynamic range (HDR) as compared to the widely used display
devices. Thus, for display purposes, dynamic range compression is
required on HDR images. Our proposed method generates few LDR
images from the HDR radiance map by clipping its values at
different exposures. Using contrast information of each LDR
image generated, the method uses an energy minimization approach
to estimate the probability map of each LDR image. These
probability maps are then used as label set to form final
compressed dynamic range image for the display device. The
results of our method were compared qualitatively and
quantitatively with those produced by widely cited and
professionally used methods
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