The selection of a track reference ins further complicated by image variations associated with changes in the viewing geometry and target characteristics. This paper compares several image-processing algorithms for the precision pointing of a near-space ATP platform that is viewing missile targets. The...
The framework is based on the OpenGL API and uses the Cg programming language to implement image-processing algorithms. However, the ideas represented in this chapter apply similarly to Microsoft's DirectX API and the HLSL programming language. Our hope is that the framework will be useful ...
The MATLAB®Image Processing Toolboxthat provides functions (Appendix C) and tools for enhancing and analyzing digital images and developing image-processing algorithms is a growing part of the MATLAB®package. It furthersimplifies the learning and teaching of image processing techniques in both acad...
Answering this question requires looking at object detection as two components: object classification and object localization. In other words, to detect and target an object in an image, the artificial intelligence needs to know what it is and where it is during machine learning image processing. ...
Supervised learning.This type of image recognition uses supervised learning algorithms to distinguish between different object categories -- such as a person or a car -- from a collection of photographs. A person can use the labels "car" and "not car," for instance, if they want the image ...
It includes features such as timing logic, exposure control, analog-to-digital conversion, shuttering, white balance, gain adjustment, and initial image processing algorithms. CMOS sensors contain rows of photodiodes coupled with individual amplifiers to amplify the electric signal from the photodiodes. ...
The MATLAB toolbox contains various algorithms for hyperspectral data processing and analysis, such as end-member extraction, abundance map estimation, spectral matching, and anomaly detection. The toolbox supports reading and writing hyperspectral data in different file formats, such as NITF, ENVI, TI...
For example, using more traditional image processing techniques, faces can be detected within an image, extracted, and resized, and the data transformed and normalized. Algorithms can then extract the fiducial points of the face—that is, the important values for positioning the face and for ...
Manolakis D, Shaw GA (2002) Detection algorithms for hyperspectral imaging application. IEEE Signal Process Mag 19(1):29–43 ArticleGoogle Scholar Manolakis D, Marden D, Shaw GA (2003) Hyperspectral image processing for automatic target detection applications. Lincoln Lab J 14(1):79–116 ...
摘要:Image segmentation is an important component of many image understanding systems. It aims to group pixels in a spatially and perceptually coherent manner. Typically, these algorithms have a collection of parameters that control the degree of over-segmentation produced. It still remains a challenge...