The most difficult stage of automated target recognition (ATR) is segmentation. Current AFIT segmentation problems include faces and tactical targets; previous efforts to segment these objects have used intensity and motion cues. This thesis develops a color preprocessing scheme to be used with the other segmentation techniques. A neural network is trained to identify the color of a desired object, eliminating all but that color from the scene. Gabor correlations and 2D wavelet transformations will be performed on stationary images; and 3D wavelet transforms on multispectral data will incorporate color and motion detection into the machine visual system. The thesis will demonstrate that color and motion cues can enhance a computer segmentation system. Results from segmenting faces both from the AFIT data base and from video taped television are presented; results from tactical targets such as tanks and airplanes are also given. Color preprocessing is shown to greatly improve the segmentation in most cases. Color, Segmentation, Neural network, Wavelet transform.

Cited by 1

Color Image Processing

2018

Cited by 12

Image Color Analysis

Henry R. Kang · Wiley Encyclopedia of Electrical and Electronics Engineering · 1999

Abstract The sections in this article are Basics of Image Color Analysis Color Description and Formulation Human Visual Responses Color Appearance Model Color Mixing Models Circular Dot Overlap Model Device‐Independent Color Imaging

Cited by 0

Color Image Segmentation

Konstantinos N. Plataniotis, A.N. Venetsanopoulos · Digital signal processing · 2000

Cited by 73

Color Image Watermarking

Qingtang Su · 2016

Cited by 0

The Color Image

Joshua Yumibe · Indiana University Press eBooks · 2018

Cited by 1

Color Image Statistics

Ming Ronnier Luo, Ming Ronnier Luo, Ming Ronnier Luo · 2016

Cited by 0

Color image enhancement

Marshall, Shelley E. · DSpace@MIT (Massachusetts Institute of Technology) · 1984

Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1984

Cited by 0

Color Image Processing

D. Sundararajan · 2017

Cited by 7

Color Image Processing

Arthur R. Weeks · SPIE eBooks · 2009

Color image processing is one of the newest and most exciting areas of electronic image processing. Until recently, the computing hardware required to store and manipulate color images was limited to a special few. Today, it is not uncommon to find a standard desktop computer system with a true-color 24-bit display, at least 8 million bytes of memory, and 2 gigabytes of hard disk storage. The use of desktop color scanners and color printers attached to a desktop computer system are also becoming commonplace. The demand has increased to integrate both color and black and white images into presentations and documents. The new operating systems that are being used with these desktop computer systems are integrating to make it easier to transfer color images from one application to another. Several completely electronic photography systems have appeared in recent years that totally eliminate the use of film and chemicals. An image is acquired by an electronic camera, digitized, and then stored digitally within the camera. These images are then transferred at a later time to a desktop computer for processing and inclusion in documents. Even though they presently do not have the resolution of film, they are becoming quite popular because of the speed with which images can be acquired and placed within a document or presentation. This chapter first presents the fundamentals of color followed by a discussion of several commonly used color models. Next, several examples of using color image processing will be given, including the correction of the tint and saturation, the spatial filtering, the detection of edges, the histogram equalization, and the color white balancing of color images. The final section discusses a technique known as pseudocoloring used to highlight specific graylevels in a grayscale image in color to enhance important features so that they are clearly observable.

Cited by 9

Color Image Enhancement

2007

This chapter contains sections titled: False Colors and Pseudocolors Enhancement of Real Color Images Noise Removal in Color Images Contrast Enhancement in Color Images References

Cited by 2

Color Image Halftoning

Niranjan Damera-Venkata, Vishal Monga, Brian L. Evans · Image processing series · 2006

Cited by 1

Scalable color image . . .

Rosa M. Figueras i Ventura, Pierre Vandergheynst, Pascal Frossard et al. · 2003

This paper presents a new scalable and highly flexible color image coder based on a Matching Pursuit expansion. The Matching Pursuit algorithm provides an intrinsically progressive stream and the proposed coder allows us to reconstruct color information from the first bit received. In order to

Cited by 0