S.E. Umbaugh. Computer Vision and Image Processing. Prentice Hall, New Jersey, 1998. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Mixed-Initiative Control of Multiple Heterogeneous.. - Murphy, Casper.. (2000) (2 citations) (Correct)
....to operate under the unconstrained USAR con ditions. SCT has been used for mobile robots and has been shown to perform well in unstructured lighting conditions such as outdoors. 21] Various skin colors segment very simi larly over SCT space, thus multiple definitions of skin color are not needed[22]. The skin color detector required a range of values for the color. These values were trained using a simple two dimensional Gaussian histogram over SCT space for 10 sample images of skin color. Once the values were trained, when the algorithm ran in real time, the five largest connected areas of ....
Scott E. C. Umbaugh, Computer Vision and Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1998.
Gesture-Tracking in Real Time with Dynamic Regional Range.. - Tsap (Correct)
....or clothing, prior knowledge of initial hand positions, or movement restrictions. A goal of our project is to exclude such simpli cations. Instead, we use the SCT Center algorithm that can handle changing illumination. It was originally developed for skin cancer detection using color features [12]. Later the algorithm was successfully tested for position estimation of micro rovers [13] Other similar solutions for decoupling intensity from color information were also recently investigated ( 9] 1.3 Motivation for Range On Demand Approach Usefulness of 3 D data in gesture analysis ....
S. E. Umbaugh. Computer Vision and Image Processing. Prentice-Hall, Englewood Cli s, NJ, 1998.
Retrospective Shading Correction Based on Entropy.. - Likar, Maintz.. (1999) (3 citations) (Correct)
....makes retrospective methods incapable of removing shading variations that are similar to variations in the true image. Image smoothing and homomorphic ltering are the two most intuitive retrospective methods for removing low frequency shading components (Russ, 1995; Madisetti Williams, 1998; Umbaugh, 1998). Image smoothing assumes that shading is an additive low frequency signal that may be obtained by low pass ltering (LPF) A simple subtraction and grey level range restoration, performed by adding the constant C, then gives the corrected image: U#x# y##N#x# y##LPF#N#x# y## # C #8# By ....
Umbaugh, S.E. (1998) Computer Vision and Image Processing. Prentice Hall, New York.
Image Analysis and Supervised Learning in the Automated.. - Katz (2000) (1 citation) (Correct)
....Cell Differentiation [Cil1] Image captured for this thesis (shown at similar size for comparison) Basophil Eosinophil Lymphocyte Monocyte Neutrophil Thesis A.R.J. Katz 4 4 Chapter 2 Literature Survey 2. 1 Overview Computer vision is the processing of image data for use by a computer [Umb1]. It is a form of computer imaging where a computer processes visual information directly, examining images and acting on the result. Computer vision can be split into two primary tasks, that of image acquisition and that of image analysis. Image acquisition is the process of capturing an image ....
....a similar algorithm that uses the zero crossings of the second derivative of the smoothed image to determine the edges. The second stage of the image analysis process, data reduction involves either reducing the data in the spatial domain, or transforming it into the frequency domain, or both [Umb1](see Figure 3) The image information may be filtered after these processes, further reducing the data and allowing the extraction of the features required for analysis. Figure 3 Data Reduction [Based on Umb1, p. 39] Feature extraction, the final step in the data reduction process, is the ....
[Article contains additional citation context not shown here]
Scott E. Umbaugh, "Computer Vision and Image Processing", Prentice Hall Inc, New Jersey, USA, 1997.
HPC-ICTM: The Interval Categorizer.. - de Aguiar.. (2005) (Correct)
No context found.
S.E. Umbaugh. Computer Vision and Image Processing. Prentice Hall, New Jersey, 1998.
Resubmitted to IEEE Transactions on Robotics and Automation.. - Title Low Order (2001) (Correct)
No context found.
Scott E. C. Umbaugh. Computer Vision and Image Processing. Prentice-Hall, Englewood Cliffs, NJ, 1998.
INVISTOR - A Distributed MultiMedia Indexing System - Westmacott (Correct)
No context found.
Scott E. Umbaugh. Computer Vision and Image Processing. Prentice Hall 1998.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC