ROBUST TECHNIQUES FOR COMPUTER VISION

lar is a popular technique for parameterizing shape, appear-ance, and motion [8, 4, 18, 19, 29]. These learned PCArepresentations have proven useful for solving problemssuch as face and object recognition, tracking, detection, andbackground modeling [2, 8, 18, 19, 20].Typically, the tr[r]

addressed the robustness of PCA. In particular, we describethe method of Xu and Yuille [30] in detail and quantita-tively compare it with our method. We show how PCAcan be modified by the introduction of an outlier process[1, 13] that can account for outliers at the pixel level. Arobust M-esti[r]

al., 2008), and stuff detections (roughly, massnouns, things like sky and grass) based on linearSVMs for low level region features.Appearance characteristics are predicted usingtrained detectors for colors, shapes, textures, andmaterials, an idea originally introduced in Farhadiet al.[r]

Benchmarking mode runs until we get significant infor-mation about all implementations according to their inputparameters such as image properties and optional operatorparameters. We use SugoiTracer [1] to collect the statistics(such as average processing time, standard deviation, totaltime ). The me[r]

Hindawi Publishing CorporationEURASIP Journal on Applied Signal ProcessingVolume 2006, Article ID 76278, Pages 1–15DOI 10.1155/ASP/2006/76278Evaluating Edge Detection through Boundary DetectionSong Wang, Feng Ge, and Tiecheng LiuDepartment of Computer Sc ience and Engineering, University of S[r]

Copyright © 2008 Mohamed F. Abdelkader et al. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.1. INTRODUCTIONActivity modeling and recog[r]

Copyright © 2008 Mohamed F. Abdelkader et al. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.1. INTRODUCTIONActivity modeling and recog[r]

12. Quarteroni, A. and Valli, A.: Numerical Approximation of Partial Differential Equations, Springer-Verlag, New York Quarteroni, A. and Sacco, R. and Saleri, F.: Numerical Mathematics, Vol. 37 of Texts in Applied Mathematics, Springer-Verlag, New York (2000). 13. Sethian, J.A. Level Set Methods an[r]

51525354555657585960 // The cascade definition to be used for detection. private static final String CASCADE_FILE = "haarcascade_frontalface_alt.xml"; public static void main(String[] args) throws Exception { // This will redirect the OpenCV errors to the Java console to give you // feedbac[r]

4. The function g(u, v)(x, y) is represented by the image guv in which pixel values of 1 are white and pixel valuesof -1 are black.Figure 8.6.2 Two-dimensional Walsh basis for n = 4.Figure 8.6.3 shows the magnitude image (right) of the two-dimensional Walsh transform of the image of a jet(lef[r]

certain that these techniques will lead to sub- stantial improvements in almost every unifica- tion based system. It is, for example, quite un- likely that unification failures are equally dis- tributed over the different nodes of the gram- mar's feature structure, which is the most im[r]

one image (a “document” in this setting). The more direct co-occurrences there are, the more related the words will be for themodel. This works for Exp. 1: Since the ESP labels are lists ofwhat subjects saw in a picture, and the adjectives of Exp. 1 aretypical colors of objects, there[r]

The rest of the paper is structured as follows. Section 2briefly reviews the prior literature. In Section 3, the Harrisscale-adaptive keypoint detector is presented. The proposedmethod is described in Section 4 and is experimentallyvalidated and compared against other competing techniquesin Section 5[r]

ment techniques to increase the signal-to-noise ratioof the noisy speech (Ortega-Garcia and Gonzalez-Rodriguez, 1996); and 2) using model-based com-pensation schemes to adapt the acoustic models tonoisy environment (Gales and Young, 1996; Aceroet al., 2000).From the information-theoretic poin[r]