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Table 1: Comparison of edge-based and appearance-based evaluation methods for vehicle tracking.

in Learning Enhanced 3D Models for Vehicle Tracking
by J. M. Ferryman, A. D. Worrall, S. J. Maybank 1998
"... In PAGE 9: ... Figure 6 illustrates two instances in which edge-based tracking fails (far-camera and significant occlusion) but appearance-based tracking succeeds. A comparison of the pros and cons of edge-based and appearance-based tracking is given in Table1 . An experiment was also performed to compare the performanceof integrated edge-based and appearance- based tracking for pose refinement.... ..."
Cited by 9

TABLE 2 Summary of appearance-based recognition techniques.

in A survey of free-form object representation and recognition techniques
by Richard J. Campbell, Patrick J. Flynn 2001
Cited by 72

Table 1: Appearance-based classi cations for images in gure 9.

in unknown title
by unknown authors

Table 1. Comparison of different appearance-based feature sets

in On Appearance-Based Feature Extraction Methods for Writer-Independent Handwritten Text Recognition
by unknown authors

Table 1: Classification results (in percent) and confusion matrix (absolute numbers) for the appearance-based approach without view- point selection

in Active Appearance-Based Object Recognition Using Viewpoint Selection
by Appearance-Based Object Recognition, M. Reinhold L, F. Deinzer L, J. Denzler, D. Paulus L, J. Pssl

Table 1 The appearance-based CML algorithm. kak is the norm of vector a, jBj is the determinant of matrix B and size(A) is the number of elements of set A.

in Appearance-based concurrent map building and localization
by J. M. Porta, B. J. A. Kröse 2004
"... In PAGE 7: ... The second case is when Xt 1 is a single Gaussian but the application of the state update procedure (Equation 5) results in a reduction of the covariance of this Gaussian. Table1 summarizes the CML algorithm we introduce in this paper. The mapping strategy just outlined allows us to build an appearance-based representation of a given environment along the paths followed by the robot.... ..."
Cited by 8

Table 1. Comparison of feature-selection techniques in the appearance-based expression recognition system of Littlewort et al (2006). Three feature selection options are compared using LDA and SVMs as the classifier

in Machine Analysis of Facial Expressions
by Maja Pantic, Marian Stewart Bartlett

Table 1. Comparison of feature-selection techniques in the appearance-based expression recognition system of Littlewort et al (2006). Three feature selection options are compared using LDA and SVMs as the classifier

in 20 Machine Analysis of Facial Expressions 1. Human Face and Its Expression
by Maja Pantic, Marian Stewart Bartlett

Table 1: COIL-100: Recognition rate (rank 1), in compari- son to appearance based methods

in OBJECT RECOGNITION METHODS BASED ON TRANSFORMATION COVARIANT FEATURES
by unknown authors
"... In PAGE 6: ... Such a configuration is benign for appearance-based methods. Table1 compares recognition rates achieved by the LAF approach with the rates of sev- eral appearance-based object recognition methods. Results are presented for five experimental set-ups, differing in the number of training views per object.... ..."

Table 1. Different properties of 2D and 3D information. While 3D information has geometric properties (position and orientation), 2D information covers also appearance based properties (color,contrast transition etc.). .

in Abstract
by Syddansk Universitet, Dirk Kraft, Syddansk Universitet, Nicolas Pugeault
"... In PAGE 7: ... Discussion The advantages of using a 2D or a 3D scene representa- tion is highly dependent on the application and the context. Both have their own advantages and disadvantages as pre- sented in Table1 . By keeping these properties in mind, we described a representation that preserves relevant aspects of 2D and 3D information to allow for switching between the dimensions according to the actual requirements.... ..."
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