A COMPARISON AND EVALUATION OF MOTION INDEXING ...

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Figure 3.2: Process of Indexing Document. for similarity between some parts of a motion clip. Figure 3.3 shows the process of finding exact hits. The technique described here is based on an exact hit and tries to find the exact match of a motion. The two motion clips are considered similar with respect to the selected feature function if they exhibit the same feature sequence. Mathematically, if w = (w0, w1, · · · , wM) and v = (v0, v1, · · · , vN) are the feature sequences of the database and the query, respectively, then an exact hit is an element k ∈ [0, M] such that v is a subsequence of consecutive feature vectors in w starting from index k. This is denoted with the expression vkw. The set of all exact hits in the database Γ is defined as: HΓ(v) := {k ∈ [0, M] |vkw}. In terms of inverted lists, which are shifted and intersected, the set of all exact hits can be expressed as: HΓ(v) = n∈[0:N] 36 (L(vn) − n) . (3.1) The input lists L(vn) are additively adjusted by −n. The more efficient way is to adjust the lists appearing as intermediate results by +1 prior to each intersection step. After the final iteration, an adjustment of the resulting set by −(N + 1) yields
the same set of exact hits. Figure 3.3: Process of computing Exact Hits. The iterative algorithm to compute exact hits has the following inputs: an admissible fuzzy query, v, and an inverted file index I Γ F for 2f inverted lists L (υ) , υ ∈ {0, 1} f . The output of the algorithm is a list of exact hits which is denoted by HΓ(v). The algorithm starts by incrementing the first inverted list denoted by L(v0) and assign it to L 0 := L(v0) + 1. After that, for all n = 0, · · · , N − 1, L n+1 is computed iteratively by intersecting L n and L(vn+1) and then incrementing it by 1: L n+1 := (L n ∩ L(vn + 1)) + 1, where n = 0, · · · , N − 1. Once L N is computed, N + 1 is decremented to adjust the inverted lists and this adjusted lists are the exact hits: HΓ(v) = L N − (N + 1). 37
Page 1 and 2: A COMPARISON AND EVALUATION OF MOTI
Page 3 and 4: To my mom, dad, brother, friends an
Page 5 and 6: ABSTRACT A COMPARISON AND EVALUATIO
Page 7 and 8: TABLE OF CONTENTS ACKNOWLEDGEMENTS
Page 9 and 10: LIST OF FIGURES Figure Page 1.1 Ske
Page 11 and 12: CHAPTER 1 INTRODUCTION Animation is
Page 13 and 14: • Segmentation: The motion captur
Page 15 and 16: In several applications, like a mod
Page 17 and 18: provide fast interpolation but do n
Page 19 and 20: • Frame: Each slice of time of a
Page 21 and 22: Figure 1.1: Skeleton with bone name
Page 23 and 24: Figure 1.4: Snapshot of a sample AM
Page 25 and 26: CHAPTER 2 PCA-BASED TECHNIQUES In t
Page 27 and 28: The first step in a PCA analysis is
Page 29 and 30: of dimensions needed to represent t
Page 31 and 32: The PCA-SVD algorithm finds the val
Page 33 and 34: where Vr is a matrix with the first
Page 35 and 36: 2. Similarly combine the two segmen
Page 37 and 38: joints are known for every pose. He
Page 39 and 40: motion. Figure 3.1: Result of boole
Page 41 and 42: 2. Feature functions describing an
Page 43 and 44: F -equivalent if the corresponding
Page 45: 3.1.6.1 Inverted File Index Inverte
Page 49 and 50: CHAPTER 4 DYNAMIC TIME WARPING TECH
Page 51 and 52: Figure 4.1: Result of DTW technique
Page 53 and 54: operator \ to determine the relatio
Page 55 and 56: zmin = ¯z + ¯x ′ · sin(θmin)
Page 57 and 58: CHAPTER 5 EXPERIMENTS AND RESULTS W
Page 59 and 60: 5.2 Indexing Accuracy Testing To ev
Page 61 and 62: Figure 5.3: False positives for PCA
Page 63 and 64: Figure 5.5: False positives for PPC
Page 65 and 66: Figure 5.7: False positives for Fea
Page 67 and 68: Figure 5.9: False positives for DTW
Page 69 and 70: Figure 5.11: False positives for DT
Page 71 and 72: Figure 5.13: False Negatives detect
Page 73 and 74: DTW[18]. DTW matching can also be i
Page 75 and 76: Animation techniques can be broadly
Page 77 and 78: directly modified or manipulated by
Page 79 and 80: B.1 Breif History Human beings are
Page 81 and 82: B.2 Motion Capture Technologies 1.
Page 83 and 84: (b) Active markers: Markers have on
Page 85 and 86: • Capture of movement: Subject we
Page 87 and 88: animation abilities, it can be help
Page 89 and 90: 74 09 127 23 138 19 141 14 75 19 79
Page 91 and 92: 10 04 walk with hands moving more 0
Page 93 and 94: 118 05 jump 118 06 jump 118 07 jump
Page 95 and 96: 143 25 waiving 143 08 jumping twist
Page 97 and 98:
[9] A. J. Hanson, “Visualizing qu
Page 99:
BIOGRAPHICAL STATEMENT Harnish Bhat
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