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Hierarchical Exploration of Volumes Using Multilevel Segmentation of the Intensity-Gradient Histograms Cheuk Yiu IpAmitabh VarshneyJoseph JaJa Slide 2 Volume Exploration Challenge Raw Volume Data Cube Meaningful Visualization [Voreen CG&A 09] Slide 3 Transfer Function Evolution How do we find the right transfer function? RGBA Intensity Slide 4 Histogram Helps Transfer Function Design [Drebin et al. SIGGRAPH 88] Slide 5 Histogram Helps Transfer Function Design [Drebin et al. SIGGRAPH 88] Slide 6 Histogram Helps Transfer Function Design [Drebin et al. SIGGRAPH 88] Slide 7 Histogram Helps Transfer Function Design Slide 8 Histograms May Not Always Help Slide 9 Volume Exploration Exhaustively explore the dataset Slide 10 Volume Exploration Seek for salient features Slide 11 Volume Exploration Seek for salient features Slide 12 Volume Exploration Seek for salient features Slide 13 Volume Exploration Feature locations can be arbitrary Slide 14 2D Transfer Functions Gradient ( f(x) ) captures boundaries Histogram shapes Volume segments Intensit y Gradient [Kindlmann & Durkin VolVis98, Kniss et al. TVCG 02] Slide 15 Advances on New Attributes Higher Derivatives: f(x) [Kindlmann & Durkin VolVis98] Specific features: Size [Correa and Ma TVCG 08] LH-transform [Sereda et al. TVCG 06], Domain specific semantic attributes [Salama et al. TVCG 06] Select good views: Visibility [Correa and Ma TVCG 10], Information divergence [Ruiz et al. TVCG 11] Slide 16 Challenges of the 2D Transfer Function Search for separated meaningful features 1 Region 1 Minute Slide 17 Histogram and Volume Features Slide 18 Skin Slide 19 Histogram and Volume Features Flesh Slide 20 Histogram and Volume Features Skull Slide 21 Histogram and Volume Features Sinus Slide 22 Histogram and Volume Features Teeth Slide 23 Approximate Histogram Transfer Functions [Wang et al. TVCG 2011] Existing approaches directly or indirectly fit the histogram User Specified [Kniss et al. TVCG 02, Fogal et al. 2010] Slide 24 Reduce Search to Classification Recursive histogram classification Tight coverage with a few segments Exhaustive exploration Slide 25 Overview 1.Segment the histogram statistics 2.Build an exhaustive multilevel hierarchy 3.User interactive exploration Slide 26 Overview Visual segmentation matches user intuition Complete coverage Users stay in a familiar feature space Slide 27 Intensity- gradient Histogram Users implicitly recognize shapes from the histogram Segment this histogram as an image Intensity Gradient Slide 28 Normalized-cut Image Segmentation Normalized-cut (ncut) image segmentation [Shi & Malik PAMI 98] Min ncut produces balanced segments Eigenanalysis approximates the min ncut for k segments B [Wang et al. PATTERN RECOGN LETT 06] Slide 29 Normalized-cut on Intensity-gradient Histogram We apply normalized-cut on 256 2 8-bit histograms k=2 separates the tooth from the volume box k=10 shows segments of the tooth crown and root k=20 shows different material boundaries Slide 30 Which k should we pick ? Iteratively picking k is tedious Increasing k may not subdivide region of user interest Try k = 2, 3, 4, Slide 31 Replace k with User-driven Exploration Multilevel Segmentation Hierarchy: Apply normalized-cut recursively Slide 32 Multilevel Segmentation Hierarchy Selectively inspect segments of choice Slide 33 Multilevel Segmentation Hierarchy [Xia & Varshney Vis 96, Hoppe SIGGRAPH 97, Luebke & Erikson SIGGRAPH 97] Any cut guarantees complete coverage View-dependent LoD hierarchies Slide 34 Multilevel Segmentation Hierarchy Slide 35 Information Guided Traversal Segment entropy High entropy Complex segment Entropy 2.8 6.8 Slide 36 What if the Entropies are Similar The segment entropies can be similar Which segment should we divide next? Use Information Gain Entropy Slide 37 Information-Gain Guided Traversal Information Gain = Entropy reduction after a subdivision High Information Gain Structural separation Information Gain 0.11 0.01 Slide 38 Interactive Exploration Explore the segmentation hierarchy Selective expansion Interactive visualizations Exhaustively explore the tooth in 1 minute Slide 39 Examples Engine blockVisible Human Male Head Slide 40 Examples TomatoHurricane Isabel Slide 41 Conclusions Computational segmentation mimics user interactions Intuitive volumetric classification Exhaustive multilevel hierarchy Information guided traversal Interactive exploration Slide 42 Future Work Improve the information content measures Automatic color assignment for segments Segment histograms with different attributes Time varying datasets Slide 43 Acknowledgements National Science Foundation: CCF 05-41120, CMMI 08-35572, CNS 09-59979 NVIDIA CUDA Center of Excellence Program Derek Juba, Sujal Bista, Yang Yang, M. Adil Yalcin, and the reviewers for improving this paper and presentation The SciVis Best Paper Award committee Thank you! Slide 44 Questions ? Source code for building the hierarchy: www.cs.umd.edu/~ipcy/software/volsegtree/ Papers and videos: Cheuk Yiu Ip www.cs.umd.edu/~ipcy/ GVIL Research Highlights www.cs.umd.edu/gvil/ Slide 45