• Publications
  • Influence
Shape distributions
TLDR
The dissimilarities between sampled distributions of simple shape functions provide a robust method for discriminating between classes of objects in a moderately sized database, despite the presence of arbitrary translations, rotations, scales, mirrors, tessellations, simplifications, and model degeneracies. Expand
Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
TLDR
A new low-distortion embedding of l<sub>2</sub><sup>d</sup> into l p (p=1,2) is introduced, called the Fast-Johnson-Linden-strauss-Transform (FJLT), based upon the preconditioning of a sparse projection matrix with a randomized Fourier transform. Expand
Matching 3D models with shape distributions
TLDR
The primary motivation for this approach is to reduce the shape matching problem to the comparison of probability distributions, which is simpler than traditional shape matching methods that require pose registration, feature correspondence or model fitting. Expand
A Functional Approach to Data Structures and Its Use in Multidimensional Searching
  • B. Chazelle
  • Mathematics, Computer Science
  • SIAM J. Comput.
  • 1 June 1988
TLDR
These results include, in particular, linear-size data structures for range and rectangle counting in two dimensions with logarithmic query time and a redefinition of data structures in terms of functional specifications. Expand
The Fast Johnson--Lindenstrauss Transform and Approximate Nearest Neighbors
TLDR
A new low-distortion embedding of $\ell-2^d$ into $\ell_p^{O(\log n)}$ ($p=1,2$) called the fast Johnson-Lindenstrauss transform (FJLT) is introduced, based upon the preconditioning of a sparse projection matrix with a randomized Fourier transform. Expand
The Bottomn-Left Bin-Packing Heuristic: An Efficient Implementation
  • B. Chazelle
  • Mathematics, Computer Science
  • IEEE Transactions on Computers
  • 1 August 1983
TLDR
This paper presents an implementation of the bottom-left heuristic for two-dimensional bin-packing which requires linear space and quadratic time, and believes that even for relatively small values of N, it gives the most efficient implementation of this heuristic, to date. Expand
Triangulating a simple polygon in linear time
  • B. Chazelle
  • Mathematics, Computer Science
  • Discret. Comput. Geom.
  • 1 December 1991
TLDR
A deterministic algorithm for triangulating a simple polygon in linear time is given, using the polygon-cutting theorem and the planar separator theorem, whose role is essential in the discovery of new diagonals. Expand
Cutting hyperplanes for divide-and-conquer
  • B. Chazelle
  • Mathematics, Computer Science
  • Discret. Comput. Geom.
  • 1 February 1993
TLDR
A deterministic algorithm for computing a (1/r)-cutting ofO(rd) size inO(nrd−1) time is presented, based on a hierarchical construction of cuttings, which also provides a simple optimal data structure for locating a point in an arrangement of hyperplanes. Expand
The discrepancy method - randomness and complexity
This book tells the story of the discrepancy method in a few short independent vignettes. It is a varied tale which includes such topics as communication complexity, pseudo-randomness, rapidly mixingExpand
The Bloomier filter: an efficient data structure for static support lookup tables
TLDR
The Bloomier filter is introduced, a data structure for compactly encoding a function with static support in order to support approximate evaluation queries and lower bounds are provided to prove the (near) optimality of the constructions. Expand
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