Showing papers on "Feature hashing published in 1990"

Model-based object recognition by geometric hashing

Abstract: The Geometric Hashing paradigm for model-based recognition of objects in cluttered scenes is discussed. This paradigm enables a unified approach to rigid object recognition under different viewing transformation assumptions both for 2-D and 3-D objects obtained by different sensors, e.g. vision, range, tactile. It is based on an intensive off-line model preprocessing (learning) stage, where model information is indexed into a hash-table using minimal, transformation invariant features. This enables the on-line recognition algorithm to be particularly efficient. The algorithm is straightforwardly parallelizable. Initial experimentation of the technique has led to successful recognition of both 2-D and 3-D objects in cluttered scenes from an arbitrary viewpoint. We, also, compare the Geometric Hashing with the Hough Transform and the alignment techniques. Extensions of the basic paradigm which reduce its worst case recognition complexity are discussed.

Random sampling from hash files

Abstract: In this paper we discuss simple random sampling from hash files on secondary storage. We consider both iterative and batch sampling algorithms from both static and dynamic hashing methods. The static methods considered are open addressing hash files and hash files with separate overflow chains. The dynamic hashing methods considered are Linear Hash files [Lit80] and Extendible Hash files [FNPS79]. We give the cost of sampling in terms of the cost of successfully searching a hash file and show how to exploit features of the dynamic hashing methods to improve sampling efficiency.

Selecting a hashing algorithm

Abstract: Hashing is so commonly used in computing that one might expect hash functions to be well understood, and that choosing a suitable function should not be difficult. The results of investigations into the performance of some widely used hashing algorithms are presented and it is shown that some of these algorithms are far from optimal. Recommendations are made for choosing a hashing algorithm and measuring its performance.

A letter-oriented perfect hashing scheme based upon sparse table compression

Abstract: In this paper, a new letter-oriented perfect hashing scheme based on Ziegler's row displacement method is presented. A unique n-tuple from a given set of static letter-oriented key words can be extracted by a heuristic algorithm. Then the extracted distinct n-tuples are associated with a 0/1 sparse matrix. Using a sparse matrix compression technique, a perfect hashing function on the key words is then constructed.

Dynamic Hashing Strategies

Abstract: This survey paper describes new types of dynamic hashing strategies for implementing dictionaries on sequential, parallel and distributed computation models. In particular, it surveys the progress in constructing and analyzing new classes of universal hash functions.

Statistical behavior of fast hashing of variable length test strings

Abstract: In information retrieval, we often have to store and search for a particular record into a large amount of information. For example, during a document indexing process or when a program is trying to spell a text, a dictionary has to be used in an efficient way. A solution to that problem resides in using a hash table. However, if we known many algorithms for manipulating or accessing hash tables [Knuth 73], [Standish 80], [Wiederhold 87], the main problem is to define a "good" hash function for a variable-length string. In order to answer that question our main goals are to present some concrete algorithms and to study their statistical behavior.