Realistic compilation by program transformation (detailed summary)
POPL '89 Proceedings of the 16th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Elements of ML programming
Two-dimensional imaging
Elk: the Extension Language Kit
Computing Systems
A tractable Scheme implementation
Lisp and Symbolic Computation
Revised report on the algorithmic language scheme
ACM SIGPLAN Lisp Pointers
Bigloo: A Portable and Optimizing Compiler for Strict Functional Languages
SAS '95 Proceedings of the Second International Symposium on Static Analysis
Verischemelog: Verilog embedded in Scheme
Proceedings of the 2nd conference on Domain-specific languages
Hancock: a language for processing very large-scale data
Proceedings of the 2nd conference on Domain-specific languages
Verischemelog: Verilog embedded in scheme
DSL'99 Proceedings of the 2nd conference on Conference on Domain-Specific Languages - Volume 2
Hancock: a language for processing very large-scale data
DSL'99 Proceedings of the 2nd conference on Conference on Domain-Specific Languages - Volume 2
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Computer vision (image understanding) algorithms are difficult to write, debug, maintain, and share. This complicates collaboration, teaching, and replication of research results. This paper shows how user-level code can be simplified by providing better programming language constructs, particularly a new abstract data type called a "sheet." These primitives have been implemented as an extension to Scheme. Implementation of sheet operations is made challenging by the fact that images are extremely large, e.g. sometimes over 5 megabytes each. Therefore, operations that loop through images must be compiled from (a specialized subset of) Scheme into C. This paper discusses how the need for extreme efficiency affects the design of the user-level language, the run-time support, and the compiler.