The Pattern Recognition Application Programmer's Interface aims to be a fully-featured, easy-to-use general C++ framework for various pattern recognition tasks, especially image analysis. It features support for many image formats, well-known image analysis methods, classification and feature analysis tools, XML serialization, etc.
Stochastic discrimination is a general methodology for constructing classifiers appropriate for pattern recognition. It is based on combining arbitrary numbers of very weak components, which are usually generated by some pseudorandom process, and it has the property that the very complex and accurate classifiers produced in this way retain the ability, characteristic of their weak component pieces, to generalize to new data as complexity increases. These utilities provide an implementation of this algorithm.
SIP provides image processing, pattern recognition, and computer vision routines for SciLab, a Matlab-like matrix-oriented programming environment. SIP is able to read/write images in almost 90 major formats, including JPEG, PNG, BMP, GIF, FITS, and TIFF. It includes routines for filtering, segmentation, edge detection, morphology, curvature, fractal dimension, distance transforms, multiscale skeletons, and more.
Allegro Common Lisp is a full ANSI Common Lisp (1994) implementation. It contains many extensions, including 32- and 64-bit native compilation, efficient built-in memory management, foreign functions (for interfacing with other languages), multiprocessing, UNICODE and locale support, XML/HTML parsers, a Web client and server, GTK+ interface (1.2 and 2.0), Java interface, OLE interface (Windows only), profiler, regular expressions, an XML RPC implementation, native Lisp RPC, sockets, DLL and shared library support, and more.
ZOE (formerly OGLE) is a simple OpenGL graphics engine written entirely in Python. Its primary focus is rapid prototyping and experimentation, so it only supports the barest essentials, with focus on wire frames. Special emphasis is placed on particle systems (in which non-interacting particles follow simple rules). Some familiarity with OpenGL is expected, although when exploiting the particle system abstractions, no specific OpenGL knowledge is required. The demos included are the obligatory spinning polyhedra, static views of conic sections and the Solar System, a 3D surface plotter, a fountain of sparks, a swarming behavior model, a random walk example, a whirlpool effect using gravity and drag, and an example of chaos theory and sensitivity to initial conditions.
RISO is an implementation of heterogeneous, distributed belief networks in Java. A belief network is a probability model defined on an acyclic directed graph; distributed means nodes can be on different hosts, and heterogeneous means allowing different conditional distributions. The calculations involved are multidimensional integrations; exact results are known for a catalog of special cases. If a partial result cannot be calculated as a special case from the catalog, RISO computes an approximate result by numerical integration. Partial results are passed from one node in the graph to another as messages; if nodes live on different hosts, the belief network is said to be distributed. Messages are passed via RMI. Many example belief networks and lengthy documents are included in the RISO release bundle.