Abstract: A method for validating input received from an XML based form using reusable XPath validation expressions. The method can include a step of identifying a reusable XPath validation expression including at least one substitution marker. The substitution marker can be mapped to one or more values specific to an XML instance. At least one field to be validated with the reusable XPath validation expression can be designated utilizing the development tool. The development tool can automatically replace the substitution marker with the appropriate value specific to an XML instance.

Abstract: A system, method, and computer program for the creation and implementation of configurable Java classes on a computer platform executing instructions based upon at least a set of Java language instructions. The configurable Java class is created as an instance of a metaclass object included within the set of Java language instructions, and the configurable metaclass object includes a plurality of subclasses and interfaces wherein each interface includes one or more methods to alter the attributes and methods of the Java class instance of the metaclass object. The metaclass object is preferably implemented as an Enterprise JavaBean.

Abstract: A system, method, and computer program for the creation and implementation of configurable Java classes on a computer platform executing instructions based upon at least a set of Java language instructions. The configurable Java class is created as an instance of a metaclass object included within the set of Java language instructions, and the configurable metaclass object includes a plurality of subclasses and interfaces wherein each interface includes one or more methods to alter the attributes and methods of the Java class instance of the metaclass object. The metaclass object is preferably implemented as an Enterprise JavaBean.

Abstract: An estimate of a threshold value for feature extraction from image data containing noise and quantized into a plurality of quantization levels is made by filtering, such as recursively averaging n adjacent quantization levels and counting the extrema of the result. The recursive averaging is repeated with increasing values of n until only three extrema are present in the filtered data. The estimate of the threshold value is refined by comparing averages or summations of n quantization levels above and below the estimate and altering the estimated threshold value based on a comparison of the averages or summations. By basing both the estimation and refinement processes on the original data and using a value of n which is smaller in the refinement process than in the estimation process, a highly effective threshold value for the feature extraction process is obtained. Adaptive adjustment of n during the estimation process minimizes loss of data as well as computing time.