New data structure and process model for automated watershed delineation

DEM analysis to delineate the stream network and its associated subwatersheds are the primary steps in the raster-based parameterization of watersheds. There are two widely used methods for delineating subwatersheds. One of these is the Upstream Catchment Area (UCA) method. The UCA method employs a user specified threshold value of upstream catchment area to delineate subwatersheds from the extracted network of streams. The other common technique is the nodal method. In this approach, subwatersheds are initiated at stream network nodes, where nodes occur at the upstream starting point of streams and at the point of intersection of streams in the network. The UCA approach and the Nodal approach do not permit watershed initiation at points of specific interests. They also fail to explicitly recognize drainage features other than single channel reaches. That is, they exclude water bodies, wetlands, braided channels and other important hydrologic features. TOPAZ (TOpographic PArameteriZation) [Garbrecht and Martz, 1992], is a typical program for raster based, automated drainage analysis. It initiates subwatersheds at source points and at points of intersection of drainage channels. TOPAZ treats lakes as spurious depressions arising out of errors in DEM, and removes them. This research addresses one important limitation of the currently used modeling techniques and tools. It adds the capability to initiate watershed delineation at points of specific interest other than junction and source points in the delineated networks from the Digital Elevation Models (DEMs). The research project evaluates qualitative and quantitative aspects of a new Object Oriented data structure and process model for raster format data analysis in spatial hydrology. The concept of incorporating a user-specified analysis in extraction and parameterization of watersheds is based on the need to have a tool to allow for studies specific to certain points in the stream network including gauging stations. It is also based on the need for an improved delineation of hydrologic features (water bodies) in hydrologic modeling. The research project developed an interface module for TOPAZ [Garbrecht and Martz, 1992] to offset the aforementioned disadvantages of the subwatershed delineation techniques. The research developed an internal hybrid, raster-based, Object Oriented data structure and processing model similar to that of vector data type. The new internal data structure permits further augmentation of the software tool. This internal data structure and algorithms provide an improved framework for discretization of the important hydrologic entities (water bodies) and the capability of extracting homogenous hydrological subwatersheds

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules