A system for automatic data acquisition from topographic maps (PROMAP-Processing of Maps) is presented. Maps are an important source of information for efficient spatial data evaluation using Geographic Information Systems (GIS). At present a lot of relevant maps have still to be digitized manually, which is a time-consuming and error-prone process. To improve the situation, the authors developed the PROMAP-system which incorporates adequate image analyzing methods. The system is capable of generating a symbolic description of the map contents that may be imported into a GIS (e.g. ARC/INFO).>

A new design procedure for shape-gain vector quantizers (SGVQs) which leads to substantially improved robustness against channel errors without increasing the computational complexity is proposed. This aim is achieved by including the channel transition probabilities in the design procedure, leading to an improved assignment of binary codewords to the coding regions as well as a change of partition and centroids. In contrast to conventional design, negative gain values are also permitted. The new design procedure is applied to adaptive transform image coding. Simulation results are compared with those obtained by the conventional design procedure. The new algorithm is particularly useful for heavily distorted or fading channels.>

We describe a possibility to reconstruct 3-D information from a single view of an 3-D bilateral symmetric object. The symmetry assumption allows us to obtain a “second view” from a different viewpoint by a simple reflection of the monocular image. Therefore, we have to solve the correspondence problem in a special case, where known feature-based or area-based binocular approaches fail. In principle, our approach is based on a frequency domain template matching of the features on the epipolar lines. During a training period, our system “learns” the assignment of correspondence models to image features. The object shape is interpolated, when no template matches to the image features. This fact is an important advantage of this methodology, because no “real world” image holds the symmetry assumption perfectly. To simplify the training process, we used single views on human faces (e.g. passport photos), but our system is trainable on any other kind of objects.

A heuristic method to obtain near-minimal covers of p-valued switching functions is introduced. First, we describe transform tools useful in the processing of MVL functions. They are: p-adic shifting, weighting, and implicant detecting transformations. Based on these tools, a direct cover algorithm is presented that uses local information for heuristic decision making. The heuristics are taken from weight coefficients calculated for canonical terms and implicants. The method allows to assign cost factors to implicants. Further, the algorithms can be modified easily, so as to correspond to various connectives (e.g., MAX, PLUS).

The elements of an array of 2n data are indexed so that adjacent elements have Hamming distance 1. Based on this indexing a class of invertible fast in-place transformations is developed. The transform coefficients depend on the Hamming distance between the indexes of input data. A class of similar transformations exists in the Walsh-Hadamard domain. Three transform operations are discussed in detail: dyadic shift transform, a subsuming and averaging transformation and a weighting operation. Applications of the method are in the processing of Boolean and fuzzy switching functions, image analysis, etc.

The role of dither threshold matrices has not been paid much attention in a recent paper [1]: These comments highlight the useful features of the so-called Nasik pattern as a threshold matrix. It is shown that the total number of Nasik type matrices is 384. The properties of Nasik patterns are invariant to dyadic shifts. Hence, random selection of matrices and adaptive selection schemes can be devised, resulting in a minimization of texture and good rendition of details.