Klaus Broelemann

SketchMapia: Qualitative Representations for the Alignment of Sketch and Metric Maps

Abstract: More and more private citizens collect and publish environmental data via web-based geographic information systems. These systems face two challenges: The user interface must be intuitive and the processing of geographic information must account for cognitive impact. We propose to use sketch maps as the medium for interaction, because they reflect a persons spatial knowledge. Information from sketch maps is distorted, schematized, incomplete, and generalized and metric maps are not. This article employs qualitative representations for the alignment of sketch and metric maps. We suggest a set of cognitively oriented aspects in sketch maps stably computed by people and evaluate qualitative representations to formalize these aspects. This allows us to align and integrate geographic information from sketch maps.

A region-based method for sketch map segmentation

Sketch maps are an intuitive way to display and communicate geographic data and an automatic processing is of great benefit for human-computer interaction. This paper presents a method for segmentation of sketch map objects as part of the sketch map understanding process. We use region-based segmentation that is robust to gaps in the drawing and can even handle open-ended streets. To evaluate this approach, we manually generated a ground truth for 20 maps and conducted a preliminary quantitative performance study.

Automatic street graph construction in sketch maps

In this paper we present an algorithm for automatic street graph construction of hand-drawn sketch maps. This detection is important for a subsequent graph matching in order to align the sketch map with another map. Our algorithm detects a number of street candidates and selects street lines by rating the candidates and their neighbors in the street candidate graph. To evaluate this approach, we manually generated a ground truth for some maps and conducted a preliminary quantitative performance study.

Graph-based markerless registration of city maps using geometric hashing

Recently, augmenting paper maps with additional dynamic information on mobile devices has become popular. A central task in this context is to register high-resolution paper maps to digital maps on a mobile device, which was typically performed by means of RFID tags or visual markers on specially prepared paper maps. In this paper we present a novel graph-based approach for a markerless registration of city maps. The goal is to find the best registration between a given image, which shows a small part of a city map, and stored map data. The proposed method creates a graph representation of a given input image and robustly finds an optimal registration using a geometric hashing technique. It is translation, scale and rotation invariant, and robust against noise and missing data. Experiments on both synthetic and real data are presented to demonstrate the algorithmic performance.

Hierarchical graph representation for symbol spotting in graphical document images

Symbol spotting can be defined as locating given query symbol in a large collection of graphical documents. In this paper we present a hierarchical graph representation for symbols. This representation allows graph matching methods to deal with low-level vectorization errors and, thus, to perform a robust symbol spotting. To show the potential of this approach, we conduct an experiment with the SESYD dataset.

Automatic understanding of sketch maps using context-aware classification

We present the first comprehensive system for offline classifying sketch map objects.We created a database of labeled sketch maps for training and evaluation purposes.Context-awareness improves the classification of sketch map objects greatly. Sketching is a natural and easy way for humans to express visual information in everyday life. Despite a number of approaches to understand online sketch maps, the automatic understanding of offline, hand-drawn sketch maps still poses a problem. This paper presents a new approach for novel sketch map understanding. To our knowledge, this is the first comprehensive work dealing with this task in an offline way. This paper presents a system for automatic understanding of sketch maps and the underlying algorithms for all steps. Major parts are a region-growing segmentation for sketch map objects, a classification for isolated objects, and a context-aware classification. The context-aware classification uses probabilistic relaxation labeling to integrate dependencies between objects into the recognition. We show how these algorithms can deal with the major problems of sketch map understanding, such as vagueness in interpretation. Our experiments demonstrate the importance of context-aware classification for sketch map understanding. In addition, a new database of annotated sketch maps was developed and is made publicly available. This can be used for training and evaluation of sketch map understanding algorithms.

Hierarchical Plausibility-Graphs for Symbol Spotting in Graphical Documents

Graph representation of graphical documents often suffers from noise such as spurious nodes and edges, and their discontinuity. In general these errors occur during the low-level image processing viz. binarization, skeletonization, vectorization etc. Hierarchical graph representation is a nice and efficient way to solve this kind of problem by hierarchically merging node-node and node-edge depending on the distance. But the creation of hierarchical graph representing the graphical information often uses hard thresholds on the distance to create the hierarchical nodes (next state) of the lower nodes (or states) of a graph. As a result, the representation often loses useful information. This paper introduces plausibilities to the nodes of hierarchical graph as a function of distance and proposes a modified algorithm for matching subgraphs of the hierarchical graphs. The plausibility-annotated nodes help to improve the performance of the matching algorithm on two hierarchical structures. To show the potential of this approach, we conduct an experiment with the SESYD dataset.

Variants of k-regular nearest neighbor graph and their construction

Abstract Neighborhood graph and its construction play an important role in numerous problems. Many methods have been proposed to improve the classic k-NN construction based on various criteria. Recently, a new structure named k-regular nearest neighbor (k-RNN) graph has been proposed not only to minimize the sum of edge weights (for keeping the creditable neighborhood relationships), but also to require the node degree to be approximately k . However, the proposed graph construction algorithm is highly heuristic, which neither minimizes the cost (sum of edge weights) in an explicit manner nor formalizes the node degree requirement. In this work we formalize the essential requirements behind k-RNN. Several variants of this formalization will be studied, all being formulated as problems of energy function minimization with efficient greedy solutions. An experimental evaluation is also presented to demonstrate the effectiveness of our methods and superior performance compared to previous works.

Graph-Based Registration of Partial Images of City Maps Using Geometric Hashing

In this paper, we present a novel graph-based approach for the registration of city maps. The goal is to find the best registration between a given image, which shows a small part of a city map, and stored map data. Such registration is important in fields like mobile computing for augmentation purposes. Until now, RFID tags, markers, or regular dot grids on specially prepared maps are typically required. In this paper we propose a graph-based method to avoid the need of special maps. It creates a graph representation of a given input image and robustly finds an optimal registration using a geometric hashing technique. Our approach is translation, scale and rotation invariant, map type independent and robust against noise and missing data.