ALPAGE: Reverse engineering dedicated to ancient color maps

It is difficult for an observer to recognize an object if he does not have a mental representation of it a priori [Neisser 1976], [Neisser 1989]. The problem is similar for an artificial interpretation system, and it is necessary to integrate this notion of a model if the aim is to obtain a representation close to the one processed by the cadastral agent. In order to carry out this "modeling", we start from the prin-ciple that the whole of the graphic document relates to a specific organization of all the graphical primitives (homogeneous color regions, strokes, lines, etc.) and to the grouping rules for these graphical entities for object representation. The main difficulty with such an interpretation methodology is the administration of seman-tic links between objects (borders between colored zones, limits between objects, overlapping objects, etc.). With regard to the cadastral maps, this physical-logical link is relatively simple, since the main relations between the different entities are neighborhood or containment relations. From the following representation of doc-ument entities (graphical and logical points of view) and the relationships within it, the model first proposes a hierarchical structure for the document. The four levels for city maps are illustrated in figure2.2. The difficulty to correctly interpret a document comes from noise and/or perturbations (pigment fading, storage-due degradations, ...) which corrupt the document image. Hence, images of documents are no longer conform to their model and generating rules are not valid anymore.

This phenomenon is showed in figure 2.3. The direct consequence of the degrada-tions is a misleading computer-generated model, however our approach provides the opportunity to evaluate this ambiguity at a logical level and so, to find out how different is the computer model from the original model of document. This model comparison provides an external or global consistency to verify the compliance of objects generated by low level algorithms. The knowledge checking is carried out through a graph matching viewpoint to take into account spatial relationships be-tween objects.

As discussed in section 2.3, the cadastral map is an association of graphical

Figure 2.2: Physical and logical structures.

primitives which build a cadastral object. For the cadastral agent, these objects are parcels, streets, frame, and quarters, which are made up of lines, characters, homogeneous color regions (areas having a similar hue), symbols, etc. As can be seen in the figure 2.2, we considered four kinds of objects in relation to the French ancient city map: the parcel (of land), the street, the quarter (or district), and the section. Our bottom-up strategy consists of three stages to interpret each cadastral object. Thus, the architecture of our system is illustrated in figure2.4. In addition, the three stages for parcel extraction from a cadastral map are put forward as follows:

1. Pre-processing

(a) Color restoration

(b) Color space and color representation

Most display and color acquisition devices, such as digital cameras and scanners, have their input or output signals in the Red Green Blue (RGB) format. It is straightforwardly derived from sensor technology, the R (re-spectively G, B) primary in RGB corresponds to the amount of the phys-ical reflected light in the red band (respectively green and blue bands).

2.4. ALPAGE: Reverse engineering dedicated to ancient color maps 21

Figure 2.3: Ideal and real documents.

Figure 2.4: An adaptation of the bottom-up strategy to the ALPAGE’s context.

This is why RGB space is widely used in the applications of image pro-cessing. However, it may not be always the most appropriate color space for computer vision algorithms. Consequently, we provide guide lines to find a suitable color model according to the kind of images we have.

(c) Color segmentation

Once the source image is transferred into a suitable hybrid color space, an edge detection algorithm is applied. This contour image is generated thanks to a vectorial gradient according to the following formalism. The gradient or multi-component gradient takes into account the vectorial nature of a given image considering its representation space (In our case a hybrid color space). The vectorial gradient is calculated from all com-ponents seeking direction for which variations are the highest. This is done through maximization of a distance criterion according to the L2 metric, characterizing the vectorial difference in a given color space. The approaches proposed by DiZenzo [Dizenzo 1986] first, and then by Lee and Cok under a different formalism are methods that determine multi-components contours by calculating a color gradient from the marginal gradients.

2. Processing

(a) Text/graphic separation

A contour image is obtained by binarization of the gradient image; this latter is issued from the color detection stage. The text/graphic segmen-tation is run on top of it, on every contour image to remove undesir-able objects: street names, street numbers, ... The mainstream of this text/graphic separation is a graph classification principle where objects to be classified are graphs extracted from the connected components of the contour image. In a learning stage, text and graphic diversities are taken into account by a prototype selection scheme for structural data, thereafter in a decision step, a standard nearest prototype classification rule is applied to categorized instances. Finally, all connected compo-nents labeled as "graphics" are included into a so called graphic image.

(b) Object detectors

Dedicated image processing algorithms are performed on the graphic im-age to locate domain objects. Thus, frame, streets, quarters and parcels are delineated by black pixels.

(c) Digital curve approximation: vectorization

Black pixels are vectorized using a polygonal approximation based on a genetic algorithm. In this method, the optimization/exploration algorithm locates breakpoints on the digital curve by minimizing simultaneously the number of breakpoints and the approximation error. Using such an approach, the algorithm proposes a set of so-lutions at its end. This set which is called the Pareto Front in the

2.5. Discussion 23

multi objective optimization field contains solutions that represent trade-offs between the two classical quality criteria of polygonal ap-proximation: the Integral Square Error (ISE) and the number of vertices.

(d) Polygonizer. From lines to polygons.

Detecting polygons defined by a set of line segments in a plane is an easy step in the analysis of vectorial drawings. To perform polygon detection from a set of line segments we divide this task in four major steps. First we detect line segment intersections. Next step creates a graph induced by the drawing. The third step finds the Minimum Cycle Basis (MCB) of the graph induced in previous step. Last step constructs a set of polygons based on cycles in the previously found MCB.

3. Post-processing (a) Correcting rules.

Polygons which are too small to be a parcel of land are merged according to their color properties.

(b) Reliability measure.

Once the vectorization is finished, we produce a graph that contains concepts and relation between them. Concepts are objects recognized in the prior operations (i.e. Text components, quarters, parcels, ...). This model instance is compared to a higher representation (a meta-model) of cadastral map thanks to a graph matching algorithm. This Model Driven Engineering angle is a well-suited candidate for image conversion by providing a common framework for representing graph structures as models conforms to meta-models elaborated by expert of the domain.

The overall method provides a distance which indicates the confidence in the raster to vector conversion quality.

2.5 Discussion

The problem of automatic raster map digitization has been discussed. We conjec-ture that the most promising line of progress toward its solution lies in successively integrating knowledge into the system. Raster to vector process requires a human judgment to control the quality of the vectorization and to adjust algorithm param-eters. This judgment can be considered as a semantic analysis. Introducing this analysis into a vectorization process must considerably improve the results. How-ever, this integration is not a trivial task. The representation by graph formalism is a powerful tool since graphs can represent different points of view of a given image, from the region layout to knowledge configuration. Estimating the quality of the vectorization thanks to a model checking approach is one of our contribution in this domain. Thus our approach could be combined with interactive approaches.

Finally, the next section will focus on pre-processing, low level tools to unleash and extract the basic constructs of our system.

Chapter 3