Yizi Chen

In

Abstract Maps have been a unique source of knowledge for centuries. Such historical documents provide invaluable information for analyzing complex spatial transformations over important time frames. This is particularly true for urban areas that encompass multiple interleaved research domains: humanities, social sciences, etc. The large amount and significant diversity of map sources call for automatic image processing techniques in order to extract the relevant objects as vector features. The complexity of maps (text, noise, digitization artifacts, etc.

Introducing the boundary-aware loss for deep image segmentation

By Minh Ôn Vũ Ngọc, Yizi Chen, Nicolas Boutry, Joseph Chazalon, Edwin Carlinet, Jonathan Fabrizio, Clément Mallet, Thierry Géraud

2021-11-28

In Proceedings of the 32nd british machine vision conference (BMVC)

Abstract Most contemporary supervised image segmentation methods do not preserve the initial topology of the given input (like the closeness of the contours). One can generally remark that edge points have been inserted or removed when the binary prediction and the ground truth are compared. This can be critical when accurate localization of multiple interconnected objects is required. In this paper, we present a new loss function, called, Boundary-Aware loss (BALoss), based on the Minimum Barrier Distance (MBD) cut algorithm.

ICDAR 2021 competition on historical map segmentation

By Joseph Chazalon, Edwin Carlinet, Yizi Chen, Julien Perret, Bertrand Duménieu, Clément Mallet, Thierry Géraud, Vincent Nguyen, Nam Nguyen, Josef Baloun, Ladislav Lenc, Pavel Král

2021-05-17

In Proceedings of the 16th international conference on document analysis and recognition (ICDAR’21)

Abstract This paper presents the final results of the ICDAR 2021 Competition on Historical Map Segmentation (MapSeg), encouraging research on a series of historical atlases of Paris, France, drawn at 1/5000 scale between 1894 and 1937. The competition featured three tasks, awarded separately. Task 1 consists in detecting building blocks and was won by the L3IRIS team using a DenseNet-121 network trained in a weakly supervised fashion. This task is evaluated on 3 large images containing hundreds of shapes to detect.

Vectorization of historical maps using deep edge filtering and closed shape extraction

By Yizi Chen, Edwin Carlinet, Joseph Chazalon, Clément Mallet, Bertrand Duménieu, Julien Perret

2021-05-17

In Proceedings of the 16th international conference on document analysis and recognition (ICDAR’21)

Abstract Maps have been a unique source of knowledge for centuries. Such historical documents provide invaluable information for analyzing the complex spatial transformation of landscapes over important time frames. This is particularly true for urban areas that encompass multiple interleaved research domains (social sciences, economy, etc.). The large amount and significant diversity of map sources call for automatic image processing techniques in order to extract the relevant objects under a vectorial shape.

Combining deep learning and mathematical morphology for historical map segmentation

By Yizi Chen, Edwin Carlinet, Joseph Chazalon, Clément Mallet, Bertrand Duménieu, Julien Perret

2021-02-16

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocks, gardens, rivers, etc.

Modern vectorization and alignement of historical maps: An application to paris atlas (1789-1950)

Introducing the boundary-aware loss for deep image segmentation

ICDAR 2021 competition on historical map segmentation

Vectorization of historical maps using deep edge filtering and closed shape extraction

Combining deep learning and mathematical morphology for historical map segmentation

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