Object Detection in Floor Plan Images

Ziran, Zahra; Marinai, Simone

doi:10.1007/978-3-319-99978-4_30

Cited by 37 publications

(31 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CVC-FP [4] contains 90 high quality floor plan images with an uniform style and a wellreadable text. Flo2Plan is a subset of the dataset proposed in [17] and contains 64 floor plans downloaded from Internet with very different styles and resolution. Both datasets have been manually labeled to provide information about text objects.…”

Section: Resultsmentioning

confidence: 99%

Text Recognition and Classification in Floor Plan Images

Ravagli

Ziran

Marinai

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

Self Cite

View full text Add to dashboard Cite

In this paper we present a method for text recognition in floor plan images. In particular, we are concerned about locating, reading, and categorizing text inside floor plan images to obtain information about the building. Furthermore, the aim of this paper is to compare traditional text detection methods, based on image processing techniques, with recent approaches relying on convolutional neural networks. To improve results we combined several methods outperforming the original ones. Text regions are also classified in four semantic classes according to their purpose. Two datasets with different features, including quality and size, were considered in the experiments performed.

show abstract

Section: Resultsmentioning

confidence: 99%

Text Recognition and Classification in Floor Plan Images

Ravagli

Ziran

Marinai

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Most of the methods are based on CNN. Similarly, in [41], the use of deep neural networks for object detection in floor plan images is investigated, evaluating the use of object detection architectures to recognize furniture objects, doors and windows in floor plans.…”

Section: Related Workmentioning

confidence: 99%

Classification of Geometric Forms in Mosaics Using Deep Neural Network

et al. 2021

View full text Add to dashboard Cite

The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.

show abstract

“…In particular, SESYD does not include occlusion, clutter, nor any symbol intra-class graphical variability. Ziran and Marinai [88] and Goyal et al [89] both utilized object detection networks for symbol spotting. Their experiments, focused on simple floor plans, did not allow for a performance assessment under heavy occlusion and clutter.…”

Section: Dl-based Symbol Detectionmentioning

confidence: 99%

Geometry-based symbol spotting in born-digital architectural floor plans

2021

View full text Add to dashboard Cite

Architectural floor plans are scale-accurate 2D drawings of one level of a building, seen from above, which convey structural and semantic information related to rooms, walls, symbols, textual data, etc. They consist of lines, curves, symbols, and textual markings, showing the relationships between rooms and all physical features, required for the proper construction or renovation of the building.First, this thesis provides a thorough study of state-of-the-art on symbol spotting methods for architectural drawings, an application domain providing the document image analysis and graphic recognition communities with an interesting set of challenges linked to the sheer complexity and density of embedded information, that have yet to be resolved.Second, we propose a hybrid method that capitalizes on strengths of both vector-based and pixel-based symbol spotting techniques. In the description phase, the salient geometric constituents of a symbol are extracted by a variety of vectorization techniques, including a proposed voting-based algorithm for finding partial ellipses. This enables us to better handle local shape irregularities and boundary discontinuities, as well as partial occlusion and overlap. In the matching phase, the spatial relationship between the geometric primitives is encoded via a primitive-aware proximity graph. A statistical approach is then used to rapidly yield a coarse localization of symbols within the plan. Localization is further refined with a pixel-based step implementing a modified cross-correlation function. Experimental results on the public SESYD synthetic dataset and real-world images demonstrate that our approach clearly outperforms other popular symbol spotting approaches.Traditional on-the-fly symbol spotting methods are unable to address the semantic challenge of graphical notation variability, i.e. low intra-class symbol similarity, an issue that is particularly important in architectural floor plan analysis. The presence of occlusion and clutter, characteristic of real-world plans, along with a varying graphical symbol complexity from almost trivial to highly complex, also pose challenges to existing spotting methods. iv Third, we address all the above issues by leveraging recent advances in deep learningbased neural networks and adapting an object detection framework based on the YOLO (You Only Look Once) architecture. We propose a training strategy based on tiles, avoiding many issues particular to deep learning-based object detection networks related to the relatively small size of symbols compared to entire floor plans, aspect ratios, and data augmentation. Experimental results demonstrate that our method successfully detects architectural symbols with low intra-class similarity and of variable graphical complexity,

show abstract

Object Detection in Floor Plan Images

Cited by 37 publications

References 17 publications

Text Recognition and Classification in Floor Plan Images

Text Recognition and Classification in Floor Plan Images

Classification of Geometric Forms in Mosaics Using Deep Neural Network

Geometry-based symbol spotting in born-digital architectural floor plans

Contact Info

Product

Resources

About