Human perception in segmentation of sketches

In this paper, we study the segmentation of sketched engineering drawings into a set of straight and curved segments. Our immediate objective is to produce a benchmarking method for segmentation algorithms. The criterion is to minimise the differences between what the algorithm detects and what human beings perceive. We have created a set of sketched drawings and have asked people to segment them. By analysis of the produced segmentations, we have obtained the number and locations of the segmentation points which people perceive. Evidence collected during our experiments supports useful hypotheses, for example that not all kinds of segmentation points are equally difficult to perceive. The resulting methodology can be repeated with other drawings to obtain a set of sketches and segmentation data which could be used as a benchmark for segmentation algorithms, to evaluate their capability to emulate human perception of sketches

a.SCatch: semantic structure for architectural floor plan retrieval

Architects’ daily routine involves working with drawings. They use either a pen or a computer to sketch out their ideas or to do a drawing to scale. We therefore propose the use of a sketch-based approach when using the floor plan repository for queries. This enables the user of the system to sketch a schematic abstraction of a floor plan and search for floor plans that are structurally similar. We also propose the use of a visual query language, and a semantic structure as put forward by Langenhan. An algorithm extracts the semantic structure sketched by the architect on DFKI’s Touch& Write table and compares the structure of the sketch with that of those from the floor plan repository. The a.SCatch system enables the user to access knowledge from past projects easily. Based on CBR strategies and shape detection technologies, a sketch-based retrieval gives access to a semantic floor plan repository. Furthermore, details of a prototypical application which allows semantic structure to be extracted from image data and put into the repository semi-automatically are provided

Features and design intent in engineering sketches

We investigate the problem of determining design intent from engineering sketches: what did the designer have in mind when sketching a component? Specifically, we consider the unidirectional reverse mapping from form features, as determined from an input sketch, to design features, representing the design intent present in the designer’s mind. We introduce a list of com- mon engineering form features. For each, we list which geometrical cues may be helpful in identifying these features in design sketches, and we list the design features which such form features commonly imply. We show that a reductionist approach which decomposes a diagram into form features can be used to deduce the design intent of the object portrayed in a drawing. We supply experimental results in support of this idea

Vectorial Signatures for Symbol Discrimination

Colloque avec actes et comité de lecture. internationale.International audienceIn this paper, we present a method based on vectorial signatures, which aims at discriminating, by a fast technique, symbols represented within technical documents. The use of signatures on this kind of document has an obvious interest. Indeed, considering a raw vectorial description of the graphical layer of a technical document (e.g. a set of arcs and segments), signatures can be used to perform a pre-processing step before a "traditional" graphics recognition processing, or can be used to establish a classification that can be sufficient to feed a further indexation step. To compute vectorial signatures, we have based our approach on a method proposed by Etemadi et al., who study spatial relations between primitives to solve a vision problem. We considerer five types of relations, invariant to transformations like rotation or scaling, between neighboring segments: parallelism with or without overlapping, collinearity, L junctions and V junctions. A quality factor is computed for each of the relations, computable with low requirements of power. The signature of all models of symbols that could be found in a given document are computed and matched against the signature of the document, in order to determine what symbols the document is likely to contain. The quality factor associated with each relation is used to prune relations whose quality factor is too low. We present finally the first tests obtained with this method, and we discuss the improvements we plan to do

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Revised selected papers from Eighth IAPR International Workshop on Graphics RECognition (GREC) 2009.The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols

Northward range expansion in spring-staging barnacle geese is a response to climate change and population growth, mediated by individual experience

The study was funded by the Norwegian Environment Agency, the Research Council of Norway (the projects ‘LANDRING 134716/720’, ‘AGRIGOOSE 165836’, ‘MIGRAPOP 204342’), the European Union (the project FRAGILE EVK2‐2001‐00235), the County Governor of Nordland, the Wildfowl and Wetlands Trust, the Fram Centre in Tromsø and a grant from the Netherlands Organization for Scientific Research awarded to TO (ref 019.172EN.011).All long-distance migrants must cope with changing environments, but species differ greatly in how they do so. In some species, individuals might be able to adjust by learning from individual experiences and by copying others. This could greatly speed up the process of adjustment, but evidence from the wild is scarce. Here, we investigated the processes by which a rapidly growing population of barnacle geese (Branta leucopsis) responded to strong environmental changes on spring-staging areas in Norway. One area, Helgeland, has been the traditional site. Since the mid-1990s, an increasing number of geese stage in another area 250 km further north, Vesterålen. We collected data on goose numbers and weather conditions from 1975 to 2017 to explore the extent to which the increase in population size and a warmer climate contributed to this change in staging area use. During the study period, the estimated onset of grass growth advanced on average by 0.54 days/year in each of the two areas. The total production of digestible biomass for barnacle geese during the staging period increased in Vesterålen but remained stable in Helgeland. The goose population has doubled in size during the past 25 years, with most of the growth being accommodated in Vesterålen. The observations suggest that this dramatic increase would not have happened without higher temperatures in Vesterålen. Records of individually marked geese indicate that from the initial years of colonization onwards, especially young geese tended to switch to Vesterålen, thereby predominating in the flocks at Vesterålen. Older birds had a lower probability of switching to Vesterålen, but over the years, the probability increased for all ages. Our findings suggest that barnacle geese integrate socially learned behaviour with adjustments to individual experiences, allowing the population to respond rapidly and accurately to global change.Publisher PDFPeer reviewe

Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks.

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents and also due to the lack of dataset availability in the public domain that can help push the research in this area. In this paper, we present a multiclass imbalanced dataset for the research community made of 2432 instances of engineering symbols. These symbols were extracted from a collection of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID). By providing such dataset to the research community, we anticipate that this will help attract more attention to an important, yet overlooked industrial problem, and will also advance the research in such important and timely topics. We discuss the datasets characteristics in details, and we also show how Convolutional Neural Networks (CNNs) perform on such extremely imbalanced datasets. Finally, conclusions and future directions are discussed

Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings.

The demand for digitisation of complex engineering drawings becomes increasingly important for the industry given the pressure to improve the efficiency and time effectiveness of operational processes. There have been numerous attempts to solve this problem, either by proposing a general form of document interpretation or by establishing an application dependant framework. Moreover, text/graphics segmentation has been presented as a particular form of addressing document digitisation problem, with the main aim of splitting text and graphics into different layers. Given the challenging characteristics of complex engineering drawings, this paper presents a novel sequential heuristics-based methodology which is aimed at localising and detecting the most representative symbols of the drawing. This implementation enables the subsequent application of a text/graphics segmentation method in a more effective form. The experimental framework is composed of two parts: first we show the performance of the symbol detection system and then we present an evaluation of three different state of the art text/graphic segmentation techniques to find text on the remaining image