Digital or analog Hough transform?

Kiryati, Nahum; Lindenbaum, Michael; Bruckstein, Alfred M.

doi:10.1016/0167-8655(91)90412-f

Cited by 19 publications

(6 citation statements)

References 17 publications

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“…More precisely, we use the mathematical measure of lines in the plane to evaluate a kind of accuracy of the DSS recognition. Similar analyses have been proposed in (Kiryati et al, 1991) to compare the continuous and the analytical Hough transforms. The authors have considered the measure of the DSS worst case in both models for recognition purposes.…”

Section: Introductionmentioning

confidence: 79%

Measure of straight lines for digital contour analysis

Cœurjolly¹,

Sivignon²

2011

Int J Imaging Syst Tech

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In digital geometry, objects such as digital straight lines can be considered as equivalent classes of Euclidean straight lines with respect to a digitization process. This article investigates the analysis of the set of straight lines whose digitization is a given digital straight segment with the help of tools from integral geometry. After a definition of a measure on such sets, we illustrate several applications of it in digital geometry for contour analysis. V

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Section: Introductionmentioning

confidence: 79%

Measure of straight lines for digital contour analysis

Cœurjolly¹,

Sivignon²

2011

Int J Imaging Syst Tech

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“…More than 2500 papers focus on its improved algorithms and applications [19]. Some improved algorithms have been developed based on classical Hough Transform, such as Generalized Hough Transform [20], Progressive Probabilistic Hough Transform [21], Random Hough Transform [22], Digital Hough Transform [23], and Fuzzy Hough Transform [24].…”

Section: Traditional Methods For Components Recognitionmentioning

confidence: 99%

A Deep Learning-Based Method to Detect Components from Scanned Structural Drawings for Reconstructing 3D Models

2020

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Among various building information model (BIM) reconstruction methods for existing building, image-based method can identify building components from scanned as-built drawings and has won great attention due to its lower cost, less professional operators and better reconstruction performance. However, this kind of method will cost a great deal of time to design and extract features. Moreover, the manually extracted features have poor robustness and contain less non-geometric information. In order to solve this problem, this paper proposes a deep learning-based method to detect building components from scanned 2D drawings. Taking structural drawings as an example, in this article, 1500 images of structural drawings were firstly collected and preprocessed to guarantee the quality of data. After that, the neural network model—You Only Look Once (YOLO) was trained, verified and tested. In addition, a series of metrics were utilized to evaluate the performance of recognition. The results of test experiments show that the components in structural drawings (e.g., grid reference, column and beam) can be successfully detected, while the average detection accuracy of the whole image is over 80% and the average detection time for each image is 0.71 s. The experimental results demonstrate that the proposed method is robust and timesaving, which provides a good basis for the reconstruction of BIM from 2D drawings.

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“…However, in the PHT algorithm, one object point maps into many parameter values ( ρ , θ ) (one-to-many mapping), while RHT uses many object points to define a straight line (many-to-one mapping) [15,16]. Although these proposed alternative algorithms could reduce resource consumption and processing time, they do not consider the location errors (errors between the actual line coordinates and digital image coordinates) [17], so that the accuracy is compromised. In order to solve this problem, DHT adopts a pattern of judging whether a given set of object points has the digital straight-line (DSL) property [18].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Straight-Line Detection for XGA-Size Videos by Hough Transform with Parallelized Voting Procedures

Guan

Zhang

et al. 2017

Sensors

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Abstract:The Hough Transform (HT) is a method for extracting straight lines from an edge image. The main limitations of the HT for usage in actual applications are computation time and storage requirements. This paper reports a hardware architecture for HT implementation on a Field Programmable Gate Array (FPGA) with parallelized voting procedure. The 2-dimensional accumulator array, namely the Hough space in parametric form (ρ, θ), for computing the strength of each line by a voting mechanism is mapped on a 1-dimensional array with regular increments of θ. Then, this Hough space is divided into a number of parallel parts. The computation of (ρ, θ) for the edge pixels and the voting procedure for straight-line determination are therefore executable in parallel. In addition, a synchronized initialization for the Hough space further increases the speed of straight-line detection, so that XGA video processing becomes possible. The designed prototype system has been synthesized on a DE4 platform with a Stratix-IV FPGA device. In the application of road-lane detection, the average processing speed of this HT implementation is 5.4 ms per XGA-frame at 200 MHz working frequency.

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Digital or analog Hough transform?

Cited by 19 publications

References 17 publications

Measure of straight lines for digital contour analysis

Measure of straight lines for digital contour analysis

A Deep Learning-Based Method to Detect Components from Scanned Structural Drawings for Reconstructing 3D Models

Real-Time Straight-Line Detection for XGA-Size Videos by Hough Transform with Parallelized Voting Procedures

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