Traffic sign segmentation and classification using statistical learning methods

Lillo-Castellano, José María; Mora-Jiménez, Inmaculada; Figuera, Carlos; Rojo-Álvarez, José Luis

doi:10.1016/j.neucom.2014.11.026

Cited by 102 publications

(72 citation statements)

References 30 publications

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“…For example, The YUV system is considered in [7] to detect blue rectangular signs. In [8] a segmentation method in both La-b and HSI color spaces is used to extract candidate blobs for chromatic signs. At the same time, white signs are detected with the help of an achromatic decomposition.…”

Section: A Traffic Sign Detectionmentioning

confidence: 99%

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Traffic Sign Detection and Recognition using Features Combination and Random Forests

Ellahyani¹,

Ansari²,

Jaafari³

et al. 2016

ijacsa

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Abstract-In this paper, we present a computer vision based system for fast robust Traffic Sign Detection and Recognition (TSDR), consisting of three steps. The first step consists on image enhancement and thresholding using the three components of the Hue Saturation and Value (HSV) space. Then we refer to distance to border feature and Random Forests classifier to detect circular, triangular and rectangular shapes on the segmented images. The last step consists on identifying the information included in the detected traffic signs. We compare four features descriptors which include Histogram of Oriented Gradients (HOG), Gabor, Local Binary Pattern (LBP), and Local Self-Similarity (LSS). We also compare their different combinations. For the classifiers we have carried out a comparison between Random Forests and Support Vector Machines (SVMs). The best results are given by the combination HOG with LSS together with the Random Forest classifier. The proposed method has been tested on the Swedish Traffic Signs Data set and gives satisfactory results.

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Section: A Traffic Sign Detectionmentioning

confidence: 99%

“…The evaluation of the detection stage is performed based on precision-recall curve, where the recall and precision values are computed as follows: recall = N umber of correctly detected signs N umber of true signs × 100 (8) precision = N umber of correctly detected signs N umber of detected signs × 100 (9)…”

Section: B Traffic Sign Detectionmentioning

confidence: 99%

Traffic Sign Detection and Recognition using Features Combination and Random Forests

Ellahyani¹,

Ansari²,

Jaafari³

et al. 2016

ijacsa

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“…Then, C r is chosen for reddish grids and g for green ones. For other grid colors, the relative color difference (rcd) is used [8]. Then, C r , g or rcd is chosen (depending on the grid color) and the top-hat morphological operator is applied with a flat, disk-shaped SE of diameter δ gl .…”

Section: Grid Cancelationmentioning

confidence: 99%

Auto:Cropping of Phone Camera Color Images to Segment Cardiac Signals in ECG Printouts

Lozano-Fernandez

Jiménez

Sanromán‐Junquera

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Digital analysis of the bio-electrical signals allows us to obtain valuable information for clinical and research purposes. Despite current devices record and store the digital electrical signal, in some cases only a printout version is available. Although previous research focused on algorithms to digitize a scanned version of the printed signal, nowadays a color image of the printout can be immediately obtained with a cell phone. Most of these algorithms digitize the signals after manually selecting from the whole image one sub-image per signal to digitize. Here, propose a procedure to automatically crop the image to get as many sub-images as signals are in the ECG printout. First, the perspective distortion is corrected. Then, an image I mainly containing the grid, is obtained from a transformation of the RGB space to another colorspace taking advantage of the grid chromatic characteristics. The grayscale image, G, and I are morphologically processed to emphasize both signal and grid in G, and just grid in I. Boundaries between contiguous signals are set by means of projections and morphological operations.Thirty one phone camera color images of 12-lead-ECG printouts were used for evaluation. Experiments were conducted with images with different spatial resolutions and quality factors according to the jpeg compressor. Our procedure worked correctly when the tracing of consecutive leads did not overlap in the amplitude axis and the spatial resolution and the quality factor were higher than 0.25 (one-fourth of the original size) and 40%, respectively.

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Section: Introductionmentioning

confidence: 99%

“…In recent studies, researchers have combined the color based method with shape based method to achieve better results [9]. Lillo Castellano et al [9] used HSI color space based segmentation along with machine learning to detect the traffic sign shape. In recent works, traffic sign detection methods were based on the local stability of the traffic sign regions rather than the boundary color [8,10].…”

Section: Introductionmentioning

confidence: 99%

Automatic and Invariant Recognition of Traffic Sign with Color-HMAX and Deep ELM Based Classifier

Khan¹,

Wang²,

Ahmed³

et al. 2017

IJMUE

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Traffic sign segmentation and classification using statistical learning methods

Cited by 102 publications

References 30 publications

Traffic Sign Detection and Recognition using Features Combination and Random Forests

Traffic Sign Detection and Recognition using Features Combination and Random Forests

Auto:Cropping of Phone Camera Color Images to Segment Cardiac Signals in ECG Printouts

Automatic and Invariant Recognition of Traffic Sign with Color-HMAX and Deep ELM Based Classifier

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