Image Segmentation and Shape Analysis for Road-Sign Detection

Khan, Jesmin; Bhuiyan, Sharif; Adhami, Reza R.

doi:10.1109/tits.2010.2073466

Cited by 140 publications

(66 citation statements)

References 39 publications

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“…However, its calculation is more complicated. YCbCr color space has also been used in the traffic signs detection because it is the native color space of many cameras, which makes color space conversion unnecessary [5].…”

Section: Color-based Approachesmentioning

confidence: 99%

Circular Traffic Signs Detection in Natural Environments

Huang¹,

Huang²

2017

dtcse

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This paper describes an efficient method for the detection of circular traffic signs on color images in different natural environments. First, a RGB image was converted into HSV color space which was segmented by the hue and saturation thresholds, so that we can extract the region of interests (ROIs). Due to which, A Circular Hough Transform (CHT) based algorithm is proposed to find circles in the image, after that the traffic signs are detected from the images under natural scenes. Experiments were conducted for the detection of traffic signs which involved two traffic sign image databases. The experimental results indicate that the proposed algorithm possess of high detection accuracy, and good robust performance.

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Section: Color-based Approachesmentioning

confidence: 99%

Circular Traffic Signs Detection in Natural Environments

Huang¹,

Huang²

2017

dtcse

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“…Most existing road sign detection methods include color processing. Color thresholding segmentation is one of the simplest methods used for road sign detection, which classifies the pixels in an image into 'object pixels' and 'background pixels' in RGB space [5]. HIS transformation is employed in [2] to reduce the influence of illumination changes.…”

Section: Road Sign Detectionmentioning

confidence: 99%

Exploiting Visual Saliency and Bag-of-Words for Road Sign Recognition

Wei

Tang

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we propose a novel method for road sign detection and recognition in complex scene real world images. Our algorithm consists of four basic steps. First, we employ a regional contrast based bottom-up visual saliency method to highlight the traffic sign regions, which usually have dominant color contrast against the background. Second, each type of traffic sign has special color distribution, which can be explored by top-down visual saliency to enhance the detection precision and to classify traffic signs into different categories. A bag-of-words (BoW) model and a color name descriptor are employed to compute the specialclass distribution. Third, the candidate road sign blobs are extracted from the final saliency map, which are generated by combining the bottom-up and the top-down saliency maps. Last, the color and shape cues are fused in the BoW model to express blobs, and a support vector machine is employed to recognize road signs. Experiments on real world images show a high success rate and a low false hit rate and demonstrate that the proposed framework is applicable to prohibition, warning and obligation signs. Additionally, our method can be applied to achromatic signs without extra processing.

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“…However dela Escalera et al, Yalic et al, Ruta et al [5] used the RGB color space with thresholds for segmentation. Khan et al used CIELab and gabor filters to represent a color image because of independent control over intensity and color information [6]. On the other hand, Hough Transform (HT) is widely used among shape based methods.…”

Section: Introductionmentioning

confidence: 99%