Unsupervised color image segmentation using a dynamic color gradient thresholding algorithm

Balasubramanian, Guru Prashanth; Saber, Eli; Mišić, Vojislav B.; Peskin, E.; Shaw, Mark Q.

doi:10.1117/12.766184

Cited by 22 publications

(12 citation statements)

References 6 publications

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“…Sobel is a kernel for gradient thresholding [22] in both and x and y-axis. Since the lane lines are probably going to be vertical, the more weight on the inclination in a yaxis is given.…”

Section: (I) Gradient Thresholdingmentioning

confidence: 99%

A Computer Vision based Lane Detection Approach

Haque¹,

Islam²,

Alam³

et al. 2019

IJIGSP

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Automatic lane detection to help the driver is an issue considered for the advancement of Advanced Driver Assistance Systems (ADAS) and a high level of application frameworks because of its importance in drivers and passerby safety in vehicular streets. But still, now it is a most challenging problem because of some factors that are faced by lane detection systems like as vagueness of lane patterns, perspective consequence, low visibility of the lane lines, shadows, incomplete occlusions, brightness and light reflection. The proposed system detects the lane boundary lines using computer vision-based technologies. In this paper, we introduced a system that can efficiently identify the lane lines on the smooth road surface. Gradient and HLS thresholding are the central part to detect the lane lines. We have applied the Gradient and HLS thresholding to identify the lane line in binary images. The color lane is estimated by a sliding window search technique that visualizes the lanes. The performance of the proposed system is evaluated on the KITTI road dataset. The experimental results show that our proposed method detects the lane on the road surface accurately in several brightness conditions.

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“…Sobel is a kernel for gradient thresholding [22] in both and x and y-axis. Since the lane lines are probably going to be vertical, the more weight on the inclination in a yaxis is given.…”

Section: (I) Gradient Thresholdingmentioning

confidence: 99%

A Computer Vision based Lane Detection Approach

Haque¹,

Islam²,

Alam³

et al. 2019

IJIGSP

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show abstract

“…So far, many graph-based approaches have been developed for color image segmentation, and their drawbacks generally lie in the high computation complexity. Hybrid approaches [13][14][15] have become a noticeable developing direction over the years. They usually mix edge and region information together with other image features or combine two segmenting techniques to achieve optimal results.…”

Section: Introductionmentioning

confidence: 99%

“…The approach JSEG in [13] quantizes the color to several classes to form a class-map and then use region growing technique to obtain the segments, but the parameters are hard to be specified. The work [15] is a hybrid approach which employs the vector-based color gradient method and Otsu's automatic threshold to perform a dynamic threshold-based segmentation. All these approaches address some of the many drawbacks in color image segmentation, such as complex computing models, computation expensiveness, and sophisticated parameters.…”

Section: Introductionmentioning

confidence: 99%

KmsGC: An Unsupervised Color Image Segmentation Algorithm Based onK-Means Clustering and Graph Cut

Liang

Zhang

2014

Mathematical Problems in Engineering

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For unsupervised color image segmentation, we propose a two-stage algorithm, KmsGC, that combinesK-means clustering with graph cut. In the first stage,K-means clustering algorithm is applied to make an initial clustering, and the optimal number of clusters is automatically determined by a compactness criterion that is established to find clustering with maximum intercluster distance and minimum intracluster variance. In the second stage, a multiple terminal vertices weighted graph is constructed based on an energy function, and the image is segmented according to a minimum cost multiway cut. A large number of performance evaluations are carried out, and the experimental results indicate the proposed approach is effective compared to other existing image segmentation algorithms on the Berkeley image database.

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“…More recently, the region growing approach based on color and texture features has been used with good results [2,4,6]. This region growing approach may combine segments that are not adjacent; see, for example, the mountains in Figure 1(b).…”

Section: Stage 1 -Segmentationmentioning

confidence: 99%

“…Our approach leverages recent advances in image segmentation [4,6]. While there have been important advances in the quality of segmentation, improvement in speed has made this technology viable for some imaging applications.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Segmentation: Combining Image Segmentation With Object Tagging

Bergman

Nachlieli

2011

IEEE Trans. on Image Process.

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Abstract:Most consumers do not want to edit their images, either because they do not have the time, or they do not have the know how. They do want to be able to press a button that will magically make the objects captured in their photo look better. At the heart of enabling such functionality, lay image analysis models. The more we know about the objects in the photo, the better we can enhance and modify it according to human preferences. We present a necessary piece of this puzzle, breaking the image into signi¯cant segments and ¯nding important perceptual objects including skin, sky, snow and foliage. Abstract Most consumers do not want to edit their images, either because they do not have the time, or they do not have the know how. They do want to be able to press a button that will magically make the objects captured in their photo look better. At the heart of enabling such functionality, lay image analysis models. The more we know about the objects in the photo, the better we can enhance and modify it according to human preferences. We present a necessary piece of this puzzle, breaking the image into significant segments and finding important perceptual objects including skin, sky, snow and foliage.

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Unsupervised color image segmentation using a dynamic color gradient thresholding algorithm

Cited by 22 publications

References 6 publications

A Computer Vision based Lane Detection Approach

A Computer Vision based Lane Detection Approach

KmsGC: An Unsupervised Color Image Segmentation Algorithm Based onK-Means Clustering and Graph Cut

Perceptual Segmentation: Combining Image Segmentation With Object Tagging

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