An unsupervised approach to color video thresholding

Du, Eliza Yingzi; Chang, Chein‐I; Thouin, Paul D.

doi:10.1109/icme.2003.1221317

Cited by 25 publications

(25 citation statements)

References 26 publications

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“…대표적인 클러스터링 방안인 K-Means 알고리즘 [1] 은 문제점을 해결하기 위해서 현재 많은 방법들 [3][4][5] 이 제안되었 으며, 그 중에서 EWFCM 알고리즘 [5] 은 군집화 반복과정에 [7] 는 히스토그램을 이용하여 색상별 성분을 두 개의 군집으로 분류한 후 군집단위의 재분류에 의한 분할방법을 제안하였다. 그리고 현재 많이 이용되는 그래프 이론을 기 반으로 하여 W. Yang [8] 과 W. Tao [9] 는 각각 워터쉐드 알고 …”

Section: 클러스터링 기반의 분할알고리즘은 화소들의 유사성이나unclassified

A Study of Post-processing Methods of Clustering Algorithm and Classification of the Segmented Regions

Oh¹,

Kim²,

Kim³

2009

The KIPS Transactions:PartB

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Some clustering algorithms have a problem that an image is over-segmented since both the spatial information between the segmented regions is not considered and the number of the clusters is defined in advance. Therefore, they are difficult to be applied to the applicable fields. This paper proposes the new post-processing methods, a reclassification of the inhomogeneous clusters and a region merging using Baysian algorithm, that improve the segmentation results of the clustering algorithms. The inhomogeneous cluster is firstly selected based on variance and between-class distance and it is then reclassified into the other clusters in the reclassification step. This reclassification is repeated until the optimal number determined by the minimum average within-class distance. And the similar regions are merged using Baysian algorithm based on Kullbeck-Leibler distance between the adjacent regions. So we can effectively solve the over-segmentation problem and the result can be applied to the applicable fields. Finally, we design a classification system for the segmented regions to validate the proposed method. The segmented regions are classified by SVM(Support Vector Machine) using the principal colors and the texture information of the segmented regions. In experiment, the proposed method showed the validity for various real-images and was effectively applied to the designed classification system.

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Section: 클러스터링 기반의 분할알고리즘은 화소들의 유사성이나unclassified

A Study of Post-processing Methods of Clustering Algorithm and Classification of the Segmented Regions

Oh¹,

Kim²,

Kim³

2009

The KIPS Transactions:PartB

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“…From Figure 2, it is clear that the blue color could be extracted by the following rule: BLUE is pixel then TB r g If (5) The guidance sign as illustrated in Figure 1 contains three kinds of information, i.e. the symbol indicating the road structure (represented by the arrows), characters (Kanji and/or Alphabet) indicating city or location, and numbers indicating the route numbers.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Since a color image is represented in three components (RGB color), the histogram-based segmentation is performed by combining three thresholds obtained from each color channel. In [5], thresholding technique is carried out in each component of Red (R), Green (G), and Blue (B), to obtain the threshold values which are used as a base to produce a set of multithresholds used for video segmentation by unsupervised clustering.…”

Section: Related Work 21 Color Segmentationmentioning

confidence: 99%

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Segmentation of Road Guidance Sign Symbols and Characters Based on Normalized RGB Chromaticity Diagram

Soetedjo¹,

Yamada²,

Limpraptono³

2010

IJCA

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In this paper, we describe a color segmentation based on the normalized RGB chromaticity diagram to extract the symbols and characters of road guidance signs. The proposed method separates blue color of the signs by utilizing the developed histogram on the normalized RGB chromaticity diagram for selecting the threshold automatically. The image morphology operator and the histogram projection technique are employed to extract the symbols and characters. From the experiments on the real scene images, the extraction rate of 97.3% is achieved. General TermsImage processing, Machine vision.

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“…One of the most recent introduced methods for threshold determination is based on the local entropy [21]. This approach has been widely used for measuring the local information content or uncertainty and the information content of a probability distribution [22], [23].…”

Section: State Of the Artmentioning

confidence: 99%

Determining adaptive thresholds for image segmentation for a license plate recognition system

Norul¹,

Omar²,

Salimi³

et al. 2016

ijacsa

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Abstract-A vehicle license plate recognition (LPR) system is useful to many applications, such as entrance admission, security, parking control, airport and cargo, traffic and speed control. This paper describe an adaptive threshold for image segmentation applied to a system for Malaysian intelligent license plate recognition (MyiLPR). Due to the different types of license plates used, the requirements of an automatic LPR system are rather different for each country. Upon receiving the input car image, this system (MyiLPR) detects and segments the license plate based on proposed adaptive threshold via image and blob histogram, and blob agglomeration, and finally, it extracts geometric character features and classifies them using neural network. The use of the proposed adaptive threshold increased the detection, segmentation and recognition rate to 99%, 94.98% and 90% correspondingly, from 95%, 78.27% and 71.08% obtained with the fixed threshold used in the originally proposed system.

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An unsupervised approach to color video thresholding

Cited by 25 publications

References 26 publications

A Study of Post-processing Methods of Clustering Algorithm and Classification of the Segmented Regions

A Study of Post-processing Methods of Clustering Algorithm and Classification of the Segmented Regions

Segmentation of Road Guidance Sign Symbols and Characters Based on Normalized RGB Chromaticity Diagram

Determining adaptive thresholds for image segmentation for a license plate recognition system

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