ABSTRACT:Image segmentation is an intermediate image processing stage in which the pixels of the image are grouped into clusters such that the data resulted from this stage is more meaningful for the next stage. Many clustering methods are used widely to segment the images. For this purpose, most clustering methods use the features of the image pixels. While some clustering method consider the local features of images by taking into account the neighborhood system of the pixels, some consider the global features of images. The algorithm of the K-means clustering method, that is easy to understand and simple to put into practice, performs by considering the global features of the entire image. In this algorithm, the number of cluster is given by users initially as an input value. For the segmentation process, if the distribution of the pixels over a histogram is used, the algorithm runs faster. The values in the histogram must be discrete in a certain range. In this paper, we use the Euclidean distance between the color values of the pixels and the mean color values of the entire image for taking advantage of the every color values of the pixels. To obtain a histogram that consists of discrete values, we normalize the distance value in a specific range and round the values to the nearest integers for discretization. We tested the versions of K-means with the gray-level histogram and the distance value histogram on an urban image dataset getting from ISPRS WG III/4 2D Semantic Labeling dataset. Comparing the two histograms, the distance value histogram proposed in this paper is better than the gray-level histogram. Key Words: Clustering, Histogram, Image segmentation, K-meansNormalize Edilmiş Uzaklık Değerleri ile Hızlı K-ortalama Renkli Görüntü Segmentasyonu ÖZ: Görüntü segmentasyonu, görüntü piksellerinin kümelere gruplandığı orta seviye bir görüntü işleme aşamasıdır. Öyle ki, bu şamadan elde edilen veri daha sonraki aşamalar için önceki veriye göre daha anlamlı hale gelmiş olur. Birçok kümeleme metodu, görüntü segmentasyonu amacıyla yaygın bir şekilde kullanılmaktadır. Bu amaçla, çoğu kümeleme metodu görüntü piksellerinin özelliklerini kullanmaktadır. Bazı kümeleme metotları piksellerin komşuluk sistemini kullanarak görüntünün yerel özelliklerini ele alırken, bazıları da görüntünün genel özelliklerini ele almaktadır. Anlaşılması kolay ve uygulaması basit olan K-ortalama algoritması, bütün görüntünün özelliklerini ele alarak segmentasyon yapmaktadır. Bu algoritmada, küme sayısı başlangıç giriş değeri olarak kullanıcı tarafından verilmektedir. Bu segmentasyon işlemi için, eğer piksellerin bir histogram üzerindeki dağılımı kullanılırsa algoritma daha hızlı çalışmaktadır. Bu histogram üzerindeki değerler belirli bir aralıkta ve ayrık olmak zorundadır. Bu çalışmada, piksellerin her bir renk değerinden faydalanmak için piksellerin renk değerleri ile görüntünün ortalama renk değerleri arasındaki Öklit uzaklığı kullanılmıştır. Ayrık değerlerden oluşan bir histogram elde etmek için, uzaklık değerlerini belirli değer ara...
Two important features of the points in the LiDAR point clouds are the spatial and the color features. The spatial feature is mostly used in the point cloud processing field due to its 3D informative and distinctive characteristic. The local geometric difference derived from the spatial features of the points is usually benefited by graph-based point cloud segmentation methods, because the geometric features of the local point groups are highly distinctive. In this paper, we use both the geometric and color differences of the adjacent local point groups at the impact rates 0.3, 0.5, and 0.7 and cooperate the Euclidean and the vector color differences within several averaging techniques for the color difference. The difference forms have been tested within a graph-based segmentation method on four point cloud segmentation datasets, two indoor and two outdoor, using their spatial and color information. The geometric mean as an averaging techniques increases the segmentation success for the all datasets except one outdoor when the color differences are used in the segmentation at the impact rate 0.3, while the harmonic mean increases the success for the all datasets the successes except the other outdoor at the same impact rate. According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.
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