Выделение малоразмерных изображений объектов нерегулярного вида

З метою підвищення стійкості, адекватності і точності виділення малорозмірних зображень об’єктів нерегулярного вигляду в автоматичному режимі в роботі запропоновано підхід, який засновано на аналізі контрастності розглядаємих зображень відносно фону з використанням спеціального виду регіональних масок, адаптованих до параметрів форми аналізуємих об’єктів нерегулярного вигляду.With the aim to increase stability, adequacy and accuracy of segmentation of small-sized images of irregular objects in automatic mode, an approach is suggested that is based on the image vs. background contrast analysis that uses the proposed regional masks being adjusted to the form parameters of the considered irregular objects

Data Fusion in a Hierarchical Segmentation Context: The Case of Building Roof Description

Automatic mapping of urban areas from aerial images is a challenging task for scientists an

Novel Image Segmentation through Agile Expanse Merging

This paper gives the explanation of novel image segmentation by agile expanse merging. Initially, the image is segmented into many expanses and the expanses of the same colour are continuously merged by the proposed algorithm. The proposed algorithm for expanse merging process will seek for homogeneity among the expanses and merges it iteratively by Sequential Probability Ratio Test (SPRT) and minimal cost condition. This merging algorithm combines the expanses in an orderly manner and stops the merging according to the criteria which is set by SPRT TEST. The merging done here goes on the agile programming technique. The final segmentation is done based on the studied or perceived image after merging process. This algorithm acquires the global standards especially the speed in which the merging is carried out by using Nearest Neighbor Graph Method (NNG)

A Novel Model of Image Segmentation Based on Watershed Algorithm

A novel model of image segmentation based on watershed method is proposed in this paper. To prevent the oversegmentation of traditional watershed, our proposed algorithm has five stages. Firstly, the morphological reconstruction is applied to smooth the flat area and preserve the edge of the image. Secondly, multiscale morphological gradient is used to avoid the thickening and merging of the edges. Thirdly, for contrast enhancement, the top/bottom hat transformation is used. Fourthly, the morphological gradient of an image is modified by imposing regional minima at the location of both the internal and the external markers. Finally, a weighted function is used to combine the top/bottom hat transformation algorithm and the markers algorithm to get the new algorithm. The experimental results show the superiority of the new algorithm in terms of suppression over-segmentation

Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation

Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively

Watershed segmentation with boundary curvature ratio based merging criterion

This paper proposes to incorporate boundary curvature ratio, region homogeneity and boundary smoothness into a single new merging criterion to improve the oversegmentation of marker-controlled watershed segmentation algorithm. The result is a more refined segmentation result with smooth boundaries and regular shapes. To pursue a final segmentation result with higher inter-variance and lower intra-variance, an optimal number of segments could be self-determined by a proposed formula. Experimental results are presented to demonstrate the merits of this method.postprintThe 9th IASTED International Conference on Signal and Image Processing (SIP 2007), Honolulu, HI., 20-22 August 2007. In Proceedings of SIP, 2007, p. 7-1

Segmentation of Sedimentary Grain in Electron Microscopy Image

This paper describes a novel method developed for the segmentation of sedimentary grains in electron microscopy images. The algorithm utilizes the approach of region splitting and merging. In the splitting stage, the marker-based watershed segmentation is used. In the merging phase, the typical characteristics of grains in electron microscopy images are exploited for proposing special metrics, which are then used during the merging stage to obtain a correct grain segmentation. The metrics are based on the typical intensity changes on the grain borders and the compact shape of grains. The experimental part describes the optimal setting of parameter in the splitting stage and the overall results of the proposed algorithm tested on available database of grains. The results show that the proposed technique fulfills the requirements of its intended application

Lokal Fuzzy Thresholding Berdasarkan Pengukuran Fuzzy Similarity Pada Interaktif Segmentasi Citra Panoramik Gigi

Dalam segmentasi citra, thresholding merupakan salah metode yang mudah dan sederhana untuk diimplementasikan. Pada citra panoramik gigi, penentuan global threshold masih kurang begitu optimal untuk diimplementasikan. Hal tersebut dikarenakan adanya factor penghambat seperti pencahayaan yang tidak merata dan citra yang kabur. Faktor-faktor tersebut  dapat menyebabkan histogram tidak bisa dipartisi dengan baik, sehingga akan berpengaruh pada hasil segmentasi. Pada penelitian ini diusulkan lokal fuzzy thresholding berdasarkan pengukuran fuzzy similarity pada interaktif segmentasi citra panoramik gigi. Metode yang diusulkan terdiri dari tiga tahapan utama, tahap pertama region splitting untuk mendapatkan lokal region. Tahap kedua adalah user marking untuk mendapat inisial seed background dan objek, Tahap terakhir adalah pengukuran fuzzy similarity pada setiap lokal region untuk mendapatkan nilai lokal threshold. Hasil uji coba pada citra panoramik gigi, metode yang diusulkan berhasil melakukan segmentasi dengan rata-rata missclasification error (ME) 5.47%