Sequential image segmentation based on minimum spanning tree representation

Sağlam, Ali; Baykan, Nurdan Akhan

doi:10.1016/j.patrec.2016.06.001

Cited by 35 publications

(16 citation statements)

References 24 publications

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“…Bu nedenle de tüzel kişilik ile onu oluşturan tabanın ayrılığı prensibi ana kuralken, perdenin aralanması ile kabul edilen ayrılık prensibi ise son derece istisnai bir çözüm olarak düşünülmelidir 79 . Dolayısıyla hakkın kötüye kullanılması söz konusu olduğunda tüzel kişilik perdesi kaldırılmalı ve tüzel kişi vasıtasıyla elde edilmek istenen amaç engellenmelidir 80…”

Section: Tüzel Kişilik Perdesinin Kaldırılmasıunclassified

Türk Şirketler Hukukundaki Bazı Durumlarda Tahkim Anlaşmasının Üçüncü Kişilere Teşmili Sorunu

Mekengeç¹

2019

Ankara Üniversitesi Hukuk Fakültesi Dergisi

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İhtiyari tahkimin en temel gereği tahkim anlaşmasıdır. Tahkim anlaşması da her sözleşme gereği kural olarak nisbidir. Ancak bazı durumlarda nisbilik ilkesinin istisnaları ortaya çıkabilir. Bu istisnalar sayesinde tahkim anlaşmasının üçüncü kişilere teşmili mümkün hâle gelir. Böylelikle üçüncü kişinin de tahkim yargılamasının tarafı olması, hakem kararının tenfizini, icrasını ya da iptalini talep etmesi gibi durumlar meydana gelebilir. Bu ihtimal farklı şekillerde ortaya çıkabilir. Biz çalışmamız kapsamında şirketler hukuku bakımından meseleyi ele aldık. Çalışmada konu, doktrindeki görüşler ve Yargıtay uygulamalarına da yer verilerek incelenmiştir

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Section: Tüzel Kişilik Perdesinin Kaldırılmasıunclassified

Türk Şirketler Hukukundaki Bazı Durumlarda Tahkim Anlaşmasının Üçüncü Kişilere Teşmili Sorunu

Mekengeç¹

2019

Ankara Üniversitesi Hukuk Fakültesi Dergisi

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“…1). Felzenszwalb and Huttenlocher have segmented the images by detecting the edges of the boundaries, which are called boundary edges [10,15]. For the purpose of to detect the boundary edges to be excluded from the MST, Felzenszwalb and Huttenlocher have used the merging criterion Eq.…”

Section: The Graph-based Segmentation Algorithmmentioning

confidence: 99%

“…By this way, a tree is separated into several sub-trees. The sub-trees represent regions in the image [5,[7][8][9]. The second way is the merging procedure in which the sub-trees that satisfies a homogeneity criterion are merged [10].…”

Section: Introductionmentioning

confidence: 99%

Parallelization of a Hierarchical Graph-Based Image Segmentation using OpenMP

Sağlam

Baykan

2016

International Journal of Applied Mathematics, Electronics and Computers

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Abstract:In many image-processing applications, image segmentation is an essential stage. In this stage, an image is partitioned into several regions according to the similarity of its pixels. In addition to the accuracy of the image segmentation, the speed is also very important for real-time image processing applications. Many computer applications take advantages of the multi-processor architecture to up to their running performance. However, to run an algorithm as parallel is very difficult in many cases. Due to using the same memory blocks, many conflicts might be happened between the processors. Moreover, each process of one processor may depend on those of another processor. For this reason, the algorithm to be parallelized must be suitable to parallel. In addition, the processing traffic that is pursued by the processors must be controlled within some parallel directives. In this paper, we provide a parallel implementation to a hierarchical graph-based image segmentation method by using its hierarchical processing steps. To achieve this goal, we utilize the OpenMP (Open Multi-Processing) Library to run the segmentation process as parallel on images of different sizes from the INRIA Holidays dataset. The experimental results show that the parallel implementation of the algorithm is more effective than the serial type according to processing time.

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“…Segmenting an object from its background has an important role in machine learning and computer vision [1]. In recent years, several unsupervised image segmentation algorithms have been presented [2,3].…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Using a Trimmed Likelihood Estimator in the Asymmetric Mixture Model Based on Generalized Gamma and Gaussian Distributions

Zhou

Zhu

2018

Mathematical Problems in Engineering

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Finite mixture model (FMM) is being increasingly used for unsupervised image segmentation. In this paper, a new finite mixture model based on a combination of generalized Gamma and Gaussian distributions using a trimmed likelihood estimator (GGMM-TLE) is proposed. GGMM-TLE combines the effectiveness of Gaussian distribution with the asymmetric capability of generalized Gamma distribution to provide superior flexibility for describing different shapes of observation data. Another advantage is that we consider the spatial information among neighbouring pixels by introducing Markov random field (MRF); thus, the proposed mixture model remains sufficiently robust with respect to different types and levels of noise. Moreover, this paper presents a new component-based confidence level ordering trimmed likelihood estimator, with a simple form, allowing GGMM-TLE to estimate the parameters after discarding the outliers. Thus, the proposed algorithm can effectively eliminate the disturbance of outliers. Furthermore, the paper proves the identifiability of the proposed mixture model in theory to guarantee that the parameter estimation procedures are well defined. Finally, an expectation maximization (EM) algorithm is included to estimate the parameters of GGMM-TLE by maximizing the log-likelihood function. Experiments on multiple public datasets demonstrate that GGMM-TLE achieves a superior performance compared with several existing methods in image segmentation tasks.

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Sequential image segmentation based on minimum spanning tree representation

Cited by 35 publications

References 24 publications

Türk Şirketler Hukukundaki Bazı Durumlarda Tahkim Anlaşmasının Üçüncü Kişilere Teşmili Sorunu

Türk Şirketler Hukukundaki Bazı Durumlarda Tahkim Anlaşmasının Üçüncü Kişilere Teşmili Sorunu

Parallelization of a Hierarchical Graph-Based Image Segmentation using OpenMP

Image Segmentation Using a Trimmed Likelihood Estimator in the Asymmetric Mixture Model Based on Generalized Gamma and Gaussian Distributions

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