Image thresholding has played an important role in image segmentation. In this paper, we present a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. Two improved implementations of the k-nearest neighbor (k-NN) algorithm are intr...

Thresholding is an important approach for image segmentation and it is the first step in the image processing for many applications. Segmentation is a low level operation that can segment an image in nonoverlapping regions. The optimal thresholds are found by maximizing Kapur's entropy-based thresholding function in a grey level image. However, the required CPU time increases exponentially with...

In this paper, we describe an input sensitive thresholding algorithm for ancient Hebrew calligraphy documents. Usually, historical document images are of poor quality since the documents have degraded over time due to storage conditions. However, the distribution of noise in one document is not uniform and the characters quality may vary. We develop tools to identify noisy characters and apply ...

The drawback of the conventional multilevel thresholding methods is high computational cost since they do exhaustive search among exponentialy growing number of possible thresholds to optimize the objective functions. In this paper a new multilevel thresholding method based on cuckoo search (CS) algorithm is proposed in order to overcome this obstacle. The optimal thresholds are found by maximi...

Due to the computational complexity of multilevel image thresholding, Swarm Intelligence Optimization Algorithm (SIOA) has been widely applied improve calculation efficiency. Therefore, more and attention paid exploring application latest SIOA in segmentation. This article takes Otsu fuzzy entropy as objective functions, using Coyote (COA) for thresholds optimization selection, through median a...

In this paper, an automatic near-real time (NRT) flood detection approach is presented, which combines histogram thresholding and segmentation based classification, specifically oriented to the analysis of single-polarized very high resolution Synthetic Aperture Radar (SAR) satellite data. The challenge of SAR-based flood detection is addressed in a completely unsupervised way, which assumes no...

We present an algorithm that enables one to perform locally adaptive block thresholding, while maintaining image continuity. Images are divided into sub-images based some standard image attributes and thresholding technique is employed over the sub-images. The present algorithm makes use of the thresholds of neighboring sub-images to calculate a range of values. The image continuity is taken ca...

Entropy-based image thresholding are used widely in image processing. Conventional methods are efficient in the case of bilevel thresholding. But they are very computationally time consuming when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. In this paper, we propose a conditional probability entropy (CPE) based on...