Motivation: The paper investigated was Hernandez, S. E., K. E. Barner, et al. (2005). "Region merging using homogeneity and edge integrity for watershed-based image segmentation." Optical Engineering 44(1): 017004-017014. The premise of the paper was to discuss a hybrid method of the watershed transform for use in image segmentation. This paper was selected due to its relevance to our method of...

We present a hybrid learning method bridging the fields of recurrent neural networks, unsupervised Hebbian learning, vector quantization, and supervised learning to implement a sophisticated image and feature segmentation architecture. This architecture is based on the competitive layer model (CLM), a dynamic feature binding model, which is applicable on a wide range of perceptual grouping and ...

In the recent years, 3D city reconstruction is one of the active researches in the field of photogrammetry. The goal of this work is to improve and extend region growing based segmentation in the X-Y-Z image in the form of 3D structured data with combination of spectral information of RGB and grayscale image to extract building roofs, streets and vegetation. In order to process 3D point clouds,...

Image segmentation is a part of digital image processing that deals to extract hidden patterns from the medical images. Medical image segmentation is a technique using to mean manually, fully or semi-automatically delineating the boundaries of tissue regions or an object. It is a process of changing the representation step of an image into some extent which is easy to analyze. In the recent res...

In the recent years, 3D city reconstruction is one of the active researches in the field of photogrammetry. The goal of this work is to improve and extend surface growing based segmentation in the X-Y-Z image in the form of 3D structured data with combination of spectral information of RGB and grayscale image to extract building roofs, streets and vegetation. In order to process 3D point clouds...

Medical Image segmentation deals with segmentation of tumor in CT and MR images for improved quality in medical diagnosis. Geometric Vector Flow (GVF) enhances the concave object extraction capability. However, it suffers from high computational requirement and sensitiveness to noise. This paper intends to combine watershed algorithm with GVF snake model to reduce the computational complexity, ...

Segmentation plays a key role in many image processing applications, including SAR image analysis. Given a high-quality segmentation, important tasks such as adaptive noise filtering and classification can be greatly simplified. However, the presence of a strong, multiplicative noise known as speckle makes segmentation of SAR images very difficult. A new, hybrid segmentation technique has permi...

Image segmentation is a part of digital image processing that deals to extract hidden patterns from the medical images. Medical image segmentation is a technique using to mean manually, fully or semi-automatically delineating the boundaries of tissue regions or an object. It is a process of changing the representation step of an image into some extent which is easy to analyze. In the recent res...

diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. thus, the need for precise segmentation of corneal layer boundaries is inevitable. obviously, manual segmentation is time‑consuming and imprecise. in this paper, the gaussian mixture model (gmm) is used for automatic segmentation of three clinically important corneal boundaries on opt...

reducing the number of colors in an image while preserving its quality, is of importance in many applications such as image analysis and compression. it also decreases memory and transmission bandwidth requirements. moreover, classification of image colors is applicable in image segmentation and object detection and separation, as well as producing pseudo-color images. in this paper, the kohene...