Enhancement of Image Segmentation osing Automatic Histogram Thresholding

This study is focused on histogram thresholding methods automatically. In computer vision, Image segmentation is an initial and vital step in a series of processes aimed at overall image understanding. In other words Segmentation refers to the process of partitioning a digital image into the multiple segments (set of pixels as known as super pixels). Two very simple image segmentation techniques that are based on the gray level histogram of an image are Thresholding and Clustering. Thresholding method is widely used for image segmentation approach. It is useful in discriminating foreground from the background. By selecting an adequate threshold value T, or automatically computing threshold value T, the gray level image can be converted in to binary image. Several methods are there to find the threshold automatically for image segmentation. Some of the methods like Otsu, Kapur, Triangle, Iterative and also manually threshold is calculated for different type of images like X-ray computed tomography (CT-Scan), magnetic resonance imaging (MRI), synthetic aperture radar (SAR), Ultrasound image were explained and the results are presented to show the validity of the methods. DOI: 10.17762/ijritcc2321-8169.16042

A Generalization of Otsu's Method and Minimum Error Thresholding

We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding. GHT works by performing approximate maximum a posteriori estimation of a mixture of Gaussians with appropriate priors. We demonstrate that GHT subsumes three classic thresholding techniques as special cases: Otsu's method, Minimum Error Thresholding (MET), and weighted percentile thresholding. GHT thereby enables the continuous interpolation between those three algorithms, which allows thresholding accuracy to be improved significantly. GHT also provides a clarifying interpretation of the common practice of coarsening a histogram's bin width during thresholding. We show that GHT outperforms or matches the performance of all algorithms on a recent challenge for handwritten document image binarization (including deep neural networks trained to produce per-pixel binarizations), and can be implemented in a dozen lines of code or as a trivial modification to Otsu's method or MET.Comment: ECCV 202

Segmentation and Extraction of Individual Leaves from Plant Images for Species Classification

Plant species classification through the examination of images of plant leaves requires as input an image of a single leaf with no stems or other non-leaf objects. Images of plants, however, usually include more than one leaf, stems, branches, flowers, and other non-leaf objects. For such images each individual leaf needs to be extracted into a unique sub-image, and these sub-images must be cleaned to remove all non-leaf objects. A target leaf could then be selected from the group of sub-images to be provided as the input to the plant species classification program. As a part of the research on this thesis, an algorithm was developed to automate the tasks of detecting and extracting leaf sub-images from plant images and to clean the leaf sub-images by removing all non-leaf objects. To implement the algorithm, software was developed in Java. The proposed algorithm produced at least one perfect leaf result in 18 of the 21 (86%) plant images used in this research, while the remaining three (14%) plant images produced acceptable leaves

A Novel Histogram-Based Multi-Threshold Searching Algorithm for Multilevel Color Thresholding

[[abstract]]Image segmentation is an important preliminary process required in object tracking applications. This paper addresses the issue of unsupervised multi‐colour thresholding design for colour‐based multiple objects segmentation. Most of the current unsupervised colour thresholding techniques require adopting a supervised training algorithm or a cluster‐number decision algorithm to obtain optimal threshold values of each colour channel for a colour‐of‐interest. In this paper, a novel unsupervised multi‐threshold searching algorithm is proposed to automatically search the optimal threshold values for segmenting multiple colour objects. To achieve this, a novel ratio‐map image computation method is proposed to efficiently enhance the contrast between colour and non¬colour pixels. The Otsu’s method is then applied to the ratio‐map image to extract all colour objects from the image. Finally, a new histogram‐based multi‐threshold searching algorithm is developed to search the optimal upper‐bound and lower‐bound threshold values of hue, saturation and brightness components for each colour object. Experimental results show that the proposed method not only succeeds in separating all colour objects-of-interest in colour images, but also provides satisfactory colour thresholding results compared with an existing multilevel thresholding method.[[notice]]補正完畢[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]電子版[[booktype]]紙

Analysis of objects in binary images

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented