scholarly journals Dental Images’ Segmentation Using Threshold Connected Component Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Vincent Majanga ◽  
Serestina Viriri
Keyword(s):  
Deep Learning ◽  
Region Of Interest ◽  
Component Analysis ◽  
Connected Components ◽  
Connected Component ◽  
Low Contrast ◽  
Connected Component Analysis ◽  
Feature Representations ◽  
Learning Techniques ◽  
Performance Results

Recent advances in medical imaging analysis, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the center of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performance in analysis of medical applications and systems. Deep learning techniques have achieved great performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps the dentist to diagnose dental caries. The performance of these deep networks is however restrained by various challenging features of dental carious lesions. Segmentation of dental images becomes difficult due to a vast variety in topologies, intricacies of medical structures, and poor image qualities caused by conditions such as low contrast, noise, irregular, and fuzzy edges borders, which result in unsuccessful segmentation. The dental segmentation method used is based on thresholding and connected component analysis. Images are preprocessed using the Gaussian blur filter to remove noise and corrupted pixels. Images are then enhanced using erosion and dilation morphology operations. Finally, segmentation is done through thresholding, and connected components are identified to extract the Region of Interest (ROI) of the teeth. The method was evaluated on an augmented dataset of 11,114 dental images. It was trained with 10 090 training set images and tested on 1024 testing set images. The proposed method gave results of 93 % for both precision and recall values, respectively.

scholarly journals FUSION OF LIDAR DATA AND MULTISPECTRAL IMAGERY FOR EFFECTIVE BUILDING DETECTION BASED ON GRAPH AND CONNECTED COMPONENT ANALYSIS

2015 ◽  
Vol XL-3/W2 ◽  
pp. 65-72 ◽  
Author(s):  
S. A. N. Gilani ◽  
M. Awrangjeb ◽  
G. Lu
Keyword(s):  
Component Analysis ◽  
Airborne Lidar ◽  
Connected Components ◽  
Lidar Data ◽  
Building Detection ◽  
Multispectral Imagery ◽  
Connected Component ◽  
Connected Component Analysis ◽  
Dense Vegetation ◽  
Black Pixel

Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusion by highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborne LiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDAR data is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as the primary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildings and trees), while the white region describes the ground (earth). The second phase begins with the process of Connected Component Analysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling. Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unit distance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to a neighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents a prospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clustering process clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step, NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building’s occluded parts. Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineate the building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australian data sets: Aitkenvale and Hervey Bay, for object-based and pixel-based completeness, correctness, and quality. The proposed technique detects buildings larger than 50 m<sup>2</sup> and 10 m<sup>2</sup> in the Aitkenvale site with 100% and 91% accuracy, respectively, while in the Hervey Bay site it performs better with 100% accuracy for buildings larger than 10 m<sup>2</sup> in area.

scholarly journals A new Connected Component Analysis based System for Text Segmentation in Degraded Historical Document Images

2020 ◽  
Vol 9 (6) ◽  
pp. 69-75
Keyword(s):  
Component Analysis ◽  
Input Image ◽  
Connected Components ◽  
Text Segmentation ◽  
Document Images ◽  
Historical Document ◽  
Connected Component ◽  
Histogram Method ◽  
Text Documents ◽  
Connected Component Analysis

Historical documents contain valuable heritage information. These documents are preserved in the manuscript preservation center and archaeological departments. They are mostly degraded in nature and hence hard to read and understand the contents. So, there is a need for text segmentation and feature extraction to convert these manuscripts into machine editable format. In this work, we present an effective way to segment historical document images into characters. It is a challenging segmentation process due to complex background images. In this paper, horizontal histogram, vertical histogram and connected component analysis is used to segment text documents images. In this algorithm, the input image is converted to gray scale image, then gray image is converted into binary image [Otsu’s method] and then all the objects containing fewer than desired pixels are removed. Line and word segmentation is implemented using horizontal and vertical histogram method respectively. Then the connected components are labeled and properties are measured for the image regions. Connected component analysis is used to segment the characters and the individual characters are extracted. The simulation result shows that the proposed segmentation method achieves an average accuracy of 93.37% for HDLAC 2011 DATASET. Moreover this method is more efficient and more suitable for real time tasks.

scholarly journals ROAD NETWORK IDENTIFICATION AND EXTRACTION IN SATELLITE IMAGERY USING OTSU'S METHOD AND CONNECTED COMPONENT ANALYSIS

2018 ◽  
Vol XLII-5 ◽  
pp. 91-98 ◽  
Author(s):  
P. Yadav ◽  
S. Agrawal
Keyword(s):  
High Resolution ◽  
Road Network ◽  
Satellite Images ◽  
Component Analysis ◽  
Morphological Operations ◽  
Connected Component ◽  
Road Detection ◽  
Connected Component Analysis ◽  
High Resolution Satellite Images ◽  
Otsu’S Method

<p><strong>Abstract.</strong> As the high resolution satellite images have become easily available, this has motivated researchers for searching advanced methods for object detection and extraction from satellite images. Roads are important curvilinear object as they are a used in urban planning, emergency response, route planning etc. Automatic road detection from satellite images has now become an important topic in photogrammetry with the advances in remote sensing technology. In this paper, a method for road detection and extraction of satellite images has been introduced. This method uses the concept of histogram equalization, Otsu's method of image segmentation, connected component analysis and morphological operations. The aim of this paper is to discover the potential of high resolution satellite images for detecting and extracting the road network in a robust manner.</p>

scholarly journals Deep Learning for Generic Object Detection: A Survey

2019 ◽  
Vol 128 (2) ◽  
pp. 261-318 ◽  
Author(s):  
Li Liu ◽  
Wanli Ouyang ◽  
Xiaogang Wang ◽  
Paul Fieguth ◽  
Jie Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Rapid Evolution ◽  
Feature Representation ◽  
Future Research ◽  
Context Modeling ◽  
Feature Representations ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Object Feature

Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

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