scholarly journals An Efficient Hardware-Oriented Single-Pass Approach for Connected Component Analysis

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3055 ◽  
Author(s):  
Fanny Spagnolo ◽  
Stefania Perri ◽  
Pasquale Corsonello
Keyword(s):  
Component Analysis ◽  
Input Image ◽  
Image Resolution ◽  
Streaming Data ◽  
Frame Rate ◽  
Connected Component ◽  
Connected Component Analysis ◽  
Single Pass ◽  
Field Programmable ◽  
On Chip

Connected Component Analysis (CCA) plays an important role in several image analysis and pattern recognition algorithms. Being one of the most time-consuming tasks in such applications, specific hardware accelerator for the CCA are highly desirable. As its main characteristic, the design of such an accelerator must be able to complete a run-time process of the input image frame without suspending the input streaming data-flow, by using a reasonable amount of hardware resources. This paper presents a new approach that allows virtually any feature of interest to be extracted in a single-pass from the input image frames. The proposed method has been validated by a proper system hardware implemented in a complete heterogeneous design, within a Xilinx Zynq-7000 Field Programmable Gate Array (FPGA) System on Chip (SoC) device. For processing 640 × 480 input image resolution, only 760 LUTs and 787 FFs were required. Moreover, a frame-rate of ~325 fps and a throughput of 95.37 Mp/s were achieved. When compared to several recent competitors, the proposed design exhibits the most favorable performance-resources trade-off.

scholarly journals A new Connected Component Analysis based System for Text Segmentation in Degraded Historical Document Images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Historical Document ◽  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Historical documents contain valuable heritage information.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 In this work, we present an effective way to segment historical document images into characters.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 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.

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