Role of Clustering Techniques in Effective Image Segmentation

2018 ◽  
pp. 128-160
Author(s):  
Bhavneet Kaur ◽  
Meenakshi Sharma
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Object Detection ◽  
Real Time ◽  
Vision Research ◽  
Medical Equipment ◽  
Additional Advantage ◽  
Clustering Techniques

Image segmentation is gauged as an essential stage of representation in image processing. This process segregates a digitized image into various categorized sections. An additional advantage of distinguishing dissimilar objects can be represented within this state of the art. Numerous image segmentation techniques have been proposed by various researchers, which maintained a smooth and easy timely evaluation. In this chapter, an introduction to image processing along with segmentation techniques, computer vision fundamentals, and its applied applications that will be of worth to the image processing and computer vision research communities has been deeply studied. It aims to interpret the role of various clustering-based image segmentation techniques specifically. Use of the proposed chapter if made in real time can project better outcomes in object detection and recognition, which can then later be applied in numerous applications and devices like in robots, automation, medical equipment, etc. for safety, advancement, and betterment of society.

Near-Band IR Small Scene Star Image Segmentation Based on Partial Histogram Grey Level

2014 ◽  
Vol 496-500 ◽  
pp. 1834-1839
Author(s):  
Zhe Wang ◽  
Zhe Yan ◽  
Wei Tan
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Real Time ◽  
Grey Level ◽  
Star Image ◽  
Star Images

The near-band IR star images segmentation and recognition is key technique in day time star navigation. Due to the scene of near-band IR star imaging relative small and stellar with high star grade are limited. Pertinence and dynamic grey level threshold is necessary for image processing arithmetic. In order to enhance near-band IR star images segmentation and recognition in real-time, this paper present the process of partial histogram grey level threshold and improve for actually near-band IR star images with scene of no more than 1.5°×1.5°. It can reduce the calculation of near-band IR star images with adjustable threshold. And get rid of disturbance of small imaging square stars and noise points.

Digital Image Zero-Watermarking Algorithm Based on Improved Wavelet Moment

2013 ◽  
Vol 347-350 ◽  
pp. 3232-3236
Author(s):  
Zheng Bao Zhang ◽  
Chao Jia
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Real Time ◽  
Digital Watermarking ◽  
Digital Image ◽  
Image Rotation ◽  
The Real ◽  
Geometric Attacks ◽  
Zero Watermarking ◽  
Local Distortions

Lots of anti-RST attacks watermarking algorithms have been proposed, but few solutions for local geometric attacks, in this paper it proposed a new algorithm combined with the the Wavelet Moment for an anti-geometric attacks. Since wavelet moment was proposed, it is widely used in the field of computer vision, image processing, but the large amount of computation must be improved to be applied to digital watermarking technology so that it can adapt to the real-time detection of digital watermarking. By image rotation, scaling, translation, shear, local distortions, filtering attack operations and so on, these attacks can be seen that the algorithm has good robustness, and the efficiency of watermark detection is relatively high. The experiments show that the algorithm is robustness, greatly accelerate the speed of operation, to unify the robust and efficient.

Hybrid Image Segmentation Within a Computer Vision Hierarchy

1993 ◽  
Vol 30 (1) ◽  
pp. 51-64
Author(s):  
Ray Thomas ◽  
Fariborz Zahedi
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Vision System ◽  
Critical Role ◽  
Generic Model ◽  
Test Results ◽  
Segmentation Approach ◽  
Edge Based

Hybrid image segmentation within a computer vision hierarchy A generic model of a computer vision system is presented which highlights the critical role of image segmentation. A hybrid segmentation approach, utilising both edge-based and region-based techniques, is proposed for improved quality of segmentation. An image segmentation architecture is outlined and test results are presented and discussed.

scholarly journals Combining dielectrophoresis and computer vision for precise and fully automated single-cell handling and analysis

Lab on a Chip ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 4016-4020 ◽  
Author(s):  
Neus Godino ◽  
Felix Pfisterer ◽  
Tobias Gerling ◽  
Christian Guernth-Marschner ◽  
Claus Duschl ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Single Cell ◽  
Real Time ◽  
Active Control ◽  
Microfluidic Channel ◽  
Real Time Image Processing ◽  
Real Time Image ◽  
Cell Handling ◽  
Time Image

We employ real-time image processing in the active control of dielectrophoretic actuation to select, isolate and arrange individual cells in a microfluidic channel.

scholarly journals Comparative Performance Analysis of Neural Network Real-Time Object Detections in Different Implementations

2020 ◽  
Vol 226 ◽  
pp. 02020
Author(s):  
Alexey V. Stadnik ◽  
Pavel S. Sazhin ◽  
Slavomir Hnatic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Performance Analysis ◽  
Object Detection ◽  
Real Time ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Comparative Performance

The performance of neural networks is one of the most important topics in the field of computer vision. In this work, we analyze the speed of object detection using the well-known YOLOv3 neural network architecture in different frameworks under different hardware requirements. We obtain results, which allow us to formulate preliminary qualitative conclusions about the feasibility of various hardware scenarios to solve tasks in real-time environments.

scholarly journals Ball Classification through Object Detection using Deep Learning for Handball

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 49-54
Author(s):  
Arzielah Ashiqin Alwi ◽  
Ahmad Najmuddin Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Performance Analysis ◽  
Object Detection ◽  
High Speed ◽  
Detection Accuracy ◽  
Narrow Angle ◽  
Target Locations ◽  
Ball Detection

Dynamic gameplay, fast-paced and fast-changing gameplay, where angle shooting (top and bottom corner) has the best chance of a good goal, are the main aspects of handball. When it comes to the narrow-angle area, the goalkeeper has trouble blocked the goal. Therefore, this research discusses image processing to investigate the shooting precision performance analysis to detect the ball's accuracy at high speed. In the handball goal, the participants had to complete 50 successful shots at each of the four target locations. Computer vision will then be implemented through a camera to identify the ball, followed by determining the accuracy of the ball position of floating, net tangle and farthest or smallest using object detection as the accuracy marker. The model will be trained using Deep Learning (DL)  models of YOLOv2, YOLOv3, and Faster R-CNN and the best precision models of ball detection accuracy were compared. It was found that the best performance of the accuracy of the classifier Faster R-CNN produces 99% for all ball positions.

scholarly journals Object Detectors’ Convolutional Neural Networks backbones : a review and a comparative study

2021 ◽  
Vol 9 (11) ◽  
pp. 1379-1386
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Crucial Role ◽  
Extended Version ◽  
Backbone Networks ◽  
Detection Algorithms ◽  
Wide Range

Computer vision is a scientific field that deals with how computers can acquire significant level comprehension from computerized images or videos. One of the keystones of computer vision is object detection that aims to identify relevant features from video or image to detect objects. Backbone is the first stage in object detection algorithms that play a crucial role in object detection. Object detectors are usually provided with backbone networks designed for image classification. Object detection performance is highly based on features extracted by backbones, for instance, by simply replacing a backbone with its extended version, a large accuracy metric grows up. Additionally, the backbone's importance is demonstrated by its efficiency in real-time object detection. In this paper, we aim to accumulate the crucial role of the deep learning era and convolutional neural networks in particular in object detection tasks. We have analyzed and have been concentrating on a wide range of reviews on convolutional neural networks used as the backbone of object detection models. Building, therefore, a review of backbones that help researchers and scientists to use it as a guideline for their works.

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