Image Segmentation Based on Supervised Discriminative Learning

2018 ◽  
Vol 32 (10) ◽  
pp. 1854027 ◽  
Author(s):  
Lijuan Song
Keyword(s):  
Image Segmentation ◽  
Sparse Representation ◽  
Greedy Algorithms ◽  
Discriminative Learning ◽  
Natural Image ◽  
Efficient Manner ◽  
Complete Representation ◽  
Nonzero Coefficient ◽  
Essential Properties ◽  
Image Datasets

In view of the complex background of images and the segmentation difficulty, a sparse representation and supervised discriminative learning were applied to image segmentation. The sparse and over-complete representation can represent images in a compact and efficient manner. Most atom coefficients are zero, only a few coefficients are large, and the nonzero coefficient can reveal the intrinsic structures and essential properties of images. Therefore, sparse representations are beneficial to subsequent image processing applications. We first described the sparse representation theory. This study mainly revolved around three aspects, namely a trained dictionary, greedy algorithms, and the application of the sparse representation model in image segmentation based on supervised discriminative learning. Finally, we performed an image segmentation experiment on standard image datasets and natural image datasets. The main focus of this thesis was supervised discriminative learning, and the experimental results showed that the proposed algorithm was optimal, sparse, and efficient.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

Block-based unsupervised natural image segmentation

2000 ◽  
Vol 39 (12) ◽  
pp. 3146 ◽  
Author(s):  
Chee Sun Won
Keyword(s):  
Image Segmentation ◽  
Natural Image ◽  
Block Based

scholarly journals LSI: Latent semantic inference for natural image segmentation

2016 ◽  
Vol 59 ◽  
pp. 282-291 ◽  
Author(s):  
Le Dong ◽  
Ning Feng ◽  
Qianni Zhang
Keyword(s):  
Image Segmentation ◽  
Natural Image ◽  
Semantic Inference

scholarly journals An Improved Mask R-CNN Model for Multiorgan Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jian-Hua Shu ◽  
Fu-Dong Nian ◽  
Ming-Hui Yu ◽  
Xu Li
Keyword(s):  
Image Segmentation ◽  
Radiation Treatment ◽  
Left Lung ◽  
Region Of Interest ◽  
Target Volume ◽  
Medical Image Segmentation ◽  
Natural Image ◽  
Target Area ◽  
Semantic Features ◽  
Segmentation Task

Medical image segmentation is a key topic in image processing and computer vision. Existing literature mainly focuses on single-organ segmentation. However, since maximizing the concentration of radiotherapy drugs in the target area with protecting the surrounding organs is essential for making effective radiotherapy plan, multiorgan segmentation has won more and more attention. An improved Mask R-CNN (region-based convolutional neural network) model is proposed for multiorgan segmentation to aid esophageal radiation treatment. Due to the fact that organ boundaries may be fuzzy and organ shapes are various, original Mask R-CNN works well on natural image segmentation while leaves something to be desired on the multiorgan segmentation task. Addressing it, the advantages of this method are threefold: (1) a ROI (region of interest) generation method is presented in the RPN (region proposal network) which is able to utilize multiscale semantic features. (2) A prebackground classification subnetwork is integrated to the original mask generation branch to improve the precision of multiorgan segmentation. (3) 4341 CT images of 44 patients are collected and annotated to evaluate the proposed method. Additionally, extensive experiments on the collected dataset demonstrate that the proposed method can segment the heart, right lung, left lung, planning target volume (PTV), and clinical target volume (CTV) accurately and efficiently. Specifically, less than 5% of the cases were missed detection or false detection on the test set, which shows a great potential for real clinical usage.

Multi-nature hierarchical approach for natural image segmentation with pattern refinement feedback

2013 ◽  
Vol 99 ◽  
pp. 325-338 ◽  
Author(s):  
F.J. Díaz-Pernas ◽  
M. Antón-Rodríguez ◽  
M. Martínez-Zarzuela ◽  
F.J. Perozo-Rondón ◽  
D. González-Ortega
Keyword(s):  
Image Segmentation ◽  
Natural Image ◽  
Hierarchical Approach

scholarly journals Using Semi-Supervised Learning in Cellular Automata for Edge Detection of Image Segmentation

2018 ◽  
Vol 7 (02) ◽  
pp. 23613-23619
Author(s):  
Draiya A. Alaswad ◽  
Yasser F. Hassan
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Edge Detection ◽  
Supervised Learning ◽  
Unlabeled Data ◽  
Machine Learning Techniques ◽  
Natural Image ◽  
Clustering Method ◽  
Learning Techniques ◽  
Better Than

Semi-Supervised Learning is an area of increasing importance in Machine Learning techniques that make use of both labeled and unlabeled data. The goal of using both labeled and unlabeled data is to build better learners instead of using each one alone. Semi-supervised learning investigates how to use the information of both labeled and unlabeled examples to perform better than supervised learning. In this paper we present a new method for edge detection of image segmentation using cellular automata with modification for game of life rules and K-means algorithm. We use the semi-supervised clustering method, which can jointly learn to fusion by making use of the unlabeled data. The learning aim consists in distinguishing between edge and no edge for each pixel in image. We have applied the semi-supervised method for finding edge detection in natural image and measured its performance using the Berkeley Segmentation Dataset and Benchmark dataset. The results and experiments showed the accuracy and efficiency of the proposed method.

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