scholarly journals 224 Automatic image feature extraction for genetic analysis in cattle

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 47-47
Author(s):  
Jessica Nye ◽  
Laura Zingaretti ◽  
Miguel Perez-Enciso
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Genetic Analysis ◽  
Coat Color ◽  
Large Data ◽  
Image Feature ◽  
Background Information ◽  
Pedigree Data ◽  
Require Time ◽  
Composite Method

Abstract Image analysis has increasingly become an important tool for increasing productivity in many industries, yet its application in breeding programs is under utilized. With coat color patterns from dairy bull images, we explore automatic image analysis that extracts features which can be used in genetic analysis. In order to remove the unnecessary background information, the current methods require time consuming human inspection. Here, we present and compare a composite method that creates a mask (i.e., removes the background portion of the image) and calculates the proportion of dark and light coloration in bulls (n = 657) from the breeds Holstein and Ayrshire in dynamic backgrounds (e.g., forest, grass, hay, snow, etc.). This composite method combines the supervised algorithm MASK-RCNN, an unsupervised image segmentation approach, and k-means color clustering. The first step identifies the region of interest removing the majority of the background noise, while the second and third steps optimize the identification of the bull and segments the color patterning. We find a very low discrepancy between the proportion of white and dark between the manual curation and the composite method (+/- 1.40%); with an immense reduction in data collection time. This automatic composite method greatly improves the efficiency of complex image segmentation and analysis without compromising the quality of the data extracted, making analysis computationally feasible for large data sets. The next step is to calculate genetic parameters from these extracted phenotypes with genomic and/or pedigree data.

The Research on Image Segmentation for Fingerprint Identification

2011 ◽  
Vol 217-218 ◽  
pp. 396-401
Author(s):  
Xiao Jie Xu ◽  
Xi Yan Dong
Keyword(s):  
Image Segmentation ◽  
Mixture Model ◽  
Hybrid Approach ◽  
Identification Accuracy ◽  
Fingerprint Identification ◽  
Image Feature ◽  
Background Information ◽  
Bayesian Decision ◽  
Fingerprint Image ◽  
Directional Information

As the precondition of fingerprint identification, the effective image segmentation plays the significant role in the following image processing. Unlike other images, the fingerprint images are obviously directional. Aiming at this feature, in this paper, an image segmentation method based on the directional information of fingerprint image is introduced, which sufficiently utilizes the directional information of fingerprint image and succeeds in separating the background information. However, owing to the absence of directional information in some local areas of fingerprint image, this method will produce large segmentation errors, even fail. Therefore, for these local regions without directional information, it is proposed to apply Bayesian decision-making theory based on minimum error probability to realize image segmentation. On the assumption that the gray values accord with the probability distribution of Gaussian finite mixture model in image feature space, EM algorithm is used to estimate the parameters of mixture model. The mixture application of two methods can effectively separate the background information from fingerprint image while saving the preprocessing time and ensuring the following identification accuracy of fingerprint. The experiments illustrate the feasibility of the hybrid approach.

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

A Survey on Solar Image Segmentation Techniques

2014 ◽  
Vol 945-949 ◽  
pp. 1899-1902
Author(s):  
Yuan Yuan Fan ◽  
Wei Jiang Li ◽  
Feng Wang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Analysis ◽  
Image Segmentation ◽  
Image Retrieval ◽  
Fundamental Issue ◽  
Solar Image ◽  
Segmentation Methods

Image segmentation is one of the basic problems of image processing, also is the first essential and fundamental issue in the solar image analysis and pattern recognition. This paper summarizes systematically on the image segmentation techniques in the solar image retrieval and the recent applications of image segmentation. Then the merits and demerits of each method are discussed in this paper, in this way we can combine some methods for image segmentation to reach the better effects in astronomy. Finally, according to the characteristics of the solar image itself, the more appropriate image segmentation methods are summed up, and some remarks on the prospects and development of image segmentation are presented.

scholarly journals A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS

2016 ◽  
Vol XLI-B7 ◽  
pp. 241-247 ◽  
Author(s):  
T. Kavzoglu ◽  
M. Yildiz Erdemir ◽  
H. Tonbul
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Classification Accuracy ◽  
Scale Parameter ◽  
Rate Of Change ◽  
Multi Scale ◽  
Object Based Image Analysis ◽  
Optimal Scale ◽  
Segmentation Process ◽  
Object Based

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

scholarly journals REGION GROWING IMAGE SEGMENTATION ON LARGE DATASETS USING GPU

2016 ◽  
Vol 15 (14) ◽  
pp. 7486-7497
Author(s):  
Gurpreet Kaur ◽  
Sonika Jindal
Keyword(s):  
Image Segmentation ◽  
Traffic Control ◽  
Cluster Computing ◽  
Region Growing ◽  
Large Data ◽  
Maximum Speed ◽  
Processing Unit ◽  
Small Areas ◽  
Best Fitting ◽  
Seed Pixel

Image segmentation is an important image processing, and it seems everywhere if we want to analyze what inside the image. There are varieties of applications of image segmentation such as the field of filtering noise from image, medical imaging, and locating objects in satellite images and in automatic traffic control systems, machine vision in problem of feature extraction and in recognition. This paper focuses on accelerating the image segmentation mechanism using region growing algorithm inside GPU (Graphical Processing Unit). In region growing algorithm, an initial set of small areas are iteratively merged according to similarity constraints. We have started by choosing an arbitrary seed pixel and compare it with neighboring pixels. Region is grown from the seed pixel by adding in neighboring pixels that are similar, increasing the size of the region. When the growth of one region stops we simply choose another seed pixel which does not yet belong to any region and start again. This whole process is continued until all pixels belong to some region. If any of the segment makers has the fusion cost lower than the maximum fusion cost (a given threshold), it is selected to grow. Avoid information overlapping like two threads attempting to merge its segment with the same adjacent segment.  Experiments have demonstrated that the proposed shape features do not imply in a significant change of the segmentation results, as long as the algorithm’s parameters are properly adjusted. Moreover, experiments for performance evaluation indicated the potential of using GPUs to accelerate this kind of application. For a simple hardware (GeForce 630M GT), the parallel algorithm reached a maximum speed up of approximately 20-30% for different datasets. Considering that segmentation is responsible for a significant portion of the execution time in many image analysis applications, especially in object-oriented analysis of remote sensing images, the experimentally observed acceleration values are significant. Two variants of PBF (Parallel Best Fitting) and PLMBF (Parallel Local Mutual Best Fitting) have been used to analyze the best merging cost of the two segments. It has been found that the PLMBF has been performed better than PBF.  It should also be noted that these performance gains can be obtained with low investment in hardware, as GPUs with increasing processing power are currently available on the market at declining prices. The parallel computational scheme is well suited for cluster computing, leading to a good solution for segmenting very large data sets.

scholarly journals Plant Diseases Recognition Based on Image Processing Technology

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Guiling Sun ◽  
Xinglong Jia ◽  
Tianyu Geng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Image Recognition ◽  
Color Image ◽  
Regional Growth ◽  
Recognition System ◽  
Plant Diseases ◽  
Image Feature

A new image recognition system based on multiple linear regression is proposed. Particularly, there are a number of innovations in image segmentation and recognition system. In image segmentation, an improved histogram segmentation method which can calculate threshold automatically and accurately is proposed. Meanwhile, the regional growth method and true color image processing are combined with this system to improve the accuracy and intelligence. While creating the recognition system, multiple linear regression and image feature extraction are utilized. After evaluating the results of different image training libraries, the system is proved to have effective image recognition ability, high precision, and reliability.

A Critical Overview of Image Segmentation Techniques Based on Transition Region

2018 ◽  
pp. 1308-1318
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Transition Region ◽  
Google Scholar ◽  
Research Directions ◽  
Distinctive Type ◽  
Critical Overview

Image segmentation is the key step from image processing to image analysis, and is an important technique of image engineering. Image segmentation based on transition region is a special or distinctive type of techniques that are different from traditional boundary-based or region-based techniques. Since the first technique using transition region proposed, there are many subsequent related researches and applications, and a series of papers in the literature citing are published worldwide. Using Google Scholar, a number of papers citing the original papers are searched, a study on the statistics of these papers is conducted. These papers are sorted first according to the publishing year, and then grouped according to their purposes and contents (with techniques used). Some questionable issues in these papers are pointed out and critically discussed, and several further research directions are indicated and analyzed.

Storage Systems for Data Warehousing

2011 ◽  
pp. 1859-1864 ◽  
Author(s):  
Alexander Thomasian
Keyword(s):  
Data Storage ◽  
Concurrency Control ◽  
Storage Systems ◽  
Large Data ◽  
Cost Effective ◽  
Background Information ◽  
Disk Storage ◽  
Large Databases ◽  
Database Size ◽  
Time Required

Data storage requirements have consistently increased over time. According to the latest WinterCorp survey (http://www/WinterCorp.com), “The size of the world’s largest databases has tripled every two years since 2001.” With database size in excess of 1 terabyte, there is a clear need for storage systems that are both cost effective and highly reliable. Historically, large databases are implemented on mainframe systems. These systems are large and expensive to purchase and maintain. In recent years, large data warehouse applications are being deployed on Linux and Windows hosts, as replacements for the existing mainframe systems. These systems are significantly less expensive to purchase while requiring less resources to run and maintain. With large databases it is less feasible, and less cost effective, to use tapes for backup and restore. The time required to copy terabytes of data from a database to a serial medium (streaming tape) is measured in hours, which would significantly degrade performance and decreases availability. Alternatives to serial backup include local replication, mirroring, or geoplexing of data. The increasing demands of larger databases must be met by less expensive disk storage systems, which are yet highly reliable and less susceptible to data loss. This article is organized into five sections. The first section provides background information that serves to introduce the concepts of disk arrays. The following three sections detail the concepts used to build complex storage systems. The focus of these sections is to detail: (i) Redundant Arrays of Independent Disks (RAID) arrays; (ii) multilevel RAID (MRAID); (iii) concurrency control and storage transactions. The conclusion contains a brief survey of modular storage prototypes.

Fuzzy Clustering-Based Approaches in Automatic Lip Segmentation from Color Images

2006 ◽  
pp. 292-317
Author(s):  
Shilin Wang ◽  
Wing Hong Lau ◽  
Alan Wee-Chung Liew ◽  
Shu Hung Leung
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Visual Information ◽  
Spatial Information ◽  
Noisy Environments ◽  
Low Contrast ◽  
Speaker Authentication ◽  
Lip Segmentation ◽  
Traditional Image

Recently, lip image analysis has received much attention because the visual information extracted has been shown to provide significant improvement for speech recognition and speaker authentication, especially in noisy environments. Lip image segmentation plays an important role in lip image analysis. This chapter will describe different lip image segmentation techniques, with emphasis on segmenting color lip images. In addition to providing a review of different approaches, we will describe in detail the state-of-the-art classification-based techniques recently proposed by our group for color lip segmentation: “Spatial fuzzy c-mean clustering” (SFCM) and “fuzzy c-means with shape function” (FCMS). These methods integrate the color information along with different kinds of spatial information into a fuzzy clustering structure and demonstrate superiority in segmenting color lip images with natural low contrast in comparison with many traditional image segmentation techniques.

Sign in / Sign up

Export Citation Format

Share Document