A multi-population genetic algorithm for robust and fast ellipse detection

Yao, Jie; Kharma, Nawwaf; Grogono, Peter

doi:10.1007/s10044-005-0252-7

Cited by 59 publications

(25 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other advanced target recognition algorithms, such as the genetic algorithm (GA) [47] or iterated RHT [48], can be combined with RHT to address this problem. The GA-based algorithm can search for the target curves more efficiently than RHT-based algorithm in the presence of multiple imperfect curves [47]. The iterated RHT updates the ROI to locate the target curves, which may detect interfering reflections of roots more accurately than the original RHT.…”

Section: Combination Of Several Advanced Algorithms To Deal With Compmentioning

confidence: 99%

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Cui

Guo

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Abstract:As a nondestructive geophysical tool, Ground penetrating radar (GPR) has been applied in tree root study in recent years. With increasing amounts of GPR data collected for roots, it is imperative to develop an efficient automatic recognition of roots in GPR images. However, few works have been completed on this topic because of the complexity in root recognition problem. Based on GPR datasets from both controlled and in situ experiments, the randomized Hough transform (RHT) algorithm was evaluated in root object recognition for different center frequencies (400 MHz, 900 MHz, and 2000 MHz) in this paper. Reasonable accuracy was obtained (both a high recognition rate and a low false alarm rate) in these datasets, which shows it is feasible to apply the RHT algorithm for root recognition. Furthermore, we evaluated the influence of root and soil factors on the recognition. We found that the performance of RHT algorithm is mainly affected by root interval length, root orientation, and clutter noise of soil. The recognition results by RHT could be applied for large scale root system distribution study in belowground ecology. Further studies should be conducted to reduce clutter noise and improve the recognition of the complex root reflections.

show abstract

Section: Combination Of Several Advanced Algorithms To Deal With Compmentioning

confidence: 99%

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Cui

Guo

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…For example, cell analysis is another important application field of the CBMIA approaches, referring to cell migration analysis (Boucher et al 1998), multiple cell detection (Yao et al 2005;Pasquale and Stander 2009), blood cell classification (Tuzel et al 2007), cell segmentation (Korzynska et al 2007), semen cell quality analysis (Witkowski 2013), cell nucleus detection (Nogueira and Teofilo 2014;John et al 2016), stem cell analysis (Huang et al 2016;Zhang et al 2016;Li et al 2017), antibody analysis (Soda et al 2009;Neuman et al 2013), pathological tissue analysis (Tasoulis et al 2014;Jothi and Rajam 2017) and so on. Material analysis is also an important application domain of the CBMIA methods, including food microstructure analysis (Ding and Gunasekaran 1998), collagen fiber analysis (Elbischger et al 2004), metallography image analysis (Grzegorzek 2010), membranes porosity evaluation (Chwojnowski et al 2012), cement quality analysis (Wang et al 2014) and so on.…”

Section: Potential Application Fields Of Cbmia Methodologymentioning

confidence: 99%

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

View full text Add to dashboard Cite

Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

show abstract

“…An example of some cell images may be seen in Figure 2. Data from the CellsDB has previously been used for image processing [26], [27].…”

Section: Image Patterns and Evaluationmentioning

confidence: 99%

Evolving novel image features using Genetic Programming-based image transforms

Kowaliw

Banzhaf

Kharma

et al. 2009

2009 IEEE Congress on Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

Abstract-In this paper, we use Genetic Programming (GP) to define a set of transforms on the space of greyscale images. The motivation is to allow an evolutionary algorithm means of transforming a set of image patterns into a more classifiable form. To this end, we introduce the notion of a Transformbased Evolvable Feature (TEF), a moment value extracted from a GP-transformed image, used in a classification task. Unlike many previous approaches, the TEF allows the whole image space to be searched and augmented. TEFs are instantiated through Cartesian Genetic Programming, and applied to a medical image classification task, that of detecting muscular dystrophy-indicating inclusions in cell images. It is shown that the inclusion of a single TEF allows for significantly superior classification relative to predefined features alone.

show abstract

A multi-population genetic algorithm for robust and fast ellipse detection

Cited by 59 publications

References 20 publications

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Evolving novel image features using Genetic Programming-based image transforms

Contact Info

Product

Resources

About