Bacteria Identification From Microscopic Morphology: A Survey

Mohamad, Noor Amaleena; Jusoh, Noorain Awang; Htike, Zaw Zaw; Win, Shoon Lei

doi:10.5121/ijscai.2014.3201

Cited by 20 publications

(14 citation statements)

References 17 publications

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“…There have been many successful attempts of using machine learning in automation of labour intensive tasks [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Image-based insect recognition has wide range of applications especially in agriculture, ecology and environmental science [5].…”

Section: Introductionmentioning

confidence: 99%

Vision Based Entomology : A Survey

Hassan¹,

Rahman²,

Htike³

et al. 2014

IJCSES

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Entomology has been deeply rooted in various cultures since prehistoric times for the purpose of agriculture. Nowadays, many scientists are interested in the field of biodiversity in order to maintain the diversity of species within our ecosystem. Out of 1.3 million known species on this earth, insects account for more than two thirds of these known species. Since 400 million years ago, there have been various kinds of interactions between humans and insects. There have been several attempts to create a method to perform insect identification accurately. Great knowledge and experience on entomology are required for accurate insect identification. Automation of insect identification is required because there is a shortage of skilled entomologists. This paper provides a review of the past literature in vision-based insect recognition and classifications. Over the past decades, automatic insect recognition and classification has been given extra attention especially in term of crop pest and disease control. This paper details advances in insect recognition, discussing representative works from different types of method and classifiers algorithm.Among the method used in the previous research includes color histogram, edge detection and feature extraction (SIFT vector). We provides discussion on the state-of-the-art and provides perspective on future research direction in insect recognition and classification problem.

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Section: Introductionmentioning

confidence: 99%

Vision Based Entomology : A Survey

Hassan¹,

Rahman²,

Htike³

et al. 2014

IJCSES

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“…To a great extent, bacteria are classified on the basis of their morphology [1] into sub groups such as, cocci, bacilli, vibrio et cetra. Microscopic study helps determine the morphological sub group of the bacteria under consideration.…”

Section: Introductionmentioning

confidence: 99%

Classifying Bacterial Species using Computer Vision and Machine Learning

Vijaykumar¹

2016

IJCA

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The study embodied in this paper, aims at making use of machine learning and computer vision algorithms in order to reliably identify the species of bacteria, from their microscopic images. The study has taken into consideration three of the most commonly occurring species of bacteria that are clinically important. The work shown further in this study can be extended to a larger number of bacteria species. The study makes use of the Speeded Up Robust Features or SURF algorithm for detecting image keypoints. The artificial intelligence classifier makes use of these keypoint vectors as its input variable. It is noteworthy that the images used are taken at the gram staining stage of bacterial identification, in order to minimize any biases in the dataset owing to a variance in staining technique, although the feature detector invariably grayscales the image prior to keypoint computation. The paper follows the study from the conception of the idea, to the formulating of the algorithm, the training and testing of the same.

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“…During the past few decades, applications of pattern recognition and machine learning techniques have emerged in many domains [8][9][10][11][12][13][14][15][16][17]. Pattern recognition and machine learning techniques have also recently become popular in the arena of microarray gene expression analysis.…”

Section: Introductionmentioning

confidence: 99%