Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey

Sultana, Farhana; Sufian, Abu; Dutta, Paramartha

doi:10.1016/j.knosys.2020.106062

Cited by 212 publications

(113 citation statements)

References 89 publications

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“…Deep learning algorithms require much larger training datasets (minimum 1000 to 10000 images), but are less sensitive to noise and idiosyncrasies of the foreground. Thus, for large and continuously growing data sets, or for recurring image analysis tasks, deep learning has become the standard approach for segmentation (Sultana et al, 2020) . Deeper networks may increase model accuracy, and thus improve the segmentation results, but have an increasing risk of overfitting the contained informationi.e.…”

Section: After Taking Imagesmentioning

confidence: 99%

Computer vision, machine learning, and the promise of phenomics in ecology and evolutionary biology

Lürig¹,

Donoughe²,

Svensson³

et al. 2020

Preprint

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For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings, and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic trait diversity, population dynamics, mechanisms of divergence and adaptation and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from the images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics - the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, is a way to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV for fast, comprehensive, and reproducible image analysis in ecology and evolution. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can most effectively capture phenomic-level data by using CV. Next, we describe the primary types of image-based data, and review CV approaches for extracting them (including techniques that entail machine learning and others that do not). We identify common hurdles and pitfalls, and then highlight recent successful implementations of CV in the study of ecology and evolution. Finally, we outline promising future applications for CV in biology. We anticipate that CV will become a basic component of the biologist’s toolkit, further enhancing data quality and quantity, and sparking changes in how empirical ecological and evolutionary research will be conducted.

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Section: After Taking Imagesmentioning

confidence: 99%

Computer vision, machine learning, and the promise of phenomics in ecology and evolutionary biology

Lürig¹,

Donoughe²,

Svensson³

et al. 2020

Preprint

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show abstract

“…Computer vision is changing human life by assisting them in various ways. Through computer vision algorithms machine could classify images [20], segmentation of an image [21] and detect objects within an image [22]. With computer vision, we can process thousands of image frames at once and assist humans to their do jobs a better, faster, and automated way.…”

Section: Computer Visionmentioning

confidence: 99%

“…A research study proposed to monitoring patients and visitors based on instance image segmentation [42]. Each instance of ICU is quantified by Mask-RCNN [21] model.…”

Section: Review Of Some Recent State-of-the-artsmentioning

confidence: 99%

Insights of Artificial Intelligence to Stop Spread of COVID-19

Sufian

Jat

Banerjee

2020

Studies in Big Data

Self Cite

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, a pandemic that has pushed down human civilization in a severe threat. As viruses of COVID-19 like diseases are transferable from human to human, so it becomes very challenging to stop spreading these pandemics. These challenges are not only limited to the treatment of infected patients but also maintaining systematic social distancing to stop spreading the disease. However, maintaining social distancing is not entirely possible everywhere, like in hospitals, emergency sectors, etc. Some critical issues such as: carefully handling the Intensive Care Unite (ICU), patient care, hygienic practice, and systematic social distancing have become very necessary to slow down the spread of the new virus as appropriate vaccines or drugs are not yet available. In this time of crisis, Artificial Intelligence (AI) could assists in many more ways in addition to assisting diagnosis, drug or vaccine discovery. Therefore, this AI, especially algorithms of machine learning, deep learning, and computer vision along with edge computing and IoT technologies could be smart solutions for such challenges. This chapter brings such solutions through some insights of AI to assist to stop these COVID-19 like pandemics.

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“…The goal is a partition of the image into coherent regions, which is an important initial step in the analysis of image content. Numerous image segmentation algorithms have been developed in the last several decades, from the earliest methods, such as image thresholding [1], region growing and merging [2]- [3], clustering [4]- [5], watershed segmentation [6]- [7], to more complex algorithms, such as active contours [8], graph cuts [9]- [10], and deep learning-based methods [11]- [12].…”

Section: Introductionmentioning

confidence: 99%

Automatic Image Thresholding Based on Shannon Entropy Difference and Dynamic Synergic Entropy

et al. 2020

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An automatic thresholding method based on Shannon entropy difference and dynamic synergic entropy is proposed to select a reasonable threshold from the gray level image with a unimodal, bimodal, multimodal, or peakless gray level histogram. Firstly, a new concept called Shannon entropy difference is proposed, and the stopping condition of a multi-scale multiplication transformation is automatically controlled by maximizing Shannon entropy difference to produce edge images. Secondly, the gray level image is thresholded by the gray levels in order from smallest to largest to generate a series of binary images, followed by extracting contour images from the binary images. Then, a series of gray level histograms that can dynamically reflect gray level distributions and pixel positions are constructed using the edge images and the contour images synergically. Finally, dynamic synergic Shannon entropy is calculated from this series of gray level histograms, and the threshold corresponding to maximum dynamic synergic entropy is taken as the final segmentation threshold. The experimental results on 40 synthetic images and 50 real-world images show that, although the proposed method is not superior to 8 automatic segmentation methods in computational efficiency, it has more flexible adaptivity of selecting threshold and better segmentation accuracy. INDEX TERMS Automatic thresholding, Principle of maximum entropy, Shannon entropy difference, Dynamic synergic entropy.

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Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey

Cited by 212 publications

References 89 publications

Computer vision, machine learning, and the promise of phenomics in ecology and evolutionary biology

Computer vision, machine learning, and the promise of phenomics in ecology and evolutionary biology

Insights of Artificial Intelligence to Stop Spread of COVID-19

Automatic Image Thresholding Based on Shannon Entropy Difference and Dynamic Synergic Entropy

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