Nuclei Recognition Using Convolutional Neural Network and Hough Transform

Żejmo, M.; Kowal, Marek; Korbicz, Józef; Monczak, Roman

doi:10.1007/978-3-319-64474-5_26

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Our ensemble model, composed of five convolutional neural networks (CNN) and five multilayer perceptron models (MLP), autonomously extracts features associated with MGPs to distinguish between circular gasometers and other circular infrastructure such as water towers, oil tanks, and church domes. Sanborn atlases are particularly amenable to automated data extraction because they use relatively uniform scales, colors, labeling, and symbology across publication years and geography, and the tools we employ are well-tested: Hough transforms combined with deep learning methods have been shown to successfully detect and classify circular structures in other fields including biology, automation and robotics [ 31 – 33 ].…”

Section: Overcoming Data Challenges To Identifying Historical Mpg Sitesmentioning

confidence: 99%

Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities

2021

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U.S. cities contain unknown numbers of undocumented “manufactured gas” sites, legacies of an industry that dominated energy production during the late-19th and early-20th centuries. While many of these unidentified sites likely contain significant levels of highly toxic and biologically persistent contamination, locating them remains a significant challenge. We propose a new method to identify manufactured gas production, storage, and distribution infrastructure in bulk by applying feature extraction and machine learning techniques to digitized historic Sanborn fire insurance maps. Our approach, which relies on a two-part neural network to classify candidate map regions, increases the rate of site identification 20-fold compared to unaided visual coding.

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Section: Overcoming Data Challenges To Identifying Historical Mpg Sitesmentioning

confidence: 99%

Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities

2021

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“…The use of the CNN is based on the model commonly used [19] [20]. It is a simplified version that uses a single layer of filters and pooling rather than three filter layers.…”

Section: B Cnnmentioning

confidence: 99%

Facial Expression Recognition on partial facial sections

Melaugh

Siddique

Coleman

et al. 2019

2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)

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Research by psychologists have shown that subjects had a preference for a side of a face when it was expressing emotions. This paper seeks to find what accuracies can be attained when only a segment of the face is considered. We show that using one side of the face only reduces accuracy by 0.34% but at half the computationally time required. Various other sections of the face are evaluated for similar performance. We demonstrate that using smaller portions of the face have an expected computation reduction but dont suffer the same degree of accuracy loss. For evaluating we train with a Convolutional Neural Network. To test what portions of a facial image are useful, the full face, half face, eyes, single eye, mouth and half of the mouth are chosen. These images come from the JAFFE, CK+ and KDEF datasets.

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Nuclei Recognition Using Convolutional Neural Network and Hough Transform

Cited by 2 publications

References 21 publications

Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities

Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities

Facial Expression Recognition on partial facial sections

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