Learn Computer Vision Using OpenCV

Gollapudi, Sunila

doi:10.1007/978-1-4842-4261-2

Cited by 59 publications

(27 citation statements)

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“…In Figure 2, we illustrate the convolution of a kernel with an input matrix of a dimension of depth one. The convolutional layer consists of filters, or kernels, that are applied along the spatial dimensions and added together over the input volume's depth dimension (11). The matrix resulting from a convolution operation between a kernel and an input matrix is called an activation map.…”

Section: Convolutional Layermentioning

confidence: 99%

“…A pooling layer is usually added after the convolutional layer. The pooling layer is responsible for reducing the input volume's spatial dimension, thereby reducing the number of parameters to be trained (11).…”

Section: Pooling Layermentioning

confidence: 99%

“…The fully connected layer can receive only one-dimensional data. In this way, the data organized in a volume with three dimensions of the last layer needs to be converted to unitary dimension (11).…”

Section: Pooling Layermentioning

confidence: 99%

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Pre-Harvest Fruit Image Processing: A Brief Review

Gremes¹,

Krummenauer²,

Andrade³

et al. 2021

BJEDIS

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Agriculture is essential for the development of human civilization. Methods that can precisely estimate the yield of a crop or to perform the harvest automatically using robots can decrease the costs involved and increase production efficiency. With the advancement of agriculture 4.0, current technologies like the internet of things, big data, and artificial intelligence have become more and more common. Systems that use image processing with Deep Learning methods are becoming viable in solving agricultural problems. Deep Learning is part of a large family of methods based on artificial neural networks that can mimic the human brain's work in data processing and pattern recognition for decision-making. Indeed, applications of Deep Learning techniques in agriculture are relatively recent. However, with the rapid advance in Deep Learning and its successful application in agriculture, many articles have been published in recent years. Thus, the main objective of this work was to carry out a brief bibliographic review of pre-harvest fruit image processing techniques, emphasizing the most recent applications using Deep Learning. As seen in the literature, Deep Learning is a promising tool for various agricultural activities, including fruit counting and automatic fruit harvesting using robots.

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Section: Convolutional Layermentioning

confidence: 99%

Section: Pooling Layermentioning

confidence: 99%

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Pre-Harvest Fruit Image Processing: A Brief Review

Gremes¹,

Krummenauer²,

Andrade³

et al. 2021

BJEDIS

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show abstract

“…where 1: represents the input sentence, whose words are mapped and stacked together with embedding vectors. Additionally, the output can be used at various tasks; for example, it is possible to obtain a representation for each data and give it to the classifier as an input, in order to train the network [23][24][25][26]. Nevertheless, the networks used in this project are convolutional and VGG16 networks.…”

Section: Fig1 Venn Diagram Of Artificial Intelligencementioning

confidence: 99%

Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

Yazdani¹,

Mehr²,

Showkatyan³

et al. 2021

IJIGSP

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The present study, the main idea of which was based on one of the questions of I.P.T.2018 competition, aimed to develop a high-precision relationship between the fluid temperature and the sound produced when colliding with different surfaces, by creating a data collection tool. In fact, this paper was provided based on a traditional phenomenological project using the well-known deep neural networks, in order to achieve an acceptable accuracy in this project. In order to improve the quality of the paper, the data were analyzed in two ways: I. Using the images of data spectrogram and the known V.G.G.16 network. II. Applying the data audio signal and a convolutional neural network (C.N.N.). Finally, both methods have obtained an acceptable precision above 85%.

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“…The Pi's camera module is used to capture facial features, and a DY50 fingerprint scanner is used for capturing fingerprint features. The pyfingerprint code [48] is used in developing the fingerprint authentication module, while OpenCV [49] is used for the face biometrics.…”

Section: Prototypementioning

confidence: 99%

Energy‐efficient multi‐biometric system for Internet of Things using trust management

et al. 2021

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The traditional password-based authentication systems are easy to breach because they rely on what the user knows and not on who is the user. This makes them prone to impersonation, as the culprit can gain access to the system once they knew the user's password, which led to the development of biometric-based authentication systems. Nonetheless, noisy data, intra-, and inter-class variations render these systems inaccurate. However, combining multiple biometric traits (multi-biometric systems) promises to increase the accuracy of biometric systems. But, this leads to an overhead in energy and latency in the system. These overheads are frowned at in embedded systems, especially those deployed in remote areas. Here, the authors propose a trust management-based multi-biometric system. The system switches between a uni-biometric and a multibiometric system to achieve high accuracy and reduced overhead (i.e. energy and latency). The system uses the uni-biometric technique for trusted users and the multibiometric method for untrustworthy users who are zero-effort impostors. It is found to reduce the false positive rate by 0.29 of Fingerprint uni-biometric system and the false negative rate by a factor of 0.5 of the multi-biometric system. Also, the energy consumption is reduced by a factor of 0.2 of the multi-biometric system.

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Learn Computer Vision Using OpenCV

Cited by 59 publications

References 0 publications

Pre-Harvest Fruit Image Processing: A Brief Review

Pre-Harvest Fruit Image Processing: A Brief Review

Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

Energy‐efficient multi‐biometric system for Internet of Things using trust management

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