Classification and Recognition of 3D Image of Charlier moments using a Multilayer Perceptron Architecture

Amakdouf, Hicham; Mallahi, Mostafa El; Zouhri, Amal; Tahiri, Ahmed; Qjidaa, Hassan

doi:10.1016/j.procs.2018.01.118

Cited by 26 publications

(7 citation statements)

References 13 publications

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“…Output signals of the last layer neurons are the response of the whole network. The number of neurons in this layer depends on the addressed problem (Amakdouf et al, 2018;Goodfellow et al, 2016). For example, in the issue considered in this paper, the price of real estate is estimated.…”

Section: Arti Cial Neural Networkmentioning

confidence: 99%

The use of urban indicators in forecasting a real estate value with the use of deep neural network

Bazan-Krzywoszańska

Bereta

2018

Reports on Geodesy and Geoinformatics

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Records of municipal planning documents directly affect the land use. In this way, the market price of the land is also shaped. Awareness of the economic and social consequences of adapting specific solutions is the primary argument that should condition the local policy in terms of spatial planning. The research results indicate that the network trained with attributes which do not describe a property value by its price was able to estimate it with acceptable and satisfactory results. The possibility to use artificial multilayer networks in spatial policy decision-making seems well founded. The research results show the relevance of the assumption that using them for modeling can be helpful in selecting the most advantageous variant of planning arrangements in a local law document which determines the land use and development, therefore impacts its value.

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Section: Arti Cial Neural Networkmentioning

confidence: 99%

The use of urban indicators in forecasting a real estate value with the use of deep neural network

Bazan-Krzywoszańska

Bereta

2018

Reports on Geodesy and Geoinformatics

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“…If k = 2 (shown in Figure 9c), (even number) it is difficult to classify the object because of achieving the same score of two classes labels. Orthogonal moments face recognition [143], object classification [144], [145], object recognition [128], [146], texture retrieval [147] • strong signal descriptors with low order elements [129] • computationally expensive [129] Krawtchouk polynomials object recognition [130], edge detection [148], object classification [149], image recognition [150] • better performance for reconstruction error [23], [130] • high computational time [23] Tchebichef polynomials image analysis [131], face Recognition [151], edge detection [132], image retrieval [152] • eliminates the necessity for numerical approximation in the image discrete domain [131], [133] • vulnerable coefficients' calculation to numerical instability for higher polynomial order [132] Charlier polynomials object recognition [153], image classification [154], image reconstruction [155], object recognition [156] • minimizes both the time of computation and the error of propagation [136] • coefficient's numerical inconsistency for higher-order polynomials [136] SKTP face detection [140] • stable in noisy environments [140] • no clear information for handling major occlusion [140] B. DECISION TREE Decision trees are developed for classification tasks.…”

Section: Classifiers a Nearest Neighbour Classifiermentioning

confidence: 99%

Vision-Based Human Detection Techniques: A Descriptive Review

Sumit

Mirjalili

2021

IEEE Access

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“…The weights and biases are initialized to small random values, and by stochastic gradient descent (SGD), they are iteratively updated on the training samples until the squared error becomes minimum. (22) 2 2…”

Section: Mlpmentioning

confidence: 99%

Sensing Analysis of Feature Extraction Types for Handwritten Character Recognition

Gao¹,

Yao²,

Zhang³

et al. 2020

Sensors and Materials

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The line density direction (LDD) feature, gradient direction (GD) feature, and deep convolution neural network-based (convNet-based) feature widely employed in handwritten character sensing recognition have acceptable accuracies. The convNet-based method determines feature expression not only from a raw pattern image but also from domain-specific LDD and GD knowledge. These methods are named convNet-based-Raw, convNet-based-LDD, and convNet-based-GD, respectively. In this paper, we present an independent sensing analysis of the five features under identical working conditions considering the preprocessing and algorithm implementation of two handwritten character databases: CASIA-HWDB1.0 (Chinese) and TUAT HANDS (Japanese). The experimental results demonstrate that convNet-based feature extraction is more robust and discriminating than LDD and GD, two traditional methods for both handwritten Chinese character recognition (HCCR) and handwritten Japanese character recognition (HJCR). Furthermore, the convNet-based-GD has the highest accuracy for both HCCR and HJCR among the three convNet-based feature extraction methods. Compared with the traditional methods, LDD and GD, the best accuracies when using convNet-based-GD are improved by 3.04 and 2.31% for HCCR, and 3.15 and 1.54% for HJCR, respectively. Similarly, compared with the two other convNet-based methods, convert-based-Raw and content-based-LDD, the best accuracies are improved by 0.44 and 0.25% for HCCR, and 0.65 and 0.08% for HJCR, respectively. Experimental comparisons of sensing analysis results are acceptable and valuable.

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Classification and Recognition of 3D Image of Charlier moments using a Multilayer Perceptron Architecture

Cited by 26 publications

References 13 publications

The use of urban indicators in forecasting a real estate value with the use of deep neural network

The use of urban indicators in forecasting a real estate value with the use of deep neural network

Vision-Based Human Detection Techniques: A Descriptive Review

Sensing Analysis of Feature Extraction Types for Handwritten Character Recognition

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