Classification of vehicles’ types using histogram oriented gradients: comparative study and modification

El-Sayed, Rania Salah; Elsayed, Mohamed Nour

doi:10.11591/ijai.v9.i4.pp700-712

Cited by 8 publications

(6 citation statements)

References 23 publications

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“…On the other hand, if the pixel intensity histogram is distributed throughout the histogram range, the image will have a higher contrast level. This indicator was used to assess the image enhancement methods, using a variety of illuminations for optimal performance [28], [85], [86], [105], [140], [166].…”

Section: Analysis Of the Image Enhancement Methodsmentioning

confidence: 99%

“…Therefore, the NIR channel was used to refine the image's contrast. The HE method enhances the image by distributing the image brightness levels equally across the brightness scale [1], [11], [15], [21], [44], [53], [54], [72], [94], [119], [136], [140], [145], [150], [159], [168], [169], [172], [175]. Furthermore, the intensity of the contrast enhancement method is measured through the root mean square (RMS), where the higher the RMS value, the better the contrast image [22], [35], [48], [178]- [181].…”

Section: Analysis Of the Image Enhancement Methodsmentioning

confidence: 99%

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Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Zulkifle

Hassan

Ahmad

et al. 2021

IJEECS

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Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.

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Section: Analysis Of the Image Enhancement Methodsmentioning

confidence: 99%

Section: Analysis Of the Image Enhancement Methodsmentioning

confidence: 99%

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Zulkifle

Hassan

Ahmad

et al. 2021

IJEECS

View full text Add to dashboard Cite

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“…The application of machine learning in this study was carried out using cross-validation to divide the dataset into two sets: training and testing data. Five classification methods were implemented, including ANN, C.45, Decision Tree, KNN, and SVM as in the previous study [29].…”

Section: Classificationmentioning

confidence: 99%

The combination of color-texture features and machine learning for detecting Dayak beads

2023

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Dayak is one of the tribes in East Kalimantan, Indonesia, which has a lot of cultural wealth. Beads craft is one of the Dayak traditional cultures made using various materials with distinctive motifs. The Dayak beads have many different motifs and color combinations. Hence not everyone can distinguish between the bead motif of Dayak and non-Dayak easily. This study aims to develop a bead detection method to differentiate between the bead types of Dayak and non-Dayak. The main processes required include preprocessing, feature extraction, and classification. The features were extracted based on color and texture. Experiments were carried out using several machine learning approaches. The highest results were achieved using the combination of color and texture features with the implementation of K-Nearest Neighbor (KNN) methods as indicated by the parameters precision, recall, and accuracy achieved of 92%, 92%, and 92.2% using Cross-Validation with a K-Fold value of 10.

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“…In [8] the rank widget scores the attributes according to their correlation with the class. Attribute scoring methods that can be used in rank widget are information gain, information gain ratio and gini [9], [10].…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

An optimization clustering and classification based on artificial intelligence approach for internet of things in agriculture

Tangwannawit

2022

IJ-AI

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This research focused on testing with maize, economical crop grown in Phetchabun province, Thailand, by installing a total of 20 sets of internet of things (IoT) devices which consist of soil moisture sensors and temperature and humidity sensors (DHT11). Data science tools such as rapidminer studio was used for data cleansing, data imputation, clustering, and prediction. Next, these data would undergo data cleansing in order to group them to obtain optimization clustering to identify the optimum condition and amount of water required to grow the maize through k-mean technique. From the analysis, the optimization result showed 3 classes and these data were further analyzed through prediction to identify precision. By comparing several algorithms including artificial neural network (ANN), decision tree, naïve bayes, and deep learning, it was found that deep learning algorithm can provide the most accurate result at 99.6% with root mean square error (RMSE)=0.0039. The algorithm obtained was used to write function to control the automated watering system to make sure that the temperature and humidity for growing maize is at appropriate condition. By using the improved watering system, it improved the efficacy of watering system which saves more water by 13.89%

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Classification of vehicles’ types using histogram oriented gradients: comparative study and modification

Cited by 8 publications

References 23 publications

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

The combination of color-texture features and machine learning for detecting Dayak beads

An optimization clustering and classification based on artificial intelligence approach for internet of things in agriculture

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