Oil tank detection based on salient region and geometric features

A small but growing area of public administration scholarship appreciates the influence of religious values on various aspects of government. This appreciation parallels a growing interest in comparative public administration and indigenized forms of government which recognizes the role of culture in different approaches to government. This article is at the crossroads of these two trends while also considering a very salient region, the Islamic world. The Islamic world is uniquely religious, which makes this discussion even more relevant, as the nations that represent them strive towards legitimacy and stability. The history and core values of Islam need to be considered as they pertain to systems of government that are widely accepted by the people. In essence, this is being done in many countries across the Islamic world, providing fertile grounds for public administration research from a comparative perspective. This paper explores these possibilities for future research on this topic.

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An Investigation on Gesture Analysis and Geometric Features Extraction

International Journal of Innovative Research in Computer and Communication Engineering ◽

10.15680/ijircce.2015.0301037 ◽

2015 ◽

Vol 03 (01) ◽

pp. 387-392

Author(s):

NOOR ADNAN IBRAHEEM ◽

MOKHTAR MOHAMMED HASAN ◽

RAFIQULZAMAN KHAN

Keyword(s):

Features Extraction ◽

Geometric Features ◽

Gesture Analysis

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Face Completion Using Semantic Segmentation and Geometric Features

International Review of Automatic Control (IREACO) ◽

10.15866/ireaco.v11i6.15738 ◽

2018 ◽

Vol 11 (6) ◽

pp. 304

Author(s):

Javier Pinzon-Arenas ◽

Robinson Jimenez-Moreno ◽

Ruben Hernandez-Beleno

Keyword(s):

Semantic Segmentation ◽

Geometric Features

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OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i2.8947 ◽

2020 ◽

Vol 5 (2) ◽

pp. 504

Author(s):

Matthias Omotayo Oladele ◽

Temilola Morufat Adepoju ◽

Olaide ` Abiodun Olatoke ◽

Oluwaseun Adewale Ojo

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Word Recognition ◽

Support Vector Machine Classifier ◽

Recognition Accuracy ◽

Recognition System ◽

Support Vector ◽

Geometric Features ◽

Total Length ◽

Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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