Car Detection Methodology in Outdoor Environment Based on Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM)

2015 ◽  
Author(s):  
S. Guzmán ◽  
A. Gómez ◽  
G. Diez ◽  
D.S. Fernández
Keyword(s):  
Support Vector Machine ◽  
Outdoor Environment ◽  
Support Vector ◽  
Histogram Of Oriented Gradient ◽  
Car Detection

Pedestrian Crowd Detection Based Unmanned Aerial Vehicle Infrared Imagery

2017 ◽  
Vol 873 ◽  
pp. 347-352
Author(s):  
Yong Hao Xiao ◽  
Hong Zhen
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Human Detection ◽  
Support Vector ◽  
Infrared Images ◽  
Histogram Of Oriented Gradient ◽  
Detection Approach ◽  
Aerial Vehicle ◽  
Geometric Filtering ◽  
Pedestrian Crowd

Human detection is a keyproblem in computer vision. Recently, some research has been focusing on the detection ofpedestrianusing infrared images. The infrared images have outstanding merit. It depends only on object's temperature, but not on color or texture. In this paper, the pedestrian crowd detection approach is proposed. The approach is compose of ROI blocks extraction and crowd block recognition. ROI blocks can be extracted with circle gradient operator and weighted geometric filtering. Crowd blocks are recognized by support vector machine, which combines histogram of oriented gradient and circle gradient. The experimental results show thatthe approach works effectively in different scenes.

BI-FEATURE VERIFICATION FOR PALMPRINT IMAGES CAPTURED IN PEGLESS SCENARIOS

2013 ◽  
Vol 27 (05) ◽  
pp. 1356007 ◽  
Author(s):  
CHIH-LUNG LIN ◽  
HSU-YUNG CHENG ◽  
KUO-CHIN FAN ◽  
CHUN-WEI LU ◽  
CHANG-JUNG JUAN ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Access Control ◽  
Control Systems ◽  
Low Cost ◽  
Experimental Results ◽  
Support Vector ◽  
Low Resolution ◽  
Classification Rate ◽  
Histogram Of Oriented Gradient ◽  
Feature Information

This paper presents a reliable and robust palmprint verification approach that involves using a bi-feature, biometric, palmprint feature-point number (FPN) and a histogram of oriented gradient (HOG). The bi-feature was fused and verified using a support vector machine (SVM) at the feature level. The approach has the advantages of capturing palm images in pegless scenarios with a low cost and low-resolution (100 dpi) digital scanner, and one sensor can capture palmprint bi-feature information. The low-resolution images result in a smaller database. Nine thousand palmprint images were collected from 300 people to verify the validity of the proposed approach. The results showed an accurate classification rate of 99.04%. The experimental results demonstrated that the proposed approach is feasible and effective in palmprint verification. Our findings will help extend palmprint verification technology to security access control systems.

scholarly journals Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhe Xu ◽  
Xi Guo ◽  
Anfan Zhu ◽  
Xiaolin He ◽  
Xiaomin Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Fine Tuning ◽  
Support Vector ◽  
Test Accuracy ◽  
Nutrient Deficiencies ◽  
Deep Convolutional Neural Networks ◽  
Histogram Of Oriented Gradient

Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.

scholarly journals HOG based object detection and classification

2018 ◽  
Vol 7 (3.3) ◽  
pp. 151
Author(s):  
N Venkateswara Rao ◽  
G Anil Kumar ◽  
B Harish
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Object Detection ◽  
Real World ◽  
Support Vector ◽  
Svm Classifier ◽  
Robust Method ◽  
Histogram Of Oriented Gradient ◽  
Detection Algorithms

The intension of the project is to classify objects in real world and to tracks them throughout their life spans. Object detection algorithms use feature extraction and learning algorithms to classification of an object category. Our algorithm uses a combination of “histogram of oriented gradient” (HOG) and “support vector machine” (SVM) classifier to classify of objects. Results have shown this to be a robust method in both classifying the objects along with tracking them in real time world.  

scholarly journals Penerapan Support Vector Machine Untuk Pendeteksian dan Klasifikasi Motif Pada Citra Batik Besurek Motif Gabungan Berdasarkan Fitur Histogram Of Oriented Gradient

Pseudocode ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 75-84
Author(s):  
Dita Retnowati ◽  
Ernawati Ernawati ◽  
Kurnia Anggriani
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Histogram Of Oriented Gradient

Batik Besurek memiliki karakter dan motif yang khas dan unik. Tujuan dari penelitian ini adalah membangun aplikasi yang dapat mendeteksi dan mengklasifikasi motif batik Besurek menggunakan Histogram of Oriented Gradient untuk ekstraksi fitur dan Support Vector Machine untuk klasifikasi motif batik besurek. Penelitian ini terfokus pada motif berikut; Kaligrafi, Rafflesia, dan Burung Kuau. Aplikasi ini dibangun dengan Matlab 2014. Berdasarkan hasil pengujian aplikasi diperoleh akurasi dari proses deteksi pada citra yang telah dilatih, citra yang belum dilatih, dan citra non Besurek masing-masing yaitu; (a) 83,06% ,(b) 86,87%, dan (c) 84,69%. Sedangkan akurasi dari proses klasifikasi pada citra yang telah dilatih, citra yang belum dilatih, dan citra non-besurek masing-masing yaitu; (a) 100%, (b) 89,33%, dan 0%.

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