Comparison of SIFT and SURF Methods for Use on Hand Gesture Recognition based on Depth Map

Sýkora, Peter; Kamencay, Patrik; Hudec, Róbert

doi:10.1016/j.aasri.2014.09.005

Cited by 65 publications

(22 citation statements)

References 3 publications

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“…: SIFT can be defined as one of the widely utilized local visual descriptors. It includes two steps in which, the first step, is about feature detection of images, the second step describes the extracted features (Sykora et al, 2014). SIFT detector is strong and stable to rotations, scaling, translation and partly stable to illumination variations and viewpoint of the camera (Mansourian et al, 2015).…”

Section: Histogram Of Oriented Gradient (Hog)mentioning

confidence: 99%

Indoor Place Recognition and Localization Using Histogram of Oriented Gradient with Deep Learning

2020

ZJPAS

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Indoor place recognition is a crucial and challenging field of computer science. It is widely used in robotics and computer vision for various applications. The challenges in indoor place recognition comes from the fact that recognizing localized places like office, corridor, and others may fall under various environmental effects of weather, illumination and others. In this paper, an indoor place recognition and localization system is proposed. The system utilizes the great recognition capabilities of Convolutional Neural Network (CNN) and AlexNet with the use of feature image for training. The feature images are constructed using Histogram of Gradient (HOG). The main contribution of this work is the use of 2D feature constructed image from HOG instead of the scene image used with CNN. The proposed system was compared to other previous systems, in which, it achieved better recognition accuracy when tested on COLD and IDOL standard indoor image datasets.

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Section: Histogram Of Oriented Gradient (Hog)mentioning

confidence: 99%

Indoor Place Recognition and Localization Using Histogram of Oriented Gradient with Deep Learning

2020

ZJPAS

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“…A multi-scale inference procedure is used to produce high-resolution object detections at a low cost by a few network applications. Sykora et al (2014) suggested two feature extraction methods: SIFT as the first method and SURF method as second. They were applied on set of depth map images of left hand gestures.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of static gestures using correlation and cross-correlation

Saqib¹,

Kazmi²

2018

Int. j. adv. appl. sci.

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Sign language recognition has been an active area of research for around two decades and numerous sign languages have been extensively studied in order to design reliable sign language recognition systems. Pakistan sign language (PSL) has been used as a case study here. A comprehensive database of static images depicting the signs for different Urdu alphabets is being used as a reference and input images are being compared to perform PSL alphabet recognition. The normalized Correlation technique is being used for image registration between input image and images from the database to find the closest match. The purpose of research is to identify static gestures of any Sign Language which can ultimately lead to an identification of words and sentences. The research starts with image acquisition, image preprocessing, and use of correlation and labeling of an identified symbol. Normalized correlation is used to find the nearest match. This paper includes experiments for 37 static hand gestures related to PSL alphabets. Training dataset consists of 10 samples of each PSL symbol in different lighting conditions, different sizes and shapes of hand by 5 different signers. This gesture recognition system can identify one hand static gestures in any complex background with a "minimum-possible constraints" approach. A comparison is also drawn between normalized correlations and normalized cross-correlation. As compared to other technique, this technique can work with a small dataset size. The technique is based on unsupervised learning.

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“…SURF juga dipilih karena memiliki descriptor dengan 64 nilai poin ambang yang lebih ringkas namun memiliki semua informasi penting, daripada SIFT yang memiliki descriptor dengan 128 nilai poin ambang. Pada MATLAB algoritma SURF yang digunakan telah ada dalam kelas Bag of Words[2].Bagof Words (BoW) dalam visi komputer adalah penyerderhanaan dalam representasi fitur sebuah gambar dalam kata-kata. Pembentukan BoW akan melewati 3 tahap yaitu deteksi fitur, deskripsi fitur dan pembuatan codebook.…”

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Rancang Bangun Game Aksi dengan Integrasi dan Pengenalan Gambar Menggunakan Algoritma Ekstraksi Fitur SURF dan Klasifikasi SVM pada Perangkat Android

Nandriawan

Kuswardayan

Suciati

2016

JTITS

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Saat ini perangkat mobile berbasis Android dan iOS sangat digemari oleh anak-anak. Mereka yang telah memiliki perangkat mobile akan menghabiskan seluruh waktunya untuk menatap layar perangkat saat bermain. Akibat yang ditimbulkan adalah penurunan kesehatan mata dan kurangnya bersosialisasi dengan teman sebaya. Menggambar adalah hal yang disukai anak-anak. Dengan menggabungkan menggambar dengan permainan perangkat mobile, anak-anak akan tetap merasa bermain meski mereka sedang tidak menatap layar untuk menggambar. Tugas akhir ini bertujuan untuk membangun sebuah permainan yang dapat mengenali gambar dan menggunakan gambar tersebut dalam permainan. Metode pengenalan gambar dalam tugas akhir ini dilakukan dengan menggunakan metode Edge Detection dengan algoritma ekstraksi fitur SURF (Speeded-Up Robust Features) dan klasifikasi SVM (Support Vector Machine). Jenis permainan yang dibangun merupakan gabungan dari aksi dan tower defense. Hasil dari tugas akhir ini dibagi menjadi 3 yaitu hasil fungsionalitas permainan, hasil daya tarik permainan dan hasil akurasi pengenalan gambar. Semua fungsi yang dibuat pada permainan dapat berjalan dengan baik, dan permainan juga telah memiliki daya tarik yang memikat. Namun hasil akurasi pengenalan gambar masih kurang dari harapan penulis.

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Comparison of SIFT and SURF Methods for Use on Hand Gesture Recognition based on Depth Map

Cited by 65 publications

References 3 publications

Indoor Place Recognition and Localization Using Histogram of Oriented Gradient with Deep Learning

Indoor Place Recognition and Localization Using Histogram of Oriented Gradient with Deep Learning

Recognition of static gestures using correlation and cross-correlation

Rancang Bangun Game Aksi dengan Integrasi dan Pengenalan Gambar Menggunakan Algoritma Ekstraksi Fitur SURF dan Klasifikasi SVM pada Perangkat Android

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