Speeded up robust features (SURF) : performance test

This work presents a performance analysis of SURF features, an algorithm for feature detection and matching. The goal is to test the performance of SURF in the presence of noise: The analysis is performed both on synthetically generated observations as well on raw images. In first place we present SURF features. Introduction covers the concept of feature extraction, what it is and the interest of it, as well the feature points detection, description and matching. After the introduction we proceed with the main part: Experiments. In the first part we use test images and we add noise (additive noise). The additive noise we work with is Gaussian and Poisson noise. We do this process several times and in each iteration we compute the SURF features of the original image and the altered one. Afterwards the performance is analyzed, considering the repeatability and the ratio of incorrect matches. We also consider the changes of illumination, out of focus blur and motion blur. Finally conclusions are taken from the results. In the second part the experiments are done with raw files. A raw file is the data obtained from the sensor, without changes, so it needs a bit of processing for be able even to preview it. It is analyzed the performance of SURF when it is changed the exposure time. In this case noise will be as a result of the camera defects and noise related to the exposure time. After the experiments, the results are analyzed as in the first part (repeatability and ratio of incorrect matches). It results that SURF is robust to noise in a wide range, and out of this range the results are poor and inaccurate. It is sensible to blur and the performance is bad in dark environments (pictures). An appendix is written for explain the process of port the SURF libraries, which are originally written in C and ready for use with OpenCV in any platform that admits OpenCV (such as C, python or Java) and make them usable in Matlab. For this purpose Matlab has a tool that is called Mex file. This tool is explained and the process in general. ______________________________________Este trabajo presenta un test de funcionamiento de la librería SURF features, un algoritmo para la detección y correcpondencia de parámetros significativos en una imagen. El objetivo es comprobar el comportamiento de SURF en presencia de ruido: El análisis es llevado a cabo con ruido sintético (generado) y también con imágenes raw (no procesadas). En primer lugar se presenta SURF features. La introducción abarca el concepto de extracción de parámetros, qué es y el interés de ello, y también la detección de puntos de interés, descripción de los mismos y la correlación entre ellos. Después de la introducción procedemos con la parte principal: Experimentos. En la primera parte usamos imágenes de prueba y añadimos ruido (ruido aditivo). El ruido aditivo con el que trabajamos es Gausiano y de Poisson. Llevamos a cabo este procedimiento varias veces y en cada iteración calculamos los parámetros SURF de la imagen original y de la imagen alterada. Después el comportamiento es analizado, considerando la repetibilidad y la relación de correspondencias incorrectas. Además consideramos los cambios de iluminación, desenfoque y movimiento. Finalmente las conclusiones extraídas de los experimentos son mostradas. En la segunda parte los experimentos son realizados con imágenes raw. Una imagen raw es una imagen obtenida directamente del sensor, sin cambios, por lo que necesita algo de procesamiento incluso para poder verla. Es analizado el comportamiento de SURF cuando es cambiado el tiempo de obturación. Después de los experimentos, los resultados son analizados igual que en la primera parte (repetibilidad y relación de correspondencias incorrectas). Resulta que SURF es robust al ruido en un amplio rango, y fuera de este rango los resultados son pobres e inexactos. Es sensible al desenfoque y el comportamiento es malo en entornos oscuros. Un apéndice es escrito para explicar el proceso de llevar la librería SURF, que originalmente han sido escritas en C y están listas para usar en OpenCV y cualquier plataforma que admita OpenCV (como C, python o Java) y hacer posible su uso en Matlab. Para este propósito Matlab dispone de una herramientda llamada Mex file. Esta herramienta es explicada y el proceso en general.Ingeniería Industria

Стійкі ознаки зображення для ідентифікації конфігурації руки в українській жестовій мові

В статті розглядається задача ідентифікації на зображенні конфігурації руки для української жестової мови. Запропоновано для розв’язку цієї задачі використовувати метод Speeded Up Robust Features (SURF), оскільки він є одним з найбільш ефективних і швидких сучасних алгоритмів. Отримані експериментальні результати показали досить високу ефективність методу для ідентифікації дактильних знаків.В статье рассматривается задача идентификации на изображении конфигурации руки для украинского жестового языка. Предложено для решения этой задачи использовать метод Speeded Up Robust Features (SURF), так как он есть одним из наиболее эффективных и быстрых современных алгоритмов. Полученные экспериментальные результаты показали достаточно высокую эффективность метода для идентификации дактильных знаков.The problem of identification of hand configuration on an image for Ukrainian Sign language is studied in this paper. To solve the problem a method Speeded Up Robust Features (SURF) is suggested to be used since it is one of the most effective and rapid of the present-day algorithms. The obtained experimental results showed a fairly high efficiency of the method for identification of dactyl signs

Aplikasi Findgo-ITATS Berbasis Android Dengan Algoritma SURF Untuk Menampilkan Informasi Lokasi Di ITATS

Institut Teknologi Adhi Tama Surabaya (ITATS) is an institute that has relatively wide territory and complicated building arrangement for outsiders especially related to identification of buildings that they want to visit. To overcome this problem, an android based application that can be used to gain information related to those buildings, locations of places or important places in real-time is required. Augmented Reality (AR) is the appropriate technology to display environment and locations information at ITATS in real-time. The implementation of Augmented Reality technology on android based smartphones using Speeded Up Robust Features (SURF) can identify pictures continuously and has proper identification speed. Speeded Up Robust Features (SURF) is an algorithm that has been commonly applied in correspondence matching because it is faster than Scale Invariant Feature Transform (SIFT) and has appropriate and accurate performance maintenance. In designing this application, there are three main stages that should be considered, namely: initialization, tracking marker, and object rendering. Initialization is the stage where images that becomes the database is preliminary processed with Speeded Up Robust Features (SURF) algorithm and  the preparation of the displayed information on the users’ smartphones. The second is tracking marker, smartphone camera takes pictures continuously while processing every inputted image applying Speeded Up Robust Features (SURF) and conducting matching process of images in the database. The final stage, after a match is found, this application displays the text information which corresponds with the matching result. The reliability of this system in recognizing locations at ITATS is 81.66% and average time required is 2.333 seconds

Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain

In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT) magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF) algorithm to allow the correct watermark detection, even if the watermarked image has been distorted. To recognize the selected object region after geometric distortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF features of the distorted image are estimated and match them with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and the Visual Information Fidelity (VIF). The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The receiver operating characteristics (ROC) curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided

Note: An object detection method for active camera

To solve the problems caused by a changing background during object detection in active camera, this paper proposes a new method based on SURF (speeded up robust features) and data clustering. The SURF feature points of each image are extracted, and each cluster center is calculated by processing the data clustering of k adjacent frames. Templates for each class are obtained by calculating the histograms within the regions around the center points of the clustering classes. The window of the moving object can be located by finding the region that satisfies the histogram matching result between adjacent frames. Experimental results demonstrate that the proposed method can improve the effectiveness of object detection.Yong Chen, Ronghua Zhang, Lei Shang, and Eric H

APLIKASI PENGENALAN RAMBU LALU LINTAS MENGGUNAKAN ALGORITMA SPEEDED UP ROBUST FEATURES (SURF)

Banyaknya kecelakaan kendaraan bermotor terutama kendaraan roda empat memunculkan ide untuk membuat sistem pemberitahuan atau peringatan dan juga memunculkan ide pengembangan auto-pilot pada mobil dengan mendeteksi rambu lalu lintas disekitarnya. Tahap awal pengembangan sistem tersebut adalah pengenalan rambu lalu lintas. Pada penelitian ini fokus pada membangun sebuah model pengenalan rambu lalu lintas menggunakan algoritma SURF. Tahap pengenalan rambu ada tiga tahap, yaitu: prapengolahan, ekstraksi fitur dan pencocokan. Pada tahap prapengolahan dengan tujuan untuk mendapatkan letak rambu dalam citra menggunakan segmentasi warna dengan ruang warna HSL dan blobcounter untuk mendapatkan letak rambu sesuai warna yang di segmentasi. Pada tahap ekstraksi fitur fast hessian digunakan untuk mendapatkan bloblike structure, dan non maximum suppression digunakan untuk mencari kandidat dari interest point. Deskriptor dihitung dengan menjumlahkan response haar wavelet disekitar interest point. Ekstraksi fitur menghasilkan interest points dan 64 descriptor untuk tiap interest point-nya. Pada tahap pencocokan hasil ekstraksi fitur citra kueri dicocokan dengan hasil ekstraksi fitur dari citra basisdata yang telah disimpan dengan menggunakan metode FLANN (Fast Library for Approximate Nearest Neighbors). Hasil pencocokan dari 192 citra rambu mendapatkan hasil akurasi sebesar 82,28%

Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery

Indonesia's various types of fruits can be met by the community. Many fruits that contain a source of vitamins are very beneficial to the body, or as an economic source for farmers. It's no wonder that many experts submit discoveries to increase the amount of productivity or just want to experiment with intelligent systems. Intelligent systems are specially designed machines in certain areas to adjust the capabilities made by the creators. This article provides the latest texture classification technique called Speeded up Robust Features (SURF) with the SVM (Support Vector Machine) method. In this concept, the representation of the image data is done by capturing features in the form of keys. SURF uses the determinant of the Hessian matrix to reach the point of interest in which descriptions and classifications are performed. This method delivers superior performance compared to existing methods in terms of processing time, accuracy, and durability. The results showed that the fruit classification by using the extraction of Speeded up Robust Features (SURF) feature and SVM (Support Vector Machine) Classification method is quite maximal and accurate. Result of 3 kinds of classification with SVM kernel function, SVM Gaussian with 72% accuracy, Polynomial SVM with 69.75% accuracy, and Linear SVM with 70.25% accuracy