Speeded Up Robust Features Feature Points Research Articles

Big Data Classification of Ultrasound Doppler Scan Images Using a Decision Tree Classifier Based on Maximally Stable Region Feature Points

The classification of ultrasound scan images is important in monitoring the development of prenatal and maternal structures. This paper proposes a big data classification system for ultrasound Doppler scan images that combines the residual of maximally stable extreme regions and speeded up robust features (SURF) with a decision tree classifier. The algorithm first preprocesses the ultrasound scan images before detecting the maximally stable extremal regions (MSER). A few essential regions are chosen from the MSER regions, along with the residual region that provides the best Region of Interest (ROI). SURF features points that best represent the region are detected using the gradient of the estimated cumulative region of interest. To extract the feature from the pixels that surround the SURF feature points, the Triangular Vertex Transform (TVT) transform is used. A decision tree classifier is used to train the extracted TVT features. The proposed ultrasound scan image classification system is validated using performance parameters such as accuracy, specificity, precision, sensitivity, and F1 score. For validation, a large dataset of 12,400 scan images collected from 1792 patients is used. The proposed method has an F1score of 94.12%, sensitivity, specificity, precision, and accuracy of 93.57%, 93.57%, and 97.96%, respectively. The evaluation results show that the proposed algorithm for classifying Doppler scan images is better than other algorithms that have been used in the past.

Implementation face based cancelable multi-biometric system

This paper suggests novel two proposed cancellable biometric realization techniques recognition and template protection. In this paper, the Homomorphic Key (HK) encoding algorithm is utilized for cancelable face system. In the first suggested scheme, the HK algorithm is applied on the face images. The resultant map is encrypted, in order to the second HK utilized in the HK is produced from the image. This approach can be used to develop a frequency domain procedure for making this system for biometric template protection. The second algorithm presents a new approach to detect the features of face images depending the Speeded Up Robust Features (SURF) and Optimum Global Thresholding (OTSU) method. This algorithm is relied on scale space analysis with the number of SURF feature points as the key parameters for classification. Simulation results using evaluation metrics False Positive Rate (FPR), False Negative Rate (FNR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Area under ROC (AROC) prove that the first proposed cancelable biometric technique with the second key are best with comparing the other keys. The obtained results clear that the second suggested technique has sucesseded in detection the features of the skin, eyes, nose, hair, ears images and the face positions.

A Novel SURF Based on a Unified Model of Appearance and Motion-Variation

The speeded-up robust features (SURFs) algorithm is the best and most efficient local invariant feature algorithm for application to 2-D images and is widely applied in the fields of 2-D image processing and computer vision. Compared to 2-D images, a video has motion information in addition to its appearance information. Here, to make full use of the video appearance and motion information, we use geometric algebra as the mathematical calculation and analysis framework to obtain the embedded appearance and motion information on a local area of a video. In our proposed model of appearance and motion variation (UMAMV), we developed SURF feature detection and description algorithms operating on the spatio-temporal domain with video appearance and motion information. First of all, a model of appearance and motion variation, which contains video appearance and motion information in the framework of geometric algebra, is proposed. Then, based on this model, we propose a novel detection algorithm, theUMAMV-SURF detector, which mainly contains Hessian matrix construction, Hessian matrix determinant approximation calculation, and non-maximal suppression determination feature points as its key steps. Then, we introduce the UMAMV-SURF description algorithm, which mainly includes determining the dominant orientation of UMAMV-SURF feature points and generating the UMAMV-SURF feature descriptors. Finally, by experimenting with the Weizman and UCF101 datasets, the experimental results show that the proposed UMAMV-SURF algorithm can detect those SURF feature points which can have unique appearance information in the spatial domain and reflect motion change in the temporal domain. Moreover, it offers a higher accuracy than other spatio-temporal interest point algorithms in human behavior recognition of video footage.

적외선 영상에서 변위추정 및 SURF 특징을 이용한 표적 탐지 분류 기법

본 논문에서는 적외선 영상에서 영상 변위를 이용하여 기동 표적 영역을 탐지하고, SURF(Speeded Up Robust Features) 특징점에 대한 BAS(Beam Angle Statistics)를 이용하여 분류하는 시스템에 대하여 설명한다. 영상 기반 기술 분야에서 대표적인 대응점 정합 알고리즘인 SURF 기법은 SIFT(Scale Invariant Feature Transform) 기법에 비해 정합 속도가 매우 빠르고 비슷한 정합 성능을 보이기 때문에 널리 사용되고 있다. SURF를 이용한 대부분의 객체 인식의 경우 특징점 추출과 정합의 과정을 수행하지만, 제안하는 기법은 표적의 기동 특성을 반영하여 영상의 변위 추정을 통하여 표적의 영역을 탐지하고 SURF 특징점 들의 기하구조를 판단함으로써 표적 분류를 수행한다. 제안하는 기법은 무인 표적 탐지/인지 시스템의 초기모델 구축을 위하여 연구가 진행되었으며, 모의 표적을 이용한 가상 영상과 적외선 실 영상을 이용하여 실험한 결과 약 73~85%의 분류 성능을 확인하였다. In this paper, we propose the target detection method using image displacement, and classification method using SURF(Speeded Up Robust Features) feature points and BAS(Beam Angle Statistics) in infrared images. The SURF method that is a typical correspondence matching method in the area of image processing has been widely used, because it is significantly faster than the SIFT(Scale Invariant Feature Transform) method, and produces a similar performance. In addition, in most SURF based object recognition method, it consists of feature point extraction and matching process. In proposed method, it detects the target area using the displacement, and target classification is performed by using the geometry of SURF feature points. The proposed method was applied to the unmanned target detection/recognition system. The experimental results in virtual images and real images, we have approximately 73~85% of the classification performance.

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.

Vision-Based Aerial Refueling Docking Based on SURF Algorithm

For the Boom and Receptacle Air Refueling, in order to locate the spatial position of the refueling receptacle, an object locating method is developed based on Speeded-up Robust Feature (SURF) algorithm. Firstly, SURF feature vector matching algorithm is used to detect and collect suitable SURF feature points in left and right images produced by binocular stereo vision system separately. Then the point that shows the same spatial position in both left and right images can be located through process such as deleting wrong matching points and calculating the image coordinate of the target point. Finally, the three-dimensional coordinates of the target points could be rebuilt in the camera’s coordinate system. According to the results of experiments, this method has good robustness and practicability.