PCA기반 검색 축소 기법을 이용한 SURF 매칭 속도 개선

Speeded Up Robust Features (SURF) algorithm is a fast robust local feature matching method. Because of the advantage of invariance of scale and rotation, the SURF algorithm has been widely used in the image registration. However, due to the acquired angle, imaging mode, different resolution, SURF algorithm becomes quite difficult in the image registration. This paper presents a normalized SURF algorithm which can reduce the impact of hue difference between remote sensing images. Combined with the idea of two-way matching, the normalized SURF is able to improve the accuracy of the image registration. The experimental results show the normalized SURF algorithm can keep greater accuracy and relatively higher speed of matching than the original SURF algorithm.

Indexing images represents a necessary tool for various domains related to computer vision such as video surveillance and movements analysis. Visual characteristics of image such as color, texture, shape are used to identify the content of these frames. The high increase of data (Big Data) makes us face a problem of curse of dimensionality. In this paper, we propose a cloud-based technique for images indexing images using PCA (Principal component analysis) and a binary tree. Our work consists of exploiting SIFT (Scale Invariant Feature Transform) and SURF (Speeded up Robust Features) descriptors as frames features, PCA as dimensionality reduction method of SIFT and SURF features and the binary tree representation of these images in order to accelerate the research phase and reduce the storage space. Our approach consists of developing a system based on three phases: indexing images with SIFT and SURF features, compression within the application of PCA and finally the image retrieval in the cloud.

위성 SAR 영상의 활용이 증가하면서 영상의 해석 및 융합을 위한 정밀 기하보정에 대한 필요성이 높아지고 있다. 특히 광역감시 목적으로 활용되기 위해 서로 다른 해상도를 갖는 SAR 영상간 정보융합도 활발해지고 있다. 일반적으로 SAR 영상의 기하보정은 위성의 궤도 및 자세정보를 활용하여 수행할 수 있지만 SAR 센서의 궤도 및 시스템 오차, 대상지형 특성에 의한 왜곡으로 인해 추가적인 보정이 필요하게 된다. SAR 영상을 통한 변화탐지나 타 영상과의 융합에 적용하기 위해서는 기하 오차 보정이 반드시 선행되어야 한다. 이를 위해 다수의 지상 기준점을 선정하고 이를 포함하는 기준 영상과 비교하여 원본 영상에서 대응점을 찾는 방식으로 정밀 기하보정을 수행할 수 있다. Speeded Up Robust Feature (SURF) 기법은 쉽고 빠르게 영상의 기준점을 찾을 수 있지만 상대적으로 해상도가 낮고 스펙클 잡음에 영향을 받는 SAR 영상에서는 활용하기가 어렵다고 알려져 있다. 본 논문에서는 SURF 기법을 위성 SAR 영상에 적용할 때 발생할 수 있는 오차를 추출하고 영상 특성에 따른 성능 변화를 분석하였다. SURF 알고리즘의 적용이 가능한 입력 변수의 적정 범위를 제시하고 그에 따른 영상 정합의 오차를 분석하여 중저해상도의 위성 SAR 영상에 대해서도 SURF 기법을 통한 기하 보정 및 영상 정합이 적용될 수 있음을 검증하였다. As applications of spaceborne SAR imagery are extended, there are increased demands for accurate registrations for better understanding and fusion of radar images. It becomes common to adopt multi-resolution SAR images to apply for wide area reconnaissance. Geometric correction of the SAR images can be performed by using satellite orbit and attitude information. However, the inherent errors of the SAR sensor's attitude and ground geographical data tend to cause geometric errors in the produced SAR image. These errors should be corrected when the SAR images are applied for multi-temporal analysis, change detection applications and image fusion with other sensor images. The undesirable ground registration errors can be corrected with respect to the true ground control points in order to produce complete SAR products. Speeded Up Robust Feature (SURF) technique is an efficient algorithm to extract ground control points from images but is considered to be inappropriate to apply to SAR images due to high speckle noises. In this paper, an attempt is made to apply SURF algorithm to SAR images for image registration and fusion. Matched points are extracted with respect to the varying parameters of Hessian and SURF matching thresholds, and the performance is analyzed by measuring the imaging matching accuracies. A number of performance measures concerning image registration are suggested to validate the use of SURF for spaceborne SAR images. Various simulations methodologies are suggested the validate the use of SURF for the geometric correction and image registrations and it is shown that a good choice of input parameters to the SURF algorithm should be made to apply for the spaceborne SAR images of moderate resolutions.

The present work demonstrates the utilization of computer-aided diagnosis system for the detection of lung cancer diseases using computer tomography (CT) images, magnetic resonance images (MRI) and X-ray images. The feature extraction process in lung cancer images has been achieved by scale invariant feature transform (SIFT), speeded up robust features (SURF), and principal component analysis (PCA) techniques. In this work, a comparative investigation of different feature extraction technique such as SIFT, SURF, and PCA has been discussed in order to find the best descriptor for feature extraction of cancerous subjects to the normal subjects in terms of two parameters named as execution time and error rate. The main aspect of these learning approaches is to find the valid key points in minimum execution time with least error. The results reveal that the SURF technique has an average execution time of 0.448 s with an average error rate value of 25.704 which is least among three techniques. Hence, SURF extraction technique is best as compared to SIFT and PCA.

Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper label. In the paper, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Oriented Fast and Rotated BRIEF (ORB) are used for experimental work of an object recognition system. A comparison among these three descriptors is exhibited in the paper by determining them individually and with different combinations of these three methodologies. The amount of the features extracted using these feature extraction methods are further reduced using a feature selection (k-means clustering) and a dimensionality reduction method (Locality Preserving Projection). Various classifiers i.e. K-Nearest Neighbor, Naive Bayes, Decision Tree, and Random Forest are used to classify objects based on their similarity. The focus of this article is to present a study of the performance comparison among these three feature extraction methods, particularly when their combination derives in recognizing the object more efficiently. In this paper, the authors have presented a comparative analysis view among various feature descriptors algorithms and classification models for 2D object recognition. The Caltech-101 public dataset is considered in this article for experimental work. The experiment reveals that a hybridization of SIFT, SURF and ORB method with Random Forest classification model accomplishes the best results as compared to other state-of-the-art work. The comparative analysis has been presented in terms of recognition accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and Area Under Curve (AUC) parameters.

Image upgrade is essential for giving improved data to other image handling methods. Also, the requirement for differentiation improvement to all-encompassing image is a usually performed examination by dental specialists and oral specialists in regular practice and is a critical indicative apparatus. It covers a more extensive zone than a traditional intraoral x-beam and, subsequently, gives important data about the maxillary sinuses, tooth situating and other bone variations from the norm. Thusly, it is anything but difficult to distinguish and perceive valuable data. In this pursuit was utilized the calculation of Principle Component Analysis (PCA) to upgrade the subtle elements of images and enhance the quality the image, by its joining with strategy for difference improvement, and highlights extraction that take from the upgrade technique to limit mistakes in images, along these lines enhance the execution of the framework, image upgrade strategies used to deliver more reasonable images for identification and extraction. Three principle computerized image process strategies: Contrast Stretching, Histogram Equalization and, Contrast Limited Adaptive Histogram Equalization , were considered to build the improvement image, and evacuate clamor, separately however low quality and the image is light angle in hues and less clear . In this exploration we will, pre-prepared images were assessed utilizing current image recognition and highlight extraction techniques Speeded Up Robust Features (SURF) calculations after go in improvement strategies. After that PCA embed the yield vector from SURF. Gotten results demonstrated that upgrade image expanded after number of separated highlights and diminished mistakes amid info image in PCA. Therefore, the impacts of various pre-process procedures were thought about in the paper and the assessment parameters are figured which demonstrate that proposed work gives better execution from the image broke down under PSNR to enhance the dependability of proposed strategy from the outcomes.

How to construct compact and powerful feature descriptors is an important research subject in the fields of machine vision and pattern recognition. After the research on SURF, the issue that the Haar descriptor of Speeded Up Robust Features(SURF) algorithm can not make full use of the information around the neighborhood of the feature points is found. To resolve the issue, this paper proposes a improved SURF based on the original SURF algorithm. The algorithm makes full use of the information around the neighborhood of the sub-block by masking with 3*3 window templates and then constructs the descriptor which can obtain better discriminative power. In addition, aimed at the primary direction of the SURF depends too much on the local region, the paper improves the confirmation of the primary direction. At last, this paper combines the nearest distance and the ratio between the nearest distance and the second nearest distance to match the interest points. The experiment shows that the algorithm proposed in this paper has better discriminative power, and the face recognition rate is enhanced.

Copy-Move forgery is the most widely used way to tamper image for creating the forged images. The meaning of the photograph is changed by forging or concealing the images. Copy move is a very common technique for image forgery; tools used for retouching are applied along with it making naked eyes difficult to detect the copy move. Hence, the verification of authenticity and integrity is very essential of the image. The copy-move forgery detection can be divided into two heads a) Block based method b) Keypoint based. In the case of blocked based method, the method is exceptionally good in detecting the forged regions with higher accuracy but uses extremely high power of computation. Speed up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) are invariant with respect to geometrical and illumination transform. In this paper we review the different keypoint approach which is a better approach or alternate to block-based approach due to invariancy property of SURF and SIFT. The keypoint based copy-move forgery detection schemes involve, identifying and relating the localized features of the images using the algorithms like SIFT and SURF.

A design of parallel architecture for image feature detection and description is proposed in this article. The major component of this architecture is a 2D cellular network composed of simple reprogrammable processors, enabling the Hessian Blob Detector and Haar Response Calculation, which are the most computing-intensive stage of the Speeded Up Robust Features (SURF) algorithm. Combining this 2D cellular network and dedicated hardware for SURF descriptors, this architecture achieves real-time image feature detection with minimal software in the host processor. A prototype FPGA implementation of the proposed architecture achieves 1318.9 GOPS general pixel processing @ 100 MHz clock and achieves up to 118 fps in VGA (640 × 480) image feature detection. The proposed architecture is stand-alone and scalable so it is easy to be migrated into VLSI implementation.

This paper presents an efficient feature detection algorithm based on the classical SURF (Speeded Up Robust Feature) detector. The image features are represented and scored with respect to its local symmetry property. The local symmetry has natural properties of scale and transformation invariants, and also insensitive to illumination change and local noise. By the proposed feature descriptor, the calculation of 64-dimensional vectors in SURF algorithm can be reduced to 16-dimensional vector respectively. The local symmetry score is defined as the sum of minimum distance between each feature point and its neighboring points in an image based on the image intensities. The algorithm is experimented with some real images and the results are compared with the original SURF algorithm to show its improvement.

In this paper, a new method for content-based video copy detection is presented. This method includes fingerprint extracting and matching phases. In the fingerprint extracting phase, a video is represented by a set of Speeded Up Robust Features (SURF), which outperforms other local features. In the fingerprint matching phase, the Locality Sensitive Hashing (LSH) is applied to efficiently detect video copy. The experimental results show that the proposed method has excellent performance in accuracy and speed.

This paper proposes a feature-based image registering technique for remote sensing satellite images. The system is composed of multiple steps in which various image processing techniques are used. Speed-up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) can be mounted alternatively to detect and define features on images. We also address the issues of registering multi-spectral and multi-temporal images. The effectiveness of the proposed technique has been studied by registering partially overlapping satellite mosaic images of the Marmara Sea. The images were captured by the recently-launched LandSat-8 satellite.

In recent years, keypoints-based features, such as speeded up robust features (SURF), capture the attention of researchers in the area of image copy-move forgery detection. They are fast to compute and are quite robust to most inter-processing and post-processing operations. However, they will fail to detect the forgery if not enough keypoints are detected, and that is the case when the forgery size is small. A small size forgery is possible if the object is far away from the camera, or if the image is scaled down as a post-processing step. In this paper, our focus is on detecting forgeries of small size using an improved SURF based copy-move forgery detection (CMFD) method. Our method improves keypoints detection by preprocessing the image using a single image super resolution (SISR) algorithm. The proposed approach has been evaluated and compared against original SURF through a comprehensive set of experiments using a dataset of small size forgeries. Experimental results indicate that our method outperforms SURF especially when the forgery size is small.

In order to improve the speed of stitching and information extraction of remote sensing images, based on the UAV (Unmanned Aerial Vehicle) image stitching algorithm, this paper proposes a UAV image matching method based on the combination of SURF (Speeded Up Robust Features) and MASC (M-Estimate Sample Consensus) algorithm. the SURF algorithm is fast, and the MASC algorithm can effectively eliminate mismatch points and has high stability. In this paper, the SIFT (Scale-invariant feature transform) algorithm, SURF algorithm, SURF and RANSAC (Random Sample Consensus) algorithm as well as SURF and MASC algorithm are used to stitch the same two images and compare them respectively. The experimental results show that the lowest image matching error rate is only 2.13% and the matching time is 4.772670s when using the combination of SURF and MASC algorithm for image stitching, which has certain advantages.

Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. We have developed two filters capable of computing pixel intensity variations, followed by the covariance matrix of the polynomial to describe the features. The superiority of CBFD is validated through precision, recall, computation time, and feature location distance. Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. Noteworthy is CBFD's recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. Finally, the plot of location feature distance illustrates that CBFD exhibits minimal distance compared to DITF and Histogram of Oriented Gradients (HOG). These results highlight the speed and robustness of CBFD across various transformations.