Fast scale invariant feature detection and matching on programmable graphics hardware

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.

Solid modeling of polyhedral objects by Layered Depth-Normal Images on the GPU

This work presents a visual information fusion approach for robust probability-oriented feature matching. It is sustained by omnidirectional imaging, and it is tested in a visual localization framework, in mobile robotics. General visual localization methods have been extensively studied and optimized in terms of performance. However, one of the main threats that jeopardizes the final estimation is the presence of outliers. In this paper, we present several contributions to deal with that issue. First, 3D information data, associated with SURF (Speeded-Up Robust Feature) points detected on the images, is inferred under the Bayesian framework established by Gaussian processes (GPs). Such information represents a probability distribution for the feature points’ existence, which is successively fused and updated throughout the robot’s poses. Secondly, this distribution can be properly sampled and projected onto the next 2D image frame in , by means of a filter-motion prediction. This strategy permits obtaining relevant areas in the image reference system, from which probable matches could be detected, in terms of the accumulated probability of feature existence. This approach entails an adaptive probability-oriented matching search, which accounts for significant areas of the image, but it also considers unseen parts of the scene, thanks to an internal modulation of the probability distribution domain, computed in terms of the current uncertainty of the system. The main outcomes confirm a robust feature matching, which permits producing consistent localization estimates, aided by the odometer’s prior to estimate the scale factor. Publicly available datasets have been used to validate the design and operation of the approach. Moreover, the proposal has been compared, firstly with a standard feature matching and secondly with a localization method, based on an inverse depth parametrization. The results confirm the validity of the approach in terms of feature matching, localization accuracy, and time consumption.

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.

Abstract. Remote sensing system fitted on UAV (Unmanned Aerial Vehicle) can obtain clear images and high-resolution aerial photographs. It has advantages of flexibility, convenience and ability to work full-time. However, there are some problems of UAV image such as small coverage area, large number, irregular overlap, etc. How to obtain a large regional map quickly becomes a major obstacle to UAV remote sensing application. In this paper, a new method of fast registration of UAV remote sensing images was proposed to meet the needs of practical application. This paper used Progressive Sample Consensus (PROSAC) algorithm to improve the matching accuracy by removed a large number of mismatching point pairs of remote sensing image registration based-on SURF (Speed Up Robust Feature) algorithm, and GPU (Graphic Processing Unit) was also used to accelerate the speed of improved SURF algorithm. Finally, geometric verification was used to achieve mosaic accuracy in survey area. The number of feature points obtained by using improved SURF based-on PROSAC algorithm was only 9.5% than that of SURF algorithm. Moreover, the accuracy rate of improved method was about 99.7%, while the accuracy rate of improved SURF algorithm was increased by 8% than SURF algorithm. Moreover, the improved running time of SURFGPU algorithm for UAV remote sensing image registration was a speed of around 16 times than SURF algorithm, and the image matching time had reached millisecond level. Thus, improved SURF algorithm had better matching accuracy and executing speed to meet the requirements of real-time and robustness in UAV remote sensing image registration.

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.

The accurate segmentation of liver vessels is an important prerequisite for creating oncologic surgery planning tools as well as medical visualization applications. In this paper, a fully automatic approach is presented to quickly enhance and extract the vascular system of the liver from CT datasets. Our framework consists of three basic modules: vessel enhancement on the graphics processing unit (GPU), automatic vessel segmentation in the enhanced images and an option to verify and refine the obtained results. Tests on 20 clinical datasets of varying contrast quality and acquisition phase were carried out to evaluate the robustness of the automatic segmentation. In addition the presented GPU based method was tested against a CPU implementation to demonstrate the performance gain of using modern graphics hardware. Automatic segmentation using graphics hardware allows reliable and fast extraction of the hepatic vascular system and therefore has the potential to save time for oncologic surgery planning.KeywordsSegmentationAutomationComputed TomographyGraphics HardwareHepatic Vessels

The paper treats a problem of building an image mosaic or a 2D map from the sequence of images taken by an Unmanned Aerial Vehicle (UAV). The proposed algorithm is composed of the following parts: feature detection and extraction using Speeded Up Robust Features (SURF) descriptor, feature matching using Fast Approximate Nearest Neighbor Search (FANN) with automatic algorithm configuration, estimation of the optimal transform between images using RANdom SAmple Consensus (RANSAC) and finally creating a mosaic by stitching images together using the obtained transformations. Summarizing results obtained in two different scenarios, it can be concluded that the proposed algorithm exhibits near real time performance for automated UAV 2D aerial mapping.

Automatic orientation and 3D modelling from markerless rock art imagery

This thesis aims to develop an embedded image processing board, which can be used to realize various image feature extraction and processing algorithms for robotic applications. The circuitry of this image board can be divided into two parts, one is for a digital signal processor (DSP) and the other for a field programmable gate array (FPGA). A low power, small size CMOS image sensor with YUV4:2:2 image format and 512x480 pixel size is adopted for image acquisition. The DSP module is mainly used for implementing image processing algorithms, while the FPGA module for lower level image processing and sensor control functions. In this work, the TMS320C6414TGLZ7 DSP from Texas Instruments Corporation and the EP2C35F672C6 FPGA from Altera Corporation were selected for the image board. In this work, speeded-up robust features (SURF) algorithm has been successfully realized on the developed image board. An USB interface with average throughput of 22MB/sec works to transfer acquired image frames and extracted SURF descriptors to a personal computer, where feature matching is executed. Experimental results show that the refresh period of frame grabbing is about 33.5ms. When the number of SURF descriptor is equal to 64, the computing time is about 417.5ms; when the number of SURF descriptor is equal to 199, the computing time is about 985ms. The typical matching rate varies from 70.2% to 20%, depending on the threshold of the patterns used in feature extraction.

Feature point extraction and matching are the key modules in the front-end of visual simultaneous localization and mapping (SLAM), which determines the accuracy of locating and map Nbuilding robots. To obtain a good performance, the image extraction algorithm being applied should have high efficiency and robustness to the changes of the surrounding environment. In this paper, three widely used feature extraction algorithms, namely Scale-invariant feature transform (SIFT), speed up robust features (SURF) and oriented FAST, and rotated BRIEF (ORB), are studied. The basic concepts and details of the three algorithms are introduced. Then the execution time of the three algorithms is compared to evaluate their efficiency. Meanwhile, the key points matching rate of the three algorithms are compared under various transformations and distortions. The experiment results show that ORB has higher efficiency than SIFT and SURF. However, SIFT has better robustness to image rotation, illumination change, scaling transformation, Gaussian noise and salt-and-pepper noise. In addition, the performance of the ORB-SLAM system is tested on different datasets. It has great ability in camera trajectory estimation and map building and therefore is suitable for deploying in industry.

This paper presents a Graphics Processing Unit (GPU) based solution for classical geometric hashing and its variation with transformation functions on Speeded Up Robust Features (SURF) space of images. GPU based classical geometric hashing provides speed-up of 14.0x to 61.0x for offline indexing and 1.08x to 10.06x for online searching compared to sequential one for different partitioning sizes. GPU based transformation by mean invariancy and principal component based alignment with geometric hashing provides speed-up of 12.12x to 63.13x for offline indexing and 1.02x to 5.82x for online searching. This paper also proposes solution to execute multiple query simultaneously. It proves to be better than the serial execution of multiple queries. GPU based implementation of multi-query provide speed-up of 1.68x to 460.45x than the sequential one for online searching for multiple queries between 1 to 10 simultaneously. Experimentation is done using standard CASIA Palm-print based images.

In this paper, we propose a Graphics Processing Unit (GPU)-based motion estimation for H.264/AVC by rearranging the encoding order of 4x4 blocks. Previous research [3] [4] assume no dependency among adjacent blocks, which is not true for H.264/AVC. The proposed method not only overcomes the dependency problem, but also can be used for both full search and fast search. Experimental results show that our implementation is forty five faster than our SDVTD optimized CPU implementation.

I start with a brief introduction to the graphics processing unit (GPU) as well as general-purpose computation on modern graphics hardware (GPGPU). Next, I explore the motivations for GPGPU programming, and the capabilities of modern GPUs (including advantages and disadvantages). Also, I give the background required for further exploring GPU programming, including the terminology used and the resources available. Finally, I include a comprehensive survey of previous and current GPGPU work, and end with a look at the future of GPU programming.