Estimation of Large Scalings in Images Based on Multilayer Pseudopolar Fractional Fourier Transform

Accurate estimation of the Fourier transform in log-polar coordinates is a major challenge for phase-correlation based motion estimation. To acquire better image registration accuracy, a method is proposed to estimate the log-polar coordinates coefficients using multilayer pseudopolar fractional Fourier transform (MPFFT). The MPFFT approach encompasses pseudopolar and multilayer techniques and provides a grid which is geometrically similar to the log-polar grid. At low coordinates coefficients the multilayer pseudopolar grid is dense, and at high coordinates coefficients the grid is sparse. As a result, large scalings in images can be estimated, and better image registration accuracy can be achieved. Experimental results demonstrate the effectiveness of the presented method

FFT-based estimation of large motions in images: a robust gradient-based approach

A fast and robust gradient-based motion estimation technique which operates in the frequency domain is presented. The algorithm combines the natural advantages of a good feature selection offered by gradient-based methods with the robustness and speed provided by FFT-based correlation schemes. Experimentation with real images taken from a popular database showed that, unlike any other Fourier-based techniques, the method was able to estimate translations, arbitrary rotations and scale factors in the range 4-6

FFT-based estimation of large motions in images: a robust gradient-based approach

A fast and robust gradient-based motion estimation technique which operates in the frequency domain is presented. The algorithm combines the natural advantages of a good feature selection offered by gradient-based methods with the robustness and speed provided by FFT-based correlation schemes. Experimentation with real images taken from a popular database showed that, unlike any other Fourier-based techniques, the method was able to estimate translations, arbitrary rotations and scale factors in the range 4-6

Three-dimensional alignment and merging of confocal microscopy stacks

pre-printWe describe an efficient, robust, automated method for image alignment and merging of translated, rotated and flipped confocal microscopy stacks. The samples are captured in both directions (top and bottom) to increase the SNR of the individual slices. We identify the overlapping region of the two stacks by using a variable depth Maximum Intensity Projection (MIP) in the z dimension. For each depth tested, the MIP images gives an estimate of the angle of rotation between the stacks and the shifts in the x and y directions using the Fourier Shift property in 2D. We use the estimated rotation angle, shifts in the x and y direction and align the images in the z direction. A linear blending technique based on a sigmoidal function is used to maximize the information from the stacks and combine them. We get maximum information gain as we combine stacks obtained from both directions

GPU Accelerated FFT-Based Registration of Hyperspectral Scenes

Registration is a fundamental previous task in many applications of hyperspectrometry. Most of the algorithms developed are designed to work with RGB images and ignore the execution time. This paper presents a phase correlation algorithm on GPU to register two remote sensing hyperspectral images. The proposed algorithm is based on principal component analysis, multilayer fractional Fourier transform, combination of log-polar maps, and peak processing. It is fully developed in CUDA for NVIDIA GPUs. Different techniques such as the efficient use of the memory hierarchy, the use of CUDA libraries, and the maximization of the occupancy have been applied to reach the best performance on GPU. The algorithm is robust achieving speedups in GPU of up to 240.6×This work was supported in part by the Consellería de Cultura, Educacion e Ordenación Universitaria under Grant GRC2014/008 and Grant ED431G/08 and in part by the Ministry of Education, Culture and Sport, Government of Spain under Grant TIN2013-41129-P and Grant TIN2016-76373-P. Both are cofunded by the European Regional Development Fund. The work of A. Ordóñez was supported by the Ministry of Education, Culture and Sport, Government of Spain, under an FPU Grant FPU16/03537S

Automatic 4-D Registration in Dynamic MR Renography Based on Over-complete Dyadic Wavelet and Fourier Transforms

Dynamic contrast-enhanced 4-D MR renography has the potential for broad clinical applications, but suffers from respiratory motion that limits analysis and interpretation. Since each examination yields at least over 10-20 serial 3-D images of the abdomen, manual registration is prohibitively labor-intensive. Besides in-plane motion and translation, out-of-plane motion and rotation are observed in the image series. In this paper, a novel robust and automated technique for removing out-of-plane translation and rotation with sub-voxel accuracy in 4-D dynamic MR images is presented. The method was evaluated on simulated motion data derived directly from a clinical patient's data. The method was also tested on 24 clinical patient kidney data sets. Registration results were compared with a mutual information method, in which differences between manually co-registered time-intensity curves and tested time-intensity curves were compared. Evaluation results showed that our method agreed well with these ground truth data

Tools for creating wide-field views of the human retina using Optical Coherence Tomography

Optical Coherence Tomography (OCT) has allowed in-vivo viewing of details of retinal layers like never before. With the development of spectral domain OCT (SD-OCT) details of nearly 2µm axial resolution and higher imaging speed have been reported. Nevertheless, a single volume scan of the retina is typically restricted to 6mm x 6mm in size. Having a larger field of view of the retina will definitely enhance the clinical utility of the OCT. A tool was developed for creating wide-field thickness maps of the retina by combining the use of already available tools like i2k Retina (DualAlign, LLC, Clifton Park, NY) and the thickness maps from Cirrus HD-OCT research browser (Carl Zeiss Meditec, Dublin, California, USA). Normal subjects (n=20) were imaged on Zeiss Cirrus HD-OCT using 512x128 Macular Cube scanning protocol. Sixteen overlapping volumetric images were obtained by moving the internal fixation target around such that the final stitched maps were 12mm x 14mm in size. The thickness maps were corrected for inter-individual differences in axial lengths measured using Zeiss IOL Master and averaged to obtain a normative map. An algorithm was also developed for montaging 3-D volume scans. Using this algorithm two OCT volume scans can be registered and stitched together to obtain a larger volume scan. The algorithm can be described as a two step process involving 3-D phase-correlation and 2-D Pseudo-polar Fourier transform (PPFT). In the first step, 3-D phase-correlation provides translation values in the x, y and z axis. The second step involves applying PPFT on each overlapping pair of B-scans to find rotation in the x-y plane. Subsequent volumes can be stitched to obtain a large field of view. We developed a simple and robust method for creating wide-field views of the retina using existing SD-OCT hardware. As segmentation algorithms improve, this method could be expanded to produce wide-field maps of retinal sub-layers, such as the outer nuclear layer or retinal nerve fiber layer. These wide-field views of the retina may prove useful in evaluating retinal diseases involving the peripheral retina (e.g., retinitis pigmentosa and glaucoma)

Multiscale Point Correspondence Using Feature Distribution and Frequency Domain Alignment

In this paper, a hybrid scheme is proposed to find the reliable point-correspondences between two images, which combines the distribution of invariant spatial feature description and frequency domain alignment based on two-stage coarse to fine refinement strategy. Firstly, the source and the target images are both down-sampled by the image pyramid algorithm in a hierarchical multi-scale way. The Fourier-Mellin transform is applied to obtain the transformation parameters at the coarse level between the image pairs; then, the parameters can serve as the initial coarse guess, to guide the following feature matching step at the original scale, where the correspondences are restricted in a search window determined by the deformation between the reference image and the current image; Finally, a novel matching strategy is developed to reject the false matches by validating geometrical relationships between candidate matching points. By doing so, the alignment parameters are refined, which is more accurate and more flexible than a robust fitting technique. This in return can provide a more accurate result for feature correspondence. Experiments on real and synthetic image-pairs show that our approach provides satisfactory feature matching performance

Estimation of Translation, Rotation, and Scaling between Noisy Images Using the Fourier–Mellin Transform

In this paper we focus on extended Euclidean registration of a set of noisy images. We provide an appropriate statistical model for this kind of registration problems, and a new criterion based on Fourier-type transforms is proposed to estimate the translation, rotation and scaling parameters to align a set of images. This criterion is a two step procedure which does not require the use of a reference template onto which aligning all the images. Our approach is based on M-estimation and we prove the consistency of the resulting estimators. A small scale simulation study and real examples are used to illustrate the numerical performances of our procedure

Around View Monitor(AVM) Based Visual SLAM For Autonomous Parking

학위논문 (석사) -- 서울대학교 대학원 : 융합과학기술대학원 지능정보융합학과, 2021. 2. 박재흥.자율주차는 인식, 계획, 제어로 이루어져있다.자율주차의 인식과정동안에 자동차는 자신의 위치를 알아야하고 주변환경을 인지할수있어야한다. 이러한 과정을 동시적위치추정및지도생성(SLAM)이라고한다. 많은 SLAM알고리즘들은 주변환경의 정확한인식을 위해 제안되어왔다. 특히 값싼 카메라를 주된센서로 사용하는 Visual SLAM은 자율주행자동차를 위한 유망한 알고리즘으로 여겨져왔다. 대부분의 Visual SLAM은 전방카메라를 사용한다. 이러한 시스템 형태에서, Visual SLAM은대부분의 환경에서 잘 작동한다. 그러나 주변환경에 특징점이 적고, 강한빛이 있는환경에서는 Visual SLAM의 성능이 하락한다. 많은수의 주차장들은 야외에 위치해있고, 주차선과 같은 단조로운 특징점들만을 가지고있다. 주차장에서의 이러한 문제들에 대처하고 Visual SLAM의 성능을 개선하기 위해서 이연구에서는 Around View Monitor(AVM)을 주요센서로 하는 새로운 Visual SLAM 알고리즘을 제안한다. AVM 시스템에서는 Top View 이미지가 생성되기 때문에 푸리에변환은 AVM이미지들로부터 동작정보를 추출하기 위해서 사용된다. 동작정보를 추정하기위해 reprojection error또는photometric error등을 비용함수로써 사용하는 기존의 Visual SLAM과는 달리 푸리에변환은 어떠한 특징점매칭이나 최적화과정없이,참조되는 이미지로부터 대상이되는 이미지로의 동작정보를 간단히 추정할수있다. 또한 자동차의 위치를 정확하게 그리고 강건하게 추정하기위해서 landmark를 이용한 위치추정방법이 사용되었다.이 연구에서 landmark라함은 주차선끼리 만나는 Cross point(교차점)을 말한다.이 연구에서 landmark를 이용한 위치추정은 세가지단계로 나뉜다.첫번째 단계는 교차점을 탐색하는것이다.주로 이미지에서 특징점을 찾기위해 Image Segmentation방법을 사용하는 기존의AVM기반의 SLAM연구와는 달리 이 연구에서는 Image Segmentation보다 훈련이 더쉽고 간단한 Object Detection네트워크인YoloV3를 사용하여 교차점을 탐색하였다.두번째 단계는 Data Association이다. SLAM에서 Data Association은 지도에 등록되어 있는 특징점과 현재 관찰된 특징점을 서로 연관시키는 작업이다. Deep SORT가 현재 관촬된 특징점을추적하기위해 사용되었지만, Deep SORT를 사용할때에는 추적되는 특징점의 ID가 비교적 자주 바뀌는 현상이 일어나서 푸리에변환으로부터의 동작정보와 현재 관찰된 특징점정보를 사용하여 Nearest Neighbor방법을 통해 추가적인 Data Association을 구현하였다. 푸리에변환으로부터의 동작정보는 비교적 정확하고, 교차점사이의 거리는 멀기때문에Data Association의 정확도는 Deep SORT만 사용했을때보다 정확해졌다.이후에 마지막단계에서는 일정개수이상의 data association이 이루어졌을때, Singular Value Decomposition을 이용하여 새롭게 동작정보가 추정된다. 기존의Visual SLAM과 제안된 SLAM알고리즘의 성능을 비교하기위해서 주차장에서 실험을진행하였고, LOAM이 알고리즘의 비교를위한 Groundtruth로서 사용되었다.Autonomous parking consists of perception, planning, control. During perception procedure in autonomous parking, vehicle should know its location and perceive surrounding environment. This is called Simultaeneous Localization And Mapping (SLAM). Many SLAM algorithms have been proposed for accurate perception of environment. Especially, Visual SLAM, which uses a cheap camera as a main sensor of SLAM algorithm, has been considered as promising algorithm for autonomous vehicle. Most of Visual SLAM use front camera setting. In this camera setting, Visual SLAM works well for most of environments. However, performance of the algorithm gets worse when environment has few features or strong sunlight condition. Most of parking lots are located outdoor and have monotonous features like parking lines, cars. To address these problems and improve accuracy of Visual SLAM for autonomous parking, this paper proposes new Visual SLAM algorithm, which uses Around View Monitor(AVM) as a main sensor. As top-view images are generated in AVM system, fourier transform is used to extract motion information from the AVM images. Compared to traditional visual motion tracking methods which use reprojection error or photometric error as a cost function to estimate motion, fourier transform can simply estimate motion from reference AVM image to target AVM image without any optimization or feature matching. Also, landmark based localization is used to estimate vehicle's motion more robustly and accurately. In this paper, landmark means cross points on parking lines. Landmark based localization in this paper consists of three procedure. First one is cross point detection. Cross points are detected using YoloV3. Compared to other AVM based SLAM methods, which use Image segmentation to detect features in parking lot, training procedure of the neural network is simpler and easier. Second one is data association. Data association means associating procedure among features in map and currently observed features in SLAM literature. Deep SORT is used to track features using currently observed cross points. As re-identification of tracked features frequently occurs when using Deep SORT, additional data association is done using current motion estimation from image registration and currently observed cross points in Nearest Neighbor literature. As motion estimation accuracy from reference image to target image is considerably accurate and distance between cross points is far, data association accuracy is improved compared to the data association without this additional association procedure. After this data association, if the number of associated features is larger than one, motion is newly estimated using Singular Value Decomposition and positions of associated features. To demonstrate improvement of proposed SLAM algorithm compared to other Visual SLAM algorithms, experiments in parking lot are suggested and compared with traditional Visual SLAM algorithms. Also, Lidar Odometry And Mapping(LOAM)is used as a groundtruth for comparing the Visual SLAM algorithms.I. Visual Simultaeneous Localization And Mapping(Visual SLAM) 1 1.1Visual SLAM이란 . . . . . . . . . . . . . . . . . . . . . . 1 1.2특징점탐색. . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3Data Association . . . . . . . . . . . . . . . . . . . . . . . 3 1.4Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.5성능평가. . . . . . . . . . . . . . . . . . . . . . . . . . . 5 II.자율주차(Autonomous Parking). . . . . . . . . . . . . . . . 7 2.1자율주차란 . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2자율주차의발전. . . . . . . . . . . . . . . . . . . . . . . 7 2.3상용자율주차. . . . . . . . . . . . . . . . . . . . . . . . 8 III.자율주차를위한AVM기반Visual SLAM. . . . . . . . . . . 9 3.1서론. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2방법. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.1제안하는SLAM파이프라인. . . . . . . . . . . . 13 3.2.2교차점의탐색과추적그리고모서리추출. . . . . 16 3.2.3푸리에변환을이용한위치추정. . . . . . . . . . 18 3.2.4Keyframe생성. . . . . . . . . . . . . . . . . . . . 20 3.2.5Data Association . . . . . . . . . . . . . . . . . . . 22 3.2.6교차점을이용한Landmark기반의위치추정. . . 24 3.3실험. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3.1실험준비. . . . . . . . . . . . . . . . . . . . . . . 25 3.3.2실험결과. . . . . . . . . . . . . . . . . . . . . . . 28 3.4고찰및결론. . . . . . . . . . . . . . . . . . . . . . . . . 33 3.4.1고찰. . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.4.2결론. . . . . . . . . . . . . . . . . . . . . . . . . . 34 참고문헌. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Maste