Object Discovery via Cohesion Measurement

Color and intensity are two important components in an image. Usually, groups of image pixels, which are similar in color or intensity, are an informative representation for an object. They are therefore particularly suitable for computer vision tasks, such as saliency detection and object proposal generation. However, image pixels, which share a similar real-world color, may be quite different since colors are often distorted by intensity. In this paper, we reinvestigate the affinity matrices originally used in image segmentation methods based on spectral clustering. A new affinity matrix, which is robust to color distortions, is formulated for object discovery. Moreover, a Cohesion Measurement (CM) for object regions is also derived based on the formulated affinity matrix. Based on the new Cohesion Measurement, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix. Then we apply the proposed method to both saliency detection and object proposal generation. Experimental results on several evaluation benchmarks demonstrate that the proposed CM based method has achieved promising performance for these two tasks.Comment: 14 pages, 14 figure

이동 물체 감지 및 분진 영상 복원의 연구

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 수리과학부, 2021. 2. 강명주.Robust principal component analysis(RPCA), a method used to decom- pose a matrix into the sum of a low-rank matrix and a sparse matrix, has been proven effective in modeling the static background of videos. However, because a dynamic background cannot be represented by a low-rank matrix, measures additional to the RPCA are required. In this thesis, we propose masked RPCA to process backgrounds containing moving textures. First- order Marcov random field (MRF) is used to generate a mask that roughly labels moving objects and backgrounds. To estimate the background, the rank minimization process is then applied with the mask multiplied. During the iteration, the background rank increases as the object mask expands, and the weight of the rank constraint term decreases, which increases the accuracy of the background. We compared the proposed method with state- of-art, end-to-end methods to demonstrate its advantages. Subsequently, we suggest novel dedusting method based on dust-optimized transmission map and deep image prior. This method consists of estimating atmospheric light and transmission in that order, which is similar to dark channel prior-based dehazing methods. However, existing atmospheric light estimating methods widely used in dehazing schemes give an overly bright estimation, which results in unrealistically dark dedusting results. To ad- dress this problem, we propose a segmentation-based method that gives new estimation in atmospheric light. Dark channel prior based transmission map with new atmospheric light gives unnatural intensity ordering and zero value at low transmission regions. Therefore, the transmission map is refined by scattering model based transformation and dark channel adaptive non-local total variation (NLTV) regularization. Parameter optimizing steps with deep image prior(DIP) gives the final dedusting result.강건 주성분 분석은 배경 감산을 통한 동영상의 전경 추출의 방법으로 이 용되어왔으나, 동적배경은저계수행렬로표현될수없기때문에동적배경 감산에성능적한계를가지고있었다. 우리는전경과배경을구분하는일계마 르코프연쇄를도입해정적배경을나타내는항과곱하고이것을이용한새로 운형태의강건주성분분석을제안하여동적배경감산문제를해결한다. 해당 최소화문제는반복적인교차최적화를통하여해결한다. 이어서대기중의미세 먼지에의해오염된영상을복원한다. 영상분할과암흑채널가정에기반하여 깊이지도를구하고, 비국소총변동최소화를통하여정제한다. 이후깊은영상 가정에기반한영상생성기를통하여최종적으로복원된영상을구한다. 실험을 통하여제안된방법을다른방법들과비교하고질적인측면과양적인측면모 두에서우수함을확인한다.Abstract i 1 Introduction 1 1.1 Moving Object Detection In Dynamic Backgrounds 1 1.2 Image Dedusting 2 2 Preliminaries 4 2.1 Moving Object Detection In Dynamic Backgrounds 4 2.1.1 Literature review 5 2.1.2 Robust principal component analysis(RPCA) and their application status 7 2.1.3 Graph cuts and α-expansion algorithm 14 2.2 Image Dedusting 16 2.2.1 Image dehazing methods 16 2.2.2 Dust model 18 2.2.3 Non-local total variation(NLTV) 19 3 Dynamic Background Subtraction With Masked RPCA 21 3.1 Motivation 21 3.1.1 Motivation of background modeling 21 3.1.2 Mask formulation 23 3.1.3 Model 24 3.2 Optimization 25 3.2.1 L-Subproblem 25 3.2.2 L˜-Subproblem 26 3.2.3 M-Subproblem 27 3.2.4 p-Subproblem 28 3.2.5 Adaptive parameter control 28 3.2.6 Convergence 29 3.3 Experimental results 31 3.3.1 Benchmark Algorithms And Videos 31 3.3.2 Implementation 32 3.3.3 Evaluation 32 4 Deep Image Dedusting With Dust-Optimized Transmission Map 41 4.1 Transmission estimation 41 4.1.1 Atmospheric light estimation 41 4.1.2 Transmission estimation 43 4.2 Scene radiance recovery 47 4.3 Experimental results 51 4.3.1 Implementation 51 4.3.2 Evaluation 52 5 Conclusion 58 Abstract (in Korean) 69 Acknowledgement (in Korean) 70Docto

On landmark selection and sampling in high-dimensional data analysis

In recent years, the spectral analysis of appropriately defined kernel matrices has emerged as a principled way to extract the low-dimensional structure often prevalent in high-dimensional data. Here we provide an introduction to spectral methods for linear and nonlinear dimension reduction, emphasizing ways to overcome the computational limitations currently faced by practitioners with massive datasets. In particular, a data subsampling or landmark selection process is often employed to construct a kernel based on partial information, followed by an approximate spectral analysis termed the Nystrom extension. We provide a quantitative framework to analyse this procedure, and use it to demonstrate algorithmic performance bounds on a range of practical approaches designed to optimize the landmark selection process. We compare the practical implications of these bounds by way of real-world examples drawn from the field of computer vision, whereby low-dimensional manifold structure is shown to emerge from high-dimensional video data streams.Comment: 18 pages, 6 figures, submitted for publicatio