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The maximum consensus problem lies at the core of several important computer vision applications as it is one of the most popular criteria for robust estimation. Although considerable efforts have been devoted to solving this problem, exact algorithms are still impractical for real-world data. Randomized hypothesize-and-test approaches such as RANSAC and its variants are therefore still the key players...
Estimating a depth map from multiple views of a scene is a fundamental task in computer vision. As soon as more than two viewpoints are available, one faces the very basic question how to measure similarity across >2 image patches. Surprisingly, no direct solution exists, instead it is common to fall back to more or less robust averaging of two-view similarities. Encouraged by the success of machine...
In this paper we present an optimization algorithm for simultaneously detecting video freeze and obtaining the minimum number of the frame required in motion intention estimation for real time robust video stabilization on multirotor unmanned aerial vehicles. A combination of a filter and a threshold is used to the video freeze detection, and for optimizing the algorithm, we find the minimum number...
Augmented reality is popular and rapidly growing direction. It is successfully used in medicine, education, engineering and entertainment. In the paper, basic principles of typical augmented reality system are described. An efficient hybrid visual tracking algorithm is proposed. The approach is based on combining of the optical flow technique with direct tracking methods. It is demonstrated that developed...
In this paper, we introduce robust and synergetic hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation. This paper systematically analyzes the effectiveness of different features, and shows how each feature can compensate for the weaknesses of other features when they are concatenated. For a full defocus map estimation,...
In this paper we study the problem of automatically generating polynomial solvers for minimal problems. The main contribution is a new method for finding small elimination templates by making use of the syzygies (i.e. the polynomial relations) that exist between the original equations. Using these syzygies we can essentially parameterize the set of possible elimination templates. We evaluate our method...
Maximum consensus estimation plays a critically important role in computer vision. Currently, the most prevalent approach draws from the class of non-deterministic hypothesize-and-verify algorithms, which are cheap but do not guarantee solution quality. On the other extreme, there are global algorithms which are exhaustive search in nature and can be costly for practical-sized inputs. This paper aims...
We present a new way to combine the propagated flow in image pyramid and dense correspondences from descriptor matching for large displacement optical flow estimation. Because the matches and the flow propagated from the coarser level in image pyramid are possibly wrong, our method uses color-based weighted linear interpolation to reduce the wrong initial flow and alleviate over-smoothing, instead...
Optical flow is one of the key components in computer vision research area. Since the seminal work proposed by Horn and Schunck [1], numerous advanced algorithms have been proposed. Many state-of-the-art optical flow estimation algorithms optimize the data and regularization terms to solve ill-posed problems. However, despite their major advances over last decade, conventional optical flow methods...
Convolutional Neural Network (CNN) has been used successfully in solving different computer vision tasks such as classification, detection, and segmentation. This paper addresses the problem of estimating object depth from a single RGB image. While stereo depth estimation is a straightforward task, predicting depth map of an object from a single RGB image is a more challenging task due to the lack...
This study presented a position estimation and control method for quadrotor using optical flow and GPS sensors. Firstly, an optical flow based location algorithm is shown that calculates relative position from continues images. Considering the drift problem of optical flow, an improved Kalman filter based position estimation method is presented to enhance performance of location system. Then a PD...
The present paper describes a low-cost algorithm for video stabilization. Like other feature based algorithms, it is robust to motion blur, noise and illumination changes. Moreover, maintaining real time processing, it is not negatively affected by moving objects in the scene, works fine even in conditions of low details in the background and it is robust to scene changes.
The primary task in preprocessing image is moving estimation and compensation of image senor, that is to say, correction problem of image background. In this paper, a new method of motion background compensation based on robust regression is proposed. The background motion velocity is calculated by the estimation of the optical flow field model. Then robust iterative weighted least square method is...
This work presents a height estimation method that uses visual information. This method is based on the global appearance of the scenes. Every omnidirectional scene is described with a global appearance descriptor without any other transformation. This approach is tested with our own image database. This database is generated synthetically based on two different virtual rooms. One of the advantages...
Specular reflection removal is indispensable to many computer vision tasks. However, most existing methods fail or degrade in complex real scenarios for their individual drawbacks. Benefiting from the light field imaging technology, this paper proposes a novel and accurate approach to remove specularity and improve image quality. We first capture images with specularity by the light field camera (Lytro...
This paper presents a simple and effective way of solving the robust subspace estimation problem where the corruptions are column-wise. The method we present can handle a large class of robust loss functions and is simple to implement. It is based on Iteratively Reweighted Least Squares (IRLS) and works in an iterative manner by solving a weighted least-squares rank-constrained problem in every iteration...
The combining method of neighborhood and non-neighborhood is proposed to be used for amending the computed results of optical flow, which has improved the robustness of flow estimation. This method uses the estimated frame based on CLG-TV model, carries out the decomposition of structure and texture for the images, proceeds weighted approach for data term and smooth term, uses coarse-to-fine method...
Optical Flow is the pixels apparent movement pattern of two consecutives images. There are differents methods to estimate the optical flow: variational methods and exaustive methods. Exhaustive methods take a neighborhood around a point in the first image of the sequence and search for the most similar in the second image. This procedure is known as correspondency estimation. This work evaluate the...
Either any of the current global or non-local stereo matching algorithms cannot be good enough to show both matching accuracy and calculation efficiency during the matching processing, especially while there are less texture regions or the images are captured from real scene. Therefore, the goal of our research is to break current bottleneck of stereo matching in aspects of the precision and speed,...
Motion estimation from underwater images is an active research area of the vision system devoted to the applications of robots. In this paper, a vision based system for tracking the motion of moving objects is presented. The aim of this paper is to give an optimal performance against radiometric features such as non-uniform lighting, blurring and noise. The moving object detection is performed by...
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