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X-ray coronary angiography (CAG) is one of widely used imaging modalities for clinical diagnosis and interventional treatment of coronary artery diseases. CAG sequences covering one or several cardiac cycles record morphological as well as dynamic information of coronary arterial vessels and related myocardium during cardiac cycles. Detection of coronary arterial dynamic information from CAG sequences...
Using endoscopic video, it is possible to perform 3D reconstruction of the anatomy using the well known epipolar constraint between matched feature points. Through this constraint, it is possible to recover the translation and rotation between camera positions and thus reconstruct the 3D anatomy by triangulation. However, these motion estimates are not stable for small camera motions. In this work,...
Motion vector estimation is an important parameter for video segmentation. Effective video compression can be achieved by choosing a correct approach for the calculation of motion vector. Here in this paper we propose an optical flow motion vector estimation through iterative Lucas-Kanade pyramidal implementation for both large & small motion in image pyramid representation a group of pixel information...
Object tracking systems require accurate segmentation of the objects from the background for effective tracking. Motion segmentation or optical flow can be used to segment incoming images. Whilst optical flow allows multiple moving targets to be separated based on their individual velocities, optical flow techniques are prone to errors caused by changing lighting and occlusions, both common in a surveillance...
Due to the limitation of the gradient constraint, only the normal flow, which is one of the optical flow components that is in the direction of the image gradient, can be computed directly. Consequently, an additional smoothness assumption has to be imposed in order to compute the optical flow. However, such assumption could yield a mismatch, especially on the boundaries of moving objects, besides...
A correlation-based optical flow algorithm using compute unified device architecture (CUDA) technology to achieve fast motion-based image segmentation is described. Using CUDA, a 240 processor GPU implementation of an optimized correlation-based optical flow algorithm allows segmentation to be achieved at high frame rates on high-resolution video sequences. Details of the mapping of the optical flow...
In conventional focus measures, focus values are locally aggregated to suppress the noise and to obtain better depth maps. However, this enlarges the difference between focus values of two consecutive frames which results in inaccurate shape. In this paper, we propose a nonparametric approach for 3D shape from image focus by applying an unsupervised formulation of kernel regression estimate. The focus...
A recently introduced optical flow based deinterlacing algorithm showed a promising performance for the sequences with well estimated optical flows. However, estimated optical flows mostly include some errors, which cause significant artifacts. To reduce such artifacts, we propose an improved algorithm by considering flow vector reliability in terms of motion linearity, motion uniqueness, and consistent...
Determining optical flow has been a wide field of research for more than 20 years now that has not been solved satisfactorily yet. In this work, we study the influence of a nonlinear smoothing process based on bilateral filtering on a Lucas & Kanade framework for the estimation of optical flow between two image frames. Different confidence measures are used to improve the computation process and...
This paper proposes a new measure that continuously measures the degree of inconsistency for a linear system. We apply the new measure to two essential vision problems. One is to predict the fidelity of a local optical flow computation system; The other is to detect motion boundaries. Experimental results on benchmark sequences validate the performance of the proposed measure on both problems.
We propose a novel inertial-aided KLT feature tracking method robust to camera ego-motions. The conventional KLT uses images only and its working condition is inherently limited to small appearance change between images. When big optical flows are induced by a camera-ego motion, an inertial sensor attached to the camera can provide a good prediction to preserve the tracking performance. We use a low-grade...
This paper presents an original method for increasing the accuracy of ego vehicle motion estimation using video data. Our algorithm takes as input a monocular video sequence on which originally combines procedures for feature detection and filtering, optical flow, epipolar geometry and estimation of the rotation from the obtained essential matrix. Imposing a movement constraint on the rotation matrix,...
This paper proposes an approach to extract motion features from sequences of images of human behavior.A novel algorithm called two-dimensional continuous dynamic programming (2DCDP) is proposed, which can obtain a set of correspondence data for all pixels between sequential images.The 2DCDP algorithm performs segmentation-free detection of objects in an input image from representations in reference...
The goal of the autonomous city explorer (ACE) is to navigate autonomously, efficiently and safely in an unpredictable and unstructured urban environment. To achieve this aim, an accurate localization is one of the preconditions. Due to the characteristics of our navigation environment, an elaborated visual odometry system is proposed to estimate the current position and orientation of the ACE platform...
This paper proposes a novel framework for frame rate up conversion. The framework contains a motion field estimator employing a hybrid of a novel predictive variable blocksize motion estimation and robust optical flow algorithm. This motion field is more robust and accurate than pure optical flow or pure motion estimation methods. Therefore, in-between frames can be estimated more accurately by the...
Scene flow is the motion of the surface points in the 3D world. For a camera, it is seen as a 2D optical flow in the image plane. Knowing the scene flow can be very useful as it gives an idea of the surface geometry of the objects in the scene and how those objects are moving. Four methods for calculating the scene flow given multiple optical flows have been explored and detailed in this paper along...
Optical flow estimation is one of the main subjects in computer vision. Many methods developed to compute the motion fields are built using standard heuristic formulation. In this paper, however, we learn a motion model. We develop a hybrid model by combining the learnt model with Markov Random Field (MRF). And then we introduce a method based on "Radial Basis Function Neural Network" (RBF)...
A GPU becomes an affordable solution for accelerating a slow process on a commercial system. The most of achievements using it for non-rendering problems are the exact re-implementation of existing algorithms designed for a serial CPU. We study about conditions of a good parallel algorithm, and show that it is possible to design an algorithm targeted to a parallel hardware, though it may be useless...
We propose an algorithm for large displacement optical flow estimation which does not require the commonly used coarse-to-fine warping strategy. It is based on a quadratic relaxation of the optical flow functional which decouples data term and regularizer in such a way that the non-linearized variational problem can be solved by an alternation of two globally optimal steps, one imposing optimal data...
In this paper we present a method for joint deformation and illumination parameter estimation from monocular image sequences exploiting direct image information. We are particularly interested in augmented reality applications, where a new texture is rendered onto a moving and deforming surface in the original video in real-time. Realistic retexturing not only requires geometric registration but also...
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