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Traditional methods for motion estimation estimate the motion field F between a pair of images as the one that minimizes a predesigned cost function. In this paper, we propose a direct method and train a Convolutional Neural Network (CNN) that when, at test time, is given a pair of images as input it produces a dense motion field F at its output layer. In the absence of large datasets with ground...
In camera-equipped teleoperated robots, it is often tedious for the operator to manage both the viewpoint and the shaky/unstable navigation, leading to disorientation. Our proposal is to create a virtual, freely rotatable camera that is decoupled from the robot's rotation. It is implemented using a complete spherical camera and removing its rotation in-image with a novel algorithm based on aligning...
This paper presents Neighbor-Guided SemiGlobal Matching (NG-fSGM), a new method for optical flow. It is based on SGM, a popular dynamic programming algorithm for stereo vision, where the disparity of each pixel is calculated by aggregating local matching costs over the entire image to resolve local ambiguity in texture-less and occluded regions. Unlike conventional SGM, NG-fSGM operates on a subset...
Mapping an ever changing urban environment is a challenging task as we are generally interested in mapping the static scene and not the dynamic objects, such as cars and people. We propose a novel approach to the problem of dynamic object removal within stereo based scene mapping that is both independent of the underlying stereo approach in use and applicable to varying object and camera motion. By...
Recently, very deep two-stream ConvNets have achieved great discriminative power for video classification, which is especially the case for the temporal ConvNets when trained on multi-frame optical flow. However, action recognition in videos often fall prey to the wild camera motion, which poses challenges on the extraction of reliable optical flow for human body. In light of this, we propose a novel...
Sparse motion estimation with local optical flow methods is fundamental for a wide range of computer vision application. Classical approaches like the pyramidal Lucas-Kanade method (PLK) or more sophisticated approaches like the Robust Local Optical Flow (RLOF) fail when it comes to environments with illumination changes and/or long-range motions. In this work we focus on these limitations and propose...
The accuracy of calibration will significantly affect the post processing capability of light field imaging. The geometry of the reconstructed scene is related to the parameters of light field closely, involving the accuracy of decoded rays and ambiguities from ray correspondences. Through exploring the ray correspondence, we derive a transformation matrix to describe the projective distortion on...
Subjective studies showed that most HDR video tone mapping operators either produce disturbing temporal artifacts, or are limited in their local contrast reproduction capability. Recently, both these issues have been addressed by a novel temporally coherent local HDR tone mapping method, which has been shown, both qualitatively and through a subjective study, to be advantageous compared to previous...
A spectral clustering based video object segmentation technique is proposed in this work. A foreground separation model is introduced which uses thresholding by different features to produce an initial labeling for each frame of the input sequence. We use a combination of color, optical flow, spatial-coordinates, spatiotemporal saliency and the initial foreground labeling to construct an interframe...
Intrinsic natures of different appearance between sub-regions of objects and non-objects in optical flows lead to more visual consistency for object proposals. Hence, visual variations in different sub-regions in video sequences over time is a good indicator for likeliness of objects. We propose a method that dynamically measures the objectness of each proposal by exploiting temporal consistency within...
Vision-based underwater navigation and object detection requires robust computer vision algorithms to operate in turbid water. Many conventional methods aimed at improving visibility in low turbid water. In this paper, we propose a novel contrast enhancement to enhance high turbid underwater images using descattering and color correction. The proposed enhancement method removes the scatter and preserves...
A learning system that is able to predict the degradation state of impact craters on optical images is presented in this paper. It is based on the extraction of visual features along the crater rim together with the decision with a SVM classifier. The algorithm achieved a sensitivity of 89% and a specificity of 96% (preserved vs non-preserved) in a dataset of annotated craters from Mars.
Motion information is a key factor for action recognition and has been eagerly pursued for decades. How to effectively learn motion features in Convolutional Networks (ConvNets) remains an open issue. Prevalent ConvNets often take several full frames of video as input at a time, which can be a heavy burden for network training. In this paper, we introduce a novel framework called Tube ConvNets, by...
Super-resolution (SR) offers an effective approach to boost quality and details of low-resolution (LR) images to obtain high-resolution (HR) images. Despite the theoretical and technical advances in the past decades, it still lacks plausible methodology to evaluate and compare different SR algorithms. The main cause to this problem lies in the missing ground truth data for SR. Unlike in many other...
Incoherent holography has recently attracted significant research interest due to its flexibility for a wide variety of light sources. In this paper, we use compressive sensing to reconstruct a three-dimensional volumetric object from its two-dimensional Fresnel incoherent correlation hologram. We show how compressed sensing enables reconstruction without out-of-focus artifacts, when compared to conventional...
Automatic diagnosis for fetal echocardiography plays an important part in diagnostic aid in the discrimination of congenital heart disease (CHD). Instead of traditional methods analyzing 2D cardiac echo video that need to find the standard view for discrimination, in this paper, we proposed a new system for automatic discrimination of CHD applying 4D original echocardiogram, which avoids the challenging...
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