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This method introduces an efficient manner of learning action categories without the need of feature estimation. The approach starts from low-level values, in a similar style to the successful CNN methods. However, rather than extracting general image features, we learn to predict specific video representations from raw video data. The benefit of such an approach is that at the same computational...
Density estimation based visual object counting (DE-VOC) methods estimate the counts of an image by integrating over its predicted density map. They perform effectively but inefficiently. This paper proposes a fast DE-VOC method but maintains its effectiveness. Essentially, the feature space of image patches from VOC can be clustered into subspaces, and the examples of each subspace can be collected...
In this paper, we propose a novel fast cost propagation algorithm on spanning tree structures. By introducing local smoothness constraint during the weighted cost aggregation process on tree structures, we overcome the shortage of the “fronto-parallel plane” assumption used in most local and non-local cost aggregation algorithms. By applying it to our stereo correspondence framework, accurate results...
In this paper, we propose a robust video colorization method automatically through limited color references in a video sequence. The proposed method first estimates motion vectors between a monochrome frame and colored reference frames for initial matching by optical flow. Then it transfers color information to matched points in the monochrome frame and further propagates color information of matched...
Color constancy is the ability of the human visual system to perceive constant colors for a surface despite changes in the spectrum of the illumination. In computer vision, the main approach consists in estimating the illuminant color and then to remove its impact on the color of the objects. Many image processing algorithms have been proposed to tackle this problem automatically. However, most of...
Multispectral demosaicking, which is an extension of color demosaicking, is a challenging problem because each band is significantly undersampled and thus precise reconstruction is needed for the restoration of high-frequency components, such as edges, textures etc. In general, existing algorithms borrow high-frequency information either from different bands via inter-color correlation or from within...
Image upsampling from one input image gathers considerable attention in the field of computer vision. The problem is ill-posed because the number of known low-resolution (LR) pixels is less than that of unknown high-resolution (HR) pixels. Therefore, quality of an upsampled image depends on prior assumptions. Image interpolation methods are one of the image upsampling technologies and are faster than...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Scene depth variation is an important factor that leads to spatially-varying camera motion blur. Most of the previous methods require auxiliary cameras or user interaction to make depth-aware deblurring tractable. In this work, we propose to use a noisy/blurred/noisy image sequence and simultaneously recorded inertial measurements to jointly estimate scene depth and remove spatially-varying blur caused...
This paper proposes a blur detection algorithm that is capable of detecting and quantifying the level of spatially-varying blur by integrating directional edge spread calculation, Just Noticeable Blur (JNB) and local probability summation. The proposed method generates a blur map indicating the relative amount of perceived local blurriness. We compare the proposed method with six other state-of-the-art...
Motion estimation across low-resolütion frames and the reconstruction of high-resolütion images are two coupled sübproblems of multi-frame super-resolütion. This paper introduces a new joint optimization approach for motion estimation and image reconstrüction to address this interdependence. Our method is formulated via non-linear least squares optimization and combines two principles of robust süper-resolütion...
Multi-object tracking is a difficult problem underlying many computer vision applications. In this work, we focus on sediment transport experiments in a flow were sediments are represented by spherical calibrated beads. The aim is to track all beads over long time sequences to obtain sediment velocities and concentration. Classical algorithms used in fluid mechanics fail to track the beads over long...
Hazy images hinder image understanding in many applications such as autonomous vehicle. In this paper, we propose an efficient method to improve image quality of hazy images. Our method estimates the transmission function based on a linear model that allows efficient computation and employs quadtree to search for a region that best represents the scatter of airlight. Experiments were conducted using...
The accuracy of end-to-end distortion (EED) estimation is crucial to achieving effective error resilient video coding. An established solution, the recursive optimal per-pixel estimate (ROPE), does so by tracking the first and second moments of decoder-reconstructed pixels. An alternative estimation approach, the spectral coefficient-wise optimal recursive estimate (SCORE), tracks instead moments...
Restoring underwater image from a single image is known to be an ill-posed problem. Some assumptions made in previous methods are not suitable in many situations. In this paper, an effective method is proposed to restore underwater images. Using the quad-tree subdivision and graph-based segmentation, the global background light can be robustly estimated. The medium transmission map is estimated based...
In this paper, we restore images degraded by scattering and absorption such as hazy, sandstorm, and underwater images. By calculating the difference between the observed intensity and the ambient light in a degraded image scene, which we call the scene ambient light differential, we estimate the transmission map. In the restoration process, we first enhance the degraded images based on the proposed...
In this paper we present a method to recover the shading and specularities in the scene from a single image. The method presented here is based on the dichromatic model and enforces a local smoothness assumption over the object surfaces in the scene. This naturally leads to a setting where the estimate of the shading at a particular pixel can be expressed in terms of its neighbours up to a pair of...
Structure tensor analysis on epipolar plane images (EPIs) is a successful approach to estimate disparity from a light field, i.e. a dense set of multi-view images. However, the disparity range allowable for the light field is limited, because the estimation becomes less accurate as the range of disparities become larger. To overcome this limitation, we propose a new method called sheared EPI analysis,...
This paper presents a new image retargeting method that explores blur information. Given the input image, we compute the blur map and estimate in-focus regions. For retargeting, we first try to crop image boundaries as much as possible (preserving in-focus regions). If cropping is not enough, we use seam carving exploring a novel blur-aware energy function that concentrates the seams in blurred regions...
Image deconvolution is the task to recover the image information that was lost by taking photos with blur motion. Especially blind image deconvolution requires no prior informations other than the blurred image. This problem is seriously ill-posed and an additional operation is required such as extracting image features. In this paper, we present a blind image deconvolution framework using a specified...
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