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Matching specific persons across scenes, known as person re-identification, is an important yet unsolved computer vision problem. Feature representation and metric learning are two fundamental factors in person re-identification. However, current person re-identification methods, which use single handcrafted feature with corresponding metric, could be not powerful enough when facing illumination,...
Associating groups of people across non-overlapping camera views is an important but unsolved problem. Compared with the similar person re-identification task, group re-identification introduces some new challenges, such as significant deformation in uncontrolled directions, great intra-group occlusions and so on. In this paper, we propose a novel patch matching based framework for group re-identification...
This paper proposes a novel model for contrast enhancement of RGB images. The average local contrast measure is increased within a variational framework which preserves the hue of the original image by coupling the channels. The user is enabled to intuitively control the level of the contrast as well as the scale of the enhanced details. Moreover, our model avoids large modifications of the original...
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...
We propose a software solution which allows the user to design a realistic illumination for a given 2D image of a face. The user paints a few strokes on the image to give clues of desired novel lighting effects. The algorithm produces an image of the face under the best possible realistic illumination, accordingly. It takes advantage of a 3D Morphable Model framework and a state of the art inverse...
Painters have reproduced High Dynamic Range (HDR) scenes for 5 centuries; photographers have used multiple exposures for 165 years; scientists have used electronic imaging modifications of range for 50 years. This paper reviews the history of the different rendition techniques. Some techniques use trial and error to find the best rendition. Others use psychophysical models of human vision, or physical...
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...
The illumination conditions of a scene create intra-class variability in outdoor visual data, degrading the performance of high-level algorithms. Using only the image, and with hyper-spectral data as a case study, this paper proposes a deep learning approach to learn illumination invariant features from the data in an unsupervised manner. The proposed approach incorporates a similarity measure, the...
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...
Metric learning is an effective method for person re-identification. It utilizes latent factors to find a suitable space for measuring distances. In general, a small number of factors are not powerful enough to match the pedestrians while a large number of factors cause high computational cost. In this paper, to balance this trade-off, a novel diversity regularized distance metric learning method...
We present a method searching for the main symmetric axis in an image based on the SAX representation which converts pixels to symbols and a classical linear time palindrome detecting algorithm. This method generates a curve outlining the axis by dynamic programming and produces a straight axis by RANSAC, a linear fitting method tolerates outliers. The computational complexity is O(mn) on an m×n image,...
Weather-dependent road conditions are a major factor in many automobile incidents; computer vision algorithms for automatic classification of road conditions can thus be of great benefit. This paper presents a system for classification of road conditions using still-frames taken from an uncalibrated dashboard camera. The problem is challenging due to variability in camera placement, road layout, weather...
Fourier ptychographic microscopy (FPM) is an attractive method to extend the resolution beyond the conventional limit defined by a microscope optics, sharing properties with ptychographic, synthetic aperture imaging and phase retrieval. The algorithm uses a sequence of low-resolution (LR) images acquired under angularly varying illumination to reconstruct a high-resolution (HR) image. However, traditional...
In this paper, we pose a new problem of video enhancement transcoding, which converts the compressed dark video into compressed normal-lighting one. Distinct statistics of dark and normal videos result in quite different coding modes, which thus enforces latent constraints on mode conversion during transcoding. Following this idea, we propose a fast mode decision algorithm to speed up computation...
Existing 3D lighting consistency based forensic methods have some practical problems. They usually require additional images and human labor to reconstruct the 3D face model for lighting estimation, and furthermore, they cannot deal with expressional faces effectively. These drawbacks make them unusable in many practical cases. In this paper, we propose a more practical 3D lighting based forensic...
In this work, a new simple but effective fusion-based strategy for enhancing single backlit image is proposed. The fundamental idea of proposed strategy is to blend different features into a single one to improve the specific quality of image. Most of existing methods are based on the modification of histogram to enhance the contrast of low light images. However, the backlit images are different from...
In this paper, we propose a new texture descriptor, completed local derivative pattern (CLDP). In contrast to completed local binary pattern (CLBP), which involves only local differences at each scale, CLDP encodes the directional variation of the local differences of two scales as a complementary component to local patterns in CLBP. The new component in CLDP, with regarded as the directional derivative...
In this communication, a fast reconstruction algorithm is proposed for fluorescence blind structured illumination microscopy (SIM) under the sample positivity constraint. This new algorithm is by far simpler and faster than existing solutions, paving the way to 3D and real-time 2D reconstruction.
We apply social ℓ-norms for the first time to the problem of hyperspectral unmixing while modeling spectral variability. These norms are built with inter-group penalties which are combined in a global intra-group penalization that can enforce selection of entire endmember bundles; this results in the selection of a few representative materials even in the presence of large endmembers bundles capturing...
A compressive video microscope based on structured illumination is built. The source-side illumination coding scheme allows the emission photons being collected by the full aperture of the microscope objective, and thus is suitable for the fluorescence readout mode. A block-wise total variation algorithm has been proposed to address the mismatch between the illumination pattern size and the detector...
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