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In distributed sensing systems that use compressed videos for video analysis tasks, the lossy compression of videos can damage the accuracy of object detection, which is an essential step for various vision applications. This paper aims at constructing a new quality model to predict the performance of object detection. To achieve this goal, a distorted video database is constructed by applying object...
Smart living and well aging represent key challenges for our society. The precursor state of adverse outcomes that characterize aging has been recognized from scientific community with the frailty syndrome, determined by the loss of physical and psychological capacities. In this paper we define gait and posture indexes that can be effectively and unobtrusively measured using computer vision and RGBD...
Non-Bayer color filter array (CFA) sensors have recently drawn attention due to their superior compression of spectral energy, ability to deliver improved signal-to-noise ratio, or ability to provide high dynamic range (HDR) imaging. Demosaicking methods that perform color interpolation of Bayer CFA data have been widely investigated. However, a bottleneck to the adaption of emerging non-Bayer CFA...
Worldwide, 11% of infants are born prematurely, with a substantially increased risk of apneas, i.e. suspension of breathing, and in need of extra care after birth. Sensors used in neonatal intensive care units today are attached to the infant and can cause false alarms due to contact difficulties. In this paper we propose to monitor the respiratory rate of infants by using video recordings and video...
Tele-immersion (TI) related technologies can change the way people interact and bridge the gap between the physical and digital worlds. However, while the technology itself advances, most developed platforms have complex setups and require large investments. In this work, a low-cost platform is introduced, integrating multiple TI-related advances. Focusing on ease of use and rapid deployment, a fast...
We propose a method for detecting obstacles by comparing input and reference train frontal view camera images. In the field of obstacle detection, most methods employ a machine learning approach, so they can only detect pre-trained classes, such as pedestrian, bicycle, etc. This means that obstacles of unknown classes cannot be detected. To overcome this problem, we propose a background subtraction...
Capturing multiple images using the burst mode of handheld cameras can be a boon to obtain a high resolution (HR) image by exploiting the subpixel motion among the captured images arising from handshake. However, the caveat with mobile phone cameras is that they produce rolling shutter (RS) distortions that must be accounted for in the super-resolution process. We propose a method in which we obtain...
This work considers reconstructing a target signal in a context of distributed sparse sources. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI). The proposed algorithm takes advantage of compressive sensing (CS) with SI and adaptive weights by solving a proposed weighted 𝓃-ℓ1 minimization. The proposed algorithm computes the adaptive...
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We propose a computationally efficient approach for the extraction of dense gradient-based features based on the use of localized intensity-weighted centroids within the image. Whilst prior work concentrates on sparse feature derivations or computationally expensive dense scene sensing, we show that Dense Gradient-based Features (DeGraF) can be derived based on initial multi-scale division of Gaussian...
Existing multi-hypothesis (MH) prediction algorithms in compressed video sensing (CVS) are all deployed in measurement domain, which restricts the flexibility of block partitioning in the reconstruction process and decreases the reconstruction accuracy. To address this issue, this paper proposes a two-stage multi-hypothesis reconstruction (2sMHR) scheme which deploys the MH prediction in measurement...
Time-of-flight (ToF) imaging is an active method that utilizes a temporally modulated light source and a correlation-based (or lock-in) imager that computes the round-trip travel time from source to scene and back. Much like conventional imaging ToF cameras suffer from the trade-off between depth of field (DOF) and light throughput-larger apertures allow for more light collection but results in lower...
Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Various greedy recovery algorithms have been proposed to achieve a lower computational complexity compared to the optimal ℓ1 minimization, while maintaining a good reconstruction accuracy. We propose a new greedy recovery algorithm for compressed sensing, called the Adaptive Reduced-set...
In this paper we consider the problem of nonlocal image completion from random measurements and using an ensemble of dictionaries. Utilizing recent advances in the field of compressed sensing, we derive conditions under which one can uniquely recover an incomplete image with overwhelming probability. The theoretical results are complemented by numerical simulations using various ensembles of analytical...
We propose a novel predictive lossless compression algorithm for regions of interest (ROIs) in the hyperspectral images via Maximum Correntropy Criterion (MCC) based Least Mean Square (LMS) filtering. Non-linearity and non-Gaussian conditions of prediction residuals of the ROI pixels in the hyper-spectral image are taken into account to improve the compression performance compared to the ordinary...
Color-guided depth completion is to refine depth map through structure light sensing by filling missing depth structure and de-nosing. It is based on the assumption that depth discontinuity and color edge at the corresponding location are consistent. Among all proposed methods, MRF-based method including its variants is one of major approaches. However, the assumption above is not always true, which...
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