This paper presents visual analysis of eye state and head pose (HP) for continuous monitoring alertness a vehicle driver. Most existing approaches to detection nonalert driving patterns rely either on closure or nodding angles determine the driver drowsiness distraction level. The proposed scheme uses features such as index (EI), pupil activity (PA), HP extract critical information nonalertness EI determines if is open, half closed, closed from ratio height height. PA measures rate deviation...

Existing methods for tensor completion (TC) have limited ability characterizing low-rank (LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes hidden in a tensor, we propose new multilayer sparsity-based decomposition (MLSTD) (LRTC). The method encodes structured of by multiple-layer representation. Specifically, use CANDECOMP/PARAFAC (CP) model to decompose into an ensemble sum rank-1 tensors, and number components is easily interpreted as...

Recently, tensor sparsity modeling has achieved great success in the completion (TC) problem. In real applications, of a can be rationally measured by low-rank decomposition. However, existing methods either suffer from limited power estimating accurate rank or have difficulty depicting hierarchical structure underlying such data ensembles. To address these issues, we propose parametric measure model, which encodes for general Laplacian scale mixture (LSM) based on three-layer transform...

This article presents U 2PNet, a novel unsupervised underwater image restoration network using polarization for improving signal-to-noise ratio and quality in imaging environments. Traditional methods require specific cues or pairs of datasets, which limit their practical applications. Our proposed method requires only one mosaicked polarized the scene does not datasets pretraining cues. We design two subnetworks (T-net B textsubscript ∞ -net) to accurately estimate transmission map...

Fuzzy control systems and neural-network for backing up a simulated truck, truck-and-trailer, to loading dock in parking lot are presented. The supervised backpropagation learning algorithm trained the neural network systems. robustness of was tested by removing random subsets training data sequences. performed well but required extensive computation training. fuzzy until over 50% their fuzzy-associative-memory (FAM) rules were removed. They also when key FAM equilibration rule replaced with...

A demand for division of focal plane (DoFP) polarization imaging technology grows rapidly as nanofabrication technologies become mature. For real-time imaging, a DoFP polarimeter often trades off its spatial resolution, which may cause instantaneous field view (IFoV) errors. To deal with such problems, interpolation methods are used to fill the missing information. This paper presents an technique using Newton's polynomial demosaicking. The is performed in difference domain error taken into...

This paper presents a joint dehazing and denoising scheme for an image taken in hazy conditions. Conventional methods may amplify the noise depending on distance density of haze. To suppress improve performance, imaging model is modified by adding process amplifying offers depth-chromaticity compensation regularization transmission map chromaticity-depth image. The proposed iterative method with polarization uses these two schemes relationship between dehazed irradiance are used to promote...

A lab-on-a-chip (LOC)-based non-invasive optical sensor for measuring glucose in saliva was fabricated. Existing sensors utilizing blood require acquisition of a sample by pricking the finger, which is painful and inconvenient. To overcome these limitations, we propose with LOC, micro-electro-mechanical system measurement technology. The proposed involves pretreatment, mixing, on single tiny chip. Saliva containing oxidase oxidation are injected through Inlets 1 2, respectively. Next, H₂O₂...

Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-valued data lies in global subspace. The so-called prior is measured by nuclear norm. Such assumption not reliable recovering low-rank (LR) data, especially when considerable elements are missing. To mitigate this weakness, article presents an enhanced model for LRTC using both local and information a latent LR tensor. In specific, we adopt doubly weighted strategy norm along each mode to characterize...

The fusion of infrared and visible images combines the information from two complementary imaging modalities for various computer vision tasks. Many existing techniques, however, fail to maintain a uniform overall style keep salient details individual simultaneously. This paper presents an end-to-end Laplacian Pyramid Fusion Network with hierarchical guidance (HG-LPFN) that takes advantage pixel-level saliency reservation global optimization capability deep learning. proposed scheme...

This paper introduces a Polarization-Driven Solution (PDS) to enhance the contrast of underwater imagery degraded by light scattering and uneven illumination. Images taken in environments suffer from reduced due combined effects non-uniform We present an Underwater Joint Degradation Model (UJDM) that effectively describes compounded impacts By exploiting polarization distinctions between objects scattered light, we mitigate deleterious scattering. Additionally, leverage information persist...

This paper describes a fusion of visual and thermal infrared (IR) images for robust face recognition. Two types methods are discussed: data decision fusion. Data produces an illumination-invariant image by adaptively integrating registered images. Decision combines matching scores individual recognition modules. In the process, eyeglasses, which block energy, detected from replaced with eye template. Three fusion-based techniques implemented tested: (Df), highest score (Fh), average (Fa). A...

Hyperspectral image (HSI) denoising is significant for correct interpretation. In this paper, a sparse representation framework that unifies and spectral unmixing in closed-loop manner proposed. While conventional approaches treat separately, the proposed scheme utilizes information from as feedback to distortion. Both act constraints others are solved iteratively. Noise suppressed via coding, fractional abundance estimated using sparsity prior of endmembers library. The used regularizer...

Hyperspectral image (HSI) noise reduction is an active research topic in HSI processing due to its significance improving the performance for object detection and classification. In this paper, we propose a joint spectral spatial low-rank (LR) regularized method denoising, based on assumption that free-noise component observed signal can exist latent low-dimensional structure while does not have property. The proposed denoising only considers traditional LR property across domain but also...

We propose a novel graph Laplacian-guided coupled tensor decomposition (gLGCTD) model for fusion of hyperspectral image (HSI) and multispectral (MSI) spatial spectral resolution enhancements. The Tucker is employed to capture the global interdependencies across different modes fully exploit intrinsic spatial-spectral information. To preserve local characteristics, complementary submanifold structures embedded in high-resolution (HR)-HSI are encoded by Laplacian regularizations. information...

This paper presents a mosaic convolution-attention network (MCAN) for demosaicing spectral images captured using multispectral filter array (MSFA) imaging sensors. MSFA-based systems acquire information of scene in single snap-shot operation. A complete image is reconstructed by an image. To avoid aliasing and artifacts demosaicing, we utilize joint spatial-spectral correlation raw The proposed MCAN includes convolution module (MCM) attention (MAM). MCM extracts features via learning...

This paper presents a simple, yet effective demosaicking technique using polarization channel difference prior for images captured by division of focal plane imaging sensors. The embodies that high frequency energy between orthogonal channels tends to be larger than non-orthogonal channels. theoretically proves this is physical valid. For each missing at pixel position, three initial predictions are recovered different differences. estimated the weighted fusion predictions, where weights...

This paper presents a deep learning-based spectral demosaicing technique trained in an unsupervised manner. Many existing techniques relying on supervised learning with synthetic images, often underperform real-world especially as the number of bands increases. comprehensive (USD) framework based characteristics mosaic images. encompasses training method, model structure, transformation strategy, and well-fitted selection strategy. To enable network to dynamically correlation while...

A simple fuzzy control system and a neural for backing up truck in an open parking lot are developed. The choice of problem was prompted by the recent, successful, network backer-upper simulation Nguyen Widrow (Proc. Int. Joint Conference on Neural Networks, vol.2, p.357-363, June, 1989). authors were unable to exactly replicate they used. Instead built best backpropagation could with essentially same kinematics compared it controller develop. compares favorably terms black-box computation...

This paper proposes a band-subset-based clustering and fusion technique to improve the classification performance in hyperspectral imagery. The proposed method can account for varying data qualities discrimination capabilities across spectral bands, utilize spatial information simultaneously. First, cube is partitioned into several nearly uncorrelated subsets, an eigenvalue-based approach evaluate confidence of each subset. Then, nonparametric used extract arbitrarily-shaped clusters...