Model-based restoration of underwater spectral images captured with narrowband filters.

Underwater spectral imaging is a promising method for mapping, classification and health monitoring of coral reefs and seafloor inhabitants. However, the spectrum of light is distorted during the underwater imaging process due to wavelength-dependent attenuation by the water. This paper presents a model-based method that accurately restores brightness of underwater spectral images captured with narrowband filters. A model is built for narrowband underwater spectral imaging. The model structure is derived from physical principles, representing the absorption, scattering and refraction by water and the optical properties of narrowband filters, lenses and image sensors. The model coefficients are calibrated based on spectral images captured underwater and in air. With the imaging model available, energy loss due to water attenuation is restored for images captured at different underwater distances. An experimental setup is built and experiments are carried out to verify the proposed method. Underwater images captured within an underwater distance of 260 cm are restored and compared with those in air. Results show that the relative restoration error is 3.58% on average for the test images, thus proving the accuracy of the proposed method.

Generalized weighted ratio method for accurate turbidity measurement over a wide range.

Turbidity measurement is important for water quality assessment, food safety, medicine, ocean monitoring, etc. In this paper, a method that accurately estimates the turbidity over a wide range is proposed, where the turbidity of the sample is represented as a weighted ratio of the scattered light intensities at a series of angles. An improvement in the accuracy is achieved by expanding the structure of the ratio function, thus adding more flexibility to the turbidity-intensity fitting. Experiments have been carried out with an 850 nm laser and a power meter fixed on a turntable to measure the light intensity at different angles. The results show that the relative estimation error of the proposed method is 0.58% on average for a four-angle intensity combination for all test samples with a turbidity ranging from 160 NTU to 4000 NTU.

Deep Differentiable Random Forests for Age Estimation.

Age estimation from facial images is typically cast as a label distribution learning or regression problem, since aging is a gradual progress. Its main challenge is the facial feature space w.r.t. ages is inhomogeneous, due to the large variation in facial appearance across different persons of the same age and the non-stationary property of aging. In this paper, we propose two Deep Differentiable Random Forests methods, Deep Label Distribution Learning Forest (DLDLF) and Deep Regression Forest (DRF), for age estimation. Both of them connect split nodes to the top layer of convolutional neural networks (CNNs) and deal with inhomogeneous data by jointly learning input-dependent data partitions at the split nodes and age distributions at the leaf nodes. This joint learning follows an alternating strategy: (1) Fixing the leaf nodes and optimizing the split nodes and the CNN parameters by Back-propagation; (2) Fixing the split nodes and optimizing the leaf nodes by Variational Bounding. Two Deterministic Annealing processes are introduced into the learning of the split and leaf nodes, respectively, to avoid poor local optima and obtain better estimates of tree parameters free of initial values. Experimental results show that DLDLF and DRF achieve state-of-the-art performance on three age estimation datasets.

Stabilization of the mesomorphic phase in a self-assembled two-component system.

We reported here the two-component self-assembling building blocks capable of forming lyotropic liquid crystal and liquid-crystalline physical gel. One of the components has a molecular characteristic of C(3)-symmetrical trisureas containing three azobenzene groups, which can form liquid-crystal phase in a temperature range of 133-215 degrees C. Another one has a trisamide core, which can self-aggregate to fibrous network through hydrogen bonds of amide moieties. The mixture of these two components performs lyotropic liquid crystal as well as liquid-crystalline physical gel in a temperature range larger than that of sole compound, suggesting that the cooperation of hydrogen bonds between these components stabilizes the mesophase of the assembly. The mechanism of formation of the mesophase was investigated by infrared spectra and small-angle X-ray scatterings.

Phase separation of poly(vinyl methyl ether) aqueous solution: a near-infrared spectroscopic study.

The thermosensitive phase separation of poly(vinyl methyl ether) (PVME) aqueous solutions has been investigated using near-infrared spectroscopy in combination with two-dimensional correlation analysis, and a two-step phase separation mechanism during gradual heating has been established. Two-dimensional near-infrared (2D NIR) analysis results indicate that during this two-step process the dehydration of CH 2 groups occurs earlier than that of CH 3 groups. This result suggests that it is the change of the hydrophobic hydrocarbon chain conformation induced by heating that indirectly leads to the dehydration of the hydrophilic ether oxygen side groups.