Multi-class remote sensing object recognition based on discriminative sparse representation.

The automatic recognition of multi-class objects with various backgrounds is a big challenge in the field of remote sensing (RS) image analysis. In this paper, we propose a novel recognition framework for multi-class RS objects based on the discriminative sparse representation. In this framework, the recognition problem is implemented in two stages. In the first, or discriminative dictionary learning stage, considering the characterization of remote sensing objects, the scale-invariant feature transform descriptor is first combined with an improved bag-of-words model for multi-class objects feature extraction and representation. Then, information about each class of training samples is fused into the dictionary learning process; by using the K-singular value decomposition algorithm, a discriminative dictionary can be learned for sparse coding. In the second, or recognition, stage, to improve the computational efficiency, the phase spectrum of a quaternion Fourier transform model is applied to the test image to predict a small set of object candidate locations. Then, a multi-scale sliding window mechanism is utilized to scan the image over those candidate locations to obtain the object candidates (or objects of interest). Subsequently, the sparse coding coefficients of these candidates under the discriminative dictionary are mapped to the discriminative vectors that have a good ability to distinguish different classes of objects. Finally, multi-class object recognition can be accomplished by analyzing these vectors. The experimental results show that the proposed work outperforms a number of state-of-the-art methods for multi-class remote sensing object recognition.

Effects of co-exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin and polychlorinated biphenyls on nonalcoholic fatty liver disease in mice.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and polychlorinated biphenyls (PCBs) are persistent organic pollutants which coexist in environment, and human are co-exposed to these chemicals. Our present study was aimed to investigate the possible enhanced nonalcoholic fatty liver disease (NAFLD) in ApoE(-/-) mice co-exposed to TCDD and PCBs and to reveal the potential mechanisms involved in. Male ApoE(-/-) mice were exposed to TCDD (15 µg/kg) and Aroclor1254 (55 mg/kg, a representative mixture of PCBs) alone or in combination by intraperitoneal injection four times over a 6-week period. Those mice co-exposed to PCBs and TCDD developed serious liver steatosis, necrosis, and inflammatory stimuli. Interestingly, all treatment induced hepatic cytochrome P450 1A1 (CYP1A1) expression, but the maximal level of CYP1A1 was not observed in the co-exposure group. Furthermore, microarray analysis by ingenuity pathway analysis software showed that the nuclear factor-erythroid 2-related factor 2 (Nrf2)-mediated oxidative stress response pathway was significantly activated following co-exposure to TCDD and PCBs. Our data demonstrated that co-exposure to TCDD and PCBs markedly worsen NAFLD in ApoE(-/-) mice.

Augmented atherogenesis in ApoE-null mice co-exposed to polychlorinated biphenyls and 2,3,7,8-tetrachlorodibenzo-p-dioxin.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and polychlorinated biphenyls (PCBs) are persistent organic pollutants found as complex mixtures in the environment throughout the world. Therefore, humans are ubiquitously and simultaneously exposed to TCDD and PCBs. TCDD and PCBs alone have been linked to atherosclerosis. However, the effects of interactions or synergism between TCDD and PCBs on atherogenesis are unknown. We investigated the possible enhanced atherogenesis by co-exposure to TCDD and PCBs and the potential mechanism(s) involved in this enhancement. Male ApoE(-/-) mice were exposed to TCDD (15 µg/kg) and Aroclor1254 (55 mg/kg, a representative mixture of PCBs) alone or in combination by intraperitoneal injection four times over six weeks of duration. Our results showed that mice exposed to TCDD alone, but not Aroclor1254 alone, developed atherosclerotic lesions. Moreover, we found that atherosclerotic disease was exacerbated to the greatest extent in mice co-exposed to TCDD and Aroclor1254. The enhanced lesions correlated with several pro-atherogenic changes, including a marked increase in the accumulation of the platelet-derived chemokine PF4, and the expression of the proinflammatory cytokine MCP-1 and the critical immunity gene-RIG-I. Our data demonstrated that co-exposure to TCDD and Aroclor1254 markedly enhanced atherogenesis in ApoE(-/-) mice. Significantly, our observations suggest that combined exposure to TCDD and PCBs may be a greater cardiovascular health risk than previously anticipated from individual studies.

A biological hierarchical model based underwater moving object detection.

Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.

High-resolution remotely sensed small target detection by imitating fly visual perception mechanism.

The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method.

Self-adaptive image reconstruction inspired by insect compound eye mechanism.

Inspired by the mechanism of imaging and adaptation to luminosity in insect compound eyes (ICE), we propose an ICE-based adaptive reconstruction method (ARM-ICE), which can adjust the sampling vision field of image according to the environment light intensity. The target scene can be compressive, sampled independently with multichannel through ARM-ICE. Meanwhile, ARM-ICE can regulate the visual field of sampling to control imaging according to the environment light intensity. Based on the compressed sensing joint sparse model (JSM-1), we establish an information processing system of ARM-ICE. The simulation of a four-channel ARM-ICE system shows that the new method improves the peak signal-to-noise ratio (PSNR) and resolution of the reconstructed target scene under two different cases of light intensity. Furthermore, there is no distinct block effect in the result, and the edge of the reconstructed image is smoother than that obtained by the other two reconstruction methods in this work.

Ischemic stroke in Williams-Beuren syndrome: a case report.

A 15-year-old girl was admitted because of an acute onset of facial palsy and right hemiparesis. The patient had a history of moderate mental retardation and developmental delay. On admission, her vital signs were stable, except for high blood pressure. Magnetic resonance imaging demonstrated an infarct involving the left internal capsule and putamen. Because of the patient's young age, an extensive stroke survey was performed. Williams-Beuren syndrome was finally confirmed by fluorescent in situ hybridization. Compared with the previously reported cases, no evidence of cerebral arterial stenosis or cardiac abnormalities was found by noninvasive imaging techniques. Because Williams-Beuren syndrome is a complex, multiple congenital anomaly syndrome with prominent cardiovascular features, regular assessment and antihypertensive treatment are necessary to minimize the lifelong cardiovascular risk in patients with this syndrome.