Pixel Classification Algorithms for Noise Removal and Signal Preservation in Low Pass Filtering

Contrast enhancement is essential to improve the image quality in most of image pre-processing. A histogram equalization process can be used to achieve a high contrast. It causes, however, also noise generation. Involving a low-pass filtering process is an effective way to achieve a high-quality contrast enhancement with low-noise, but it leads to the conflict between noise removal and signal preservation. To perform discriminative low-pass filtering operations with the presence of noises and signal variations in different regions, it is thus necessary to develop good algorithms to classify the pixels. In this thesis, two classification algorithms are proposed. They aim at low-contrast images where gradient signals are severely degraded by various causes during the acquisition process. They are to classify the pixels according to the initial gray-level homogeneity of their regions. The basic classification method is done by gradient thresholding, and the threshold values are generated by means of gradient distribution analysis. To tackle the problems of various gradient degradation patterns in low-contrast images, image pixels are grouped in a particular way that, in the same group, pixels in homogeneous regions can be easily distinguished from those in non-homogeneous regions by the basic method of simple gradient thresholding. Two algorithms based on different grouping methods are proposed. The first algorithm aims at high dynamic range images. The pixels are first grouped according to their gray-level ranges, as the gradient degradation is, in such a case, gray-level-dependent. The gradient distribution of each sub-range is obtained and a pixel classification is then made to adapt to their original gray-level signals in the sub-range. The other algorithm is to tackle a wider range of low-contrast images. In this algorithm, a gray-level histogram thresholding is performed to divide the pixels into two groups according to their likelihood to homogeneous, or non-homogeneous, pixels. Thus, in one group a majority of homogeneous pixels is established and in the other group the majority is of non-homogeneous pixels. The classification done in each group is to identify those in the minority. Both proposed algorithms are very simple in computation and each of them is incorporated into the contrast enhancement procedure to make the integrated low-pass filters effectively remove the noise generated in the histogram equalization while well preserving the signal details. The simulation results demonstrates, by subjective observation and objective measurements, that the proposed algorithms lead to a superior quality of the contrast enhancement for varieties of images, with respect to two advanced enhancement schemes

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

Zero-shot learning (ZSL) methods have been studied in the unrealistic setting where test data are assumed to come from unseen classes only. In this paper, we advocate studying the problem of generalized zero-shot learning (GZSL) where the test data's class memberships are unconstrained. We show empirically that naively using the classifiers constructed by ZSL approaches does not perform well in the generalized setting. Motivated by this, we propose a simple but effective calibration method that can be used to balance two conflicting forces: recognizing data from seen classes versus those from unseen ones. We develop a performance metric to characterize such a trade-off and examine the utility of this metric in evaluating various ZSL approaches. Our analysis further shows that there is a large gap between the performance of existing approaches and an upper bound established via idealized semantic embeddings, suggesting that improving class semantic embeddings is vital to GZSL.Comment: ECCV2016 camera-read

Large-Margin Determinantal Point Processes

Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) of a DPP from labeled training data. We make two contributions. First, we show how to reparameterize a DPP's kernel matrix with multiple kernel functions, thus enhancing modeling flexibility. Second, we propose a novel parameter estimation technique based on the principle of large margin separation. In contrast to the state-of-the-art method of maximum likelihood estimation, our large-margin loss function explicitly models errors in selecting the target subsets, and it can be customized to trade off different types of errors (precision vs. recall). Extensive empirical studies validate our contributions, including applications on challenging document and video summarization, where flexibility in modeling the kernel matrix and balancing different errors is indispensable.Comment: 15 page

Human Mitochondrial tRNA Mutations in Maternally Inherited Deafness

AbstractMutations in mitochondrial tRNA genes have been shown to be associated with maternally inherited syndromic and non-syndromic deafness. Among those, mutations such as tRNALeu(UUR)3243A>G associated with syndromic deafness are often present in heteroplasmy, and the non-syndromic deafness-associated tRNA mutations including tRNASer(UCN)7445A>G are often in homoplasmy or in high levels of heteroplasmy. These tRNA mutations are the primary factors underlying the development of hearing loss. However, other tRNA mutations such as tRNAThr15927G>A and tRNASer(UCN)7444G>A are insufficient to produce a deafness phenotype, but always act in synergy with the primary mitochondrial DNA mutations, and can modulate their phenotypic manifestation. These tRNA mutations may alter the structure and function of the corresponding mitochondrial tRNAs and cause failures in tRNAs metabolism. Thereby, the impairment of mitochondrial protein synthesis and subsequent defects in respiration caused by these tRNA mutations, results in mitochondrial dysfunctions and eventually leads to the development of hearing loss. Here, we summarized the deafness-associated mitochondrial tRNA mutations and discussed the pathophysiology of these mitochondrial tRNA mutations, and we hope these data will provide a foundation for the early diagnosis, management, and treatment of maternally inherited deafness

Pixel classification algorithms for noise removal and signal preservation in low-pass filtering for contrast enhancement

With a view to obtaining a high quality contrast enhancement, low-pass filters are used to remove the noise generated in a high-gain histogram equalization process. To preserve signal variations, the LP operation applied to the pixels in non-homogeneous regions should have less smoothing strength than that in homogeneous regions. The pixel classification according to the gray level homogeneity is thus a critical part in the LP filtering. In this paper, two algorithms for pixel classification according to the gray level homogeneity of their regions are proposed. In each of them, image pixels are grouped in such a way that, in the same group, pixels in homogeneous regions can be easily distinguished from those in non-homogeneous regions by a simple gradient thresholding, despite the complexity of signal gradient degradation in images. The two proposed classification algorithms are very simple, requiring very small quantity of computation. Their effectiveness has been proven by the simulation results

Principal Component Regression Analysis of Nutrition Factors and Physical Activities with Diabetes

The associations of nutrition factors and physical activities with adult diabetes are inconsistent; while most of these factors are inter correlated. The aims of this study are to overcome the disturbance of the multicollinearity of the risk factors and examine the associations of these factors with diabetes using the principal component analysis (PCA) and regression analysis with principal component scores (PCS). Totally, 659 adults with diabetes and 2827 non-diabetic were selected from the 2012 Health Information National Trends Survey (HINTS 4, Cycle 2). PCA was utilized to deal with multicollinearity of the risk factors. Weighted univariate and multiple logistic regression analyses were used to estimate the associations of potential factors and PCS with diabetes. The odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. The first 3 PCs for nutrition factors and physical activities could explain 70% variances. The first principal component (PC1) is a measure of nutrition factors (fruit and vegetables consumption), PC2 is a measure for physical activities (moderate exercise and strength training), and PC3 is about calorie information use and soda use. Weighted multiple logistic regression showed that African Americans, middle aged adults (45-64 years), elderly (65+), never married, and with lower education were associated with increased odds of diabetes. After adjusting for others factors, the PC1 showed marginal association with diabetes (OR=0.84, 95% CI=0.70-1.01); while PC2 and PC3 revealed significant associations with diabetes (OR=0.73, 95% CI=0.61-0.86 and OR=0.85, 95% CI=0.74-0.99, respectively). In conclusion, PCA can be used to reduce the indicators in complex survey data. The first 3 PCs of nutrition factors and physical activities were associated with diabetes. Promotion of health food and physical activities should be encouraged to help decrease the prevalence of diabetes

Attenuation of Diabetic Nephropathy in Otsuka Long-Evans Tokushima Fatty (OLETF) Rats with a Combination of Chinese Herbs (Tangshen Formula)

Diabetic nephropathy is one of the most significant microvascular complications in patients with type 2 diabetics. The concise mechanism of diabetic nephropathy is unknown and there is no successful treatment. The objective of study was to investigate effects of Chinese herbs (Tangshen Formula) on diabetic nephropathy in Otsuka Long-Evans Tokushima Fatty (OLETF) rats. OLETF rats and LETO rats were divided into four groups: LETO control, OLETF diabetics, OLETF diabetics treated with Tangshen Formula, and OLETF diabetics treated with Monopril. Body weight, blood glucose, and 24 h urinary proteins were measured once every four weeks. Blood samples and kidney tissues were obtained for analyses of total cholesterol, triglyceride, whole blood viscosity, plasma viscosity, and pathohistological examination at 36 and 56 weeksrespectively. Untreated OLETF rats displayed diabetic nephropathy over the study period. Treatment of OLETF rats with Tangshen Formula attenuated the increases in blood glucose, body weight, 24 h urinary protein content, serum total cholesterol, whole blood viscosity and plasma viscosity at certain time. Treatment with Tangshen Formula also reduced glomerulosclerotic index and interstitial fibrotic index seen in OLETF rats. In conclusion, Tangshen Formula could attenuate the development of diabetic nephropathy in OLETF rat diabetic model

Competition between DNA Methylation, Nucleotide Synthesis, and Antioxidation in Cancer versus Normal Tissues

Global DNA hypomethylation occurs in many cancer types, but there is no explanation for its differential occurrence or possible impact on cancer cell physiology. Here we address these issues with a computational study of genome-scale DNA methylation in 16 cancer types. Specifically, we identified (i) a possible determinant for global DNA methylation in cancer cells and (ii) a relationship between levels of DNA methylation, nucleotide synthesis, and intracellular oxidative stress in cells. We developed a system of kinetic equations to capture the metabolic relations among DNA methylation, nucleotide synthesis, and antioxidative stress response, including their competitions for methyl and sulfur groups, based on known information about one-carbon metabolism and trans-sulfuration pathways. We observed a kinetic-based regulatory mechanism that controls reaction rates of the three competing processes when their shared resources are limited, particularly when the nucleotide synthesis rates or oxidative states are high. The combination of this regulatory mechanism and the need for rapid nucleotide synthesis, as well as high production of glutathione dictated by cancer-driving forces, led to the nearly universal observations of reduced global DNA methylation in cancer. Our model provides a natural explanation for differential global DNA methylation levels across cancer types and supports the observation that more malignant cancers tend to exhibit reduced DNA methylation levels. Insights obtained from this work provide useful information about the complexities of cancer due to interplays among competing, dynamic biological processes