Histogram Bins Research Articles

Writer Verification Using Spatial Domain Features under Different Ink Width Conditions

In this paper, we present a comparative study of spatial domain features for writer identification and verification with different ink width conditions. The existing methods give high error rates, when comparing two handwritten images with different pen types. To the best of our knowledge, we are the first to design the feature with different ink width conditions. To address this problem, contour based features were extracted using a chain code method. To improve accuracy at higher levels, we considered histograms of chain code and variance in bins of histogram of chain code as features to discriminate handwriting samples. The system was trained and tested for 1,000 writers with two samples using different writing instruments. The feature performance is tested on our newly created dataset of 4,000 samples. The experimental results show that the histogram of chain code feature is good compared to other methods with false acceptance rate of 11.67%, false rejection rate of 36.70%, average error rates of 24.18%, and average verification accuracy of 75.89% on our new dataset. We also studied the effect of amount of text and dataset size on verification accuracy.

Learning discriminability-preserving histogram representation from unordered features for multibiometric feature-fused-template protection

Multi-biometric feature-level fusion exploits feature information from more than one biometric source to improve recognition performance and template security. When ordered and unordered feature sets representing different biometric sources are involved, feature fusion becomes problematic. One way to mitigate this incompatibility problem is to transform the unordered extracted feature sets to ordered feature representation without sacrificing the discrimination power of the original features so that a feature fusion on ordered features can subsequently be applied. Existing unordered-to-ordered feature transformation methods are designed for three-dimensional minutiae point sets and are mostly not adaptable to high-dimensional feature input. This paper proposes a feature transformation scheme to learn a histogram representation from an unordered feature set. Our algorithm estimates the component-wise correspondences among the sample feature sets of each user and then learns a set of bins per user based on the distribution of the mutually-corresponding feature instances. Given the learnt bins, the histogram representation of a sample can be generated by concatenating the normalized frequency of unordered features falling into histogram bins. Experimental results on seven unimodal and three bimodal biometric databases show that our feature transformation scheme is able to preserve the discrimination power of the original features more promisingly than state-of-the-art transformation schemes.

Dimensionality reduced local directional pattern (DR-LDP) for face recognition

Local Directional Pattern (LDP) is a descriptor used for face recognition. It assigns a code for each pixel in the image, and the resultant LDP-encoded image is divided into regions for which each a histogram is generated. The histogram bins of all the regions are concatenated to form the final descriptor. In contrast to LDP, a dimensionality reduced local directional pattern (DR-LDP) is proposed in this paper. The proposed descriptor computes single code for each block by X-ORing the LDP codes obtained in a single block. During the process, restructuring of the patterns is done by slightly modifying the LDP coding pattern constraints. The significance of DR-LDP is the compact code generation for efficient face recognition. The experiments were carried out on standard databases like FERET, extended YALE-B database and ORL. The resultant DR-LDP descriptor provided better recognition rates, outperforming the existing local descriptor-based methods and proving its efficacy. The compact code can be further extended to provide biometric security.

SU-G-BRC-08: Evaluation of Dose Mass Histogram as a More Representative Dose Description Method Than Dose Volume Histogram in Lung Cancer Patients

Purpose: Dose-volume-histogram (DVH) is widely used for plan evaluation in radiation treatment. The concept of dose-mass-histogram (DMH) is expected to provide a more representative description as it accounts for heterogeneity in tissue density. This study is intended to assess the difference between DVH and DMH for evaluating treatment planning quality. Methods: 12 lung cancer treatment plans were exported from the treatment planning system. DVHs for the planning target volume (PTV), the normal lung and other structures of interest were calculated. DMHs were calculated in a similar way as DVHs expect that the voxel density converted from the CT number was used in tallying the dose histogram bins. The equivalent uniform dose (EUD) was calculated based on voxel volume and mass, respectively. The normal tissue complication probability (NTCP) in relation to the EUD was calculated for the normal lung to provide quantitative comparison of DVHs and DMHs for evaluating the radiobiological effect. Results: Large differences were observed between DVHs and DMHs for lungs and PTVs. For PTVs with dense tumor cores, DMHs are higher than DVHs due to larger mass weighing in the high dose conformal core regions. For the normal lungs, DMHs can either be higher or lower than DVHs depending on the target location within the lung. When the target is close to the lower lung, DMHs show higher values than DVHs because the lower lung has higher density than the central portion or the upper lung. DMHs are lower than DVHs for targets in the upper lung. The calculated NTCPs showed a large range of difference between DVHs and DMHs. Conclusion: The heterogeneity of lung can be well considered using DMH for evaluating target coverage and normal lung pneumonitis. Further studies are warranted to quantify the benefits of DMH over DVH for plan quality evaluation.

Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

Image registration is an essential step in the process of image fusion, environment surveillance and change detection. Finding correct feature matches during the registration process proves to be difficult, especially for remote sensing images with large background variations (e.g., images taken pre and post an earthquake or flood). Traditional registration methods based on local intensity probably cannot maintain steady performances, as differences are significant in the same area of the corresponding images, and ground control points are not always available in many disaster images. In this paper, an automatic image registration method based on the line segments on the main shape contours (e.g., coastal lines, long roads and mountain ridges) is proposed for remote sensing images with large background variations because the main shape contours can hold relatively more invariant information. First, a line segment detector called EDLines (Edge Drawing Lines), which was proposed by Akinlar et al. in 2011, is used to extract line segments from two corresponding images, and a line validation step is performed to remove meaningless and fragmented line segments. Then, a novel line segment descriptor with a new histogram binning strategy, which is robust to global geometrical distortions, is generated for each line segment based on the geometrical relationships,including both the locations and orientations of theremaining line segments relative to it. As a result of the invariance of the main shape contours, correct line segment matches will have similar descriptors and can be obtained by cross-matching among the descriptors. Finally, a spatial consistency measure is used to remove incorrect matches, and transformation parameters between the reference and sensed images can be figured out. Experiments with images from different types of satellite datasets, such as Landsat7, QuickBird, WorldView, and so on, demonstrate that the proposed algorithm is automatic, fast (4 ms faster than the second fastest method, i.e., the rotation- and scale-invariant shape context) and can achieve a recall of 79.7%, a precision of 89.1% and a root mean square error (RMSE) of 1.0 pixels on average for remote sensing images with large background variations.

Publishing histograms with outliers under data differential privacy

AbstractHistograms are important tools for data mining and analysis. Several differentially private publishing schemes for histograms have been proposed recently. Existing differentially private histogram publication schemes have shown that histogram reconstruction is a promising idea for the improvement of publication histograms' accuracy. However, none of these have properly considered the problem outliers in the original histogram, which can cause significant reconstruction errors. Based on the problem, the publication of histogram outliers under differential privacy, this paper puts forward a publication method for histograms with outliers under differential privacy: Outlier‐HistoPub. Our method deals with the count sequence of the original histogram first, using a “global sort” to reduce the degree of alternative distribution (a concept proposed in this paper), which may eliminate the influence of outliers during reconstruction. To avoid individual privacy leakage in the reconstruction process, an exponential mechanism is used to select the most similar adjacent bins of the uniformity distribution histogram to merge each time, and the Laplace mechanism is utilized to generate noisy data to perturb the count sequence of the reconstruction histogram. Experiments prove that the method proposed in this paper can improve the efficiency and accuracy of histogram publication. Copyright © 2016 John Wiley & Sons, Ltd.

Efficient Reversible Data Hiding Algorithms Based on Dual Prediction

In this paper, a new reversible data hiding (RDH) algorithm that is based on the concept of shifting of prediction error histograms is proposed. The algorithm extends the efficient modification of prediction errors (MPE) algorithm by incorporating two predictors and using one prediction error value for data embedding. The motivation behind using two predictors is driven by the fact that predictors have different prediction accuracy which is directly related to the embedding capacity and quality of the stego image. The key feature of the proposed algorithm lies in using two predictors without the need to communicate additional overhead with the stego image. Basically, the identification of the predictor that is used during embedding is done through a set of rules. The proposed algorithm is further extended to use two and three bins in the prediction errors histogram in order to increase the embedding capacity. Performance evaluation of the proposed algorithm and its extensions showed the advantage of using two predictors in boosting the embedding capacity while providing competitive quality for the stego image .

Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers.

BackgroundColour is the most important feature used in quantitative immunohistochemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to confirm malignancy.MethodsStatistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was first trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classifier is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identified using IHC and histochemistry.ResultsThe purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesophageal cancer, colon cancer and liver cirrhosis with different colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations.ConclusionsA robust and effective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a specified colour automatically, is easy to use and available freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html. Testing to the tool by different users showed only minor inter-observer variations in results.

Use of time‐at‐temperature data to describe dive behavior in five species of sympatric deep‐diving toothed whales

This paper develops and validates a method of using time-at-temperature (TAT) histograms from satellite transmitter tags to describe the dive activity patterns and approximate depth distributions of five deep-diving toothed whale species in the northern Bahamas. TAT histograms represent a bandwidth-conserving method of recovering a long-term proxy record of dive activity. However, using temperature to interpret TAT on a scale of approximate depths required the complex estimation of TAT histogram bin boundary depths in a dynamic oceanographic region. Here we evaluated the relative performance of four interpolation methods and a global reanalysis data assimilation model in estimating climatological isotherm depth surfaces within our study area. TAT-derived approximate time-at-depth (TAD) distributions aligned closely with directly observed TAD distributions from a smaller sample of depth-recording satellite tags deployed on separate individuals of each species. TAT-derived approximate depth distributions were also consistent with various published accounts for this suite of species. Estimating dive ranges and time budgets are important components of (1) understanding habitat overlap between species, (2) evaluating the potential role of these predators in meso- and bathypelagic ecosystems, and (3) assessing vulnerability and exposure to anthropogenic impacts.

Efficient visibility-driven medical image visualisation via adaptive binned visibility histogram

‘Visibility’ is a fundamental optical property that represents the observable, by users, proportion of the voxels in a volume during interactive volume rendering. The manipulation of this ‘visibility’ improves the volume rendering processes; for instance by ensuring the visibility of regions of interest (ROIs) or by guiding the identification of an optimal rendering view-point. The construction of visibility histograms (VHs), which represent the distribution of all the visibility of all voxels in the rendered volume, enables users to explore the volume with real-time feedback about occlusion patterns among spatially related structures during volume rendering manipulations. Volume rendered medical images have been a primary beneficiary of VH given the need to ensure that specific ROIs are visible relative to the surrounding structures, e.g. the visualisation of tumours that may otherwise be occluded by neighbouring structures. VH construction and its subsequent manipulations, however, are computationally expensive due to the histogram binning of the visibilities. This limits the real-time application of VH to medical images that have large intensity ranges and volume dimensions and require a large number of histogram bins. In this study, we introduce an efficient adaptive binned visibility histogram (AB-VH) in which a smaller number of histogram bins are used to represent the visibility distribution of the full VH. We adaptively bin medical images by using a cluster analysis algorithm that groups the voxels according to their intensity similarities into a smaller subset of bins while preserving the distribution of the intensity range of the original images. We increase efficiency by exploiting the parallel computation and multiple render targets (MRT) extension of the modern graphical processing units (GPUs) and this enables efficient computation of the histogram. We show the application of our method to single-modality computed tomography (CT), magnetic resonance (MR) imaging and multi-modality positron emission tomography-CT (PET-CT). In our experiments, the AB-VH markedly improved the computational efficiency for the VH construction and thus improved the subsequent VH-driven volume manipulations. This efficiency was achieved without major degradation in the VH visually and numerical differences between the AB-VH and its full-bin counterpart. We applied several variants of the K-means clustering algorithm with varying Ks (the number of clusters) and found that higher values of K resulted in better performance at a lower computational gain. The AB-VH also had an improved performance when compared to the conventional method of down-sampling of the histogram bins (equal binning) for volume rendering visualisation.

A Power-Saving Histogram Adjustment Algorithm for OLED-Oriented Contrast Enhancement

For the modern multimedia devices, display resolution and image quality are actively improved nowadays. Although such improvement can produce the high visual perception for the observer, the power consumption becomes an inevasible problem as it is rising progressively. In order to achieve a good balance between visual perception and power consumption, we propose a histogram-based power saving algorithm to improve the image contrast for OLED display panels. The proposed algorithm modifies the empty bins of the image histogram as a pre-process of power reduction. Furthermore, the visual effect is compensated using the power saving histogram equalization algorithm. Experimental results show that the proposed algorithm not only decreases the display power to be lower than that of compared algorithms, but also generates the highly perceptual contrast of the images.

Application of local binary patterns in digital images to estimate botanical composition in mixed alfalfa–grass fields

Botanical composition in mixed stands of alfalfa and grass is a critical parameter in equations estimating harvest fiber concentration for dairy rations. Composition is difficult to estimate by visual observation. Digital image analysis in mixed stands could reduce botanical composition uncertainty and improve spring harvest management decisions. Mixed stands were sampled (n=168) in farmers’ fields in Tompkins County, New York in May 2011. A digital image was taken of standing samples at 5-Megapixels resolution using a Canon PowerShot A3100IS, and alfalfa and grass height relationships were recorded. After clipping representative samples at 10-cm above ground level, samples were manually separated into alfalfa (Medicago sativa L.) and timothy grass (Phleum pratense L.), and dried to calculate fractions on a dry matter basis. Uniform rotation invariant local binary patterns (LBP) were extracted from whole images and 64×64pixel tiles, and were used to develop regression equations estimating grass fraction. Tiles were manually classified as alfalfa (0), grass (1) or unclassifiable. An iterative process selected most accurate local binary pattern operator settings. Grass fraction was estimated in three regression model development approaches: (1) using average tile LBP histogram bins from whole images and botanical height relationships, (2) developing a binary tile classification model from tile LBP histogram bins, and using tile model-predicted grass probability averaged for tiles in whole images (grass coverage estimate) and botanical height relationships as inputs in whole image models, and (3) using LBP histogram bins extracted directly from whole images (1024 by 1024pixel square) and height relationships. Predictive accuracy in whole image models using tile LBP histogram averages was highest for models generated from LBP tile histogram bin means (R2pred up to 0.847), followed closely by combined tile models and whole image models (R2pred up to 0.807), with pairwise correlations between tile model-generated grass coverage estimates and sample grass fraction up to 0.895. Local binary patterns are effective in differentiating alfalfa and grass under field conditions, because the method is robust to changes in color and illumination. Furthermore, key LBP histogram bins (e.g., symmetric edges) strongly differentiate alfalfa and grass in tiles. The LBP method is promising based on this study, but further evaluation under diverse field conditions, including different cameras and grass species, is necessary to assess usefulness.

Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks and genetic algorithms

The use of genetic algorithm (GA) to simplify the structures of artificial neural network-based modulation format identification is proposed in next-generation dynamic and heterogeneous fiber-optic networks. Simulation results show that with 80 asynchronous amplitude histogram bins, by virtue of GA, the identification error rate decreases from 4.24 to 1.04 %.

GLOBAL MODELING OF NEBULAE WITH PARTICLE GROWTH, DRIFT, AND EVAPORATION FRONTS. I. METHODOLOGY AND TYPICAL RESULTS

ABSTRACT We model particle growth in a turbulent, viscously evolving protoplanetary nebula, incorporating sticking, bouncing, fragmentation, and mass transfer at high speeds. We treat small particles using a moments method and large particles using a traditional histogram binning, including a probability distribution function of collisional velocities. The fragmentation strength of the particles depends on their composition (icy aggregates are stronger than silicate aggregates). The particle opacity, which controls the nebula thermal structure, evolves as particles grow and mass redistributes. While growing, particles drift radially due to nebula headwind drag. Particles of different compositions evaporate at “evaporation fronts” (EFs) where the midplane temperature exceeds their respective evaporation temperatures. We track the vapor and solid phases of each component, accounting for advection and radial and vertical diffusion. We present characteristic results in evolutions lasting 2 × 105 years. In general, (1) mass is transferred from the outer to the inner nebula in significant amounts, creating radial concentrations of solids at EFs; (2) particle sizes are limited by a combination of fragmentation, bouncing, and drift; (3) “lucky” large particles never represent a significant amount of mass; and (4) restricted radial zones just outside each EF become compositionally enriched in the associated volatiles. We point out implications for millimeter to submillimeter SEDs and the inference of nebula mass, radial banding, the role of opacity on new mechanisms for generating turbulence, the enrichment of meteorites in heavy oxygen isotopes, variable and nonsolar redox conditions, the primary accretion of silicate and icy planetesimals, and the makeup of Jupiter’s core.

Reversible data hiding by adaptive group modification on histogram of prediction errors

In this work, the conventional histogram shifting (HS) based reversible data hiding (RDH) methods are first analyzed and discussed. Then, a novel HS based RDH method is put forward by using the proposed Adaptive Group Modification (AGM) on the histogram of prediction errors. Specifically, in the proposed AGM method, multiple bins are vacated based on their magnitudes and frequencies of occurrences by employing an adaptive strategy. The design goals are to maximize hiding elements while minimizing shifting and modification elements to maintain image high quality by giving priority to the histogram bins utilized for hiding. Furthermore, instead of hiding only one bit at a time, the payload is decomposed into segments and each segment is hidden by modifying a triplet of prediction errors to suppress distortion. Experimental results show that the proposed AGM technique outperforms the current state-of-the-art HS based RDH methods. As a representative result, the proposed method achieves an improvement of 4.30dB in terms of PSNR when 105,000 bits are hidden into the test Lenna image.

Multilevel Thresholding Technique for Contrast Enhancement in Thermal Images to Facilitate Accurate Image Segmentation

The most challenging task in InfraRed Thermography based condition monitoring of electrical equipment lies in the accurate detection of the anomaly. It necessitates an efficient preprocessing technique for increasing the contrast between the background and the anomaly regions in thermographs. In this paper, an efficient multi-level thresholding based enhancement technique is proposed for highlighting the anomaly region from the background. In contrast to the two levels in binary enhancement, n levels are determined based on the histogram bins. Enhanced intensity is calculated based on the range of the level rather than selecting randomly. Strength of the proposed technique is evident in its ability to detect the true edges corresponding to the anomaly. Also the anomaly region is accurately detected to its true size which in turn resulted in the precise estimation of the real time temperature. The error in temperature is approximately 0.5 at an emissivity of 0.7. The proposed technique can be adapted irrespective of thermal profiling of thermographs. However the intensity range must be chosen appropriately. Relationship between emissivity and error in temperature is also provided at the room temperature

A 3D Steganalytic Algorithm and Steganalysis-Resistant Watermarking.

We propose a simple yet efficient steganalytic algorithm for watermarks embedded by two state-of-the-art 3D watermarking algorithms by Cho etal. The main observation is that while in a clean model the means/variances of Cho etal.'s normalized histogram bins are expected to follow a Gaussian distribution, in a marked model their distribution will be bimodal. The proposed algorithm estimates the number of bins through an exhaustive search and then the presence of a watermark is decided by a tailor made normality test or a t-test. We also propose a modification of Cho etal.'s watermarking algorithms with the watermark embedded by changing the histogram of the radial coordinates of the vertices. Rather than targeting a continuous statistics such as the mean or variance of the values in a bin, the proposed watermarking modifies a discrete statistic, which here is the height of the histogram bin, to achieve watermark embedding. Experimental results demonstrate that the modified algorithm offers not only better resistance against the steganalytic attack we developed, but also an improved robustness/capacity trade-off.

Enhancing the Robustness of Visual Degradation Based HAM Reversible Data Hiding

Purposely visual degradation of image quality is a newfangled idea in the area of Histogram Association Mapped (HAM) reversible data hiding. Such scheme first divides the color scale, i.e., 0-255, into some partitions by the range, difference of minimum and maximum value of a block. Next, the histogram of a block resided to a partition, original partition, is moved, e.g., shifted, to another partition named reflective partition. First, an association between the original and reflected partition are defined by a chunk of secret message and then the translations of the bins of the histogram of the original partition to the reflective partition are performed to complete the message concealment. In such schemes, the blocks are classified as Reflective Block (RB) and Non-Reflective Block (NRB) based on whether the range is less than 127 or not. Experimentally, it is observed that most of the RB blocks occupy the portion of two partitions which introduce a dilemma in defining original partition as well as associating and hence translating bins. Again, at NRB the range of occupied bins in the histogram of a block is enough longer than the available bins where to reflect. Therefore, the policy of associations and then translations of bins of histogram may force the pixel values to exceed the extremes of gray scale which is indeed impossible. Solutions to these issues are not presented by any literature so far we have studied. Besides, the side information database that is practiced to assist in decoding at receiver is made longer by storing bundle of information and sometimes it becomes near or more than both the secret data and size of image. Resolutions to these stated observations are presented in this article. Experimental results justify the necessity of mitigating these observations.

GPU implementation of Kampmann-Wagner numerical precipitation models

A Kampmann-Wagner type numerical precipitation model (KWN) has been implemented using NVIDIA's CUDA framework for numerical programming of the graphics processing unit (GPU). Different implementation strategies are discussed and subjected to performance When the KWN model is used in combination with other calculations that are processed by the CPU, the performance improvements can be such that the KWN model incurs only emph{negligible} additional execution time. Also if the KWN model is used standalone for a large case, the GPU implementation achieves good scalability and performance.measurements. We study two representative cases corresponding to a large and a small workload. The model is found to be well suited for a GPU implementation, provided that there is enough work to keep the device busy and the right parallelization strategy is chosen. For our hardware, we recommend a minimum work load of more than $2^{16}$ histogram bins (as the total of multiple histograms) which corresponds to 146 histogram bins per GPU core.

Two-level dimensionality reduced local directional pattern for face recognition

Face recognition can be done efficiently using local approaches. Local directional pattern LDP is one such approach that serves as a descriptor for face recognition. It assigns a code for each pixel and the image is encoded. Histogram binning is done on the LDP encoded image to represent the face. A two-level dimensionality reduced local directional pattern TL-DR-LDP is proposed in this paper. The proposed TL-DR-LDP is robust in recognising the faces with maximum recognition rate. The proposed descriptor codes the image by dividing the image into regions and for each region, a code is defined. The same process is repeated for one more level and hence named as TL-DR-LDP. At each level, the dimensions of the feature vector are drastically reduced and performance of the descriptor maintains the higher recognition rate. The proposed descriptor is tested on standard benchmark databases like FERET, Extended YALE B and ORL. The results obtained prove that the TL-DR-LDP is exemplary.