A Novel 3-D Color Histogram Equalization Method With Uniform 1-D Gray Scale Histogram

81 8-354-2674 Abstract - Simple models for the patterns as well as their cumulative gain probability and probability density functions of the JPUNASA Deep Space Network (DSN) antennas are developed. These a~ needed for the study and evaluation of interference from unwanted sources such as the emerging terrestrial system, High Density Fixed Service (HDFS), with the Ka-band receiving antenna systems in Goldstone Station of the JPLNASA Deep Space Network. Several different models for the high gain ground antennas of the JPUNASA Deep Space Network @SN) are considered. The model definitions are then used to evaluate cumulative probability functions and probability density functions for the gain values at a receiving antenna randomly located anywhere in the far field of the model antenna. Both two-dimensional models which simulate the pattern in a plane cut through the boresight of the antenna and the range of the angle from boresight to *180 degrees and three- dimensional ones in which the pattem is given anywhere in the half-space above the ground, with the range of angle from zero at boresight to 90 degrees are considered. In both cases, regular as well as smooth interpolated models have been developed. The gain value as seen by a receiver at a random location and exposed to the radiation from a transmitting antenna with a given gain profile, can be thought of as a uniformly distributed random variable. Formulas are derived and various plots are presented for the gain models and their associated cumulative probability functions and probability density functions.

To assess the echogenicity of the cervical stroma and area surrounding the cervical canal (cervical gland area - CGA) using grey scale histogram (GSH) in pregnancies between 20 and 25 weeks. Cross-sectional study involving 149 pregnancies in the second trimester. Transvaginal sonography was performed to measure cervical length (biometric evaluation), assess the CGA and detect cervical funnelling (morphologic evaluation). The GSH was used for the objective assessment of the cervical stroma and CGA in the mid portion of the cervix. Mean, minimum, maximum and standard deviation measurements were obtained on the GSH and the CGA/stroma ratio was calculated. Intra-observer and inter-observer reproducibility were assessed using the intraclass correlation coefficient (ICC). Mean cervical length was 42.37 ± 7.05 mm (range 12-54 mm). Funnelling was detected in four women (2.7%) and one of these also had absent CGA. The mean GSH stroma value was 103 ± 25 (53-160) and the mean CGA value was 64.5 ± 23 (13-167). The mean CGA/stroma ratio was 0.62 ± 0.14 (0.24-1.29). Intra- and inter-observer reproducibility was excellent with ICC of 0.975 and 0.922 respectively. There are large differences in the texture of the cervical stroma and CGA on GSH allowing the objective differentiation of the two areas. The CGA/stroma ratio allows the objective assessment of the absence or presence of the CGA.

Three hundred and eighteen grey scale histograms of thyroid glands in 121 patients have been analysed. The arithmetic average grey scale value (GWMi) and the most commonly appearing grey value (GWMa) were determined electronically. The normal range of GWMi was 24 +/- 4.6 (x +/- s) grey scale units (GWE), for GWMa it was 26 +/- 5.6 GWe (x +/- s). Macrofollicular thyroid adenomas (8 cases) and thyroids with diffuse autonomy (12 cases) differed with a GWMi of 21 +/- 3.8 and 23 +/- GEW (x +/- s) respectively and a GWMa of 23 +/- 3.9 and 25 +/- 4.8 GWE and were within the normal range (p greater than 0.05). Microfollicular thyroid adenomas (12 cases), immunological thyroid lesions (25 cases) and parathyroid adenomas (6 cases) had reduced GWMi (6 +/- 3.1 and 8 +/- 1.8 GWE respectively) and GWMa (5 +/- 3 and 7 +/- 2 GWE, p less than 0.005). Thyroid carcinomas (9 cases) and lymph node metastases (8 cases) were also significantly below normal, with a GWMi of 11 +/- 2.3 and 11 +/- 3.4 respectively (p less than 0.05). Analysis of grey scale histograms makes it possible to evaluate changes in the thyroid without reference to surrounding tissues.

This paper proposes an improved color image enhancement algorithm based on 3-D color histogram equalization algorithm. When the existed 3-D color histogram equalization algorithms in the literatures are applied in processing dim color images, the processed color images often turn pale due to the decrease of color-saturations and have false contours due to gray-scale merging phenomenon in the histogram equalization algorithm. In this paper, the proposed algorithm can make more pixels of the processed color images keep their color-saturations and reduce the gray-scale merging with Logarithmic histogram equalization algorithm. Test results with dim color images present a better effect of image enhancement.

As wind turbines more widely used with newer manufactured types and larger electrical power scales, a brief mathematical modelling for these wind turbines operating power curves is needed for optimal site matching selections. In this paper, 24 commercial wind turbines with different ratings and different manufactures are modelled using single cumulative probability density functions modelling equations. A new mean of a composite cumulative probability density function is used for better modelling accuracy. Invasive weed optimization algorithm is used to estimate different models designing parameters. The best cumulative density function model for each wind turbine is reached through comparing the RMSE of each model. Results showed that Weibull-Gamma composite is the best modelling technique for 37.5% of the reached results.

Histogram equalization (HE) is one of the simplest and most effective techniques for enhancing gray-level images. For color images, HE becomes a more difficult task, due to the vectorial nature of the data. We propose a new method for color image enhancement that uses two hierarchical levels of HE: global and local. In order to preserve the hue, equalization is only applied to intensities. For each pixel (called the ‘‘seed’’ when being processed) a variable-sized, variable-shaped neighborhood is determined to contain pixels that are ‘‘similar’’ to the seed. Then, the histogram of the region is stretched to a range that is computed with respect to the statistical parameters of the region (mean and variance) and to the global HE function (of intensities), and only the seed pixel is given a new intensity value. We applied the proposed color HE method to various images and observed the results to be subjectively ‘‘pleasant to the human eye,’’ with emphasized details, preserved colors, and with the histogram of intensities close to the ideal uniform one. The results compared favorably with those of three other methods (histogram explosion, histogram decimation, and three-dimensional histogram equalization) in terms of subjective visual quality.

Histogram equalization is an important image enhancement technique widely used in image processing. It is good method of image enhancement indeed; but it doesn't consider about human visual response to light. We know that human visual response to light is non-linear, which is somewhat likes natural logarithmic function. So the gray levels of histogram equalized, image is not really equally distributed for human eyes. In fact, the ordinary histogram equalization is not visually equalized. According to the human visual model, this article puts forward a new algorithm or image histogram modification, with which modified image has visually equal gray level distribution. This algorithm is conducted while assuming exiting a visual image whose histogram can be counted and is equal distributed. Experiment proves that this algorithm is better than ordinary image histogram equalization technique in image enhancement. It fits human visual response well. It can be widely used in digital image processing.

In this paper, we have derived and studied new probability distributions by extending the 2‐dimensional Rayleigh distribution (RD). First, we extend the RD to 3 dimensions and then generalize it to k dimensions for any positive integer k ≥ 3. The distributions are named the 3‐dimensional Rayleigh distribution (3‐DRD) and k‐dimensional Rayleigh distribution (k‐DRD), respectively. For both 3‐DRD and k‐DRD, detailed mathematical and statistical properties including derivations of the corresponding cumulative distribution, probability density, survival, and hazard functions, moments, moment generating functions, mode, skewness, kurtosis, and differential entropy are obtained in closed forms. Parameter estimation is done for both models using the maximum likelihood estimation method and some statistical properties of the estimator are discussed for each case. Interestingly, the commonly known Normal, Rayleigh, Maxwell–Boltzmann, chi‐square, gamma, and Erlang distributions are related to the newly developed extended RDs as special cases. For the 3‐DRD, plots of cumulative distribution, probability density, survival, and hazard functions are exhibited, a simulation study is carried out, and random samples are generated using the standard accept–reject (AR) algorithm to check the efficiency of the maximum likelihood estimates of the parameter. Moreover, the new 3‐DRD model is fitted to one simulated and three real datasets, revealing good performance compared to four existing Rayleigh‐based distributions. This study will contribute new knowledge to the field of applied statistics and probability, and the findings will be used as a basis for future research in the field.

Traffic sign recognition is an important key technology in the unmanned driver technology. This paper uses TT100K traffic sign dataset to realize intelligent detection and automatic recognition of multi category natural road traffic signs. First we enhanced the image based on YUV color space and histogram equalization which solve the blurry or low brightness problem of the image; secondly, located the traffic sign area with Hough transformation based on the spatial characteristics of the image; then, using the SVM classifier to get the training model with HOG features extracted from traffic signs; finally, the test set and the number of ownership in TT100K dataset are used. Validated the reliability of the training model with test set and own data sample set in TT100K dataset, test results prove the reliability of training samples.

With the development of high spatial resolution satellites, such as IKONOS, we are able to make more use of spatial features to improve classification accuracy, especially for land-use mapping. In this paper, we compared the texture spectrum (TS) approach with 4 different schemes of gray level reduction, which are min-max linear compression (LC), gray level binning (BN), histogram equalization (HE) and piece-wise nonlinear compression (PC), on the panchromatic band of the sampled IKONOS CARTERRA imagery in terms of overall accuracy and kappa coefficient. The classification algorithm is on the basis of a frequency-based contextual approach that utilizes neighboring information within a particular window. The ranking of 5 different approaches is TS, PC, HE, BN and LC. TS achieves an overall classification accuracy of 72% at a window size of 53 while PC reaches an overall accuracy of 68% at a window size of 35 and LC reaches its highest accuracy of 65% at a window size of 29. TS approach needs a relatively larger window size to achieve higher classification accuracy while the other 4 gray level reduction approaches reach their higher accuracy at smaller window sizes. Within 9 land-use classes, a relatively large window size is more applicable to classify heterogeneous land-use types or areas having first order trend while smaller window sizes work better for more homogeneous land-use types or area with second order effect. The TS approach was found to be more capable of capturing first order trends while the other 4 approaches were more sensitive to local gray level variations thus better capturing the second order effect.

Estimating a person age is a major concern for many researchers due to his importance in many applications, whereas finding a person age is crucial in making specific decision. This paper conducts a comparative study between two classification-algorithms for age estimation which applied on extracted Local Binary Patterns (LBP). It was also divided dataset into 3 classes in order to improve results and increasing accuracy of system, Root Mean Squared Error, Mean Absolute Error and other parameters are used to measure the precision of the system. The proposed methodology of this work is divided into three phases. First phase consists pre-processing methods in which selected image is handled using color to gray scale image conversion and histogram equalization. Result image of which is manipulated by standard Viola-Jones algorithm to detect and crop face area from the whole image. While to ensure same size for all images, detected face image is resized to 256 × 256. Second phase tends to extract LBP features to be fed in the next classification phase. In third phase, two classifiers Logistic Model Tree (LMT) as well as Sequential Minimal Optimization (SMO) have been applied on the extracted features for age estimation.

Principle objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. image enhancement used in Quality Control ,Problem Diagnostics, Research and Development ,Insurance Risk Assessment ,Risk Management Programme, Digital infrared thermal imaging in health care, Surveillance in security, law enforcement and defence. Various enhancement schemes are used for enhancing an image which includes gray scale manipulation, filtering and Histogram Equalization (HE), fast Fourier transform. Image enhancement is the process of making images more useful. The reasons for doing this include, Highlighting interesting detail in images, removing noise from images, making images more visually appealing, edge enhancement and increase the contrast of the image.

ABSTRACTThe left-sided truncated normal distribution is especially important in quality engineering where we remove the left side of the distribution, for example, due to estimation of the life of used products. Although the theoretical background of truncated normal distribution is already established, there is very little work on mathematical approximation of the probability density and the cumulative probability density functions. In this article, a high accuracy mathematical approximation of the left-sided truncated normal distribution is proposed. A full analysis of errors and recommendations for implementation using Microsoft Excel are provided at the end of this article.

This research uses boundary cutting, spatial-temporal segmentation, block based searching, and gray scale histogram techniques to extract the eyes, nose, and mouth images. In this research, the gray scale histogram is used to extract the eye, nose, and mouth features. A spatial-temporal template is designed to segment the eye, nose, and mouth images to extract eye, nose, and mouth images. In order to obtain better results, the generic algorithm and spatial region partition technique are used to remove noise and precisely bind the object region to obtain a more accurate extraction result.

Objective: to assess the classification capability dealing with CT images, by means of Decision Tree classifier. Methods: the CT images were provided by the hospital was used as the data source. In this study.normal liver, Single cystic hepatic hydatid and Polycystic hepatic hydatid CT images were selected.each 200 pieces. Then the texture features were extracted by gray gradient co-occurrence matrix(GGCM) and gray scale histogram. At last. Decision tree classifier were used for classification, which aimed to verify Which feature is more suitable for decision tree classification, and Parameter estimation is used to evaluate the classifier model. Results: For normal liver, Single cystic hepatic hydatid and Polycystic hepatic hydatid, the classification accuracy of gray level co-occurrence matrix was 71%,69% and 69%, respectively. the classification accuracy of gray scale histogram was 74%,63.5% and 69%.the classification accuracy of comprehensive features was 75%, 70.5% and 80.5%. Conclusion: The classification accuracy of comprehensive feature is 11.5% higher than that of single features, which are more suitable for the classification of polycystic hepatic hydatid images. This algorithm can provide reference for the classification of CT images.