Maximum Histogram Research Articles

Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition

Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under extremely low visibility conditions exacerbated by inhomogeneous illumination, which degrades deep learning performance due to inconsistent signal intensities. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (SDIF) to enhance only the vital signal information. The grayscale banding is eliminated by incorporating a visual optimization strategy based on image gradients. Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. We evaluated our algorithm using data collected from both a fog chamber and outdoor environments and performed comparative analyses with existing methods. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications.

Dynamic data hiding capacity enhancement for the Hybrid Near Maximum Histogram image steganography based on Multi-Pixel-Pair approach

AbstractIncreasing data hiding capacity and making it difficult to detect presence of any confidential data in stego images are the key objectives in contemporary image steganography research ever-improving the embedding efficiency. With regard to these crucial and challenging points, a new Multi-Pixel-Pair (MPP)-based data hiding approach is proposed in this paper. It can dynamically increase data embedding capacity of the classical Hybrid Near Maximum Histogram (HNMH) image steganography method while well-maintaining the embedding efficiency. In the proposed MPP approach, the peak value as a reference point in the histogram distribution of a cover image is obtained, and accordingly the pixel-pairs of interest where secret data is to be embedded are determined. Then, the pixels of the cover image are scanned sequentially and data hiding is performed on the relevant pixels by using the Least Significant Bit (LSB) method. In an extensive experimental study of the proposed MPP approach, it is shown that the classical HNMH data hiding capacity is dynamically improved 4.74 to 37.48 times while the Peak Signal to Noise Ratio (PSNR) decreases between 6.62 and 13.75 dB, implying both a reasonable trade-off and 7.61% enhanced embedding efficiency performance. Moreover, using the well-known cover image partitioning technique, offering significant improvements in image steganography, and the proposed MPP approach together, can further extend the data hiding capacity about 7.05%.

HNMH: A New Hybrid Approach Based on Near Maximum Histogram and LSB Technique for Image Steganography

HNMH: A New Hybrid Approach Based on Near Maximum Histogram and LSB Technique for Image Steganography

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second method respectively. The goal was to generate a Mamdani type fuzzy inference system the membership function (MF) of each fuzzy set and implementing the AV of western Iraqi territory training data sets. A pixel-by-pixel comparison of each method with traditional maximum likelihood method (ML) was made on data sets comprising six bands of satellite imagery of the western Iraqi region taken by the Landsat-5 satellite. Simulation results of these performance comparisons singled out that the method using Gaussian MFs together with peak and STD of the AV as the best achiever with a similarity of 83.16 percent for band (3) of the studied area.

Operation Status Monitoring of Transmission Tower in Power System based on Data Fusion

This paper studies the application of data fusion technology in power system to solve some difficult problems in this complex energy system. A transmission tower identification and bird nest detection method based on corner, line, color and shape features is proposed. Through LSD (Line Segment Detection) and Harris corner detection method, the straight line segment and corner point in the image are extracted respectively. Combined with triangle method, the actual tilt angle of tower is measured; According to the nesting rule of birds in transmission towers, the basic unit segmentation algorithm of transmission towers is proposed, and the basic unit segmentation of transmission towers is realized by using the local maximum of the target pixel row statistical histogram. The algorithm proposed in this paper can effectively solve the problems of on-line measurement of tilt angle of transmission tower and on-line detection of bird's nest, which will lay a theoretical foundation for on-line monitoring of transmission tower status.

Metaheuristic Algorithms Applied to Color Image Segmentation on HSV Space.

In this research, we propose an unsupervised method for segmentation and edge extraction of color images on the HSV space. This approach is composed of two different phases in which are applied two metaheuristic algorithms, respectively the Firefly (FA) and the Artificial Bee Colony (ABC) algorithms. In the first phase, we performed a pixel-based segmentation on each color channel, applying the FA algorithm and the Gaussian Mixture Model. The FA algorithm automatically detects the number of clusters, given by histogram maxima of each single-band image. The detected maxima define the initial means for the parameter estimation of the GMM. Applying the Bayes’ rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. After processing each color channel, we recombined the segmented components in the final multichannel image. A further reduction in the resultant cluster colors is obtained using the inner product as a similarity index. In the second phase, once we have assigned all pixels to the corresponding classes of the HSV space, we carry out the second step with a region-based segmentation applied to the corresponding grayscale image. For this purpose, the bioinspired Artificial Bee Colony algorithm is performed for edge extraction.

Automatic Infrared Ship Target Segmentation Based on Structure Tensor and Maximum Histogram Entropy

The existing infrared (IR) ship target segmentation methods may suffer serious performance degradation in the situation of diverse background clutters and ship targets. To cope with this problem, a novel ship target segmentation method is proposed in this paper. Initially, the IR image is transformed into the map of large eigenvalues of structure tensor (STLE), where the horizon line and ship target boundary can be explicitly characterized. According to the scene context clue, the automatic horizon line detection (AHLD) is proposed to efficiently judge the existence of horizon line and remove sky/land region clutters. Then, based on the intensity distribution of ship target and sea background, the adaptive maximum histogram entropy (AMHE) is presented to accurately perceive the brightness (dark or bright) of ship target, and coarsely segment the bright or dark ship target from sea background. After that, considering the ship target boundary information, the regions-of-interest (ROI) of ship target is located and the ship foreground map (SFM) is developed to address the under-segmentation. Finally, a new Watershed algorithm namely structure tensor and maximum histogram entropy modified Watershed transform (TEWT) is constructed to completely extract the whole ship target. Extensive experiments show that the proposed method outperforms the state-of-the-art methods, especially for IR images with intricate background clutter and heavy noise. Moreover, the proposed method can work stably for ship target with unknown brightness, uneven intensities, low contrast, variable quantities, sizes, and shapes.

Effect of shadow removal by gamma correction in SMQT algorithm in environmental application

Environmental data can be achieved from remote sensing. Shadows are main problems in achieving environmental data by remote sensing. In this research, an algorithm is proposed for preparing environmental data from shadow-covered objects. The proposed algorithm provides some information about digital number (DN) of shadow-covered objects by reducing the shadow effect. The successive mean quantization transform (SMQT) algorithm, gamma correction and combination of images form the basis of the proposed algorithm. SMQT algorithm eliminates gain and bias features by indicating the data structure. The data structure remains unchanged, and the differences between terrains are highlighted by compressing the dynamic range and stretching the histogram of the image in parts with different terrains of same DN. The output of the SMQT algorithm is a gray image. A new image is obtained by combining this image with the original image through HSV color space. In the second step, gamma correction is applied to the entire image according to brightness and contrast. But the gamma correction rate is not the same in all parts of an image. As a result, gamma correction should be done locally. However, local gamma correction and the use of a kernel of a specific dimension increase computation time. In addition, if there is a noise in the image, it will cause a significant deviation in the correction. To solve this problem, the output of SMQT algorithm is combined with the image obtained from gamma correction. Shadows in the input image cause the image histogram to be compressed in areas near zero. After performing the process in the study, the histogram was stretched and the maximum histogram reached from 190 to 250.

Influence of surface charges on the emission polarization properties of single CdSe/CdS dot-in-rods

We report an experimental investigation of the influence of surface charges on the emission polarization properties of single CdSe/CdS dot-in-rods (DRs), which is important for their polarization-based practical applications. By covering the single DRs with N-type semiconductor indium tin oxide (ITO) nanoparticles, the surface of single DRs is charged by ITO through interfacial electron transfer. This is confirmed by the experimental observations of the reduced photoluminescence intensities and lifetimes as well as the suppressing blinking. It is found that the full width at half maximum of histogram of polarization degrees of the single DRs is broadened from 0.24 (on glass) to 0.41 (in ITO). In order to explain the exprimental results, the band-edge exciton fine structure of single DRs is calculated by taking into account the sample parameters, the emission polarization, and the surface charges. The calculation results show that the level ordering of the emitting states determines the polarization degrees tending to increase or decrease under the influence of surface electrons. The surface electrons can induce an increase in the spacing between the emitting levels to change the populations and thus change the polarization degrees. In addition, different numbers of surface electrons may randomly distribute on the long CdSe/CdS rods, leading to the heterogeneous influences on the single DRs causing the broadening of polarization degrees also.

A visual cognizance based multi-resolution descriptor for human action recognition using key pose

Human activity recognition using videos sequences is a well-known phenomenon which has many real-life applications such as daily assistive living, security and surveillance, patient monitoring, robotics, and sports analysis. Recently, single or still images based action recognition is becoming very popular due to spatial cues present in an image and required less computation. Hence, a robust framework is constructed by computation of textural and spatial cues of still images at multi-resolution. A fuzzy inference model is used to select the single key image from action video sequences using maximum histogram distance between stacks of frames. To represent, these key pose images the textural traits at various orientations and scales are extracted using Gabor wavelet while shape traits are computed through a multilevel approach called Spatial Edge Distribution of Gradients (SEDGs). Finally, a hybrid model of action descriptor is developed using shape and textural evidence, which is known as Extended Multi-Resolution Features (EMRFs) model. The highest classification accuracy is achieved through SVM classifier on various human action datasets: Weizmann Action (100%), KTH (95.35%), Ballet (92.75%), and UCF YouTube (96.36%). The highest accuracy achieved on these datasets are compared with similar state-of-the-art approaches and EMRFs shows superior performance.

Space–time inhomogeneous background intensity estimators for semi-parametric space–time self-exciting point process models

Histogram maximum likelihood estimators of semi-parametric space–time self-exciting point process models via expectation–maximization algorithm can be biased when the background process is inhomogeneous. We explore an alternative estimation method based on the variable bandwidth kernel density estimation (KDE) and EM algorithm. The proposed estimation method involves expanding the semi-parametric models by incorporating an inhomogeneous background process in space and time and applying the variable bandwidth KDE to estimate the background intensity function. Using an example, we show how the variable bandwidth KDE can be estimated this way. Two simulation examples based on residual analysis are designed to evaluate and validate the ability of our methods to recover the background intensity function and parametric triggering intensity function.

An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support

Land-use/land-cover information is the basis of global-change research and regional governmental management. Automatic approaches are always required to update land maps for large-scale areas, and change detection techniques are the most important component of land-updating methods. Previous research has confirmed that simple change detection based on Landsat images from two different years with two different phenophases yields unsatisfactory results and may induce many misclassifications and pseudo-change identifications because of the phenological differences between remote sensing images. With the support of the Google Earth Engine (GEE), we propose a land-use/land-cover type discrimination method based on a classification and regression tree (CART), apply change-vector analysis in posterior probability space (CVAPS) and the best histogram maximum entropy method for change detection, and further improve the accuracy of the land-updating results in combination with NDVI timing analysis, which indicates the annual growth of ground vegetation. In the case study, we select western China as the research area and obtain a 2014 land map based on the ESA GlobCover 2009 dataset. The results confirm that the accuracy of the land-renewal results based on the CART-CVAPS-NDVI method reach 78.6–88.2%, which is 4–10% higher than that of the CART-CVPAS method without NDVI timing analysis. The CART-CVAPS-NDVI method has more detailed and accurate resolutions for land-change detection.

Binary Plane Technique Based Color Quantization for Content Based Image Retrieval

Extracting accurate informative file from a high volume of graphic files is a challenging task. This paper focus on presenting a new color indexing approach using the histogram features. Two histogram features like maximum color histogram and minimum color histogram are computed and are vector quantized to constitute a feature vector. Bit plane technique is used to map these features based upon it value at the respective position. The ultimate goal of any retrieval method is to attain higher precision within a short span of time that could be achieved if the data is in compressed to accomplish this the image is compressed using binary plane technique. The result analysis depicts the performance of the proposed approach under lossy and lossless modes and found that when operated in lossy it attain effective precision rate in a speculated amount of time.

DEEP MIXING IN STELLAR VARIABILITY: IMPROVED METHOD, STATISTICS, AND APPLICATIONS

The preliminary results on deep-mixing manifestations in stellar variability are tested using our improved method and extended data set. We measure the timescales τ m of the stochastic change in the spectral power of rotational harmonics with numbers m ≤ 3 in the light curves of 1361 main-sequence stars from the Kepler mission archive. We find that the gradient has a histogram maximum at −2/3, demonstrating agreement with Kolmogorov's theory of turbulence and therefore confirming the manifestation of deep mixing. The squared amplitudes of the first and second rotational harmonics, corrected for integral photometry distortion, also show a quasi-Kolmogorov character with spectral index ≈−5/3. Moreover, the reduction of τ 1 and τ 2 to the timescales τ lam1 and τ lam2 of laminar convection in the deep stellar layers reveals the proximity of both τ lam1 and τ lam2 to the turnover time τ MLT of standard mixing length theory. Considering this result, we use the obtained stellar variability timescales instead of τ MLT in our analysis of the relation between stellar activity and the Rossby number P/τ MLT. Comparison of our diagrams with previous results and theoretical expectations shows that best-fit correspondence is achieved for τ lam1, which can therefore be used as an analog of τ MLT. This means that the laminar component (giant cells) of stellar turbulent convection indeed plays an important role in the physics of stars. Additionally, we estimate the diffusivity of magnetic elements in stellar photospheres.

Sampling Strategies for 3D Partial Shape Matching and Retrieval Using Bag-of-Words Model

This paper investigates the feature sampling strategies for 3D partial shape retrieval using bag-of-words model. The SHREC 09’ parts query models [3] are tested for comparison. These parts models are obtained by cutting parts from complete models, which are different from range scans. Dense sampling and pyramid sampling are proposed to extract local salient features from the depth images of the 3D models. Bag-of-words model is used to represent of both of parts query and complete target models. The optimal sampling configurations for the proposed feature extraction strategies are obtained by comparing the retrieval accuracy using maximum histogram intersection distance (MHID). The results suggest that extracting more features does not guarantee better retrieval accuracy using the bag-of-words model. The feature sampling configurations also have significant impacts on the retrieval accuracy.

Rockfall detachment susceptibility map in El Hierro Island, Canary Islands, Spain

A semi-quantitative heuristic methodology is developed to map a rockfall detachment susceptibility zonation of El Hierro Island (Canary Archipelago). The rationalized procedure, which we called non-weighted bounded indicators, is based on overlapping thematic maps of conditioning factors to mass movement, which are appropriately and individually rescaled and then composed by addition to obtain a susceptibility numerical index through a GIS. As the consistency of the geomorphological analysis depends on the expert subjective criteria and the appropriate interpretation of the landscape, the use of this methodology reduces subjectivity and quantifies the degree of susceptibility. The main factors affecting the mass movement phenomena (rockfalls events), also recognized in the field and, therefore, considered in the presented GIS arrangement, are slope, profile curvature, lithology, vegetation cover and dykes density. To calculate the slope threshold or minimum angle characteristic of rockfall source areas, mixed Gaussian slope frequency decomposition is used. The curvature index reveals stepwise areas. Qualitative geomechanical characteristics are linked to a quantitative index according to a volcanic lithological-complexes classification. Both destabilization (root-wedging) and stabilization effects are considered into the vegetation cover index. The dyke density index incorporates the bearing rock capacity decrease produced in the halo around a dyke network intrusion. Slope, curvature and vegetation indexes thresholds have been fitted following field observations. A rockfall detachment susceptibility map is obtained and classified based on the histogram maxima. The rockfall inventory, based on rockfall events reported within the island, was used for the model validation. A 12 % of the whole island shows medium to very high susceptibility.

Reversible Data Hiding Scheme Based on Maximum Histogram Gap of Image Blocks

In this paper a reversible data hiding scheme based on histogram shifting of host image blocks is presented. This method attempts to use full available capacity for data embedding by dividing the image into non-overlapping blocks. Applying histogram shifting to each block requires that extra information to be saved as overhead data for each block. This extra information (overhead or bookkeeping information) is used in order to extract payload and recover the block to its original state. A method to eliminate the need for this extra information is also introduced. This method uses maximum gap that exists between histogram bins for finding the value of pixels that was used for embedding in sender side. Experimental results show that the proposed method provides higher embedding capacity than the original reversible data hiding based on histogram shifting method and its improved versions in the current literature while it maintains the quality of marked image at an acceptable level.

Image segmentation of maximum inter-class cross entropy based on 2D histogram oblique segmentation

With the approach based on 2D histogram oblique segmentation, a fast image thresholding method based on maximum inter-class entropy was presented in this paper. Firstly the thresholding method was formulated, based on maximum inter-class entropy and 2D histogram oblique segmentation, and then its fast recurring algorithm was deduced, finally the defined array operations and the algorithm were combined to search the optimal threshold vector. The experimental results show that the proposed method can not only get better generalization, but also has higher efficiency than the current thrsholding method based on 2D oblique segmentation.

Preliminary lidar observations of Na meteor trails at Wuhan (30.5°N, 114.4°E), China

Na lidar measurements of atom meteor trails with an integration period of 3.2 s were conducted at Wuhan (30.5°N, 114.4°E), China. A total of 125 Na meteor trail events were registered from 166 hours (16 nights) of lidar data. These Na trails show peak densities ranging from 4040 to 39170 cm−3 with a mean of 16430 cm−3, while their occurrence altitudes vary from 77.2 to 111.6 km with a distribution centroid at 92.6 km. The upper edge of the Na trail altitude distribution resembles that of the altitude profile of the simultaneously observed mean Na layer. In particular, the trail altitude histogram maximum occurs around the mean Na layer peak. This is consistent with early lidar observations of K and Fe trails, which shows that meteoroids entering the atmosphere tend to yield more atom meteor trails detectable by ground-based lidars around the peak of the regular metal layers than elsewhere. It was found that the formation of the Nas layers was usually accompanied by a bunch of Na meteor trails, and that they occurred near the altitude of the Nas layer peak.

High capacity lossless data embedding technique for palette images based on histogram analysis

Recently data embedding over images has drawn tremendous interest, using either lossy or lossless techniques. Although lossy techniques can allow large hiding capacity, host image cannot be recovered with high fidelity. Some applications require exact recovery of the host image, i.e. in medicine patient data can be embedded without affecting the medical image. In general lossless data hiding techniques suffer from limited capacity as the host image should be kept intact. In this paper a lossless embedding technique is proposed. In this technique image histograms are analyzed to identify the embedding capacity of different image types. Histogram maxima and minima are used in embedding capacity estimation. The proposed technique gives hiding capacity that can reach up to 50% of the host image size for images with large homochromatic regions (cartoons-like). In fact, our study showed that the embedding capacity is not only affected by the host image size but also by its histogram distribution. The data embedding and extraction is performed using simple processing operations that can save on power consumption for wireless devices.