Histogram Matching Method Research Articles

Duplicate video detection for large-scale multimedia

Since rapid growth of IT technologies, the use of multimedia data such as image and videos are explosively increasing. It is an important aspect of not only for users but also researchers. Duplicate images and videos are rapidly increasing and it causes difficulties in retrieval and management as well. It also causes copyright problems. In this paper, we discus prior duplicate video detection techniques and overcome previous research problems using block histogram and dynamic matching approach duplicate video detection method. We improved excessive abstract of previous block mean-value based feature extraction method to be robust in various video transformations. Also, we created feature vector of timely histogram by unit of blocks to reflect video features. We proposed dynamic matching algorithm to match videos which is suitable for large-scale video data. To evaluate our proposal, we used VIREO video datasets which is provided by Hong Kong City University and Carnegie Mellon University and MUSCLE-VCD-2007 dataset which is provided by INRIA. Our method showed 90 % of accuracy on duplicate video detection. Our proposed method showed robustness especially in various video transformations. Also, we tested video clustering test to prove our method and dynamic matching method showed 5 times fast compare to existing method which is suitable for real-time and large-scale video detection process.

Assessment of Landsat 7 Scan Line Corrector-off data gap-filling methods for seagrass distribution mapping

Methods to predict and fill Landsat 7 Scan Line Corrector (SLC)-off data gaps are diverse and their usability is case specific. An appropriate gap-filling method that can be used for seagrass mapping applications has not been proposed previously. This study compared gap-filling methods for filling SLC-off data gaps with images acquired from different dates at similar mean sea-level tide heights, covering the Sungai Pulai estuary area inhabited by seagrass meadows in southern Peninsular Malaysia. To assess the geometric and radiometric fidelity of the recovered pixels, three potential gap-filling methods were examined: (a) geostatistical neighbourhood similar pixel interpolator (GNSPI); (b) weighted linear regression (WLR) algorithm integrated with the Laplacian prior regularization method; and (c) the local linear histogram matching method. These three methods were applied to simulated and original SLC-off images. Statistical measures for the recovered images showed that GNSPI can predict data gaps over the seagrass, non-seagrass/water body, and mudflat site classes with greater accuracy than the other two methods. For optimal performance of the GNSPI algorithm, cloud and shadow in the primary and auxiliary images had to be removed by cloud removal methods prior to filling data gaps. The gap-filled imagery assessed in this study produced reliable seagrass distribution maps and should help with the detection of spatiotemporal changes of seagrasses from multi-temporal Landsat imagery. The proposed gap-filling method can thus improve the usefulness of Landsat 7 ETM+ SLC-off images in seagrass applications.

Reducing Semantic Gap in Video Retrieval with Fusion: A Survey

Abstract: Multimedia provides a rich content of information and huge amount of data's are available in the field of video retrieval. Now day's enormous videos are available on web and online to accessible from internet or retrieve videos from smart phones, digital cellular assistants. There is the drastic growth in the amount of multimedia field of improving data storage, acquisition and communication technologies, which are all supported by major improvements in processing of video and audio. Researches focused on more efforts in video retrieval that contain certain visual information rather than image of their interest. Such a search is facilitated by Content Based Video Retrieval (CBVR) methods. Specifically segmentation of video is the most prominent step as the retrieved results are based on the segmentation boundaries. The shot boundary detection can be performed using various different techniques like Motion/hybrid DCT, edge tracking, histogram, HSV Model, Motion vector and Block matching methods. This paper mainly presents a study of different methods/algorithm that has been proposed in literature for video retrieval to reduce the semantic gap between low and high level features. Semantic gap between these two feature level is improving by its efficiency with the help of advanced algorithms and techniques using machine learning with fusions.

Automatic dynamic range adjustment for ultrasound B-mode imaging

In medical ultrasound imaging, dynamic range (DR) is defined as the difference between the maximum and minimum values of the displayed signal to display and it is one of the most essential parameters that determine its image quality. Typically, DR is given with a fixed value and adjusted manually by operators, which leads to low clinical productivity and high user dependency. Furthermore, in 3D ultrasound imaging, DR values are unable to be adjusted during 3D data acquisition. A histogram matching method, which equalizes the histogram of an input image based on that from a reference image, can be applied to determine the DR value. However, it could be lead to an over contrasted image. In this paper, a new Automatic Dynamic Range Adjustment (ADRA) method is presented that adaptively adjusts the DR value by manipulating input images similar to a reference image. The proposed ADRA method uses the distance ratio between the log average and each extreme value of a reference image. To evaluate the performance of the ADRA method, the similarity between the reference and input images was measured by computing a correlation coefficient (CC). In in vivo experiments, the CC values were increased by applying the ADRA method from 0.6872 to 0.9870 and from 0.9274 to 0.9939 for kidney and liver data, respectively, compared to the fixed DR case. In addition, the proposed ADRA method showed to outperform the histogram matching method with in vivo liver and kidney data. When using 3D abdominal data with 70 frames, while the CC value from the ADRA method is slightly increased (i.e., 0.6%), the proposed method showed improved image quality in the c-plane compared to its fixed counterpart, which suffered from a shadow artifact. These results indicate that the proposed method can enhance image quality in 2D and 3D ultrasound B-mode imaging by improving the similarity between the reference and input images while eliminating unnecessary manual interaction by the user.

Estimating Regional Winter Wheat Leaf N Concentration with MERIS by Integrating a Field Observation-Based Model and Histogram Matching

<abstract><title><italic>Abstract.</italic></title> Leaf nitrogen status is a key indicator for evaluating crop growth, yield potential, and grain quality. Non-destructive and rapid assessment of leaf nitrogen is required for improving nitrogen management in wheat production. The Medium-Resolution Imaging Spectrometer (MERIS), one of the payloads on ENVISAT, has an average 20 nm spectral resolution in the red edge region, a 300 m spatial resolution, and a 3-day repeat cycle, making MERIS a potentially valuable sensor for fast measurement and monitoring of crop N status at regional to global scales. The red edge can provide information related directly to the N concentration of vegetation. To develop a model for estimating crop N from field observations, winter wheat (Triticum aestivum L.) leaf nitrogen concentration (LNC) and spectral reflectance data in the North China Plain were collected. A comparison between spectral reflectance, its first derivative, and red edge parameters for estimating wheat LNC found that the red well position (RWP) is the best indicator of LNC (R² = 0.60). A model was developed for LNC estimation with RWP, and the model was calibrated to the satellite imagery using a histogram matching method. A geo-rectified LNC map of Shandong and Henan provinces in the North China Plain was then generated from MERIS data. Validation with independent field observation data showed that the accuracy was higher than 95%, with root mean square error (RMSE) of 0.11 g N g^-1 DW and a coefficient of determination (R²) of 0.90. This research will contribute to improving N fertilizer usage by mapping large-area winter wheat N status.

Illumination compensation for multi-view video based on layered histogram matching with depth information

Recently, 3-dimensional, stereo, and multi-view videos have been gathering attention for next-generation imaging services. However, they suffer from illumination mismatches caused by differences in camera positions for different viewpoints and imperfect camera calibration. Multi-view videos are more susceptible to these problems. They usually decrease the performance of further processes such as data compression.We propose several component techniques to reduce the effects of these problems, built on the basis of time-constant histogram matching methods. Our method consists of a pre-processing filter to increase the pixel correlation; a color conversion technique to consider the value range of each color component; an image separation technique to separate an image into several layered sub-images using corresponding depth information; and a technique to group the temporal frames as appropriate processing units for illumination compensation.To show the performance of the proposed techniques, we conduct experiments that apply the methods to several multi-view video test sequences for illumination compensation, which are followed by a data compression process using a standard tool. In these experiments we compose the proposed techniques into four combinations on the basis of the time-constant histogram matching method. The experimental results show that all four combinations increased the image quality more than 2dB in PSNR at the same bit-rate, or decreased the bit-rate by about 20% at the same image quality in comparison to the JMVM, which includes an illumination compensation technique. In comparison with previous studies, all four combinations showed better performance. Thus, we can conclude that each of the proposed techniques contributes to illumination compensation and multi-view video coding.

도심지역의 그림자 영향을 고려한 다시기 고해상도 위성영상의 선택적 히스토그램 매칭

도심지 모델링과 분석을 효과적으로 수행하기 위해서는 다른 시기나 다른 지역의 추가적인 고해상도 위성영상이 반드시 필요하다. 그러나 같은 지상 개체라 하더라도 서로 다른 영상에서 방사적인 불일치가 존재하며 이는 영상 처리와 분석의 정확도를 저하시키는 원인이 된다. 더욱이 도심지의 경우 건물, 수목, 교량, 기타 구조물 등 높이를 갖는 개체들은 영상 전체에 걸쳐 그림자를 발생시키며 이는 상대 방사 정규화의 질을 저하시킨다. 본 연구에서는 태양과 위성의 기하학적 위치 정보, 부가적인 수치 표고 모델이 없어도 적용이 가능한 단영상 기반의 그림자 추출기법을 적용하고 그림자의 영향을 배제한 선택적인 히스토그램 매칭 기법을 제안하였다. 건물의 에지 버퍼 영역에 대한 인접 정보와 분할을 통해 생성된 객체의 공간 및 분광인자를 이용하여 그림자를 추출한 후, 아스팔트 도로와 같이 그림자로 잘못 추출된 이상 객체를 제거하였다. 최종적으로 그림자 지역이 마스킹 된 Quickbird-2 다시기 영상을 이용하여 비그림자 지역만을 이용하여 선택적 히스토그램 매칭을 수행하였다. Additional high resolution satellite images, other period or site, are essential for efficient city modeling and analysis. However, the same ground objects have a radiometric inconsistency in different satellite images and it debase the quality of image processing and analysis. Moreover, in an urban area, buildings, trees, bridges, and other artificial objects cause shadow effects, which lower the performance of relative radiometric normalization. Therefore, in this study, we exclude shadow areas and suggest the selective histogram matching methods for image based application without supplementary digital elevation model or geometric informations of sun and sensor. We extract the shadow objects first using adjacency informations with the building edge buffer and spatial and spectral attributes derived from the image segmentation. And, Outlier objects like a asphalt roads are removed. Finally, selective histogram matching is performed from the shadow masked multi-temporal Quickbird-2 images.

Daytime Precipitation Estimation Using Bispectral Cloud Classification System

Abstract Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitude–longitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bispectral (visible and infrared) rain estimation scenarios were compared to investigate the technique’s ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude–longitude) scales. Overall, the results using daytime data during June–August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively.

High quality solid texture synthesis using position and index histogram matching

The synthesis quality is one of the most important aspects in solid texture synthesis algorithms. In recent years several methods are proposed to generate high quality solid textures. However, these existing methods often suffer from the synthesis artifacts such as blurring, missing texture structures, introducing aberrant voxel colors, and so on. In this paper, we introduce a novel algorithm for synthesizing high quality solid textures from 2D exemplars. We first analyze the relevant factors for further improvements of the synthesis quality, and then adopt an optimization framework with the k-coherence search and the discrete solver for solid texture synthesis. The texture optimization approach is integrated with two new kinds of histogram matching methods, position and index histogram matching, which effectively cause the global statistics of the synthesized solid textures to match those of the exemplars. Experimental results show that our algorithm outperforms or at least is comparable to the previous solid texture synthesis algorithms in terms of the synthesis quality.

Retrieval of Identical Clothing Images Based on Non-Static Color Histogram Analysis

In this paper, we present a non-static color histogram method to retrieve clothing images that are similar to a query clothing. Given clothing area, our method automatically extracts major colors by using the octree-based quantization approach[16]. Then, a color palette that is composed of the major colors is generated. The feature of each clothing, which can be either a query or a database clothing image, is represented as a color histogram based on its color palette. We define the match color bins between two possibly different color palettes, and unify the color palettes by merging or deleting some color bins if necessary. The similarity between two histograms is measured by using the weighted Euclidean distance between the match color bins, where the weight is derived from the frequency of each bin. We compare our method with previous histogram matching methods through experiments. Compared to HSV cumulative histogram-based approach, our method improves the retrieval precision by 13.7 % with less number of color bins.

A method for color classification of fruits based on machine vision

A dominant color histogram matching method for fruits classification was presented in this paper. In classification of fruits based on machine vision, image was acquired with a color CCD camera tha...

Efficient multi-resolution histogram matching for fast image/video retrieval

Most content-based image/video retrieval systems use histogram matching method to compute the similarity between two histograms. The matching of two images can be accomplished by matching their corresponding histograms. A good image/video retrieval system requires two factors: fast response time and high accuracy. A fast search algorithm called MRSA was proposed previously by applying a multi-resolution structure to gain speed-up and to have the same retrieval accuracy as the exhaustive search algorithm. In this paper, we improve the retrieving speed of MRSA while maintaining the global retrieval accuracy. The retrieving speed is improved by using the non-uniform quantization method to obtain lower resolution histograms and the non-uniform quantization method is proven to be able to reduce the number of comparisons at lower resolution levels. Furthermore, we not only extend the multi-resolution concept from uniform quantization to non-uniform quantization but also employ another similarity measurement, χ 2 distance, to construct the multi-resolution structure. Due to the thresholding mechanism, the proposed non-uniform quantization based method relieves the over-smooth problem suffering from downsampling. Hence, our method will reduce noticeable unnecessary comparisons at low resolution levels than MRSA by selecting a proper quantization table. The employing of additional similarity measurement and different quantization criterion increases the flexibility and the efficiency of the algorithm. Experiments demonstrate the validity and efficiency of our algorithm in some typical image/video retrieval applications.

SU‐EE‐A3‐06: Deformable Image Registration in Cone‐Beam CT Images for Image‐Guided Adaptive Radiotherapy

Purpose: With the availability of on‐board imaging devices capable of constructing cone‐beam CT (CBCT) images, it is expected that there will be great interest in using volumetric CBCT for image‐guided adaptive radiotherapy. In order to fully utilize CBCT, automatic segmentation on CBCT images is one of key steps toward this goal. The purpose of this study is to implement a robust deformable image registration for auto‐segmentation. Method and Materials: In four head and neck cancer patients, we used our previously developed, image intensity‐based deformable image registration algorithm to register the planning CT with the 3–5 daily CBCT in three scenarios. First, the daily CBCT was directly used without modification. Second, we applied a generic look‐up‐table transformation to map the CBCT image intensity to the conventional CT intensity using the measured electron density calibration curves for both the conventional and CBCT scanners. In the third scenario, we proposed a wavelet‐based dynamic window/level histogram matching algorithm to map the CT number from CBCT image to the conventional CT image. Then the deformable image registration was performed in the modified CBCT images to map the anatomical structures from the planning CT to the corresponding CBCT images. Results: Without pre‐processing, we found that the CT numbers in CBCT images were inconsistent, especially in soft tissue regions and in patients with large body circumferences. The deformable image registration using the window/level histogram matching method performed the best with good consistency in delineating soft tissue structures. The algorithm is also computationally efficient. Conclusion: We implemented a wavelet‐based window/level histogram matching algorithm to pre‐process the CBCT to allow for more robust deformable image registration of with the reference planning CT. This implementation allows for volumetric CBCT‐guided adaptive radiotherapy.

Topographic information of sand dunes as extracted from shading effects using Landsat images

Topographic variations affect the reflectance properties of the Earth's surface and are often removed in remote sensing studies, especially when significant terrain variations exist. In this study, however, we show that shading effects assessed by Landsat can be treated as a signal that stores important topographic information, especially when the spectral characteristics of a surface are homogenous. The coastal transverse dunes of the Ashdod area, and the desert linear dunes of Nizzana (both located in Israel), were selected to investigate the abovementioned idea. The dune heights in these areas are 10 m on average (relative to their surroundings) and have maximum slopes of 33°. An innovative method for extracting slope, aspect, and height data for sand dunes using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images was developed, based on the regularity and periodicity of dunes' landscapes. Using two Landsat images representing different sun zenith and azimuth angles, reflectance values of each image were converted to cos( i) values ( i=incident angle between the surface normal and the solar beam radiation), applying histogram matching methods. The slope and aspect of each pixel were determined as those that give the best prediction of the observed value of cos( i). Height profiles were then extracted, using simple trigonometric relationships. The accuracies of heights and slopes along selected profile lines were to the order of 1 m and 3°, respectively (at a spatial resolution of 15 m). Best results were obtained when the images included one from the summer and the other from the winter, corresponding to maximum difference in solar zenith and azimuth angles. Errors in heights were attributed to surface heterogeneity (e.g., presence of biogenic soil crusts in the rainy season), geometric correction errors, cast shadows, and Bidirectional Reflectance Distribution Function (BRDF) effects. Comparison to Advanced Thermal Emission and Reflection Radiometer (ASTER) 3D information showed that the proposed method is better in representing the topographic variation of the area than the digital elevation model (DEM) produced by ASTER.

Multidimensional Histograms for Density Modification

Publisher Summary Density modification improves the quality of an approximate electron-density map by imposing physical constraints based on some conserved features of the correct electron density map. Density modification methods generally require an initial map with substantial phase information. In most cases, these phases are obtained from multiple isomorphous replacement (MIR) or multiwavelength anomalous dispersion (MAD), and these phases are of pivotal importance when the experimental source of phase information is bimodal as it is in single-wavelength anomalous scattering (SAD) and single isomorphous replacement methods (SIR). The most commonly used density modification method is solvent flattening that exploits the observation that the solvent region of an electron density map is featureless at medium resolution because of the high thermal motion and disorder of the solvent molecules. Xiang and Carter proposed extending the density histogram to include relationships between neighboring density values via multidimensional histograms defined as joint probabilities of the density values and their higher-order derivatives. In the conventional one-dimensional (1D) electron density histogram matching method, a one-to-one mapping is made on the original electron density to the new electron density so that the density histogram of the modified map matches that of the ideal histogram. The two-dimensional (2D) histogram specifies not only the probability of the electron density value for a given grid point in the map but also the local environment around that grid point as reflected by the density gradients that arise from chemical bonding. The strategy of using alternating 1D histogram matching to achieve 2D histogram matching can be generalized to the matching of higher dimension histograms.

Maximum-likelihood density modification using pattern recognition of structural motifs.

The likelihood-based approach to density modification [Terwilliger (2000), Acta Cryst. D56, 965-972] is extended to include the recognition of patterns of electron density. Once a region of electron density in a map is recognized as corresponding to a known structural element, the likelihood of the map is reformulated to include a term that reflects how closely the map agrees with the expected density for that structural element. This likelihood is combined with other aspects of the likelihood of the map, including the presence of a flat solvent region and the electron-density distribution in the protein region. This likelihood-based pattern-recognition approach was tested using the recognition of helical segments in a largely helical protein. The pattern-recognition method yields a substantial phase improvement over both conventional and likelihood-based solvent-flattening and histogram-matching methods. The method can potentially be used to recognize any common structural motif and incorporate prior knowledge about that motif into density modification.

Carrier-to-interference ratio measurement using moments or histograms

Cochannel interference is often the predominant factor that limits the capacity of a cellular mobile radio system. Power control is thus needed so that carrier-to-cochannel interference ratio (CIR) can be maintained within a tolerable bound. Many power control algorithms that require real-time CIR measurement have been proposed but there is very few mentioning the measurement method. This paper presents viable solutions for real-time CIR estimations under various channel conditions. These solutions are based on either the method of moments or the histogram matching concept. We provide numerical results to demonstrate the usefulness and to compare the performance of the proposed algorithms. It is found that the histogram matching method with a properly-designed nonuniform quantizer offers excellent CIR estimation.

A two-dimensional histogram-matching method for protein phase refinement and extension.

A new method has been developed for protein phase improvement using the joint distribution of the electron density and its gradient (two-dimensional histogram) as a constraint in a density-modification procedure. Matching the two-dimensional (2D) histogram of a given map to that of an ideal 2D histogram was achieved through alternating applications of one-dimensional (1D) histogram matching on electron density and on density gradient. The 2D histogram-matching method was compared with the 1D density histogram-matching method for phase refinement and extension starting from either medium-resolution or high-resolution data on three different types of phases. These included phase refinement and extension using MIR phases of T(6) insulin and phases with randomly generated errors. The test results demonstrated significant improvement of the phases and the overall map quality using the 2D histogram-matching method compared with the 1D density histogram-matching method in each of the three test cases.

The two-dimensional histogram as a constraint for protein phase improvement.

The joint distribution of electron density and its gradient in a protein electron-density map was examined. This joint distribution was represented by a two-dimensional histogram (2D histogram) of electron-density values and the modulus of the gradient. 16 structures representing distinct protein-fold families were selected to study the dependence of the 2D histogram on resolution, overall temperature factor, structural conformation and phase error. The similarity between the histograms for a pair of structures was measured by correlation coefficient, and the residual provided a measure of the difference. The 2D histogram was found to vary with resolution and overall temperature factor, but was found to be insensitive to structure conformation. The average correlation coefficient between pairs of 2D histograms at three different resolutions examined was 0.90 with a standard deviation of 0.04. The average residual for the same condition was 0.13 with a standard deviation of 0.03. The 2D histogram was also found to be sensitive to phase error. The average correlation coefficient and residual between 2D histograms with 10 degrees phase difference are 0.71 and 0.18, respectively. The variation of the 2D histogram resulting from structure-conformation changes was estimated to be equivalent to that of a 4 degrees phase error. This establishes the minimal phase error that a 2D histogram-matching method could achieve. The conservation of the 2D histogram with respect to structure conformation enables the prediction of the ideal 2D histogram for unknown structures. The sensitivity of the 2D histogram to phase error suggests that it could be used as a target for the density-modification method and also could be used as a figure of merit for phase selection in ab initio phasing.

Correction for variations in MRI scanner sensitivity in brain studies with histogram matching.

Quantitative comparisons of abnormalities in MRI scans between patients or within patients serially are affected by variations in MR scanner performance. A histogram matching method is proposed to correct for variation in scanner sensitivity. It is demonstrated that this histogram matching method reduced the variation in white matter intensities across normal subjects from 7.5 to 2.5% and provided a method to remove the threshold dependency in lesion volume measurement with global thresholding in patients with multiple sclerosis (MS). The effectiveness of the method was compared with three other possible correction schemes. The histogram matching method was shown to be 2 to 5 times better.