Histogram matching

A histogram association and mapping based embedding policy implants a group of bits, called message chunk, in a block. The strategy first calculates a distribution of pixel values of each block in the grayscale, called histogram. Each gray value acts as a bin of the histogram. Though the histogram makes up of 256 bins, only several bins hold these pixel values of a block. The implantation rules shift the pixel value containing bins as a single object to a new position in the histogram. The number of implanted bits is dependent on the range of pixel values of the working block. Implanting bits' number increases for a smaller value of range and vice versa. Being motivated, this research does the identical embedding task by the range of absolute-valued prediction errors. The research implements a predictor and measures the prediction errors in their absolute values. Computed errors' range is smaller than the range of the pixel values. The proposed method does the histogram-shifting task by the range of error values. The proposed method increases the number of embedded bits by a multiple of 1.31 to 2.04. In the second phase, it further applies the same predictor to the last calculated absolute-valued errors. The method yields a smaller range of absolute-valued errors by repeating the procedure for a number of times. The algorithm applies that smaller range in implementing HAM policy. The approach increases the number of implanted bits.

Content based image retrieval is an active research issue that had been famous from 1990s till present. The main target of CBIR is to get accurate results with lower computational time. This paper discusses on the comparative method used in color histogram based on two major methods used frequently in CBIR which are; normal color histogram using GLCM, and color histogram using K-Means. A set of 9960 images are used to test the accuracy and the precision of each methods. Using Euclidean distance, similarity between queried image and the candidate images are calculated. Experiment results shows that color histogram with K-Means method had high accuracy and precise compared to GLCM. Future work will be made to add more features that are famous in CBIR which are texture, color, and shape features in order to get better results.

One of the important requirements in the object detection and tracking is the extracting of efficient features to track the target in video sequence. The feature of colour in image is one of the most visual features widely used. The using of colour histogram is the most popular method for representing color feature. One of the problems of using colour histogram to represent feature is its lack of spatial information where it is used to represents statistical distribution of the colours only. In this paper a new similarity metric with multi block colour histogram of image is proposed. This metric will be used by an object tracking method where the similarity will be applied to get a decision of choosing the correct solution (location) of the object from many candidate locations

An approach to classifying retinal images using a histogram based representation is described. More specifically, a two stage Case Based Reasoning (CBR) approach is proposed, to be applied to histogram represented retina images to identify Age-related Macular Degeneration (AMD). To measure the similarity between histograms, a time series analysis technique, Dynamic Time Warping (DTW), is employed. The advocated approach utilises two "case bases" for the classification process. The first case base consists of green and saturation histograms with retinal blood vessels removed. The second case base comprises the same histograms, but with the Optic Disc (OD) removed as well. The reported experiments demonstrate that the proposed two stage classification process outperforms the single stage classification process with respect to a number of evaluation metrics: specificity, sensitivity and accuracy.

In recent studies of multi-target tracking, highorder association and its corresponding high-order affinity (or similarity) is often preferred over pairwise comparisons to capture high-order discriminative information. A naturally raised challenge is to calculate affinity (or similarity) among more than two target candidates. When target appearance is represented by histograms, such as the popular SIFT and HOG descriptors, pairwise matching measurements, such as Histogram Intersection (HI) etc.are often combined to fit the high-order request in an ad hoc way. However, such combinations may be ineffective and inefficient. In this paper, we address the pairwise matching issue by proposing a novel multi-histogram similarity named Multiway Histogram Intersection (MHI). MHI naturally extends HI by summing over the "min" value of all histograms in each bin. MHI applies to any number of histograms, fits the request of multitarget tracking better and requires less time than previously used affinities. To demonstrate its superiority, we integrate MHI into a recently proposed rank-1-tensor-approximation multi-tracking framework and apply it to vehicle tracking in wide aerial video surveillance. The advantage of using MHI is clearly supported by the experimental results against six common approaches on two public benchmark datasets.

Content based image retrieval is an active research issue that had been famous from 1990s till present. The main target of CBIR is to get accurate results with lower computational time. This paper discusses on the comparative method used in color histogram based on two major methods used frequently in CBIR which are; normal color histogram using GLCM, and color histogram using K-Means. A set of 9960 images are used to test the accuracy and the precision of each methods. Using Euclidean distance, similarity between queried image and the candidate images are calculated. Experiment results shows that color histogram with K-Means method had high accuracy and precise compared to GLCM. Future work will be made to add more features that are famous in CBIR which are texture, color, and shape features in order to get better results.

Many methods based on the histogram equalization have been introduced for the use in contrast enhancement. However, a technique that can produce proper natural enhancement simultaneously in images with brightness ranges of dark, medium, and bright, has been less presented. For this purpose, a powerful contrast enhancement algorithm based on the histogram equalization, called triple dynamic clipped histogram equalization (TDCHE) method has been proposed in this paper. In the proposed method, the histogram of the input image is first partitioned based on the mean or median into three portions. Then, the histogram clipping process is performed in each sub-histogram. Finally, prior to performing the equalization process of each sub-histogram independently, each sub-histogram is mapped to a new dynamic range. The proposed method is introduced to achieve multiple objectives of maximum average information content (entropy), enhancement rate control and reasonable brightness preservation. In addition, this method leads to natural enhancement by producing clear images and preserving maximum details. Performance assessment of the proposed method in terms of the entropy, structural similarity index as well as visual quality based on mean opinion score (MOS) demonstrates perceived superiority of the proposed algorithm in comparison with the previously presented techniques.

Many histogram equalisations-based methods have been proposed to be applied in contrast enhancement. However, a few methods that can simultaneously create a natural enhancement in images with low, median, and high brightness ranges are suggested. Here, a robust contrast enhancement algorithm, which is called triple clipped dynamic histogram equalisation based on standard deviation (TCDHE-SD), is proposed. In the proposed method, the histogram of the input image is partitioned into three parts with approximately the same number of pixels based on the standard deviation. Then, the process of histogram clipping is performed on each sub-histogram. After that, all histograms will be mapped to a new dynamic range by means of applying some simple calculations, and finally, the sub-histogram equalisation process will be performed independently. This method is proposed to achieve the multiple purposes of the maximum average information content (entropy), controlling the enhancement ratio and preserving reasonable brightness. It also provides a natural enhancement by generating clear images with maximum details. The performance assessment of the proposed method in terms of entropy as well as visual quality based on the mean opinion score (MOS) indicates a significant advantage of it over the previous ones.

This paper presents a novel methodology for land-use pattern comparison with the application of image smoothing algorithms and histogram similarity. This method measures the degree of similarity between land-use patterns with integrative consideration of the composition and the spatial relationship of land-use units. This paper verified the effectiveness of the proposed method through experiments of the land-use pattern comparison with simulated land-use maps, demonstrated the applicability of the method in urban studies by a sample application of this method to the land-use pattern comparison of 6 Shinkansen station areas, and clarified the limitations of this method.

Image Segmentation is an essential part of image processing to recognize an image. It is the process to find the subsets of an image on the basis of its characteristics such that values or/and position of pixels. The computational complexity of these type of problems are considered as an NP-complete problem, it is a very hard task to find the solution to such types of problems. A heuristic algorithm like GA, ACO and PSO can be used to find the solution of such type of problems. In the proposed approach the Dynamic Particle Swarm Optimization (DPSO) was used to find the approximate solutions for Image segmentation. Cumulative Histogram will be used to find initial random populations of the Swarm. The result found from this algorithm is tested at different levels of measurement and compare with existing PSO algorithm

We consider the problem of image segmentation using active contours through the minimization of an energy criterion involving both region and boundary functionals. These functionals are derived through a shape derivative approach instead of classical calculus of variation. The equations can be elegantly derived without converting the region integrals into boundary integrals. From the derivative, we deduce the evolution equation of an active contour that makes it evolve towards a minimum of the criterion. We focus more particularly on statistical features globally attached to the region and especially to the probability density functions of image features such as the color histogram of a region. A theoretical framework is set for the minimization of the distance between two histograms for matching or tracking purposes. An application of this framework to the segmentation of color histograms in video sequences is then proposed. We briefly describe our numerical scheme and show some experimental results.

Content based image retrieval is an active research issue that had been famous from 1990s till present. The main target of CBIR is to get accurate results with lower computational time. This paper discusses on the comparative method used in color histogram based on two major methods used frequently in CBIR which are; normal color histogram using GLCM, and color histogram using K-Means. A set of 9960 images are used to test the accuracy and the precision of each methods. Using Euclidean distance, similarity between queried image and the candidate images are calculated. Experiment results shows that color histogram with K-Means method had high accuracy and precise compared to GLCM. Future work will be made to add more features that are famous in CBIR which are texture, color, and shape features in order to get better results.

This paper proposes an algorithm which enhances the contrast of an input image using inter-pixel contextual information. The algorithm uses a two-dimensional (2D) histogram of the input image constructed using mutual relationship between each pixel and its neighbouring pixels. A smooth 2D target histogram is obtained by minimizing the sum of Frobenius norms of the differences from the input histogram, and the uniformly distributed histogram. The enhancement is achieved by mapping the diagonal elements of the input histogram to the diagonal elements of the target histogram. Experimental results show that the algorithm produces better or comparable enhanced images than four state-of-the-art algorithms.

High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the reconstructed HDR images, which are overor underexposed in the input LDR images. To this end, we propose a simple and effective method, HistoHDR-Net, to recover the fine details (e.g., color, contrast, saturation, and brightness) of HDR images via a fusion-based approach utilizing histogram-equalized LDR images along with selfattention guidance. Our experiments demonstrate the efficacy of the proposed approach over the state-of-art methods.

Colour image enhancement is an emerging research area in various fields like forensics, atmospheric science, astrophotography, oceanography and medical imaging. In this paper an efficient colour image enhancement technique is proposed based on Undecimated Wavelet Transform (UWT). In order to preserve the chromatic information, at first the given RGB image is converted into HSI (Hue, Intensity and Saturation) colour space and the intensity component alone is transformed by the UWT transform into lower and higher sub bands. Then clustering is taken place for lower band coefficient using K-means clustering (KMC) where number of cluster (K) is predicted by hill climbing algorithm. To enhance the image, each clustered partition is enhanced using histogram equalization (HE). Finally, the enhanced RGB colour image is obtained from the enhanced intensity component along with the original hue and saturation components. The performance is evaluated by Peak Signal to Noise Ratio (PSNR), Structural SIMilarity (SSIM) index, Universal Quality Index (UQI) and Entropy. Experimental results show that the proposed system enhances the given image with very good visual quality and achieves over 90% SSIM and UQI with very good PSNR.

For the arrangement processing of image using its histogram, we previously have presented a Histogram Matching method based on Gaussian Distribution (HMGD). However, in the case where the brightness histogram of input image has multiple peaks, the HMGD processing does not always bring good results. In this paper, we present an improved histogram matching method using the reference histogram that is made by appropriate moving average (HMMA) processing over the histogram of input image. Also in this paper, we show the experimental results.

For the arrangement processing of image using its histogram, we previously have presented a Histogram Matching method based on Gaussian Distribution (HMGD). However, in the case where the brightness histogram of input image has multiple peaks, the HMGD processing does not always bring good results. In this paper, we present an improved histogram matching method using the reference histogram that is made by appropriate moving average (HMMA) processing over the histogram of input image. Also in this paper, we show the experimental results.

We review construction of a Compressed Histogram of Gradients (CHoG) image feature descriptor, and study quantization problem that arises in its design. We explain our choice of algorithms for solving it, addressing both complexity and performance aspects. We also study design of algorithms for decoding and matching of compressed descriptors, and offer several techniques for speeding up these operations.

In existing principle component analysis (PCA) methods for histogram-valued symbolic data, projection results are approximated based on Moore's algebra and fail to reflect the data's true structure, mainly because there is no precise, unified calculation method for the linear combination of histogram data. In this paper, we propose a new PCA method for histogram data that distinguishes itself from various well-established methods in that it can project observations onto the space spanned by principal components more accurately and rapidly by sampling through a MapReduce framework. The new histogram PCA method is implemented under the same assumption of "orthogonal dimensions for every observation" with the existing literatures. To project observations, the method first samples from the original histogram variables to acquire single-valued data, on which linear combination operations can be performed. Then, the projection of observations can be given by linear combination of loading vectors and single-valued samples, which is close to accurate projection results. Finally, the projection is summarized to histogram data. These procedures involve complex algorithms and large-scale data, which makes the new method time-consuming. To speed it up, we undertake a parallel implementation of the new method in a multicore MapReduce framework. A simulation study and an empirical study confirm that the new method is effective and time-saving.

Visual observation through Closed-Circuit Television (CCTV) systems is an important method for security and monitoring in various fields such as transportation, banking and public security. However, in lighting situations that are not ideal, the image quality produced by CCTV can decrease due to low lighting contrast. Therefore, an effective image processing technique is needed to increase lighting contrast and optimize visual information that can be obtained from CCTV footage. This paper proposes the application of the histogram normalization method as a solution to increase lighting contrast in visual observations through CCTV. By normalizing the histogram, the exposure contrast is significantly increased, so that details hidden by low lighting become more visible. This method is applied adaptively to each frame of CCTV footage, resulting in a consistent increase in contrast throughout the footage. To evaluate the performance of the normalized histogram method, experiments were carried out on a number of CCTV recordings with varying lighting conditions. The experimental results show that the application of a normalized histogram effectively increases the lighting contrast in CCTV footage. Compared to the original recording, the recording that has been processed with a normalized histogram shows improved sharpness, legibility, and better object identification ability. By using the normalized histogram method, CCTV systems can produce clearer images and more useful visual information in low lighting conditions. This increase in lighting contrast has the potential to improve the performance of surveillance systems, identify important objects, and support more accurate decision making in the field of security and monitoring.

Image enhancement is an indispensable technique in improving the quality, brightness, contrast and clarity of satellite images. The object that appears in images and variation caused by shadow, occlusion, camouflage in satellite images are the fundamental challenges posed by image enhancement techniques. The aim of this research work was to enhance satellite images of Sambisa using histogram equalization technique. MATLAB 2021 was used to implement the experiment. The results show that histogram equalization method has an excellent processing effect and it improved the brightness, contrast and clarity of the images as compared original images and the enhanced images.

In this papcr, iui inhrmation theoretic framework for image segmentation is presented. This approach is bascd 0 1 1 the inlormation chatinel that goes from Ihc image Intensity histogram LO the regions of the partitioned image. It allows us to dcfinc a new I'Jmily of scgmentation methods which maximize the mutual u~formation of thc chaonel. Firstly, a greedy top-down algorithm which partitions PI image into homogeneous regions is uitroduced. Secondly, a histogram quantization algorithm which clusters color bins Ui a greedy bottom-up way is defiiied. Finally, the resulting regions U i rhc partitioning algorithm c m optionally he merged using the quantized histogram. This result demonstrates that no processing of Y. detenuinistic or random, can increase the information that Y contains about S.

In recent years, several lossless data hiding techniques have been proposed for images. Lossless data embedding can take place in the spatial domain or in the transform domain. They utilized characteristics of the difference image or the transform coefficient histogram and modify these values slightly to embed the data. However, after embedding message bits these steganography changed the nature of the difference image histogram or the transform coefficient histogram gradually. In this paper, we introduce two new steganalytic techniques based on the difference image histogram and the transform coefficient histogram. The algorithm can not only detect existence of secret messages in images which are embedded by above methods reliably, but also estimate the amount of hidden messages exactly.

In recent years, several lossless data hiding techniques have been proposed for images. Lossless data embedding can take place in the spatial domain or in the transform domain. They utilized characteristics of the difference image or the transform coefficient histogram and modify these values slightly to embed the data. However, after embedding message bits these steganography changed the nature of the difference image histogram or the transform coefficient histogram gradually. In this paper, we introduce two new steganalytic techniques based on the difference image histogram and the transform coefficient histogram. The algorithm can not only detect existence of secret messages in images which are embedded by above methods reliably, but also estimate the amount of hidden messages exactly.

With advance of high speed computer networks, multimedia, transmission technology, an amount of information is being communicated in digital form .Numerous watermarking techniques have been developed to protect images with high security performance. For the proposed method, shifting is applied for embedding stage, add to that our contribution in this method involved the threshold concept to improve the visual quality, embedding capacity, data loss of the host image. The performance of the proposed watermarking schemes have been evaluated by using the watermarked images of size 512×512(standard images) and also applied (medical images), and the watermark (payload) is of the same size as the host image. Simulation results demonstrate that the proposed algorithm generates high performances in the visual quality, PSNR,SSIM between original image and watermarked image, and the hiding capacity compared to the previous works.

With advance of high speed computer networks, multimedia, transmission technology, an amount of information is being communicated in digital form .Numerous watermarking techniques have been developed to protect images with high security performance. For the proposed method, shifting is applied for embedding stage, add to that our contribution in this method involved the threshold concept to improve the visual quality, embedding capacity, data loss of the host image. The performance of the proposed watermarking schemes have been evaluated by using the watermarked images of size 512×512(standard images) and also applied (medical images), and the watermark (payload) is of the same size as the host image. Simulation results demonstrate that the proposed algorithm generates high performances in the visual quality, PSNR,SSIM between original image and watermarked image, and the hiding capacity compared to the previous works.

Histogram of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching & recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose to represent each model object as a collection of shapeme histograms, and match the query histogram to this representation in two steps: (i) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model, and (ii) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.

Recent work has shown that effective methods for recognising objects or spatio-temporal events can be constructed based on receptive field responses summarised into histograms or other histogram-like image descriptors. This paper presents a set of composed histogram features of higher dimensionality, which give significantly better recognition performance compared to the histogram descriptors of lower dimensionality that were used in the original papers by Swain & Ballard (1991) or Schiele & Crowley (2000). The use of histograms of higher dimensionality is made possible by a sparse representation for efficient computation and handling of higher-dimensional histograms. Results of extensive experiments are reported, showing how the performance of histogram-based recognition schemes depend upon different combinations of cues, in terms of Gaussian derivatives or differential invariants applied to either intensity information, chromatic information or both. It is shown that there exist composed higher-dimensional histogram descriptors with much better performance for recognising known objects than previously used histogram features. Experiments are also reported of classifying unknown objects into visual categories. * The support from the Swedish Research Council and from the Royal Swedish Academy of Sciences as well as the Knut and Alice Wallenberg Foundation is gratefully acknowledged. We would also like to thank Barbara Caputo for valuable discussions and for granting access to her modified version of libSVM.

This research presents new three proposed approaches to enhancement the visibility of the Infrared (IR) night vision images. The first proposed approach depends on Hybrid Adaptive Gamma Correction (AGC) with Histogram Matching (HGCHM). The second proposed approach stands up Merging Gamma Correction with Contrast Limited Adaptive Histogram Equalization (MGCCLAHE). The HM uses a reference visual image for converting of night vision images into daytime images. The third approach mixes the benefits of the CLAHE with the undecimated Additive Wavelet Transform (AWT) Using Homomorphic processing (CSAWUH). The quality assessments for the suggested approaches are entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy, lightness order error and the similarity of edges. Simulation results clear that the third proposed approach gives superior results to the two proposed approaches from entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy and the computation time perspectives. On the other hand, the second proposed approach takes long computation time in the implementation with respect to the two proposed approaches. The second proposed approach gives better results to the first proposed approach entropy, average gradient, contrast improvement factor, Sobel edge magnitude, and spectral entropy perspectives. The first proposed approach gives better results to the two proposed approaches from lightness order error and the similarity of edges perspectives.

With the development of computer technology, the growing need of large databases emphasizes utmost automation in face recognition system. Labeling a huge number of people is definitely a great overhead for such system. To reduce the burden of manual labeling of faces, in this paper, we propose a PCA based semi-supervised face recognition technique using edge histogram descriptor (EHD) features. It promotes automation of system by achieving good performance toiling at minimum wage. Moreover, we establish that EHD features are not only useful for face representation but also greatly reduce the dimensionality of the representation compared with traditional pixel value representation. Thus, the space and computation complexity decrease in further stages. PCA is then applied on the EHD features instead of raw pixel intensity values of faces which traditional methods do. Using this process, we build a PCA based classifier that can iteratively update itself after classifying unlabeled training instances. We check the performance of the system using three different similarity measures. We also test our system with different levels of noise to simulate practical environment. To evaluate the proposed method, we have used ORL, Yale, Grimace and JAFFE face databases and achieve superior performance.

Image Segmentation is an essential part of image processing to recognize an image. It is the process to find the subsets of an image on the basis of its characteristics such that values or/and position of pixels. The computational complexity of these type of problems are considered as an NP-complete problem, it is a very hard task to find the solution to such types of problems. A heuristic algorithm like GA, ACO and PSO can be used to find the solution of such type of problems. In the proposed approach the Dynamic Particle Swarm Optimization (DPSO) was used to find the approximate solutions for Image segmentation. Cumulative Histogram will be used to find initial random populations of the Swarm. The result found from this algorithm is tested at different levels of measurement and compare with existing PSO algorithm

Retrieving a corresponding photo from a forensic sketch remains a great challenge. One of the difficulties is that the retrieval rate reduces significantly on data sets with shape exaggeration and lighting variation. The goal of this paper is twofold. First, we attempt to reduce the factor of the shape exaggeration problem by introducing new fiducial points for geometric face alignment. Next, to minimize the illumination effects, we describe the image using the difference of Gaussian-oriented gradient histogram. The matching results using the simplest distance measure on two public databases indicate that our proposed face sketch to photo matching method achieves significantly better accuracy than the state-of-the-art methods. It is also proved that shape exaggeration influences can be reduced by employing outer points face alignment. INDEX TERMS Difference of Gaussian, identity of interest, image matching, oriented gradient histogram, sketch to photo.

The current paper proposes recognition of partially invisible objects in images using image enhancement techniques. The problem mainly arises in night vision images which comprise poor contrast standards. Also during daytime, the object which is captured under sunlight is the lone survivor and the rest of information is not captured by camera properly. Image enhancement techniques to improve visual quality have been popularized with the proliferation of digital imagery and computers. Histogram Equalization (HE) is a versatile image improvement technique that can be incorporated for converting the partial visible objects/invisible objects into a proper vision. Further for enriching the information in image obtained by the HE image, a Contrast Limited Adaptive Histogram Equalization (CLAHE) is incorporated and finally for smoothing purpose, the image thus obtained is passed through a Gaussian filter. Results on various set of images show that above two techniques HE and CLAHE along with a Gaussian filter significantly improve the quality of image and hence assist to discover the partially visible/invisible objects.

Histogram Equalization (HE) has been an essential addition to the Image Enhancement world. Enhancement techniques like Classical Histogram Equalization(CHE),Adaptive Histogram Equalization (AHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE) methods enhance contrast, brightness is not well preserved, which gives an unpleasant look to the final image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization (MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input image using a unique logic, applied CHE in each of the sub-images and then finally interpolated them in correct order. The final image after MDHE gives us the best results based on contrast enhancement and brightness preservation aspect compared to all other techniques mentioned above. We have calculated the various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported by bar graphs, histograms and the parameter calculations at the end.

This paper presents a novel approach for improving the quality of depth video. Given a high-quality color image and its corresponding low-quality depth image, we handle various artifacts which may exist on the depth video by applying a weighted mode filtering method based on a joint histogram. When the histogram is generated, the weight based on color similarity between reference and neighboring pixels on the color image is computed and then used for counting each bin on the joint histogram of the depth map. A final solution is determined by seeking a global mode on the histogram. Experimental results show that the proposed method has outstanding performance and is very efficient in various applications such as depth video enhancement and compression.

For faster updates of large area forest change maps, L-band satellite Synthetic Aperture Radar (SAR) is a potential alternative to present operational methods (such as optical ones). As satellite SAR systems are active systems having repeat pass cycles on the order of weeks and acquire data next to unaffected by weather and lighting conditions, they can provide high-quality images all year around with a high update rate. One downside is the fact that long wavelength satellite SAR images often are poorer in resolution compared to, e.g. optical or hyperspectral data. Also, radar images are affected by speckle noise, as the radar backscatter signal consists of the incoherent sum of all radar responses from scatterers within a resolution cell. In this study, a bi-temporal change detection approach based on image rationing and histogram techniques, both for radiometric/environmental normalization and thresholding, is evaluated. To tackle low resolution and speckle, pre-applied filters are tested in combination with a SAR data fusion method that exploits both spatial and spectral information from multiple SAR channels. HH and HV polarized Fine Beam Dual images acquired 34 degrees off nadir (FBD34) by the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) are used to find clearcuts in Swedish boreal forest. A large area change detection method should be highly automated, computationally cheap and require a low number of input parameters. This is to be combined with robustness and high accuracy. For classification of change and no change areas the automatic histogram based generalized Kittler and Illingworth minimum-error thresholding algorithm (GKIT) for SAR amplitude ratio data proposed in [1] was chosen. More specifically, the lognormal variant LN-GKIT was chosen partly due to its closed form simplicity. Previous work has shown that clear-cuts present backscatter drops in both HH and HV polarized data [2]. Moreover, higher dynamic range or contrast was found for HV polarized backscatter than HH. Nonetheless, the HH polarized component bears information and is included in this study. This is done both separately and in combination with HV, using the Markovian Data Fusion (MDF) approach for SAR change detection proposed in [3], again under a lognormal assumption. Although data acquisition by SAR systems can be regarded as independent of weather conditions, the radar backscatter is sensitive to differences in environmental conditions such as precipitation, moisture content, frozen/unfrozen, snow cover and season [4]. This combined with possible sensor calibration issues should be accounted and adjusted for through some kind of relative radiometric (or environmental) normalization procedure before comparing images. As signatures from different land cover types potentially respond differently to, e.g. water content, a non-linear approach would be preferred. For this histogram matching was chosen, as it serves good for both non-linearity, is computationally cheap and can even be applied across sensors [5]. The off-the

Histogram equalization is an efficient contrast enhancement algorithm. Global equalization is effective, but enhances only overall contrast, while local histogram equalization is more powerful, but computationally expensive. In this paper, a Block-based Binomial Filtered Histogram Equalization (BBFHE) algorithm is presented. The image is divided into blocks, and for each block a histogram is obtained by means of a binomial filter applied on the block histogram itself and on other neighboring block histograms. A binomial filter achieves better results than other low pass filters and can be computed very efficiently. Experimental results show that the proposed algorithm is several times faster-depending on the selected image size-with a comparable visual quality, than the previous fastest method.

Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0.  Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution) function. As a result, the mean brightness of the input image does not change significantly by the histogram equalization. Additionally, noise is prevented from being greatly amplified. Experimental results on medical images demonstrate that the proposed method can enhance the images effectively. The result is also compared with the result of image enhancement technique using local statistics....

It might seem that image enhancement-gram transformation of video pictures in real-time would be a very demanding task requiring a high performance computer. up the two speed critical, but computationally simple, tasks of histogram gathering and image transformation as semi-independent subsystems controlled by a microprocessor, while leaving the computationally intensive task of determining the transformation to the microprocessor, it becomes possible to perform video enhancement dynamically by simple, inexpensive means. and image transformation go on continually. During the vertical blank of every field, the contents of histogram memory are accmlated in main memory and zeroed to avoid premature saturation. After accumulating histogram data for one or more fields, a new transformation function is computed during the next frame, and loaded into the image transformation RAM during the vertical blank preceding the following frame, when the new function becomes effective. computation is described, and is applicable to both histogram equalization and histogram hyperbolization. monstrating considerable improvement in visual clarity. Particularly impressive results were obtained when a rectangular window was applied to areas of interest showing poor contrast. hancement obtained is illustrated by comparing input and output video pictures. However, by setting Histogram gathering The algorithm used for this The system was built and tested, de-The en

Image enhancement aims at improving the information content of original image for a specific purpose. This purpose could be for visual interpretation or for effective extraction of required details. Nevertheless, some acquired images are often associated with pixels of low dynamic range and as such result in low contrast images. Enhancing the contrast therefore tends to increase the dynamic range of the gray levels in the acquired image so as to span the full intensity range. Techniques such as Histogram Equalization (HE) and fuzzy technique can be adopted for contrast enhancement. HE adjusts the contrast of an input image by modifying the intensity distribution of its histogram. It is characterized by providing a global approach to image enhancement, computationally fast and easy to implement approach but can introduce unnatural artifacts and other undesirable elements to the resulting image. Fuzzy technique on its part enhances image by mapping the image gray level intensities into a fuzzy plane using membership functions; modifying the membership functions as desired and mapping back into the gray level plane. Thus, details at desired areas can be enhanced at the expense of increase in computational cost. This paper explores the effect of the use of HE and fuzzy technique to enhance low contrast images. Their performances are evaluated using the M ean squared error (M SE), Peak to signal noise ratio (PSNR), entropy and Absolute mean brightness error (AM BE).

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Extending the concept of texture to the geometry of a mesh manifold surface is an emerging topic in computer vision. This concept is different from gluing images to the surface, but rather indicates the presence of relief patterns that locally change the surface geometry, showing some regular and repetitive patterns. The representation and the analysis of such relief patterns have several potential applications. In this paper, we propose an original and comprehensive framework to address this novel task, which redefines a large variety of local binary patterns on the mesh manifold domain. We also propose an efficient mesh re-sampling technique that enables uniform surface tessellation. We assess the different descriptive variants derived with this framework in terms of uniformity, repeatability and discriminative power. Afterward, we conduct an extensive experimentation on different datasets showcasing the competitiveness of our framework in classification and retrieval tasks, in terms of both accuracy and computational complexity, with respect to state-of-the-art methods.