Image Processing

The use of image processing schemes as part of the security systems have been increasing, to detect, classify as well as to tract object and human motion with a high precision. To this end several approaches have been proposed during the last decades using image processing techniques, because computer vision let us to manipulated digital image sequences to extract useful information contained in a video stream. In this paper we present a motion detection algorithm using the movement vectors estimation which are subsequently filtered to obtain better information about real motion into a given scenes. Experimental results show that the accuracy of proposed system.

We develop reconfigurable mesh (RMESH) algorithms for window broadcasting, data shifts, and consecutive sum. These are then used to develop efficient algorithms to compute the histogram of an image and to perform histogram modification. The histogram of an N×N image is computed by an N×N RMESH in O (√ ¡ B log √ ¢ ¢ B (N/√ £ B ) for B < N, Ο(√ ¤ N ) for B = N, and Ο(√ £ B ) for B > N. B is the number of gray scale values. Histogram modification is done in O (√ ¤ N ) time by an N×N RMESH.

We report the results of observations made at Magdalena Ridge Observatory using the prototype Wide Area Space Surveillance System (WASSS) camera, which has a 4 x 60 field-of-view , &lt; 0.05 resolution, a 2.8 cm 2 aperture, and the ability to view within 4 o of the sun. A single camera pointed at the GEO belt provided a continuous night-long record of the intensity and location of more than 50 GEO objects detected within the camera&#39;s 60 field-of-view, with a detection sensitivity similar to the camera&#39;s shot noise limit of m v =13.7. Performance is anticipated to scale with aperture area, allowing the detection of dimmer objects with larger-aperture cameras. The sensitivity of the system depends on multi-frame averaging and a Principal Component Analysis based image processing algorithm that filters out space objects based on their different angular velocities from those of celestial objects. Results are presented for a full night of viewing on October 16, 2012. Close to ...

Number Plate recognition, also called License Plate realization or recognition using image processing methods is a potential research area in smart cities and Internet of Things. Many of the existing automated number plate recognition systems work only in a controlled environment where images are captured from a straight angle with good illumination, clarity and standard fonts. Another drawback of existing works is that, they are based on UK number plates and may not suite for Indian number plates. This paper presents a novel image processing system for Indian number plate detection and recognition that can deal with, noisy, low illuminated, cross angled, non-standard font number plates. This work employs several image processing techniques such as, morphological transformation, Gaussian smoothing, and Gaussian thresholding in the pre-processing stage. Next, for number plate segmentation, contours are applied by border following and contours are filtered based on character dimensions and spatial localization. Finally, after the region of interest filtering and de-skewing, K-nearest neighbor algorithm is used for character recognition. The proposed methods demonstrated promising results.

Motivation Large projects produce a major number of individual results. It is desirable to try and test the individual approaches in a close-to-reality demonstration. How the interfaces work and which gaps are to be filled in future is to be determined. In a large exercise on earthquake preparedness in central Hungary (IRIS 2011) the application of the IRIS technologies has been tested over a period of three days using live satellite images and employing 500 forces of civil protection. A very successful exercise has been delivered.

We have performed end-to-end laboratory and numerical simulations to demonstrate the capability of differential photometry under realistic operating conditions to detect transits of Earth-sized planets orbiting solar-like stars. Data acquisition and processing were conducted using the same methods planned for the proposed Kepler Mission. These included performing aperture photometry on large-format CCD images of an artificial star field obtained without a shutter at a readout rate of 1 megapixel/sec, detecting and removing cosmic rays from individual exposures and making the necessary corrections for nonlinearity and shutterless operation in the absence of darks. We will discuss the image processing tasks performed 'onboard" the simulated spacecraft, which yielded raw photometry and ancillary data used to monitor and correct for systematic effects, and the data processing and analysis tasks conducted to obtain lightcurves from the raw data and characterize the detectability of transits. The laboratory results are discussed along with the results of a numerical simulation carried out in parallel with the laboratory simulation. These two simulations demonstrate that a system-level differential photometric precision of 1 O on five-hour intervals can be achieved under realistic conditions.

In this paper, we proposed an efficient radix-2 FFT architecture using CORDIC algorithm for FPGA implementation of FFT. CORDIC algorithm is used for twiddle factor multiplication; it will reduce the computation time and make faster processor. The proposed algorithm uses a new addressing scheme and the angle generator logic in order to remove the ROM usage to store twiddle factors. The CORDIC algorithm offers opportunity to calculate all the functions in a simple and elegant fashion. By using only additions and shift operations CORDIC algorithm calculates the rotation of a vector and it is an iterative algorithm. The design architecture is written in verilog HDL code using Modelsim and XILINX ISE 14.2 tools.

Edge is defined as the boundary pixels that connect two separate regions. Edges are local changes in the image intensity. Edges characterize boundaries and are therefore a problem of fundamental importance in image processing.. Edge detection plays a very important role in image processing The edges detected by algorithms are used by advanced computer vision,medical field,Brain Tumor detection,geologic formation extraction,biometrix and many more fields.In this article we are going to survey various edge detection techniques such as sobel, Prewitt, Robert,, Marr Hildrith and Canny operators.Althought each edge detection technique has its own merits and limitations .In this paper we would like to present various edge detection techniques with emphasis on Canny because the performance of canny edge detection technique is judged as the best in the field of image processing.

Edge is defined as the boundary pixels that connect two separate regions. Edges are local changes in the image intensity. Edges characterize boundaries and are therefore a problem of fundamental importance in image processing.. Edge detection plays a very important role in image processing The edges detected by algorithms are used by advanced computer vision,medical field,Brain Tumor detection,geologic formation extraction,biometrix and many more fields.In this article we are going to survey various edge detection techniques such as sobel, Prewitt, Robert,, Marr Hildrith and Canny operators.Althought each edge detection technique has its own merits and limitations .In this paper we would like to present various edge detection techniques with emphasis on Canny because the performance of canny edge detection technique is judged as the best in the field of image processing.

The proposed methodology of multi light intensity image fusion of the single image for high dynamic range imaging uses four to five input images with different light exposure time interval of the single same image with different time interval at the same time the aperture of the camera is fixed the resulting method gives the high dynamic range image as well as overcomes the displacement of pixel in motion images, underexposure and overexposure pixels in image and allow to calculate the possible luminance value of every pixel in an image using maximum Posterior Estimation (MAP) technique and reduce the noise in an image using proposed work method we can obtain more informative and high quality image than used input different light exposure images of single image. Using contrast stretching and histograms in contrast stretching the intensity of the pixel is estimated

This paper deals with the integration of image understanding (IU) programs using a knowledge-based approach. The basic concepts of program integration are discussed, and a simple problem-solving model for program integration is outlined. Two types of reasoning, planning and execution control, are identified. A system developed using this model, called OCAPI (Optimizing, Controlling and Automating the Processing of Images), is introduced. OCAPI is in an AI environment in which the reasoning used by the IU specialist is formally represented using frames and production rules. An example of an application developed using OCAPI is presented, and the advantages and shortcomings of this approach are discussed.

This paper presents the results of ISAR processing of RCS measurements performed over a broad frequency band (12 GHz - 40 GHz). The main purpose is to reach the highest resolution in reflectivity maps in order to identify scattering mechanisms in the target structure.

Machine vision application in mechanized tree fruits harvesting operations can be implemented by image processing algorithms. In this paper, a combination of color and shape processing was used to segment the red apples images in order to detect an apple fruit in each image and to determine its location. Thirty images had been taken, randomly. Images were filtered, converted to binary images, and noise reduced. The algorithm was finally assessed. The algorithm could detect apple fruits in the 83.33% of images and ...

In order to detect a fruit, a well-defined segmentation algorithm is required. The segmentation process requires finding the adequate thresholds in order to extract the wanted features. In this project 15 methods were applied to determine the thresholds of thirty images in order to detect the tart-cherry fruits. Then, the resulted binary images were assessed. The images were acquired under uncontrolled daylight conditions and the main idea was to find at least one fruit feature in each image. All of the algorithms were based ...

Kohanz is a domestic apple variety which is grown in Iran. It is sensitive to packing conditions. Bad packages normally cause decline in its quality. Therefore, apple&#x27;s geometric properties are of important consideration in the design of the fruit&#x27;s packaging facilities. In this study, a sample of 38 freshly harvested apple fruits was obtained, 82 images of their sides were acquired and geometric dimensions were measured. Their axial dimensions, equivalent diameter and sphericity were obtained using a vernier caliper with accuracy of ...

In this study, edge detection and combination of color and shape analyses was utilized to segment images of red apples obtained under natural lighting. Thirty images were acquired from an orchard in order to find an apple in each image and to determine its location. Two algorithms ...

Secara praktis pada umumnya pengguna kurang memerlukan penyelesaian eksak, yang mereka inginkan adalah suatu penyelesaian pendekatan dalam waktu yang relative cepat. Sehingga terkait dengan ini berarti metode heuristic atau metaheuristik adalah sesuai. Beberapa pendekatan pendekatan ini teleh memberikan jaminan kualtitas tertentu penyelesaian dan dalam waktu polynomial. Sayangnya, sifat teoritis demikian ini biasanya berakibat performance yang secara relative buruk. Dengan kata lain heuristic sederhana adakalanya lebih cepat dan menghasilkan penyelesaian lebih baik dari pada algoritma pendekatan, walaupun heuristic sederhana tidak menjamin kualitas dan dalam banyak kasus ia menghasilkan penyelesaian buruk. Artinya pendekatan seperti ini "problem sensitive". Untuk itu perlu diajukan metode metaheuristik dapat mengatasi kelemahan dalam hal performancenya, oleh karena itu untuk permasalahan kombinatorial adalah masalah yang mempunyai himpunan solusi layak (feasible) yang terhingga. Meskipun secara prinsip solusi dari masalah ini bisa didapatkan dengan enumerasi lengkap, pada masalah kompleks dibutuhkan waktu yang tidak bisa diterima secara praktis.

Secara praktis pada umumnya pengguna kurang memerlukan penyelesaian eksak, yang mereka inginkan adalah suatu penyelesaian pendekatan dalam waktu yang relative cepat. Sehingga terkait dengan ini berarti metode heuristic atau metaheuristik adalah sesuai. Beberapa pendekatan pendekatan ini teleh memberikan jaminan kualtitas tertentu penyelesaian dan dalam waktu polynomial. Sayangnya, sifat teoritis demikian ini biasanya berakibat performance yang secara relative buruk. Dengan kata lain heuristic sederhana adakalanya lebih cepat dan menghasilkan penyelesaian lebih baik dari pada algoritma pendekatan, walaupun heuristic sederhana tidak menjamin kualitas dan dalam banyak kasus ia menghasilkan penyelesaian buruk. Artinya pendekatan seperti ini “problem sensitive”. Untuk itu perlu diajukan metode metaheuristik dapat mengatasi kelemahan dalam hal performancenya, oleh karena itu untuk permasalahan kombinatorial adalah masalah yang mempunyai himpunan solusi layak (feasible) yang terhingga.  Me...

This paper aims to detect anemia using images of the lip mucosa, where the skin tissue is thin, and to confirm the feasibility of detecting anemia noninvasively and in the home environment using machine learning (ML). Data were collected from patients, including women and men. Six ML algorithms: artificial neural network (ANN), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM) which are widely used in medical applications, were used to classify the collected data. Two di erent data types were obtained from participants' images (RGB red color values and HSV saturation values) as features, with age, sex, and hemoglobin levels utilized to perform classification. The ML algorithm was used to analyze and classify images of the lip mucosa quickly and accurately, potentially increasing the e ciency of anemia screening programs. The accuracy, precision, recall, and F-measure were evaluated to assess how well ML models performed in predicting anemia. The results showed that NB reported the highest accuracy ( %) among the other ML models used. DT, KNN and ANN reported an accuracies of ( %), while LR and SVM had an accuracy of ( %) and ( %) receptively. This research suggests that employing ML approaches to identify anemia will help classify the diagnosis, which will then help to create e cient preventive measures. Compared to blood tests, this noninvasive procedure is more practical and accessible to patients. Furthermore, ML algorithms may be created and trained to assess lip mucosa photos at a minimal cost, making it an a ordable screening method in regions with a shortage of healthcare resources.

X-ray diffraction (XRD) and electron backscatter diffraction (EBSD) are commonly used to perform texture analysis of thin films. However, due to principle differences in data acquisition these techniques can yield disagreeing results. In this paper, we aim to highlight possible error sources with given examples for aluminum and copper thin films.

The problem of partial occlusion in face recognition has received less attention over the last few years. Partial occlusion is an important challenge of the face recognition, in which certain parts of a face are hidden by the objects such as sunglasses, hats, scarves, and a mask that can cause significant degradation in the performance of the recognition system. This paper specifically addresses face recognition with partial occlusion. The proposed algorithm is an approximate version of conventional dynamic time warping (DTW), which is an exact algorithm and based on dynamic programming. An exact algorithm provides an exact result and involves huge computation efforts when there is a gallery with more images. Hence, a faster approximation algorithm based on greedy approach is proposed to solve the partially occluded face recognition problem by finding a near optimal solution with a guarantee on its performance. Many image processing applications are real-time and need a near-optimal solution. The proposed work has two contributions, the first one is in designing a string generation algorithm for converting a face into a sequence of strings and the second is designing an approximation algorithm based on greedy approach for matching strings. The proposed work uses standard face databases such as FEI, IAB, ORL, and Extended Yale-B for evaluating the effectiveness of the system.

We propose an approach for efficient word retrieval from printed documents belonging to Digital Libraries. The approach combines word image clustering (based on Self Organizing Maps, SOM) with Principal Component Analysis. The combination of these methods allows us to efficiently retrieve the matching words from large documents collections without the need for a direct comparison of the query word with each indexed word.

This work presents the use of remote sensing data for land cover mapping with a case of Central Apennines, Italy. The data include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018-2024). The operational workflow included satellite image processing which were classified into raster maps with automatically detected 10 classes of land cover types over the tested study. The approach was implemented by using a set of modules in Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). To classify remote sensing (RS) data, two types of approaches were carried out. The first is unsupervised classification based on the MaxLike approach and clustering which extracted Digital Numbers (DN) of landscape feature based on the spectral reflectance of signals, and the second is supervised classification performed using several methods of Machine Learning (ML), technically realised in GRASS GIS scripting software. The latter included four ML algorithms embedded from the Python's Scikit-Learn library. These classifiers have been implemented to detect subtle changes in land cover types as derived from the satellite images showing different vegetation conditions in spring and autumn periods in central Apennines, northern Italy.

Factor is one of the important parameters in fracture mechanics. One of the common geometries in evaluating of fracture mechanics problems is contact of tilted wedge with half plane that in this article Unsymmetrical tilted wedge geometry has been studied and digital Shearography as a non-destructive technique is applied to determine mixed mode stress intensity factors in a half plane with vertical edge crack which is subjected to an unsymmetrical tilted wedge. Optical setup with two in-plane illumination source is used to determine purposed strain variable and consequently obtain the mixed mode stress intensity factors as a result of applying linear elastic fracture mechanics (LEFM). The results of Shearography technique contains some fringes which contain the information needed to obtain the strain variables. To utilize the advantage of whole field Shearography and to minimize random experimental errors, the least square method is used to obtain SIF. In order to verify the obtained results accuracy, the experimental results were compared with the results of finite element method. The difference between two results is nearly 19%. The comparison of the experimental results and the numerical results proves the credibility and potential of this nondestructive method in measurement applications. Then the effect of crack size and gap of tilted wedge from crack tip on Stress Intensity Factors have been investigated. Finally it shown that in this problem Stress Intensity Factor in first mode of fracture is zero, so only second mode of fracture should be considered.

In an earlier work, a recursive filter to compensate for the offset nonuniformity (NU) noise corrupting the output of infrared (IR) imaging system was presented. Such a filter was derived assuming an estimation time-window short enough so that the offset NU can be regarded as a constant corrupted by additive noise. In this paper, the assumption on the stationarity of the offset NU noise has been relaxed by characterizing the dynamics of the offset NU using a Gauss-Markov random process. Based upon this model, a recursive filter that uses blocks of IR data to estimate the offset NU has been derived. A rigorous theoretical analysis of the filter has been conducted in order to provide expressions for appropriately selecting the parameters of the filter. The ability of the block-recursive filter to compensate for NU noise has been tested using raw IR videos from different IR cameras. In addition, the filter has been implemented and tested on-line in a prototype spectrally tunable IR camera.

High dynamic range (HDR) displays are be-coming more common, which has given rise to a number of reverse tone mapping tech-niques. The goal of these techniques is to ex-pand the dynamic range of all the existing low dynamic range content to fit that of these dis-plays. ...

The use of intelligent controllers applied to indus-trial systems has been getting usual, not only in the process control as in the literature. The most utilized control methodology, within the artificial intelligent techniques, is based in fuzzy logic. Fuzzy logic controllers consist of the ...

In (2) the phrases from (l) are represented as sequences of feet. The digit I stands for the primary stress and 2 for secondary (or tertiary) stresses (as in Kraska-Szlenk 1995 or Rubaeh and Booij 1985). Polish words have penultimate stress, i.e. a prosodie word (henceforth PW d) has a prominent trochaic foot at the right edge. 2 Following McCarthy and Prince (1993) and Selkirk (1995), I assurne that feet are binary and that some unstressed syllables remain unparsed, i.e. -10in (2a) and -szczesin (2b).

During past earthquakes, many instances of building damage as a result of earthquake surface fault rupture have been observed. The results of investigating a potential mitigation scheme are presented in this paper. Such plan provides a wall in the soil with the aim of surface displacement localization in the narrow pre-determined location. This may reduce the risk of the future rupture downstream the wall. To evaluate the efficiency of the method, this paper (i) provides validation through successful class "A" predictions of 1g model tests for fault deviation by weak wall; and (ii) conducts sensitivity analyses on fault position, fault offset and wall shear strength. It is shown that wall can be designed to deviate rupture path; even downstream of the wall can be protected.

With the exponential growth of user-generated content, moderating sensitive material in videos has become both an ethical and technical challenge. Manual screening is not only time-consuming and inconsistent but also exposes reviewers to psychologically distressing content. To address these limitations, this paper presents a dual-model framework for automated sensitive content detection and localization in videos. Our approaches (custom Convolutional Neural Network (CNN) and YOLOv11-based model) are useful for different types of sensitive content. Furthermore, a modified binary search algorithm is introduced to efficiently identify continuous segments of sensitive content, enhancing post-processing accuracy. Experimental results demonstrate the effectiveness of the proposed system, with competitive detection performance and low inference times. This hybrid pipeline offers a scalable and ethical solution for real-time content moderation in digital platforms.

The design and simulation of winner-take –all Current Mode (WTA) circuit is proposed. Inputs and outputs of the Circuits are current and voltage respectively, which makes the circuit appropriated forlow voltage neural hardware computation. The circuit was designed and simulated using Cadence gpdk090. The proposed novel current mode CMOS WTA operates at 0.7v with resolution of 0.001nA and a dynamic range from input current 10nA---1μA, which are much better than existing realizations. It is also observed that dynamic range can be raised further with increasing of power supply. Simulation results along withappropriate mathematical relations are reported.

Deblurring of X-rays digital images has always been a problem of crucial interest. A specific solution to the problem of image restoration is generally determined by the nature of degradation phenomena. So, it is highly dependent on the nature of the noise present there. In this work Lucy Richardson algorithm (LRA) is implemented on the X-ray image and deblurring is processes is observed. Deblurring is an important step of image processing especially when the diagnosis needs to be classified based on the result of X-ray image. LRA is an iterative procedure in which the pixels of the observed image are represented using the PSF. Image restoration is an emerging field of image processing in which the focus is on recovering an original image from a degraded image. The blurred image can be a result of a known degradation or unknown degradation. Hence image deblurring can be defined as a process of recovering a sharp image from a degraded image which is blurred by a degradation function,...

Au vu de l'émergence rapide des nouvelles technologies mobiles et la croissance des offres et besoins d'une société en mouvement, les travaux se multiplient pour identifier de nouvelles plateformes d'apprentissage pertinentes afin d'améliorer et faciliter l'apprentissage à distance. La prochaine étape de l'apprentissage à distance est naturellement le port de l'e-learning (apprentissage électronique) vers les nouveaux systèmes mobiles. On parle de m-learning (apprentissage mobile). Nos travaux portent sur le développement d'une nouvelle architecture pour le m-learning dont l'objectif est d'adapter et recommander des parcours de formations selon les contraintes contextuelles de l'apprenant.

We introduce a new classification method that is applicable to classify image pixels. This work was motivated by the test-based classification (TBC) introduced by Liao and Akritas (2007). We found that direct application of TBC on image pixel classification can lead to high mis-classcification rate. We propose a method that combines the minimum distance and evidence from hypothesis testing to classify image pixels. The method is implemented in R programming language. Our method eliminates the drawback of Liao and Akritas (2007). Extensive experiments show that our modified method works better in the classification of

Computerized analysis of Vincent van Gogh's painting brushstrokes ] s image data acquisition technology has advanced in the past decade, museums have routinely begun to assemble vast digital libraries of images of their collections. The cross-disciplinary interaction of image analysis researchers and art historians has reached a stage where technology developers can focus on image analysis tasks supportive of the art historian's mission of painting analysis in addition to activities in image acquisition, storage, and database search [2], . In particular, the problem of artist identification seems ripe for the use of image processing tools. In making an attribution, experts often use not only current knowledge of the artist's common practices, in combination with meticulous comparisons of a variety of technical data (acquired, e.g., through ultraviolet fluorescence, infrared reflectography, x-radiography, paint sampling, and/or canvas weave count), but they also include a visual assessment of the presence of the artist's "handwriting" in the brushwork. This suggests that mathematical analysis of a painting's digital representation could assist the art expert in the process of attribution. A survey of the literature [3], [17], [19], [22], [26], reveals that image processing tools aimed at supplementing the art historian's toolbox are currently in the earliest stages of development. In part, this is because the necessary data for research has not been made widely available. To jumpstart the development of such methods, the Van Gogh and Kröller-Müller Museums in The Netherlands graciously agreed to make a data set of 101 high-resolution gray-scale scans

DEPARTAMENTO DE ASTROFISICA Universidad de La Laguna Estudio de flujos horizontales en regiones solares activas basado en t´ecnicas de alta resoluci´on para reconstrucci´on de im´agenes Study of horizontal flows in solar active regions based on high-resolution ...

We are introducing new effective computer model for extracting nationality from frontal image candidate using face part color, size and distances based on deep research. Determining face part size, color, and distances is depending on a variety of factors including image quality, lighting condition, rotation angle, occlusion and facial emotion. Therefore, first we need to detect a face on the image then convert an image into the real input. After that, we can determine image candidate's gender, face shape, key points and face parts. Finally, we will return the result, based on the comparison of sizes and distances with the sample's measurement table database. While we were measuring samples, there were big differences between images by their gender and face shapes. Input images must be the frontal face image that has smooth lighting and does not have any rotation angle. The model can be used in military, police, defense, healthcare, and technology sectors. Finally, Computer can distinguish nationality from the face image.

Для забезпечення ефективної роботи ІВС діагностики, були проведені наукові дослідження, спрямовані на обгрунтування математичних моделей, які б узгоджувалися із властивостями об‟єктів дослідження та розв‟язуваними задачами. Розглянуто задачу розробки методів і відповідних алгоритмів оцінювання точносних характеристик ІВС на базі моделей субгауссових процесів

This study explores the distinction between Fully Connected (FC) and Dense layers in neural network-based image processing. Traditional FC layers from early deep learning models connect every neuron to all previous layer neurons, leading to high computational demands and increased risk of overfitting. Dense layers, a more refined form of FC layers, help lower parameter counts, enhancing efficiency in handling large-scale image data. The research examines how each layer type influences image classification performance metrics. Findings show Dense layers offer faster training and improved efficiency compared to general FC layers. However, FC layers often yield better outcomes in complex tasks requiring strong model expressiveness. The study highlights the trade-off between speed and accuracy in image-based deep learning, offering valuable insights for experienced developers.

This research contrasts convolutional neural networks (CNNs) and generative adversarial networks (GANs) in the context of image processing. CNNs have transformed image classification and recognition due to their effectiveness in such tasks. Meanwhile, GANs excel at producing realistic synthetic images, aiding in image enhancement and artistic applications. The study highlights the distinct strengths and challenges of both models in sectors like healthcare, security, and entertainment. Findings indicate that CNNs outperform in classification tasks, while GANs dominate image generation efforts, especially in areas like image inpainting and restoration. Together, CNNs and GANs complement each other by offering distinct solutions for diverse image processing demands.

The isotonic regression problem has applications in statistics, operations research, and image processing. In this paper a general framework for the isotonic regression algorithm is proposed. Under this framework, we discuss the isotonic regression problem in the case where the directed graph specifying the order restriction is a directed tree with n vertices. A new algorithm is presented for this case, which can be regarded as a generalization of the PAV algorithm of Ayer et al. Using a simple tree structure such as the binomial heap, the algorithm can be implemented in O(n log n) time, improving the previously best known O(n 2 ) time algorithm. We also present linear time algorithms for special cases where the directed graph is a path or a star.

We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with an intrinsically low signal-to-noise ratio. Image pre-processing, denoising and bubble segmentation are described in detail, with practical recommendations. Experimental validation is presented—stationary and moving reference bodies with neutron-transparent cavities are radiographed with imaging conditions representative of the cases with bubbles in liquid metal. The new methods are applied to our experimental data from previous and recent imaging campaigns, and the performance of the methods proposed in this paper is compared against our previously achieved results. Significant improvements are observed as well as the capacity to reliably extract physically meaningful information from measurements performed under highly adverse imaging conditions. The showcased image processing solution and separate elements thereof are readily ext...

Although this huge development in medical imaging tools, we find that there are some human mistakes in the process of filming medical images, where some errors result in distortions in the image and change some medical image properties which affect thedisease diagnosis correctly.Medical images are one of the fundamental images, because they are used in the most sensitive field which is a medical field. The objective of the study is to identify the effect of implement non-linear filters in enhancingmedical images, using the strongest and most popular program MATLAB, and because of its advantages in image processing. After implementation the researcher concluded that we will get the best result for medical image enhancement by using median filter which is one of the non-linear filters,non-linear filters implemented using Matlab functions.

In this paper, we propose a method for pose-invariant facial expression recognition from monocular video sequences. Existing methods require stereo cameras or the preparation of a large amount of learning data to generate an accurate face model. With the aim of solving these problems, we propose a novel face model, called the variable-intensity template, which can be generated with very little time and effort. The variable-intensity template consists of a face shape model, a set of discrete interest points defined in the vicinity of facial parts, and an intensity model which describes how the intensity of interest points vary with different facial expressions. By using this model in the framework of a particle filter, our method is capable of estimating facial poses and expressions simultaneously. Experiments demonstrate the effectiveness of our method. A recognition rate of about 90% was achieved for horizontal facial orientations over the range of ±40 degrees from the frontal view.