Image Processing Features Research Articles

Analysis and Study of the Steel Plate Surface Defects Image Processing and the Fractal Dimension Characteristics

Steel surface defect detection is the key point of this research. The paper mainly focuses on the image processing and image feature extraction of the steel plate surface. The paper also focuses on the calculating procedure and results of the fractal dimension in different defects images. It can be concluded from the results of the study, fractal dimension of the defect images becomes an important feature of the steel plate surface image pattern recognition.

Encryption and Hiding Watermarking Using A Chaotic Modified Wavelet Transform

In this letter, a new digital watermarking algorithm is proposed to hide binary image watermark inside gray image to increase authentication and robustness, DWT are used in embedding because it has many features in image processing. To increase the effective of this algorithm chaotic logistic map are used to select the embedding position in host chaotically which increase the security and make it hard to be detected, experimental result which be measured by using (Mean Square error, Peak signal to noise ratio , Signal to Noise Ratio, CORLLATION) has value (0.3195, 53.0867, 45.9152, 0.9998) respectively reflect the effective of this algorithm, the watermarked image has no noticeable change, some image processing operation such as(filter ,noise and compression) are used to test the robustness which measured by calculate similarity between extracted watermark after attack and the original one, Programming based on Matlab9.

3D‐Sutra—Interactive Analysis Tool for a Web Atlas of Scanned Sutra Inscriptions in China

AbstractBuddhist stone inscriptions of the 8th to 12th centuries ad are important cultural assets of China that need to be documented, analysed and interpreted archaeologically, art‐historically and linguistically. On the one hand, such Buddhist stone inscriptions have to be conserved for future generations, but on the other hand, further possibilities for analysing the data could be achieved if the inscriptions were made accessible to a wider community, for instance, for understanding of the historical growth of Buddhism in China. In this paper, innovative techniques for the documentation of stone inscriptions located in the province of Sichuan, in the south‐west of China, are demonstrated. The tasks to be performed include capturing the stone inscriptions by using high precision measuring techniques to generate copies of the original inscriptions, together with processing of the data enabling improvement in the legibility of characters and thus aiding the interpretation of the inscriptions. The concept and features of image processing as applied to the captured inscriptions are outlined. In order to present the outcome to as many people as possible and to allow individual interpretations of the results of the stone inscription reconstruction, their current interpretation and additional 2D/3D maps are published within an interactive web platform.

Technical note: DIRART--A software suite for deformable image registration and adaptive radiotherapy research.

Recent years have witnessed tremendous progress in image guide radiotherapy technology and a growing interest in the possibilities for adapting treatment planning and delivery over the course of treatment. One obstacle faced by the research community has been the lack of a comprehensive open-source software toolkit dedicated for adaptive radiotherapy (ART). To address this need, the authors have developed a software suite called the Deformable Image Registration and Adaptive Radiotherapy Toolkit (DIRART). DIRART is an open-source toolkit developed in MATLAB. It is designed in an object-oriented style with focus on user-friendliness, features, and flexibility. It contains four classes of DIR algorithms, including the newer inverse consistency algorithms to provide consistent displacement vector field in both directions. It also contains common ART functions, an integrated graphical user interface, a variety of visualization and image-processing features, dose metric analysis functions, and interface routines. These interface routines make DIRART a powerful complement to the Computational Environment for Radiotherapy Research (CERR) and popular image-processing toolkits such as ITK. DIRART provides a set of image processing/registration algorithms and postprocessing functions to facilitate the development and testing of DIR algorithms. It also offers a good amount of options for DIR results visualization, evaluation, and validation. By exchanging data with treatment planning systems via DICOM-RT files and CERR, and by bringing image registration algorithms closer to radiotherapy applications, DIRART is potentially a convenient and flexible platform that may facilitate ART and DIR research. 0 2011 Ameri-

A MODEL VISION OF SORTING SYSTEM APPLICATION USING ROBOTIC MANIPULATOR

Image processing in today’s world grabs massive attentions as it leads to possibilities of broaden application in many fields of high technology. The real challenge is how to improve existing sorting system in the Moduler Processing System (MPS) laboratirium which consists of four integrated stations of distribution, testing, processing and handling with a new image processing feature. Existing sorting method uses a set of inductive, capacitive and optical sensors do differentiate object color. This paper presents a mechatronics color sorting system solution with the application of image processing. Supported by OpenCV, image processing procedure senses the circular objects in an image captured in realtime by a webcam and then extracts color and position information out of it. This information is passed as a sequence of sorting commands to the manipulator (Mitsubishi Movemaster RV-M1) that does pick-and-place mechanism. Extensive testing proves that this color based object sorting system works 100% accurate under ideal condition in term of adequate illumination, circular objects’ shape and color. The circular objects tested for sorting are silver, red and black. For non-ideal condition, such as unspecified color the accuracy reduces to 80%.

Constructing efficient linear local features in image processing and analysis problems

We give formalize the problem of creating single and sets of linear local features for digital signals and images. A linear local feature is a pair consisting of a finite impulse characteristics (FIC) and an algorithm for computing the linear convolution of the signal with that FIC. Efficient linear local features should show optimal behavior: the algorithm should have minimal computational complexity, and the FIC should best accommodate the quality criterion for a specific applied problem. We offer formulations for the problems of creating single features and sets of efficient linear local features, where the process of computing features is connected with sequences of a special kind. We give examples of such sequences.

EDICAM fast video diagnostic installation on the COMPASS tokamak

A new camera system ‘event detection intelligent camera’ (EDICAM) is being developed by the Hungarian Association and has been installed on the COMPASS tokamak in the Institute of Plasma Physics AS CR in Prague, during February 2009. The standalone system contains a data acquisition PC and a prototype sensor module of EDICAM. Appropriate optical system have been designed and adjusted for the local requirements, and a mechanical holder keeps the camera out of the magnetic field. The fast camera contains a monochrome CMOS sensor with advanced control features and spectral sensitivity in the visible range. A special web based control interface has been implemented using Java spring framework to provide the control features in a graphical user environment. Java native interface (JNI) is used to reach the driver functions and to collect the data stored by direct memory access (DMA). Using a built in real-time streaming server one can see the live video from the camera through any web browser in the intranet. The live video is distributed in a Motion Jpeg format using real-time streaming protocol (RTSP) and a Java applet have been written to show the movie on the client side. The control system contains basic image processing features and the 3D wireframe of the tokamak can be projected to the selected frames. A MatLab interface is also presented with advanced post processing and analysis features to make the raw data available for high level computing programs. In this contribution all the concepts of EDICAM control center and the functions of the distinct software modules are described.

A Computer-aided Design Tool with Semiautomatic Image-Processing Features for Visualizing Biological Pathways

The explosion in biological data resulting from highthroughput experiments requires new software tools to manipulate and display pathways in a way that can integrate disparate sources of information. A visual Java-based CAD tool for drawing and annotating biological pathways with semiautomatic image-processing features is described in this paper. The result of the image-editing process is an XML file for the appropriate links. This tool integrates the pathway images and XML file sources. The system has facilities for linking graphical objects to external dat abases and is capable of reproducing existing visual representations of pathway maps.

Calculation of Silicone Breast Implant Volumes Using Breast Magnetic Resonance Imaging

Patients needing silicone gel-filled breast implant surgical revision often do not have information as to the size of their implants. Clinical estimates may be erroneous and an accurate, in vivo method of determining breast implant volume would be very useful in planning surgery. Our objective was to determine whether magnetic resonance imaging (MRI) scans of the breast in combination with a computer-assisted detection (CAD) system could provide accurate estimation of implant volumes in patients with silicone gel-filled implants, using known implant volumes for comparison. All MRI scans were performed for medical necessity before implant revision surgery or for follow-up. Scans and silicone implant volume calculations were performed in 20 patients (39 breasts). Fifteen patients (n = 27 breasts) had known implant volumes. The MRI images were analyzed blindly by a single radiologist using the image processing features of an MRI CAD system (DynaCAD; Invivo, Birmingham, MI) in a novel way. Computed implant volumes were compared to known implant volumes obtained from documented preoperative notes (n = 17) or following implant removal via implant markings (n = 10). Average deviation of the 27 known implant volumes from those calculated by MRI DynaCAD analysis was only 0.82% (SD, 3.95%). In four of 27 breasts (14.8%), MRI calculations matched the clinical volumes exactly. The widest volume deviation range for all implants was +11.4% to -4.44%. A variety of implant types were found, with 11 confirmed ruptures. The calculation of silicone gel-filled breast implant volume using breast MRI scans and a commercially-available CAD system appears to be sufficiently accurate that it may have significant clinical benefit in planning revision implant surgery. Calculations can be easily obtained in five minutes. To our knowledge, this is the first report describing this method to reliably measure silicone implant volumes preoperatively.

ANN analysis in a vision approach for potato inspection

An image processing methodology for the extraction of potato properties is explained. The objective is to determine their quality evaluating physical properties and using Artificial Neural Networks (ANN’s) to find misshapen potatoes. A comparative analysis for three connectionist models (Backpropagation, Perceptron and FuzzyARTMAP), evaluating speed and stability for classifying extracted properties is presented. The methodology for image processing and pattern feature extraction is presented together with some results. These results showed that FuzzyARTMAP outperformed the other models due to its stability and convergence speed with times as low as 1 ms per pattern which demonstrates its suitability for real-time inspection. Several algorithms to determine potato defects such as greening, scab, cracks are proposed which can be affectively used for grading different quality of potatoes.

A Survey of Inverse Rendering Problems

Abstract Inverse rendering problems usually represent extremely complex and costly processes, but their importance in many research areas is well known. In particular, they are of extreme importance in lighting engineering, where potentially costly mistakes usually make it unfeasible to test design decisions on a model. In this survey we present the main ideas behind these kinds of problems, characterize them, and summarize work developed in the area, revealing problems that remain unsolved and possible areas of further research. ACM CSS: I.3.6 Computer Graphics Methodology and Techniques I.3.7 Computer Graphics—Three‐Dimensional Graphics and Realism I.4.1 Image Processing and Computer Vision Digitization and Image Capture I.4.7 Image Processing and Computer Vision Feature Measurement I.4.8 Image Processing and Computer Vision Scene Analysis

Image processing using light-sensitive chemical waves

Basic principles of information processing by chemical light-sensitive reaction–diffusion media and dynamic modes of these media adequate to information processing operations are studied. Specialized experimental laboratory technique is elaborated optimum for image processing investigations. New modes of image evolution in the process of its transformation by reaction–diffusion medium are observed. Basic features of image processing by chemical reaction–diffusion media could be qualitatively explained using simple Field–Korosh–Noyes model.

NEURAL NETWORKS USED FOR CLASSIFICATION OF POTTED PLANTS

In the framework of the EC-funded AIR-project Objective plant quality measurement by image processing feature extraction and decision system software is developed. The decision system is a neural network (NN) which is used for sorting (classification) of plants. The NN sorts plants, based on some relevant plant features, extracted from digital plant images. To be able to sort plants correctly the NN needs to be trained first. Weights in the NN are adapted so that for a batch of plants, the inputs of the NN (plant features) generate the desired output, which is the class the plant is assigned to by a human expert. In general, there is no ranking order in the classes and therefore classes are nominal data. The NN has an output neurone for each of the classes the expert distinguished. For detection of the NN adapting to the inconsistencies in sorting by the expert, the available plants are randomly divided in a trainset and a testset. The NN is used for classifying Ficus benjamina in marketable stage. It is trained to imitate each of six human experts who sorted the 300 available plants. By using up to three features as input for the NN, an 89% performance was reached. The NN sorted 89% of the plants the same as the human expert. For the expert whose sorting was most difficult to imitate a 71% performance was reached. These results are acceptable considering the fact that the human experts introduce inconsistencies when sorting plants. The NN is used to create uniform growth groups in Ficus benjamina plants at a half-grown stages. The NN was trained on half-grown plants from a reference experiment. Results are varied, probably because the half-grown plants from the reference experiment do not resemble the actual half-grown Ficus benjamina plants.

Texture analysis and morphological processing of magnetic resonance imaging assist detection of focal cortical dysplasia in extra‐temporal partial epilepsy

In many patients, focal cortical dysplasia (FCD) is characterized by minor structural changes that may go unrecognized by standard radiological analysis. To increase the sensitivity of magnetic resonance imaging (MRI) for the detection of subtle lesions of FCD, we developed voxel-based image postprocessing methods, including first-order texture analysis and morphological processing modeled on known MRI features of FCD. We selected 16 patients with histologically proven FCD. Image processing features were calculated over a neighborhood for each voxel in the three-dimensional T1-weighted MRI. Three feature maps were generated: (1) gray matter thickness map to model cortical thickening, (2) gradient map to model blurring of the gray matter–white matter junction, and (3) relative intensity map to model the hyperintense signal within the lesion. Two observers detected lesions on conventional MRI in 8/16 and on ratio maps in 14/16 patients. Sensitivity was 87.5% (14/16) for the ratio maps compared to 50% (8/16) for MRI (p < 0.003). Specificity was 95% (19/20) for ratio maps and 100% (20/20) for MRIs. Cohen's kappa was 0.53 for MRIs, indicating moderate agreement, and 0.83 for ratio maps, indicating strong agreement beyond chance between the 2 observers. The image-processing methods developed in this study improve visual detection of FCD, even in cases where no lesion is obvious on MRI. These techniques could increase the number of patients with partial epilepsy who could benefit from surgery.

Texture analysis and morphological processing of magnetic resonance imaging assist detection of focal cortical dysplasia in extra-temporal partial epilepsy

In many patients, focal cortical dysplasia (FCD) is characterized by minor structural changes that may go unrecognized by standard radiological analysis. To increase the sensitivity of magnetic resonance imaging (MRI) for the detection of subtle lesions of FCD, we developed voxel-based image postprocessing methods, including first-order texture analysis and morphological processing modeled on known MRI features of FCD. We selected 16 patients with histologically proven FCD. Image processing features were calculated over a neighborhood for each voxel in the three-dimensional T1-weighted MRI. Three feature maps were generated: (1) gray matter thickness map to model cortical thickening, (2) gradient map to model blurring of the gray matter-white matter junction, and (3) relative intensity map to model the hyperintense signal within the lesion. Two observers detected lesions on conventional MRI in 8/16 and on ratio maps in 14/16 patients. Sensitivity was 87.5% (14/16) for the ratio maps compared to 50% (8/16) for MRI (p < 0.003). Specificity was 95% (19/20) for ratio maps and 100% (20/20) for MRIs. Cohen's kappa was 0.53 for MRIs, indicating moderate agreement, and 0.83 for ratio maps, indicating strong agreement beyond chance between the 2 observers. The image-processing methods developed in this study improve visual detection of FCD, even in cases where no lesion is obvious on MRI. These techniques could increase the number of patients with partial epilepsy who could benefit from surgery.

バレル研磨面の知的認識法

This paper describes an intelligent visual system for evaluating barrel-finished surfaces using a fuzzy-neural network. If the features of image processing are recognized by the vision system and fuzzy-neural network, the degree of surface roughness, brilliance, and/or types of barrel-finishing conditions will be clarified in relation to surface image data and the surface integrity of barrel-finished surfaces is cleared by correlation analyses between features of surface image processing dataThe objectives of the paper are to analyze the image of barrel-finished surfaces using both texture and color texture analyses, and to construct a fuzzy-neural network system able to evaluate surface roughness, brilliance and types of finishing.It was found that the features of image data of barrel-finished surfaces is decided by texture analyses. Results show it is possible to construct a fuzzy-neural network system composed of surface image features as input data and the degree of surface roughness, brilliance, and/or types of finishing as output data.

Continuous class pattern recognition for pathology, with applications to non-hodgkin's follicular lymphomas

Continuous class pattern recognition is a new analytic method that differs from existing techniques by recognizing and exploiting the continuous relationship among classes along diagnostic scales. Continuous class pattern recognition was applied to a wide variety of image processing features extracted from lymph node biopsy images that were digitized using a Coulter diff3/50 automated research microscope. The resultant classifiers correctly subtyped 89% of a set of 37 follicular lymphomas, compared to individual pathologist rates that ranged from 57% to 81%. This study demonstrates that continuous class pattern recognition can significantly reduce this diagnostic error rate.

Extraction of Non-Parametric Image Features for Plant Growth Control

The interpretation of image information of plants can be possibly done based on image features extracted from the original pictorial image, This research work focused its interest on techniques of non-parametric image extraction, Two different types of non-parametric image features were discussed. First, the textural analysis which can be recognized as one of conventional non-parametric image feature extractors was examined. Secondly, The finite element method was introduced as an information processing tool for image feature extraction. The finite element application in image processing features a unique image data compression which is apparently similar to the function of human retina.

Kommunikation und Integration medizinischer Dokumente für Telekonferenz-Anwendungen

The collaboration between physicians is supported in the BERMED project by implementing the remote access to distributed patient data and the realisation of computer-based medical conferencing. This requires the integration of the multimedia data in form of a meta-patient record for our medical application systems. These applications are supported by a distributed information management promoting access to different information systems, imaging modalities and digital archival storage systems. The features of image processing are concerned with quantification, segmentation and 3-D-visualisation to obtain additional information. This paper gives an overview of the actual state of the teleconferencing system in radiology.

Bayesian inference for model-based segmentation of computed radiographs of the hand

We present a method for medical image understanding by computer that uses model-based, hierarchical Bayesian inference to accurately segment imaged anatomy. A first application is a prototype system that automatically segments and measures symptoms of arthridities in hand radiographs. This is potentially useful in radiological diagnosis and tracking of arthridities. Key steps of the model-based, Bayesian inference approach are: (1) prediction of imagery features from 3D models of anatomy, parameterized by population statistics, (2) local image feature extraction in predicted sub-regions, and (3) the use of a probabilistic calculus to accrue results of image processing and image feature matching procedures in support or denial of hypotheses about the imaged anatomy. The prototype system for hand radiograph analysis accurately segments normal and somewhat degenerated hand anatomy. Results are shown of the ability of the automated system to ‘fail soft’, recognizing when segmentation is inadequate for accurate measurement. This self evaluation capability improves reliability of measurements for potential clinical use.