Edge Detection

Since the beginning of the movement for quality management programs based on ISO 9000 or/and ISO/IEC 17025 that were adopted by many type of industries, calibration laboratories have been under pressure to increase productivity. This work is about a computational system to automate the entire instrument calibration process. The computer vision system proposed is to be used to read the display of both analogue as well as digital instruments that do not have communication interfaces to computers such as GPIB or RS-232. In order to figure out instrument indication, the system employs an optical character recognition technique in digital displays and Canny's method for edge detection and the Hough's transform method for line localization in analogue displays, accelerating the data acquisition process and makes it less prone to errors. Consequently, it contributes to improve calibration and reduce costs, increasing the number of instruments with quality assured measurements.

An assignment on development of Cellular mobile communication Generations, Namely, From 0 G to 5 G. Including a briefing on GSM, EDGE, AMPS, GPRS, And all others categories related to this field. This is totally a briefing that was just created in 15 min. ;)

This paper presents a study for the orientation effectiveness on the detected areas for many sampled objects when many type of the edges detection are applied. The Canny, Laplace, Prewitt and Sobel are applied for three objects (pencils' sharpeners with different colors). The MBR (Minimal Bounding Rectangular) are used to calculate the area in pixels, centroid and the orientation. The MR (Misclassification Ratio) is used to find the different between the edges detection techniques. The Canny edges detection technique gives the best result for the three used object using all orientations.

Conoscopic holography is an interferometric technique that permits the recording of three-dimensional objects. A two-step scheme is presented to recover an opaque object's shape from its conoscopic hologram, consisting of a reconstruction algorithm to give a first estimate of the shape and an iterative restoration procedure that uses the object's support information to make the reconstruction more robust. The existence, uniqueness, and stability of the solution, as well as the convergence of the restoration algorithm, are studied. A preliminary experimental result is presented.

It is common practice to utilize evidence from biological and psychological vision experiments to develop computational models for low-level feature extraction. The receptive profiles of simple cells in mammalian visual systems have been found to closely resemble Gabor filters. ...

Although current literature abounds in a variety of edge detection algorithms, they do not always lead to acceptable results in extracting various features in an image. In this paper, we address the problem of detecting blood vessels in retinal images. Blood vessels nsually have poor local contrast and the application of existing edge detection algorithms yield results which are not satisfactory. We introduce an operator for feature extraction based on the optical and spatial properties of objects to be recognized. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. We construct 12 different templates that are used to search for vessel segments along all possible directions. We discuss various issues related to the implementation of these matched filters. The results are compared to those obtained with other methods. The automatic detection of blood vessels in the retina could help physicians in diagnosing ocular diseases.

Combining both spatial and intensity information in image, we present an MRI brain image segmentation approach based on multiresolution edge detection, region selection, and intensity threshold methods. The detection of white matter structure in brain is emphasized in this paper. First, a multi-resolution brain image representation and segmentation procedure based on a multi-scale image filtering method is presented. Given the nature of the structural connectivity and intensity homogeneity of brain tissues, region-based methods such as region growing and subtraction to segment the brain tissue structure from the multi-resolution images are utilized. From the segmented structure, the region-of-interest (ROI) image in the structure region is derived, and then a modified segmentation of the ROI based on an automatic threshold method using our threshold selection criterion is presented. Examples on both T1 and T2 weighted MRI brain image segmentation is presented, showing finer brain tissue structures. ᭧

In this paper, a new algorithm for Automatic License Plate Localisation and Recognition (ALPR) is proposed on the basis of isotropic dilation that can be achieved using the binary image Euclidean distance transform. In a blob analysis problem, any two Region of Interest (RoIs) that is discontinuous are typically treated as separate blobs. However, the proposed algorithm combine with Connected Component Analysis (CCA) are coded to seek for RoI within a certain distance of other RoI to be treated as non-unique. This paper investigates the design and implementation of several pre-processing techniques and isotropic dilation algorithm to classify moving vehicles with different backgrounds and varying angles. A multi-layer feed-forward back-propagation Neural Network is used to train the segmented and refined characters. The results obtained can be used for implementation in the vehicle parking management system.

We present a method for figure-ground segregation of moving objects from monocular video sequences. The approach is based on tracking extracted contour fragments, in contrast to traditional approaches which rely on feature points, regions, and unorganized edge elements. Specifically, a notion of similarity between pairs of curve fragments appearing in two adjacent frames is developed and used to find the curve correspondence. This similarity metric is elastic in nature and in addition takes into account both a novel notion of transitions in curve fragments across video frames and an epipolar constraint. This yields a performance rate of 85% correct correspondence on a manually labeled set of frame pairs. Color/intensity of the regions on either side of the curve is also used to reduce the ambiguity and improve efficiency of curve correspondence. The retrieved curve correspondence is then used to group curves in each frame into clusters based on the pairwise similarity of how they transform from one frame to the next. Results on video sequences of moving vehicles show that using curve fragments for tracking produces a richer segregation of figure from ground than current region or feature-based methods.

A novel method of parametric active contours with geometric shape prior is presented in this paper. The main idea of the method consists in minimizing an energy function that includes additional information on a shape reference called a prototype. Shape prior introduced through a similarity measurement between evolving contour and Procrustes mean shape of desired object. This similarity measurement is full Procrustes distance between these two contours that is invariant with respect to similarity transformations (translation, scaling, and rotation). This extra shape knowledge enhances the model robustness to noise, occlusion and complex background. We also use gradient direction information in addition to gradient magnitude for more robustness to complex background. In this paper we introduce one important application of this new snake "3D object tracking". We obtain promising results for 3D object tracking which show the robustness of our method against noise, complex background, similarity transformations, occlusion, and changing viewpoint of 3D object.

This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In specific, an analysis is made not only of the properties of the divergence used, but also of the method's sensitivity to the spatial variation, as well as the detection-error risk associated with the operating conditions due to the randomness of the spatial configuration of the pixels. Although the paper deals with the procedure based on the Jensen-Shannon divergence, some problems are also related to other methods based on local detection with a sliding window, and part of the study is focused to noisy and textured images.

In this paper, we present an edge detection and characterization method based on wavelet transform. This method relies on a modelization of contours as smoothed singularities of three particular types (transition, peak and line). Using the wavelet transform modulus maxima lines of the edge models, position and descriptive parameters of each edge point can be inferred. Indeed, on the one hand, the proposed algorithm allows to detect and locate edges at a locally adapted scale. On the other hand, it is able to identify the type of each detected edge point and to measure both its amplitude and smoothness degree. The latter parameters represent, respectively, the contrast and the blur level of the edge point. Evaluation of the method is performed on both synthetic and real images. Synthetic data are used to investigate the influence of different factors and the sensitivity to noise, whereas real images allow to highlight the performance and interests of the method. In particular, we point out that the measured smoothness degree provides a cue to recover depth from defocused images or a cue to diffusion measurements in images of cloud structures. Moreover, from an indoor scene, we demonstrate the relevance of type identification for segmentation purposes. r

In this paper, we have proposed a multilevel soft thresholding technique for noise removal in Daubechies complex wavelet transform domain. Two useful properties of Daubechies complex wavelet transform, approximate shift invariance and strong edge representation, have been explored. Most of the uncorrelated noise gets removed by shrinking complex wavelet coefficients at the lowest level, while correlated noise gets removed by only a fraction at lower levels, so we used multilevel thresholding and shrinkage on complex wavelet coefficients. The proposed method firstly detects strong edges using imaginary components of complex coefficients and then applies multilevel thresholding and shrinkage on complex wavelet coefficients in the wavelet domain at non-edge points. The proposed threshold depends on the variance of wavelet coefficients, the mean and the median of absolute wavelet coefficients at a particular level. Dependence of these parameters makes this method adaptive in nature. Results obtained for one-dimensional signals and two-dimensional images demonstrate an improved denoising performance over other related methods available in the literature.

PSF (point spread function) based image reconstruction causes an overshoot at sharp intensity transitions (edges) of the object. This edge artifact, or ringing, has not been fully studied. In this work, we analyze the properties of edge artifacts in PSF-based reconstruction in an effort to develop mitigation methods. Our study is based on 1D and 2D simulation experiments. Two approaches are adopted to analyze the artifacts. In the system theory approach, we relate the presence of edge artifacts to the null space and conditioning of the imaging operator. We show that edges cannot be accurately recovered with a practical number of image updates when the imaging matrices are poorly conditioned. In the frequency-domain analysis approach, we calculate the object-specific modulation transfer function (OMTF) of the system, defined as spectrum of the reconstruction divided by spectrum of the object. We observe an amplified frequency band in the OMTF of PSF-based reconstruction and that the band is directly related to the presence of ringing. Further analysis shows the amplified band is linearly related to kernel frequency support (the reciprocal of the reconstruction kernel FWHM), and the relation holds for different objects. Based on these properties, we develop a band-suppression filter to mitigate edge artifacts. We apply the filter to simulation and patient data, and compare its performance with other mitigation methods. Analysis shows the band-suppression filter provides better tradeoff of resolution and ringing suppression than a low-pass filter.

Cardiac contractile dysfunction is frequently reported in human patients and experimental animals with type-1 diabetes mellitus. The aim of this study was to investigate the voltage-dependence of contraction in ventricular myocytes from the streptozotocin (STZ)-induced diabetic rat. STZ-induced diabetes was characterised by hyperglycaemia and hypoinsulinaemia. Other characteristics included reduced body and heart weight and raised blood osmolarity. Isolated ventricular myocytes were patched in whole cell, voltage-clamp mode after correcting for membrane capacitance and series resistance. From a holding membrane potential of −40 mV, test pulses were applied at potentials between −30 and +50 mV in 10 mV increments. L-type Ca 2+ current (I Ca,L ) density and contraction was measured simultaneously using a video-edge detection system. Membrane capacitance was not significantly altered between control and STZ-induced diabetic myocytes. The I Ca,L density was significantly (p < 0.05) reduced throughout voltage ranges (−10 mV to +10 mV) in myocytes from STZ-treated rats compared to age-matched controls. Moreover, the amplitude of contraction was significantly reduced (p < 0.05) in myocytes from STZ-treated rats at all test potentials between −20 mV and +30 mV. However, in electrically field-stimulated (1 Hz) myocytes, the amplitude of contraction was not altered by STZ-treatment. It is suggested that in field-stimulated myocytes taken from STZ-induced diabetic hearts, prolonged action potential duration may promote increased Ca 2+ influx via the sodium-calcium exchanger (NCX), which may compensate for a reduction in Ca 2+ trigger through L-type-Ca 2+ -channels and lead to normalised contraction. (Mol Cell Biochem xxx: 1-9, 2004) 7

A framework is presented for estimating the pose of a camera based on images extracted from a single omnidirectional image of an urban scene, given a 2D map with building outlines with no 3D geometric information nor appearance data. The framework attempts to identify vertical corner edges of buildings in the query image, which we term VCLH, as well as the neighboring plane normals, through vanishing point analysis. A bottom-up process further groups VCLH into elemental planes and subsequently into 3D structural fragments modulo a similarity transformation. A geometric hashing lookup allows us to rapidly establish multiple candidate correspondences between the structural fragments and the 2D map building contours. A voting-based camera pose estimation method is then employed to recover the correspondences admitting a camera pose solution with high consensus. In a dataset that is even challenging for humans, the system returned a top-30 ranking for correct matches out of 3600 camera pose hypotheses (0.83% selectivity) for 50.9% of queries.

Modeling the properties of the Internet topology aims at generating large scale artificial IP networks that mimic properties of real ones for simulation purposes. Current models typically consider the Internet as a simple graph where edges are point-to-point connections between routers. This approach does not take into account point-to-multipoint connections that exist at lower layers in the network, e.g. layer-2 clouds, such as Ethernet switches or MPLS networks. Instead, such physical point-to-multipoint connections are modeled as several logical IP level point-to-point connections.

Coronary calcified plaque (CP) is both an important marker of atherosclerosis and major determinant of the success of coronary stenting. Intracoronary optical coherence tomography (OCT) with high spatial resolution can provide detailed volumetric characterization of CP. We present a semiautomatic method for segmentation and quantification of CP in OCT images. Following segmentation of the lumen, guide wire, and arterial wall, the CP was localized by edge detection and traced using a combined intensity and gradient-based level-set model. From the segmentation regions, quantification of the depth, area, angle fill fraction, and thickness of the CP was demonstrated. Validation by comparing the automatic results to expert manual segmentation of 106 in vivo images from eight patients showed an accuracy of 78 ± 9%. For a variety of CP measurements, the bias was insignificant (except for depth measurement) and the agreement was adequate when the CP has a clear outer border and no guide-wire overlap. These results suggest that the proposed method can be used for automated CP analysis in OCT, thereby facilitating our understanding of coronary artery calcification in the process of atherosclerosis and helping guide complex interventional strategies in coronary arteries with superficial calcification.

This paper discusses a new approach for detecting edges with sub-pixel accuracy in color images using the principal axis analysis and the moment-preserving principle. First, the principal axis of the color vectors in a three-dimensional space is obtained analytically for each image block. Based on the projection scores, the direction of the line edge is obtained by analyzing the three-dimensional principal axis of a color image block. Next, by preserving some spatial color moments of the pixels of each square block, analytic formulas are derived to extract the edge feature. Experiments on both synthetic and real images show that the accuracy of the proposed method is comparable to other color edge detectors. The proposed algorithm can be performed very fast for real-time applications with no need for special hardware.

A ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region-and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to "see" around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors.

Edge detection is a fundamental procedure in image processing, machine vision, and computer vision. Its application area ranges from astronomy to medicine in which isolating the objects of interest in the image is of a significant importance. However, performing edge detection is a non-trivial task for which a large number of techniques have been proposed to solve it. This paper investigates the use of Ant Colony Optimization-a prominent set of optimization heuristics-to solve the edge detection problem. We propose two modified versions of the algorithm Ant Colony System (ACS) for an efficient and a noise-free edge detection.

In this work, we propose carrying out tests on the encoding sequence of a video sequence to improve data flow and average PSNR. We experiment with the choice of reference images, in the process of video compression, by using only the intra and predicted images extracted from sequences. For each intra and predicted image, we perform edge detection. Each image of the sequence is compared with the other images by subtracting corresponding edges. The choice of the reference image is based on the result of subtraction. We adopt the criterion of minimum pixels if the resulting images present only points and the criterion of minimum distance between the lines if they present parallel lines and possibly points. Testing this approach on News and Kiss cool video sequences revealed an improvement in data flow and average PSNR as compared to the original encoding and choosing reference images based on the mean square error.

An occupancy map provides an abstract top view of a scene and can be used for many applications such as domotics, surveillance, elderly-care and video teleconferencing. Such maps can be accurately estimated from multiple camera views. However, using a network of regular high resolution cameras makes the system expensive, and quickly raises privacy concerns (e.g. in elderly homes). Furthermore, their power consumption makes battery operation difficult. A solution could be the use of a network of low resolution visual sensors, but their limited resolution could degrade the accuracy of the maps. In this paper we used simulations to determine the minimum required resolution needed for deriving accurate occupancy maps which were then used to track people. Multi-view occupancy maps were computed from foreground silhouettes derived via an analysis of moving edges. Ground occupancies computed from each view were fused in a Dempster-Shafer framework. Tracking was done via a Bayes filter usin...

This paper addresses the challenge of bridging the seman- tic gap between the rich meaning users desire when they query to locate and browse media and the shallowness of media descriptions that can be computed in today&#39;s content management systems. To facilitate high-level semantics-based content annotation and interpretation, we tackle the prob- lem of automatic decomposition of motion pictures into

Abstract: 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 ...

This paper proposes a fine edge-preserving deinterlacing algorithm that is based on local gradient features. The proposed algorithm uses horizontal and vertical gradient operators to find the various edges, and then determines the dominant edge using the surrounding edges of the interpolating pixel, to increase the edge direction accuracy. The proposed method uses different reference pixels and weighting factors depending on the dominant edge direction. Experimental results demonstrate that the proposed method outperforms the conventional deinterlacing methods 1 .

This paper presents an on-line calibration method of the absolute extrinsic parameters of a stereovision system suited for vision based vehicle applications. The method uses as prior knowledge the intrinsic parameters and the relative extrinsic parameters (relative position and orientation) of the two cameras, which are calibrated using off-line procedures. These parameters are remaining unchanged if the two cameras are mounted on a rigid frame (stereo-rig). The absolute extrinsic parameters are defining the position and orientation of the stereo system relative to a world coordinate system. They must be calibrated every time after mounting the stereorig in the vehicle and are subject to changes due to static (variable load) and dynamic (acceleration, bumpy road) factors. The proposed method is able to perform on-line the estimation of the absolute extrinsic parameters by driving the car on a flat and straight road, parallel with the longitudinal lane markers. The edge points of the longitudinal lane markers are extracted after a 2D image classification process and reconstructed by stereovision in the stereo-rig coordinate system. After filtering out the noisy 3D points the normal vectors of the world coordinate system axes are estimated in the stereo-rig coordinate system by 3D data fitting. The output of the method is the height and the orientation of the stereo rig relative to the world coordinate system.

SOMMARIO: Il lavoro illustra lo sviluppo di un banco di prova per lo studio di tenute radiali a labbro che utilizza le tecniche della visione industriale per misurare le caratteristiche del distacco tra labbro ed albero che possono condurre a perdite di lubrificante e quindi al danneggiamento dei componenti meccanici coinvolti. Le prove sono state condotte sia su componenti in NBR, così da poter essere confrontate con studi precedenti che confermano i nuovi riscontri sperimentali, sia con tenute in FPM. I risultati mostrano come le grandezze studiate dipendono dalla temperatura del lubrificante.

Image segmentation has traditionally been thought of us a low/mid-level vision process incorporating no high level constraints. However, in complex and uncontrolled environments, such bottom-up strategies have drawbacks that lead to large misclassification rates. Remedies to this situation include taking into account (1) contextual and application constraints, (2) user input and feedback to incrementally improve the performance of the system. We attempt to incorporate these in the context of pipeline segmentation in industrial images. This problem is of practical importance for the 3D reconstruction of factory environments. However it poses several fundamental challenges mainly due to shading. Highlights and textural variations, etc. Our system performs pipe segmentation by fusing methods from physics-based vision, edge and texture analysis, probabilistic learning and the use of the graph-cut formalism

We consider the problem of simultaneous bitrate allocation for multiple video streams. Current methods for multiplexing video streams often rely on identifying the relative complexity of the video streams to improve the combined overall quality. In such methods, not all the videos benefit from the multiplexing process. Typically, the quality of high motion videos is improved at the expense of a reduction in the quality of low motion videos. In our approach, we use a competitive equilibrium allocation of bitrate to simultaneously improve the quality of all the video streams by finding trades between videos across time. A central controller collects rate-distortion information from each video user and makes a joint bitrate allocation decision. Each user encodes and transmits his video at the allocated bitrate through a shared channel. The proposed method uses information about not only the differing complexity of the video streams at every moment but also the differing complexity of each stream over time. Using the competitive equilibrium bitrate allocation approach for multiple video streams, simulation results show that all the video streams perform better or at least as well as with individual encoding. The results of this research will be useful both for ad hoc networks that employ a cluster head model and for cellular architectures.

Corner detection is widely used in image processing and machine vision. Hence, different corner detectors are proposed. But the performance of such corner detectors is sensitive to round effect and curve shape of the edges. In this paper we propose a novel corner detector that either over come corner detection problems and produces some information about detected corners that is very useful in segmentation and object recognition.

The job of this research is to filter the diseased part of the leaf from the leaf images. The authors proposed and implemented image processing technique using OpenCV for separating the diseased part of the leaf from the image of the leaf. Foreground Extraction, Edge Detection, Color filtering and Combination of Edge Detection with Color Filtering is done for wheat images.This novel technique can facilitate the process of the detection of diseases in wheat plants. The system allows to follow a particular pattern of capturing images of plants so that threats will be analysed quickly. Apart from helping humans to detect the diseases in plants, this research work can automate the process of detection without the need of human resources for feeding dataset to model, which is a much reliable process. This research will ultimately contribute in automation of agriculture processes faster and will make farmers to cultivate more in less amount of time.

This paper presents an adaptive method for polygonization of implicit surfaces. The method insists on the shape of triangles and the accuracy of resulting approximation as well. The main advantages of the triangulation presented are simplicity and the stable features that can be used for next expanding. The implementation is not complicated and only the standard data structures are used. The presented algorithm is based on the surface tracking scheme and it is compared with the other algorithms based on the similar principle, such as the Marching cubes and the Marching triangles algorithms.

This paper describes a new identity authentication technique by a synergetic use of lip-motion and speech. The lip-motion is defined as the distribution of apparent velocities in the movement of brightness patterns in an image and is estimated by computing the velocity components of the structure tensor by 1D processing, in 2D manifolds. Since the velocities are computed without extracting the speaker's lip-contours, more robust visual features can be obtained in comparison to motion features extracted from lip-contours. The motion estimations are performed in a rectangular lip-region, which affords increased computational efficiency. A person authentication implementation based on lip-movements and speech is presented along with experiments exhibiting a recognition rate of 98%. Besides its value in authentication, the technique can be used naturally to evaluate the ''liveness'' of someone speaking as it can be used in text-prompted dialogue. The XM2VTS database was used for performance quantification as it is currently the largest publicly available database (%300 persons) containing both lip-motion and speech. Comparisons with other techniques are presented.

An edge-detection method is proposed using the time -frequency domain method that expands the image intensity function in a series of Hermite functions. Test calculations and the application of the method to edge detection in real images demonstrate its noise tolerance.

In this paper, we describe a new unsupervised sentence boundary detection system and present a comparative study evaluating its performance against different systems found in the literature that have been used to perform the task of automatic text segmentation into sentences for English and Portuguese documents. The results achieved by this new approach were as good as those of the previous systems, especially considering that the method does not require any additional training resources.

In today's technological life, everyone is quite familiar with the importance of security measures in our lives. So in this regard, many attempts have been made by researchers and one of them is flying robots technology. One well-known usage of flying robot, perhaps, is its capability in security and care measurements which made this device extremely practical, not only for its unmanned movement, but also for the unique manoeuvre during flight over the arbitrary areas. In this research, the automatic landing of a flying robot is discussed. The system is based on the frequent interruptions that is sent from main microcontroller to camera module in order to take images; these images have been distinguished by image processing system based on edge detection, after analysing the image the system can tell whether or not to land on the ground. This method shows better performance in terms of precision as well as experimentally.

In the agricultural field of Japan, there is two issues are facing; population aging and labor shortage are progressing, rapidly. In order to solve these problems, it is indispensable to create a method that improves productivity and labor-saving technology in agricultural works. A weeding robot is one of an approach for achievement of these problems, it can be considered that labors will be released from tough or dangerous works. In this study, autonomously controlling the weeding robot will be focused on. To achieve this, an algorithm will be proposed that autonomously constructs a state-action space based on various sensor information and can apply it to actually work. The main contribution of this paper is in the development of vision-based navigation and integrated control system for straight-running or turning behavior to guarantee performance during of the working. The presented system benefits from a magnetic compass and a fixed camera. An estimation method of boundary of the workspace distance obtains view of in-front-of the robot using a monocular camera to enable detect edges of the workspace. Moreover, an obstacle detection method obtains in-front-of view of the robot using the camera. In this method is applied a deep-learning module to detect and cognition an obstacle. A rotation controller is developed to counter-rotation turn the robot such that at the target boundary point, to avoid the robot reach out of the workspace, or avoid an obstacle. In verification experiment, a state-action space, including the position of the obstacle was constructed and can avoid an obstacle, in each work. Further, we have been confirmed that the robot can counterrotation turn 90 degrees (spin-turn) at the boundary point of workspace.

This paper presents a system for a design and implementation of Optical Arabic Braille Recognition(OBR) with voice and text conversion. The implemented algorithm based on a comparison of Braille dot position extraction in each cell with the database generated for each Braille cell. Many digital image processing have been performed on the Braille scanned document like binary conversion, edge detection, holes filling and finally image filtering before dot extraction. The work in this paper also involved a unique decimal code generation for each Braille cell used as a base for word reconstruction with the corresponding voice and text conversion database. The implemented algorithm achieve expected result through letter and words recognition and transcription accuracy over 99% and average processing time around 32.6 sec per page. using matlab environment.

Edge detection is one of the most important tasks in machine vision and image processing. The paper presents novel approach to construction of algorithm for detection edges in images. In order to solve the problem the concepts of the fuzzy similarity relation and homogeneity region were exploited. The results of application of the proposed edge calculation method are presented in this paper. Additionally, this paper presents an interesting artistic effect obtained by application of the image processing method based on such the detection of edges. Possible artistic effects of application of this algorithm are exemplified.

Object detection is one of the most important preprocessing steps in object recognition and identification systems. This can be done by searching and indexing still image or video containing object in various size, position and background. This paper deals the beverage cans detection system on the moving conveyor belt using combination of sharpening and edge detection method. Experimental results show that the accuracy of the cans detection system is strongly enough which is based on the quality and quantity of the data used in the database. It also shows that the sharpening and edge detection algorithm improves the detecting speed significantly.

Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue fro m the point of view of industry productivity and competitiveness. Computer vision-based automatic quality inspection is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require co mplicated image p rocessing techniques. But in real-time applicat ion, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable nu mber of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image. A b inary image can be obtained by thresholding the original image into two levels. In this paper, we have developed an image processing system using edge-based image segmentation techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applicat ions to pass the accepted and rejected capsules.

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of

The problem of creating artifact-free upscaled images appearing sharp and natural to the human observer is probably more interesting and less trivial than it may appear. The solution to the problem, often referred to also as "single image super-resolution", is related both to the statistical relationship between low resolution and high resolution image sampling and to the human perception of image quality.