Line Detection Research Papers - Academia.edu (original) (raw)

On-line detection of bacterial adhesion in a shear flow gradient was measured with an engineered bioluminescent bacterium. Parallel plate flow cells showed colonization by a Pseudomonas fluorescens strain containing the Vibriofisheri (lux) bioluminescent operon. When induced by sodium salicylate, bulk phase and sessile cells were assayed quantitatively with an ammeter-photomultiplier-fiber optic system. The detection limit in a shear force gradient was 2x l05 attached celis.cm-2 on glass coupons. Light production was found to correlate with biofilm lipid synthesis on a per unit area basis (IA4C-acetate incorporation). Light correlated directly with sessile bacterial acridine orange direct count; in the range of 10 5-10 7 cells.cm-2, providing on-line detection of both biofilm and bulk phase biom jss and specific metabolic activity when induced.

- by
- •
- Microbiology, Medical Microbiology, Pseudomonas Fluorescens, Line Detection

File is a component of a computer system that has importance value of its own, either in terms of availability, integrity, confidentiality and functionality to a system and application. If unintended changes happen on the related file, it may affect the security of related computer system. File integrity monitor (FIM) tools is widely used to minimize the file security risk. This paper proposed dynamic schedule for FIM. This paper presents a dynamic scheduling for FIM by combining on-line and off-line monitoring based on related files security requirement. Files are divided based on their security level group and integrity monitoring schedule is defined based on related groups. The initial testing result shows that our system is effective in on-line detection of file modification.

The paper presents the design and evaluation of PECOS, a PreEmptive COntrol Signature technique for on-line detection of control flow errors. The technique uses assertions that can be embedded in the assembly language code and that are triggered by control flow instructions in the code. The PECOS target error model is any corruption that causes the application to take an incorrect control flow path. This includes corrupted control flow instructions as well as any other corruption that subsequently affects the control flow. The proposed technique is shown to handle both static and dynamic control flow constructs. PECOS is evaluated through software-based error injection-both directed control flow injections and random injections into the text segment of the running application. The injected errors model the impact of failures in the address and data lines between a processor and memory. The effectiveness of PECOS is illustrated on a real application: the Dynamic Host Configuration Protocol (DHCP) server. It is shown that PECOS detects more than 87% of control flow errors, reducing the incidence of fail-silence violations from 3.6% to 0.1% and of process crashes from 54.6% to 7.1%. Performance studies show a degradation of 15-29% with instrumentation of the entire DHCP server, and a degradation of 5-13% with instrumentation of only the critical DHCP protocol engine.

- by Zbigniew Kalbarczyk
- •
- Flow Control, Dynamic control, Text Segmentation, Line Detection

Acoustic emission (AE) is considered one of the main methods of on-line detection of catastrophic tool failure (CTF). Some strategies have claimed a subsequent increase of the root mean square value of the AE signal (AE RMS ) which in turn has been used as a measure of the CTF. However this measure was found to be not always sensitive to CTF. The aim of this paper is to present a method of catastrophic tool failure detection which uses symptoms other than the direct AE RMS signal. The method is based on the statistical analysis of the distributions of the AE RMS signal. The i distribution which was assumed in this study has been used with a density function of two parameters. The skews and kurtosis of the i distribution were the main measures employed. They were found to be highly sensitive to changes in tool condition and have given promising results with regard to chipping and tool breakage detection.

Line detection is a very basic, yet important problem in image processing. It is an approach based on whether sets of pixels lie on curves of a specified shape. Once detected, these curves form the edges of interest. Most of the shape information of an image is enclosed in edges. So first detect these edges in an image and by using these filters and then by enhancing those areas of image. Which contains edges, sharpness of the image will increase and image will become clearer. However the output from an edge detector is still an image described by its pixels. If lines, ellipses and so forth could be defined by their characteristic equations, the amount of data would be reduced even more. Line detection is an algorithm that takes a collection of an edge points and finds all the lines on which these edge points lie. The most popular line detectors are the Hough transform and convolution based techniques. While edges (i.e. boundaries between regions with relatively distinct gray levels) are by far the most common type of discontinuity in an image, instances of thin lines in an image occur frequently enough that it is useful to have a separate mechanism for detecting them. Here present a convolution based technique which produces an image description of the thin lines in an input image. Note that the Hough transform can be used to detect lines; however, in that case, the output is a parametric description of the lines in an image.

An approach of building complex image processing algorithms using independent blocks is presented. It is assumed that blocks are written in C++ programming language and the integration is performed due to means of Microsoft .NET Framework. A method of presentation of separate algorithm as a .NET dynamic loading library is described. The main advantage of this approach is ability of high-level algorithm construction and without the need of code recompilation. A new image object format suggested. This format is very convenient for data interchange between classes running in different dll's. It also eliminates the need of image copying from one container into another. For system demonstration purposes an implementation of straight line detection algorithm described.

- by Dmitry Yurin
- •
- Computer Vision, Image Processing, Image Filtering, Line Detection

The problem of detecting rectangular structures in images arises in many applications, from building extraction in aerial images to particle detection in cryo-electron microscopy. This paper proposes a new technique for rectangle detection using a windowed Hough Transform. Every pixel of the image is scanned, and a sliding window is used to compute the Hough Transform of small regions of the image. Peaks of the Hough image (which correspond to line segments) are then extracted, and a rectangle is detected when four extracted peaks satisfy certain geometric conditions. Experimental results indicate that the proposed technique produced promising results for both synthetic and natural images.

Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which brings great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack-an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep con-volutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger-scale feature maps and more holistic representations are made in smaller-scale feature maps. We build DeepCrack net on the encoder-decoder architecture of SegNet, and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale. We train DeepCrack net on one crack dataset and evaluate it on three others. The experimental results demonstrate that DeepCrack achieves F-Measure over 0.87 on the three challenging datasets in average and outperforms the current state-of-the-art methods.

- by Qin Zou
- •
- Machine Learning, Crack detection, Deep Learning, Edge Detection

This paper describes two approaches for Amharic word recognition in unconstrained handwritten text using HMMs. The first approach builds word models from concatenated features of constituent characters and in the second method HMMs of constituent characters are concatenated to form word model. In both cases, the features used for training and recognition are a set of primitive strokes and their spatial relationships. The recognition system does not require segmentation of characters but requires text line detection and extraction of structural features, which is done by making use of direction field tensor. The performance of the recognition system is tested by a dataset of unconstrained handwritten documents collected from various sources, and promising results are obtained.

- by Josef Bigun
- •
- Cognitive Science, Signal Processing, Pattern Recognition, Speech Recognition

This paper studies the detection of broken rotor bars in induction motors. The hypothesis on which detection is based is that the apparent rotor resistance of an induction motor will increase when a rotor bar breaks. Here, the apparent rotor resistance is that in the balanced steady-state single-phase electricol model of an induction motor. To detect broken rotor bars, measurements of stator voltage, stator current, stator excitation frequency, and rotor velocity are taken over a small range of velocity. These measurements are processed by a near-leastsquare-error estimator to produce estimated motor states and parameters. In particular, rotor resistance is estimated and compared with its nominal value to detect broken rotor bars. As part of this estimation process, it is necessary to compensate for the thermal variation in rotor resistance. The broken-rotor-bar detector is evaluated experimentally using one stator and three rotors from identical 3-hp induction motors. In one rotor, a bar is broken by milling into the rotor. The apparent rotor resistance, as estimated, is clearly greater for the rotor with the broken bar.

- by Aswathi Poozhikkuth
- •
- Engineering, Algorithms, Artificial Intelligence, System Identification

We review methodological approaches commonly employed for the determination of human-body-odor (BO) components. As a first step, we evaluate the common types of BO and the sampling and/or preconcentration strategies for them. We also... more

- by Sudhir Pandey
- •
- Analytical Chemistry, Strategy, Sampling, Human Body

The Hough transform (HT) is a popular tool for line detection due to its robustness to noise and missing data. However, the computational cost associated to its voting scheme has prevented software implementations to achieve real-time performance, except for very small images. Many dedicated hardware designs have been proposed, but such architectures restrict the image sizes they can handle. We present an improved voting scheme for the HT that allows a software implementation to achieve real-time performance even on relatively large images. Our approach operates on clusters of approximately collinear pixels. For each cluster, votes are cast using an oriented elliptical-Gaussian kernel that models the uncertainty associated with the best-fitting line with respect to the corresponding cluster. The proposed approach not only significantly improves the performance of the voting scheme, but also produces a much cleaner voting map and makes the transform more robust to the detection of spurious lines.

- by Leandro Fernandes
- •
- Image Processing, Pattern Recognition, Hardware Design, Software Implementation

The onset of Unmanned Ground Vehicles (UGVs) date back to World War II. These full autonomous robots or remote-controlled robots provide many services for military purposes. The deployment of UGVs in battlefield keeps the soldiers safe from harm, navigates the target points and works as a path tracker. As time goes by, researchers are encouraged to apply UGVs in other domains as in the industrial, road services and the urban domains. The autonomy of UGVs comes from the collective sensory resources and the manipulators that are used to perform specialized tasks. This paper presents the design of a fully autonomous vehicle called “E500”, which has been implemented to compete in the 22nd Intelligent Ground Vehicle Competition (IGVC), held at Oakland University, Rochester, Michigan in June 2014. The E500's body and chassis are custom made. Its power plant is based on two scooter electric motors that are driven through Pulse Width Modulation (PWM). It receives the information from a camera, few ultrasonic range finding sensors and global positioning system (GPS) receiver. The unmanned vehicle also incorporates vision and navigation systems. They are implemented to meet the design requirements of the IGVC competition. The E500's vision system acquires the images through a Microsoft camera then processes them on an onboard laptop. The vehicle was able to extract the features of the road, detecting the white lines and the position of obstacles then figure out the best path to avoid collisions. A navigation algorithm has been developed to achieve high accuracy up to 10 cm using a Samsung mobile phone running android. The algorithms were tested in a green area with two white lines and some obstacles distributed in a random way.

- by SDIWC Organization
- •
- Image Processing, Unmanned Ground Vehicles, Line Detection, Ugv

The present paper reports a comparative evaluation of four multivariate statistical process control (SPC) techniques for the on-line monitoring of an industrial sugar crystallization process. The process itself is challenging since it is carried out in multiple phases and there exists strong non-linear and dynamic effects between the variables. The methods investigated include classical on-line univariate statistical process control, batch dynamic principal component analysis (BDPCA), moving window principal component analysis (MWPCA), batch observation level analysis (BOL) and time-varying state space modelling (TVSS). The study is focused on issues of on-line detection of changes in crystallization process operation, the early warning of process malfunctions and potential production failures; problems that have not been directly addressed by existing statistical monitoring schemes. The results obtained demonstrate the superior performance of the TVSS approach to successfully detect abnormal events and periods of bad operation early enough to allow bad batches and related losses in amounts of recycled sucrose to be significantly reduced.

Accurate and efficient vectorization of line drawings is essential for any higher level processing in document analysis and recognition systems. In spite of the prevalence of vectorization and line detection methods, no standard for their performance evaluation protocol exists. We propose a protocol for evaluating both straight and circular line extraction to help compare, select, improve, and even design line detection algorithms to be incorporated into line drawing recognition and understanding systems. The protocol involves both positive and negative sets of indices, at pixel and vector levels. Time efficiency is also included in the protocol. The protocol may be extended to handle lines of any shape as well as other classes of graphic objects.

- by Dov Dori
- •
- Cognitive Science, Performance Evaluation, Document Analysis, Graphics Recognition

RFID systems are complex hybrid systems, consisting of analog and digital hardware and software components. RFID technologies are often used into critical domains or into harsh environments. But RFID system is only based on low cost equipments which then do not allow achieving robust communications. All these points make the on-line testing of RFID systems a very complex task. Thus, this article proposes the on-line characterization of a statistical system parameter, the Read-Error-Rate, to peiform the on-line detection of faulty RFID components.

- by Ioannis Parissis
- •
- Line Detection, Bit Error Rate, Hybrid System

A drowsiness detection system using both brain and visual activity is presented in this paper. The brain activity is monitored using a single electroencephalographic (EEG) channel. An EEG-based drowsiness detector using diagnostic techniques and fuzzy logic is proposed. Visual activity is monitored through blinking detection and characterization. Blinking features are extracted from an electrooculographic (EOG) channel. Features are merged using fuzzy logic to create an EOG-based drowsiness detector. The features used by the EOG-based detector are voluntary restricted to the features that can be automatically extracted from a video analysis of the same accuracy. Both detection systems are then merged using cascading decision rules according to a medical scale of drowsiness evaluation. Merging brain and visual information makes it possible to detect three levels of drowsiness: "awake," "drowsy," and "very drowsy." One major advantage of the system is that it does not have to be tuned for each driver. The system was tested on driving data from 20 different drivers and reached 80.6% correct classifications on three drowsiness levels. The results show that EEG and EOG detectors are redundant: EEG-based detections are used to confirm EOG-based detection and thus enable the false alarm rate to be reduced to 5% while the true positive rate is not decreased, compared with a single EOG-based detector.

- by Sylvie Charbonnier
- •
- Engineering, Visualization, Fuzzy Logic, Electroencephalography

Detection of lane markings based on a camera sensor can be a low cost solution to lane departure warning and lateral control. However, reliable detection is difficult due to cast shadows, vehicles occluding the marks, wear, vehicle motion, etc. The contribution of this paper is twofold. Firstly, we propose to explore another low-level image descriptor, namely, the ridgeness, instead of the gradient magnitude with the aim of getting a more reliable lane marking detection under adverse circumstances. Besides, the proposed measure comes with an associated orientation which is less noisy than the gradient one. Secondly, we have adapted RANSAC, a generic robust estimation method, to fit a parametric model to the image lane lines using both ridgeness and orientation as input data. In short, in this paper a better feature type and a robust fitting method are proposed, which contribute to improve the lane lines detection reliability, and still achieving real-time.

- by C. Canero and +2
- •
- Line Detection, Real Time

This paper describes a document recognition system for the modern neume based notation of Byzantine music. We propose algorithms for page segmentation, lyrics removal, syntactical symbol grouping and the determination of characteristic page dimensions. All algorithms are experimentally evaluated on a variety of printed books for which we also give an optimal feature set for a nearest neighbour classifier. The system is based on the Gamera framework for document image analysis. Given that we cover all aspects of the recognition process, the paper can also serve as an illustration how a recognition system for a non standard document type can be designed from scratch.

This paper presents the concept of optical music recognition (OMR), called "COMSCAN" which takes an electronically printed sheet of musical notes and processes it by using the four basic steps usually used by almost all OMR systems. The COMSCAN OMR system presented in this paper is fully based on our three proposed algorithms. First algorithm is for stave line detection which is a new form of horizontal projection. Second algorithm called "averaging" is for stave line removal which is done immediately after the detection. The third algorithm is used for musical notes' symbol identification/recognition step. It is an enhanced form of template matching technique. After a detailed description of the working of COMSCAN and the proposed algorithms, results are reported to analyze the efficiency and accuracy of proposed system. Finally, the comparison is done with the system that uses an algorithm of adding more stave lines instead of removing them.

- by muddassar sharif
- •
- Symbols, Optical Music Recognition, Line Detection, Template matching

We have developed a low cost, WebCam based optical barcode reader, which can extract and decode the sequence on a cluttered background. It is composed of three functions: barcode localization from the raw image, transformation of the localized barcode and decoding the sequence with an intelligent algorithm. The localization method is based on detecting the areas with the maximum density difference in two normal directions. The transformation method, capable of identifying any orientation, is based on the Hough line detection method. The decoding method is based on the peak/valley detection method of the barcode waveform and a consistency checking method. The consistency checking method, a constraint network, employs artificial intelligence searching methods. The algorithms used in the barcode reader have been tested on hundreds of images with an accuracy of more than 99%.

- by Chathura De Silva
- •
- Artificial Intelligent, Line Detection, Hough Transform, Consistency Checking

The paper presents a Computer Vision System based on texture segmentation and on a variation of the Standard Hough Transform, in which the choice of the parameters that determine the straight line that better represents the image is based on the contour conditions of the particular case of weld line detection on fuel storage tanks, aiming to allow their correct detection by the computer vision system even in the absence of the reinforcement structure, usually found in those cases. The proposed vision system provides the necessary information to keep a set of ultrasonic sensors, used to inspect the weld line, in the necessary position in order to improve the inspection reliability.

- by Lucas Molina
- •
- Computer Vision, Robot Vision, Line Detection, Texture Segmentation

The chiral resolving ability of the amylose-based Chiralpak IA chiral stationary phase towards omeprazole and other proton pump inhibitors under reversed-phase conditions was investigated. Organic modifier-buffer demonstrated to be a valid alternative elution mode with respect to conventional polar organic and normal-phases. No evidence of deterioration of performance of the enantioselective column after several multimodal cycles of elution was observed. Mobile phase composition was systematically changed in order to modulate the enantiomer elution order of set of compounds studied. A very simple method based on on-line detection of optical rotational sign during enantioselective HPLC was developed to assign the absolute configuration and enantiomeric elution order.

- by emiliana de santis
- •
- Engineering, Technology, Electrochemistry, Amylose

This paper describes quality of airborne laser scanning concerning analysis methods, digital terrain model quality, quality of building extraction and possibilities for laser scanning based change detection. Analysis methods include the use of portable brightness targets for intensity calibration, and co-registration of laser points with image data using an interactive orientation method. A quality of DTMs derived both from first

- by Harri Kaartinen
- •
- Airborne Laser Scanning, Change detection, Laser Scanning, Line Detection

The photoplate detection system of a spark source mass spectrometer has been recently replaced by a detector array consisting of 20 separate small channeltrons for simultaneous ion counting of up to 20 trace elements. The new multi-ion counting -spark source mass spectrometry (MIC-SSMS) technique combines the advantages of conventional SSMS with modern on-line detection of elements. It has important analytical features, such as simple and fast solid-state sample preparation, high precision (about 1-2%) and accuracy (4%) using multielement isotope dilution, high sensitivity which leads to short measuring times (10-50 min) and low detection limits (about 0.001-0.01 µg/g).

- by Klaus Jochum
- •
- Mass Spectrometry, Sample Preparation, Line Detection, Isotope Dilution

This paper proposes the Cross-Parity check as a method for an on-line detection of multiple bit-errors in registers or register files of microprocessors. Transient or 'soft' errors caused by radiation as single event upsets (SEUs) or electromagnetic coupling are in the focus of this work. Especially for register files or register groups, an easy implementable error correction method is proposed, which can be implemented by software routines or additional hardware. The method is based on the logical interpretation of Cross-Parity vectors.

Most existing approaches on sports video analysis have concentrated on semantic event detection. Sports professionals, however, are more interested in tactic analysis to help improve their performance. In this paper, we propose a novel approach to extract tactic information from the attack events in broadcast soccer video and present the events in a tactic mode to the coaches and sports professionals. We extract the attack events with far-view shots using the analysis and alignment of web-casting text and broadcast video. For a detected event, two tactic representations, aggregate trajectory and play region sequence, are constructed based on multi-object trajectories and field locations in the event shots. Based on the multi-object trajectories tracked in the shot, a weighted graph is constructed via the analysis of temporal-spatial interaction among the players and the ball. Using the Viterbi algorithm, the aggregate trajectory is computed based on the weighted graph. The play region sequence is obtained using the identification of the active field locations in the event based on line detection and competition network. The interactive relationship of aggregate trajectory with the information of play region and the hypothesis testing for trajectory temporal-spatial distribution are employed to discover the tactic patterns in a hierarchical coarse-to-fine framework. Extensive experiments on FIFA World Cup 2006 show that the proposed approach is highly effective.

- by guangyu zhu
- •
- Engineering, Information Retrieval, Graph Theory, Broadcasting

Laundering e-mail spam through open-proxies or compromised PCs is a widely-used trick to conceal real spam sources and reduce spamming cost in the underground e-mail spam industry. Spammers have plagued the Internet by exploiting a large number of spam proxies. The facility of breaking spam laundering and deterring spamming activities close to their sources, which would greatly benefit not only e-mail users but also victim ISPs, is in great demand but still missing. In this article, we reveal one salient characteristic of proxy-based spamming activities, namely packet symmetry, by analyzing protocol semantics and timing causality. Based on the packet symmetry exhibited in spam laundering, we propose a simple and effective technique, DBSpam, to online detect and break spam laundering activities inside a customer network. Monitoring the bidirectional traffic passing through a network gateway, DBSpam utilizes a simple statistical method, Sequential Probability Ratio Test, to detect the...

- by Heng Siong Yin
- •
- Information Systems, SPAM, Computer Software, Noise reduction

Abstract Document analysis is done to analyze entire forms (eg intelligent form analysis, table detection) or to describe the layout/structure of a document. In this paper document analysis is applied to snippets of torn documents to... more

- by Markus Diem
- •
- Document Analysis, Line Detection, Texture Synthesis, Degeneration

A generic integrated line detection algorithm (GILDA) is presented and demonstrated. GILDA is based on the generic graphics recognition approach, which abstracts the graphics recognition as a stepwise recovery of the multiple components of the graphic objects and is specified by the object-process methodology. We define 12 classes of lines which appear in engineering drawings and use them to construct a class inheritance hierarchy. The hierarchy highly abstracts the line features that are relevant to the line detection process. Based on the "Hypothesis and Test" paradigm, lines are detected by a stepwise extension to both ends of a selected first key component. In each extension cycle, one new component which best meets the current line's shape and style constraints is appended to the line. Different line classes are detected by controlling the line attribute values. As we show in the experiments, the algorithm demonstrates high performance on clear synthetic drawings as well as on noisy, complex, real-world drawings.

- by Dov Dori
- •
- Cognitive Science, Graphics Recognition, Line Detection, High performance

Many music works produced in the past still exist only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a digital format easy-to-manage. The manual process to carry out this task is very time consuming and error prone. Optical music recognition (OMR) is a form of structured document image analysis where music symbols are isolated and identified so that the music can be conveniently processed. While OMR systems perform well on printed scores, current methods for reading handwritten musical scores by computers remain far from ideal. One of the fundamental stages of this process is the staff line detection. In this paper a new method for the automatic detection of music stave lines based on a shortest path approach is presented. Lines with some curvature, discontinuities, and inclination are robustly detected. The proposed algorithm behaves favourably when compared experimentally with wellestablished algorithms.

- by Ana Rebelo
- •
- Image Analysis, Optical Music Recognition, Line Detection, System performance

In extracting partial discharge (PD) signals embedded in excessive noise, the need for an online and automated tool becomes a crucial necessity. One of the recent approaches that have gained some acceptance within the research arena is the Wavelet multiresolution analysis (WMRA). However selecting an accurate mother wavelet, defining dynamic threshold values and identifying the resolution levels to be considered in the PD extraction from the noise are still challenging tasks. This paper proposes a novel wavelet-based technique for extracting PD signals embedded in high noise levels. The proposed technique enhances the WMRA by decomposing the noisy data into different resolution levels while sliding it into Kaiser's window. Only the maximum expansion coefficients at each resolution level are used in de-noising and measuring the extracted PD signal. A small set of coefficients is used in the monitoring process without assigning threshold values or performing signal reconstruction. The proposed monitoring technique has been applied to a laboratory data as well as to a simulated PD pulses embedded in a collected laboratory noise.

- by Ayman El-hag and +1
- •
- Wavelet Analysis, Line Detection, Partial Discharge, Signal Reconstruction

We propose the “Line Space” as a novel
parameterization for lines in 2D images. The
approach has similarities to the well known Hough
Transform; however, we use a linear
parameterization instead of angular representations
leading to a better quality and less redundancy. The
Line Space is very well suited for GPU
implementation since all potential lines in an image
are captured through rasterization. In addition, we
improve the efficiency by introducing the term
“Cascaded Line Space”, where the image is
subdivided into smaller Line Spaces which are
finally merged to the global Line Space. We
implemented our approaches exploiting modern
GPU facilities (i.e. compute shader) and we will
describe the details in this paper. Finally, we will
discuss the enormous potential of the Line Space for
further extensions.

Accurate feature detection is key to higher level decisions regarding image content. Within the domain of spectrogram track detection and classification, the detection problem is compounded by low signal to noise ratios and high track appearance variation. Evaluation of standard feature detection methods present in the literature is essential to determine their strengths and weaknesses in this domain. With this knowledge, improved detection strategies can be developed. This paper presents a comparison of line detectors and a novel linear feature detector able to detect tracks of varying gradients. It is shown that the Equal Error Rates of existing methods are high, highlighting the need for research into novel detectors. Preliminary results obtained with a limited implementation of the novel method are presented which demonstrate an improvement over those evaluated. ⋆ This research has been supported by the Defence Science and Technology Laboratory (DSTL) 1 and QinetiQ Ltd. 2 , with special thanks to Duncan Williams 1 for guiding the objectives and Jim Nicholson 2 for guiding the objectives and also providing the synthetic data.

- by Thomas Lampert and +1
- •
- Feature Detection, Line Detection, Signal to Noise Ratio, Equal Error Rate

The subject of on-line detection and location of inter-turn short circuits in the stator windings of three-phase induction motors is discussed, and a noninvasive approach, based on the computer-aided monitoring of the stator current Parks Vector, is introduced. Experimental results, obtained by using a special fault producing test rig, demonstrate the effectiveness of the proposed technique, for detecting inter-turn stator winding faults in operating three-phase induction machines. On-site tests conducted in a power generation plant, using the diagnostic instrumentation system developed, are also reported.

- by Sergio Cruz
- •
- Power Generation, Fault diagnosis, Line Detection, System Development

Biochemical detection (BCD) methods are commonly used to screen plant extracts for specific biological activities in batch assays. Traditionally, bioactives in the most active extracts were identified through time-consuming bio-assay guided fractionation until single active compounds could be isolated. Not only are isolation procedures often tedious, but they could also lead to artifact formation. On-line coupling of BCD assays to high performance liquid chromatography (HPLC) is gaining ground as a high resolution screening technique to overcome problems associated with pre-isolation by measuring the effects of compounds post-column directly after separation. To date, several on-line HPLC-BCD assays, applied to whole plant extracts and mixtures, have been published. In this review the focus will fall on enzyme-based, receptor-based and antioxidant assays.

- by Christiaan Malherbe
- •
- Engineering, Genetics, Technology, Drug Discovery

This paper is concerned with how non-linear physical state space models can be applied to on-line detection of fouling in heat exchangers. The model parameters are estimated by using an extended Kalman filter and measurements of inlet and outlet temperatures and mass flow rates. In contrast to most conventional methods, fouling can be detected when the heat exchanger operates in transient states. Measurements from a clean counterflow heat exchanger are first used to optimize the Kalman filter. Then fouling is considered. The results show that the proposed method is very sensitive, hence well suited for fouling detection.

- by Gudmundur Jonsson and +2
- •
- Engineering, Heat Exchanger, Heat and Mass Transfer, Parameter estimation

This paper proposes an active contour algorithm for spectrogram track detection. It extends upon previously published work in a number of areas, previously published internal and potential energy models are refined and theoretical motivations for these changes are offered. These refinements offer a marked improvement in detection performance, including a notable reduction in the probability of false positive detections. The result is feature extraction at signal-to-noise ratios as low as− 1dB in the frequency domain.

- by Thomas Lampert
- •
- Remote Sensing, Pattern Recognition, Image Analysis, Time-Series Analysis
- by Nesimi Ertugrul
- •
- Engineering, Algorithms, Artificial Intelligence, System Identification

Lines, thin regions of approximately-uniform width
and colour, are important in the visual interpretation
of information. Lines are a primary feature of many
objects, and are used to convey semantic
information in the human world, e.g. road markings.
In order to interpret such information, an efficient
method is desired for the detection of lines under
this basic definition, independent of the colour and
shape of the lines to be detected. A method of
obtaining the centre-line and width of arbitrarilycoloured and -curving salient lines is described in
this paper. However, this method alone is shown to
be inadequate for its originally-intended purpose,
the detection of wires in the disassembly of end-oflife electronics, due to complications such as
twisted wires and the large number of other lines
present in images taken during assembly. Examples
are also provided of the application of the described
method to other contexts.

- by SDIWC Organization
- •
- Robotics, Computer Science, Information Technology, Computer Vision
- by Sandhya Samarasinghe
- •
- Principal Component Analysis, Mastitis, Artificial Neural Networks, Som

The proper operation of the industrial polymerization reactor is a challenging problem and a significant business opportunity for Process System Engineering application, which is in a broad sense, commonly called Polymerization Reactor Engineering. The technical challenges are specific to the particular case, but they are mainly due to some general characteristics such as their complex nonlinear, multivariable and interactive dynamic behavior, their potential open-loop instability and multiple steady-states. Also, they involve highly exothermic reactions, varying process conditions, unknown reaction kinetics and high viscosity, which often lead to a difficult operation of the reactor. Although there are a quite large number of studies on polymerization reactor engineering, they are mainly dedicated to such aspects as designing, modeling, simulation, optimization and control. Very few or almost none of these studies have been focused on the monitoring of critical process parameters. Changes in these parameters can be detrimental to the safety, reliability and efficiency of the process operation. This paper deals with the robust on-line detection and isolation of abnormal situations in an industrial continuous styrene polymerization reactor through a bank of unknown input observers that detect changes on the most relevant process parameters and external disturbances. A model predictive control scheme is implemented aiming at to stabilize the system. This may become an additional difficulty to the detection of abnormal situations as the controller usually hides the effects of changes on the parameters on the system output. In the design of the unknown input observers a linearized model of the process is utilized. The observers are tuned to detect the change of a particular parameter of the reactor model. The procedure takes into account possible uncertainties in these parameters such that a robust detection strategy of the abnormal situation is obtained. Simulation results show a very promising perspective to the proposed strategy.

This paper investigates the detection of parametric bridging and delay faults affecting the functional block of CMOS self-checking circuits (SCCs). As far as these faults are concerned, classical definitions are shown to become ambiguous because they are entirely based on logic considerations. Thus, new definitions are proposed here to consider the analog and dynamic effects of such faults, and to

- by Cecilia Metra
- •
- Fault Detection, Computer Hardware, Line Detection, Error Correction

The collection of geomechanical data in the mining industry is often hindered by limited access to rock exposures. Consequently, there is a need for a tool that permits fast and safe acquisition of information that best characterizes a geological structural regime. This paper presents a digital face mapping methodology used to construct discontinuity trace maps from photographs of rock faces. The method is applicable under a range of ground conditions, while at the same time trying to keep user intervention to a minimum.

- by F. Lemy
- •
- Civil Engineering, Cartography, Rock Mechanics, Process Optimization

Hough has proposed an interesting and computationally efficient procedure for detecting lines in pictures. This paper points out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further. It also shows how the method can be used for more general curve fitting, and gives alternative interpretations that explain the source of its efficiency.

- by Richard Duda
- •
- Pattern Recognition, Line Detection, Hough Transform, Curve fitting

In this paper, we propose a book dewarping model based on the assumption that the book surface is warped as a cylinder. This model extends the model proposed by Cao and makes Cao's model a special case of our model. This extension removes the constraint of Cao's model that the camera lens must be strictly parallel to the book surface, which is hard to make in practice, therefore enables a user to take a picture from different point of views conveniently. The main idea of the model is to build up the correspondence between a rectangle region on a flat surface and its curved region on the distorted book image and the dewarping task is to flatten the curved region to its original rectangle shape. The experimental results demonstrate the effectiveness of our proposed book dewarping approach.

- by Zhuoqun Yang
- •
- Line Detection, Point of View

CITATIONS 48 READS 42 5 authors, including:

- by Carlos Guedes
- •
- Information Systems, Algorithms, Artificial Intelligence, Music

We propose a new content-aware image resizing scheme, Stream Carving, which is based on the well-known seam carving method. Our algorithm may introduce larger seams in the retargeted image, i.e. seams with a width larger than one pixel, that we call “streams”. The resulting holes are then recovered using an inpainting method. Our retargeting algorithm is also more related to human perception by exploiting an adaptive importance map that merges several features like gradient magnitude, saliency, face, edge and straight line detection. Our approach induces an increase in the quality of the retargeted image when compared to the original seam carving method and provides similar or better results than other actual image retargeting techniques.

- by Daniel Domingues
- •
- Line Detection, Human Perception