Hough Transform

The objective of this project is the development of an algorithm for the automatic recognition of traffic signs in digital images. The program An.Si. was created (from the Greek words Anagnorisi Simaton which means Sign Recognition). Up to now, many algorithms for the traffic sign detection and classification have been introduced. Extensive research is being made by major car manufacturing companies in collaboration with Universities and other institutes on real-time and automatic recognition of traffic signs, so that it can be a part of the so called "Driver Support Systems" ([7]). Two major problems exist in the whole detection process. Road signs are frequently occluded partially by other vehicles. Many objects are present in traffic scenes which make the sign detection hard (pedestrians, other vehicles, buildings and billboards may confuse the detection system by patterns similar to that of road signs). Colour information from traffic scene images is affected by varying illumination caused by weather conditions, time (daynight) and shadowing (buildings) ([7]). The proposed method detects the location of the sign in the image, based on its geometrical characteristics and recognises it using colour information. Partial occlusion is dealt by the use of the Hough Transform and suggestions are made for future improvements so that the robustness of the algorithm in light condition changes can be increased.

The high granularity of the CALICE Semi-Digital Hadronic CALorimeter (SDHCAL) provides the capability to reveal the track segments present in hadronic showers. These segments are then used as a tool to probe the behaviour of the active layers in situ, to better reconstruct the energy of these hadronic showers and also to distinguish them from electromagnetic ones. In addition, the comparison of these track segments in data and the simulation helps to discriminate among the different shower models used in the simulation. To extract the track segments in the showers recorded in the SDHCAL, a Hough Transform is used after being adapted to the presence of the dense core of the hadronic showers and the SDHCAL active medium structure.

We develop parallel algorithms to compute the Hough transform on a reconfigurable mesh with buses (RMESH) multiprocessor. The p angle Hough transform of an N×N image can be computed in O (plog(N/p)) time by an N×N RMESH, in O ((p/N)logN) time by an N×N 2 RMESH with N copies of the image pretiled, in O ((p/√ ¡ N )logN) time by an N 1.5 ×N 1.5 RMESH, and in O ((p /N)logN) time by an N 2 ×N 2 RMESH. Hough transform, reconfigurable mesh with buses, parallel algorithms, complexity.

In this paper we describe a new technique for detecting and characterizing ellipsoidal shapes automatically from any type of image. This technique is a single pass algorithm which can extract any group of ellipse parameters or characteristics which can be computed from those parameters without having to detect all five parameters for each ellipsoidal shape. Moreover, the method can explicitly incorporate any a priori knowledge the user may have concerning ellipse parameters. The method is based on techniques from Projective Geometry and on the Hough Transform. This technique can significantly reduce interpretation and computation time by automatically extracting only those features or geometric parameters of interest from images and making exact use of a priori information.

In this paper, we propose to investigate and analyze a new method for performing quantified projection and back-projection in emission tomography. This method is based on using non-summative kernels, capacities and asymmetric Choquet integral to obtain imprecise projected values (i.e. intervals instead of usual reconstructed pixel values). Validation studies using numerical and physical single photon computed emission tomography (SPECT) phantoms were used to demonstrate links between the length of these reconstructed intervals and the stochastic noise level in reconstructed slices.

In this paper, we present blind image restoration algorithms for motion blur caused by a motion between the imaging equipment and the object during the exposure time. We explain the motion degradation analysis and how to estimate parameters using Cepstral analysis, Radon transform and Hough transform.

Lane marking detection is fundamental for both advanced driving assistance systems and traffic surveillance systems. However, detecting lane is highly challenging when the visibility of a road lane marking is low, obscured or often invisible due to real-life challenging environment and adverse weather. Most of the lane detection methods suffer from four types of challenges: (i) light effects i.e. shadow, glare of light, reflection etc. created by different light sources like streetlamp, tunnel-light, sun, wet road etc.; (ii) Obscured visibility of eroded, blurred, dashed, colored and cracked lane caused by natural disasters and adverse weather (rain, snow etc.); (iii) lane marking occlusion by different objects from surroundings (wiper, vehicles etc.); and (iv) presence of confusing lane like lines inside the lane view e.g., guardrails, pavement marking, road divider etc. In this paper, we proposed a simple, real-time, and robust lane detection and tracking method to detect lane marking considering the abovementioned challenging conditions. In this method, we introduced three key technologies. First, we introduce a comprehensive intensity threshold range (CITR) to improve the performance of the canny operator in detecting different types of lane edges e.g., clear, low intensity, cracked, colored, eroded, or blurred lane edges. Second, we propose a two-step lane verification technique, the anglebased geometric constraint (AGC) and length-based geometric constraint (LGC) followed by Hough Transform, to verify the characteristics of lane marking and to prevent incorrect lane detection. Finally, we propose a novel lane tracking technique, to predict the lane position of next frame by defining a range of horizontal lane position (RHLP) along the x axis which will be updating with respect to the lane position of previous frame. It can keep track of the lane position when either left or right or both lane markings are partially and fully invisible. To evaluate the performance of the proposed method we used the DSDLDE [1] and SLD dataset with 10801920 and 480×720 resolutions at 24 and 25 frames/sec respectively where the video frames containing different challenging scenarios. Experimental results show that the average detection rate is 97.55%, and the average processing time is 22.33 msec/frame, which outperform the stateof-the-art method. Comprehensive intensity threshold range (CITR), ROI, Angle based geometric constraint (AGC), Length based geometric constraint (LGC), Canny edge detector; Lane Detection and Tracking, Novel Lane tracking technique: defining range of horizontal lane position (RHLP); Intelligent Vehicles

his paper describes the application of digital image processing and pattern recognition techniques to assist in diagnosing neurological disorders. In medical practices the posture and movement of a human subject through his/her gait cycle contains information that is used by an experienced clinician to determine the mental health of a patient. An image processing based system can be used to automate this process and produce quanti®ed results. This is achieved by processing, extracting and classifying joint angle information from still images of a human subject's gait. Several new algorithms are devised to process and extract the required information from the images: a new edge extraction technique is used to assist in image segmentation; a technique based on correlating images from dierent parts of a gait is used to obtain complete information about a subject's posture; and an algorithm based on the Hough Transform is used to obtain the limb joint angles and swing distances of a subject. Joint angles and swing distances obtained from normal and patient subjects are then used in training and verifying classi®cations using a feed-forward neural network and a fuzzy clustering algorithm.

Most of the Indian rural and sub urban roads are not ideal for driving due to faded lanes, irregular potholes, improper and invisible road signs. This has led to many accidents causing loss of lives and severe damage to vehicles. Many techniques have been proposed in the past to detect these problems using image processing methods. But there has been little work specifically carried out for detecting such issues of Indian roads. To address this acute problem, the study is undertaken with the objectives like, to make a survey of Indian roads, to suggest the method to detect lanes, potholes and road signs and their classification and to suggest automated driver guidance mechanism. In this regard, Hough Transformation method is adopted for Lane detection, where as Color Segmentation and Shape Modeling with Thin Spline Transformation (TPS) is used with nearest neighbor classifier for road sign detection and Classification. Further, K-means clustering based algorithm is adopted for pothole detection. Therefore, the attempt is made to invent an automated driver guidance mechanism to make the driving safe and easier in Indian roads. The experimental results obtained are tested with real time image database collected across different roads in sub-urban areas in India and found satisfactory.

We develop an efficient computational algorithm that produces efficient Markov chain Monte Carlo (MCMC) transition matrices. The first level of efficinecy is measured in terms of the number of operations needed to produce the resulting matrix. The second level of efficiency is evaluaded in terms of the asymptotic variance of the resulting MCMC estimates. The idea is then extended from finite to general state space.

Currently, a wider and more frequent use of Computed Tomography (CT) volumes are used for planning the radiotherapy treatment of oncological patients. These image volumes are instrumental in positioning the patient and the precise incidence of radiation fields. Any error in such a process will adversely affect the result of the treatment and, consequently, the health of the patient. Therefore, quality control is mandatory throughout this process. As regards image acquisition, the controls are typically executed manually, implying a tedious repetitive task. This problem spurred the need to develop an algorithm that allows eliminating this issue while it evaluates and corrects the main concern as well. Particularly, an algorithm for automatic control of the entire positioning process is proposed here which prevents errors from deviation on both the longitudinal and transversal axes of the arrangement. The alignment will vary according to the anatomical part to be treated and the type of stretcher support, among other aspects. The application was developed by resorting to free software tools (GLP) and the libraries for processing, segmenting and recording of medical images (ITK). The algorithm detects these deviations on both axes by applying threshold methods, morphologic operations and Hough's Transform. This algorithm is currently operative in the Medical Image Processing Server of the Nuclear Medicine School Foundation (FUESMEN) of Mendoza, Argentina.

Various sensing modalities are presently used or conceptualized for use in roadway management projects. These include, among others, bind-type (utilizing inductive loops, reflected light, etc.), acoustical, and radar sensors. However, due to lower cost; flexibility in installation, maintenance and use; high information content; lack of emissions; and wide sensing area, some researchers have argued that the use of video cameras and computer vision techniques offer the best choice. Nonetheless, due to various issues - including lighting condition sensitivity and computational complexity - even the latter approach can be less than optimal. One possible reason that a computer vision scheme can become overly complex and sensitive to lighting conditions is that too much local information needs to be captured/analyzed by the algorithm. Utilizing a patent-pending approach, this paper discusses a just-enough-smart, largely-global technique based on a simplified version of the fly's eye ...

Now-a-day, the importance of automatic traffic monitoring has drawn the attention to the intelligent transport systems. A number plate is the unique identification of a vehicle. Automatic Number Plate Recognition (ANPR) is designed to locate and recognize the number plate of a moving vehicle automatically. For Bangladeshi Vehicles there are no fixed rules for number plates like other countries. Because of the variety of conditions and patterns, the recognition process of number plates for Bangladeshi vehicles is so difficult. We develop a ANPR system which is divided into three part such as number plate Localization, Extraction and Recognition. After successfully complete localization and extraction of number plate we send that portion to a Bangla OCR to fetch the text of the number plate for further processing. In this paper we present a complete ANPR system for Bangladesh Vehicles using standard algorithms with some customization and with our own algorithm for Number Plate extraction which can detect Number Plate region and successfully extract detected region and then able to recognize that number plate text.

Acta Universitatis Sapientiae, Film and Media Studies, 6 (2013) 65−90.

As a possible cross section of cinematic narratology and the theory of intermediality the following essay assesses some of the effects of foregrounding the single, intermedial image within film. Specific figurations of intermediality that are experienced via the consciousness of single images, or some kind of “imageness” (i.e. as “intermedial references”) are presented as well as the modes in which they can interact with the narrative structure of a film. This interaction ranges from a momentary interference with the narrative structure of a film to disrupting it altogether; it may consist in constituting the gravity centre of a plot, or even in becoming the “canvas” for a palimpsest of narrative modes and narratives.

Television has deep impact to the audience, moreover the audience is in the powerless conditions. Indonesia is the real example, that the audience is in the powerless/impowering conditions, on the contrary the television is so power.full. This condition has to be changed, until reach ballance position when the television considers public interest, producing the good quality programs, public appreciates the programs because of the quality and their need, rating of the programs reach high level, and the advertisers interest to these programs. The qouermeni dan Indonesian Broadcast Commission (Komisi Penyiaran Indonesia) must work hard together to realise it.

A research on-detecting on-tree mango fruits is presented in this paper. The digital image is used as an input for this research. The method chose is texture analysis and Randomized Hough Transform (RHT). The output for this system is the centre of ellipse which is later defined as location of the mango fruit. This paper tackled the issue of separating overlapping mango fruit and mango fruit that is occluded by leaves. The centre of the ellipse is generated as the output and this information will be used as the grasping point for the fruit picking robot during the picking process. By using this methods and flow, the system successfully detect the fruit including the overlapping one.

This paper presents a method of detecting overlapping mango fruits from the complex background image. This research uses image that is obtained from a digital camera. This method is based on pre-processing the input image using the texture analysis to determine the boundary of each overlapping fruits. The image is processed to determine the actual boundary, converted to binary images, and utilise dilation and erosion to determine the object. We use ellipse fitting by applying Randomized Hough Transform to search the potential area of the mango fruit. Ellipse fitting are chosen because the shape of the mango fruit is similar to ellipse shape. Using these techniques, the fruit is successfully detected including the fruits that are overlapping with each other.

This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D 1 H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological samples. In this paper, we present a new state of the art for solving this problem using the generalized fuzzy Hough transform (GFHT). This paper describes significant improvements since the method was introduced for NMR datasets of plasma in Csenki et al. ) and is now capable of synchronizing peaks from more complex datasets such as urine as well as plasma data. We present a novel way of globally modeling peak shifts using principal component analysis, a new algorithm for calculating the transform and an effective peak detection algorithm. The algorithm is applied to two real metabonomic 1 H-NMR datasets and the properties of the method are compared to bucketing. We implicitly prove that

In metabolic profiling, multivariate data analysis techniques are used to interpret one-dimensional (1D) 1 H NMR data. Multivariate data analysis techniques require that peaks are characterised by the same variables in every spectrum. This location constraint is essential for correct comparison of the intensities of several NMR spectra. However, variations in physicochemical factors can cause the locations of the peaks to shift. The location prerequisite may thus not be met, and so, to solve this problem, alignment methods have been developed. However, current state-of-the-art algorithms for data alignment cannot resolve the inherent problems encountered when analysing NMR data of biological origin, because they are unable to align peaks when the spatial order of the peaks changes-a commonly occurring phenomenon. In this paper a new algorithm is proposed, based on the Hough transform operating on an image representation of the NMR dataset that is capable of correctly aligning peaks when existing methods fail. The proposed algorithm was compared with current state-of-the-art algorithms operating on a selected plasma dataset to demonstrate its potential. A urine dataset was also processed using the algorithm as a further demonstration. The method is capable of successfully aligning the plasma data but further development is needed to address more challenging applications, for example urine data.

This work addresses the subject of time-series analysis of comprehensive 1 H-NMR data of biological origin. One of the problems with toxicological and efficacy studies is the confounding of correlation between the administered drug, its metabolites and the systemic changes in molecular dynamics, i.e., the flux of drug-related molecules correlates with the molecules of system regulation. This correlation poses a problem for biomarker mining since this confounding must be untangled in order to separate true biomarker molecules from dose-related molecules. One way of achieving this goal is to perform pharmacokinetic analysis. The difference in pharmacokinetic time profiles of different molecules can aid in the elucidation of the origin of the dynamics, this can even be achieved regardless of whether the identity of the molecule is known or not. This mode of analysis is the basis for metabonomic studies of toxicology and efficacy. One major problem concerning the analysis of 1 H-NMR data generated from metabonomic studies is that of the peak positional variation and of peak overlap. These phenomena induce variance in the data, obscuring the true information content and are hence unwanted but hard to avoid. Here, we show that by using the generalized fuzzy Hough transform spectral alignment, variable selection, and parallel factor analysis, we can solve both the alignment and the confounding problem stated above. Using the outlined method, several different temporal concentration profiles can be resolved and the majority of the studied molecules and their respective fluxes can be attributed to these resolved kinetic profiles. The resolved time profiles hereby simplifies finding true biomarkers and bio-patterns for early detection of biological conditions as well as providing more detailed information about the studied biological system. The presented method represents a significant step forward in time-series analysis of biological 1 H-NMR data as it provides almost full automation of the whole data analysis process and is able to analyze over 800 unique features per sample. The method is demonstrated using a 1 H-NMR rat urine dataset from a toxicology study and is compared with a classical approach: COW alignment followed by bucketing.

This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D 1 H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological samples. In this paper, we present a new state of the art for solving this problem using the generalized fuzzy Hough transform (GFHT). This paper describes significant improvements since the method was introduced for NMR datasets of plasma in Csenki et al. ) and is now capable of synchronizing peaks from more complex datasets such as urine as well as plasma data. We present a novel way of globally modeling peak shifts using principal component analysis, a new algorithm for calculating the transform and an effective peak detection algorithm. The algorithm is applied to two real metabonomic 1 H-NMR datasets and the properties of the method are compared to bucketing. We implicitly prove that

Normally, in parallel implementations of the Hough transform either the transform space or the set of image features can be distributed among the processing elements. A method is proposed to link parallel access to feature points in the image, and parallel access to the transform space. A synchronous processing sequence is suggested such that both can be distributed. Real-time performance has been obtained on a MIMD distributed memory architecture. Symbolic Reusoning in Object Exksction.

The number of fetal-neonatal death in Indonesia is still high compared to developed countries. This is caused by the absence of maternal monitoring during pregnancy. This paper, we present an automated measurement for fetal head circumference (HC) and abdominal circumference (AC) from ultrasonography (USG) image. This automated measurement is beneficial to detect early fetal abnormalities during the pregnancy period. We used the convolutional neural network (CNN) method, to preprocess the USG data. After that, we approximate the head and abdominal circumference using the Hough transform algorithm and the difference of Gaussian Revolved along Elliptical Path (Dogell) Algorithm. We used the data set from national hospitals in Indonesia and for the accuracy measurement, we compared our results to the annotated images measured by professional obstetricians. The result shows that by using CNN, we reduced errors caused by a noisy image. We found that the Dogell algorithm performs better than the Hough transform algorithm in both time and accuracy. This is the first HC and AC approximation that used the CNN method to preprocess the data.

The current context ofIraqi exile migration can be conceptualised as a continuation and amplification ofprevious trends characterised by more insecurity in Iraq, the growing number ofexiles who compete for asylum, and less international protection. This presentation puts current Iraqi ...

Coins square measure integral a part of our day to day life. We tend to use coins everyplace like grocery market, banks, buses, trains etc. Therefore it is a basic want that coin is recognized and counted. The target of this paper is to classify the Indian coins of different denomination discharged recently. The objective is to notice the Indian coins and count its total worth. The system is projected to design coin recognition by applying Advanced HarrisHessian Algorithm, supported the parameters of Indian coins such as size, shape, weight, surface and so on . This paper presents a coin recognition methodology with rotation invariance. For circle detection use Hough Transform. Keywords-smoothing, Interest point detection, Hough Transform technique

This paper presents a novel algorithm for line extraction from 2D laser range finder which has excellent precision to determine of line boundary, while having efficient speed and low complexity. This algorithm is more robust in measurement noise and cluttered data and fit a line to a set of uncertain points via Hough transform and recursive split formalism. In this algorithm first the data from laser is segmented to separate regions by determining start and end points throw Hough transform. After removing noisy or sparse points lines are extracted with a fast recursive split algorithm. Furthermore, it has good real-time capability to integrate the information of laser scanner into the navigation algorithm of the mobile robot and it is implemented on NAJI V rescue robot platform.

For successful printed circuit board (PCB) reverse engineering (RE), the resulting device must retain the physical characteristics and functionality of the original. Although the applications of RE are within the discretion of the executing party, establishing a viable, non-destructive framework for analysis is vital for any stakeholder in the PCB industry. A widely-regarded approach in PCB RE uses non-destructive x-ray computed tomography (CT) to produce three-dimensional volumes with several slices of data corresponding to multi-layered PCBs. However, the noise sources speciic to x-ray CT and variability from designers hampers the thorough acquisition of features necessary for successful RE. This article investigates a deep learning approach as a successor to the current state-of-the-art for detecting vias on PCB x-ray CT images; vias are a key building block of PCB designs. During RE, vias ofer an understanding of the PCB's electrical connections across multiple layers. Our method is an improvement on an earlier iteration which demonstrates signiicantly faster runtime with quality of results comparable to or better than the current state-of-the-art, unsupervised iterative Hough-based method. Compared with the Hough-based method, the current framework is 4.5 times faster for the discrete image scenario and 24.1 times faster for the volumetric image scenario. The upgrades to the prior deep learning version include faster feature-based detection for real-world usability and adaptive post-processing methods to improve the quality of detections. CCS Concepts: • Hardware → PCB design and layout; • Computing methodologies → Object detection.

Summary Iris localization is one of the most important step in iris based recognition systems. Iris localization means locating the inner boundary (pupil localization) and outer boundary of iris. Both boundaries of the iris are nearly circular which are surrounded by pupil, sclera, ...

Currently, a wider and more frequent use of Computed Tomography (CT) volumes are used for planning the radiotherapy treatment of oncological patients. These image volumes are instrumental in positioning the patient and the precise incidence of radiation fields. Any error in such a process will adversely affect the result of the treatment and, consequently, the health of the patient. Therefore, quality control is mandatory throughout this process. As regards image acquisition, the controls are typically executed manually, implying a tedious repetitive task. This problem spurred the need to develop an algorithm that allows eliminating this issue while it evaluates and corrects the main concern as well. Particularly, an algorithm for automatic control of the entire positioning process is proposed here which prevents errors from deviation on both the longitudinal and transversal axes of the arrangement. The alignment will vary according to the anatomical part to be treated and the type of stretcher support, among other aspects. The application was developed by resorting to free software tools (GLP) and the libraries for processing, segmenting and recording of medical images (ITK). The algorithm detects these deviations on both axes by applying threshold methods, morphologic operations and Hough's Transform. This algorithm is currently operative in the Medical Image Processing Server of the Nuclear Medicine School Foundation (FUESMEN) of Mendoza, Argentina.

Nowadays, the automation system is growing rapidly in manufacturing industries. It uses a lot of automated processes to prevent humans from doing dangerous tasks such as handling metal plates during the cutting process. Therefore, to fulfill the industries requirement a cartesian robot with two axis motion that is integrated with the computer vision to provide automated alignment process during the cutting process is proposed in this work. However, this paper only focuses on the software development based on the edge-detection method to provide alignment process for conveyor line robot. The technique uses a camera to capture the current position of the plate and calculate the error that needs to be aligned by the robot. The system will avoid human injury, time wasting and improve the production rate as well. As a result, this method has been proven to provide alignment error values that need to be aligned in terms of x and y axis by using the system accurately. In a nutshell, the uses of this method can easily improve the alignment time to ensure the improvement in production rate of an industry.

This study proposes a crack-monitoring system to quantify the change of cracks from multitemporal images during the monitoring period. A series of images were taken from an off-the-shelf digital camera. Concrete cracks were extracted from the digital images by employing a series of image-processing techniques. The image coordinates and orientation of same cracks can be changed since the position and direction of the portable camera vary at every exposure time. To monitor the crack changes (width and length), it is critical to transform the image coordinates of cracks extracted from each image into the same object coordinates of the concrete surface. In this study, such a geometric relationship was automatically recovered using the two-dimensional (2D) projective transformation based on the modified iterated Hough transform (MIHT) algorithm, the result of which solved the transformation parameters. To improve the computational operation of MIHT, regions of parameter estimation were also investigated. The developed algorithms were applied to monitor the crack of the concrete specimen. As a result, the change of cracks on the concrete specimen was successfully detected and accurately quantified.

This study proposes a crack-monitoring system to quantify the change of cracks from multitemporal images during the monitoring period. A series of images were taken from an off-the-shelf digital camera. Concrete cracks were extracted from the digital images by employing a series of image-processing techniques. The image coordinates and orientation of same cracks can be changed since the position and direction of the portable camera vary at every exposure time. To monitor the crack changes (width and length), it is critical to transform the image coordinates of cracks extracted from each image into the same object coordinates of the concrete surface. In this study, such a geometric relationship was automatically recovered using the two-dimensional (2D) projective transformation based on the modified iterated Hough transform (MIHT) algorithm, the result of which solved the transformation parameters. To improve the computational operation of MIHT, regions of parameter estimation were also investigated. The developed algorithms were applied to monitor the crack of the concrete specimen. As a result, the change of cracks on the concrete specimen was successfully detected and accurately quantified.

Este artículo presenta un método de segmentación de rectas, basado principalmente en un algoritmo de extracción de líneas robusto denominado transformada de Hough, para obtener los parámetros preliminares de las rectas compuestas por un conjunto de puntos suministrados por un escáner láser 2D, como los utilizados en sistemas de navegación de robots. Los parámetros de las rectas obtenidos por la transformada de Hough son ajustados por el algoritmo de Mínimos Cuadrados Totales (mejorando la precisión), y los puntos asociados a estos parámetros son nuevamente procesados por un algoritmo de Seguimiento de Bordes Sucesivos, con el fin de obtener las coordenadas cartesianas de los puntos extremos de las líneas que describen el entorno.

Significant geometric structures can be compactly described by global wireframes in the estimation of 3D room layout from a single panoramic image. Based on this observation, we present an alternative approach to estimate the walls in 3D space by modeling long-range geometric patterns in a learnable Hough Transform block. We transform the image feature from a cubemap tile to the Hough space of a Manhattan world and directly map the feature to the geometric output. The convolutional layers not only learn the local gradient-like line features, but also utilize the global information to successfully predict occluded walls with a simple network structure. Unlike most previous work, the predictions are performed individually on each cubemap tile, and then assembled to get the layout estimation. Experimental results show that we achieve comparable results with recent state-of-the-art in prediction accuracy and performance. Code is available at .

Several applications of silicon microstructures in areas such as neurobiology and electrophysiology have been stimulating the development of microsystems with the objective of mechanical support to monitor and control several parameters in cell cultures. In this work a multi-microelectrode arrays was fabricated over a glass plate to obtain the growth of neuronal cell monitoring their behavior during cell development. To identify the neuron core and axon an approach for implementation of edge detectors algorithms associated to images is described. The necessity of efficient and reliable algorithms for image processing and interpretation is justified by its large field of applications in several areas as well as medicine, robotics, cellular biology, computational vision and pattern recognition. In this work, it is investigated the adequacy of some edge detectors algorithms such as Canny, Marr-Hildreth. Some alterations in those methods are propose to improve the identification of both cell core and axonal growth measure. We compare the operator to edge detector proposed by Canny, Marr-Hildreth operator and application of Hough Transform. For evaluation of algorithms adaptations, we developed a method for automatic cell segmentation and measurement. Our goal is to find a set of parameters defining the location of the objects to compare the original and processed images.

Liang (Bill) Tseng and academic advisor Dr. Ming-Ying Leung who gave me continuous support of my UTEP life. Their guidance helped me in all the time to continue my academic activities alongside my research and writing of this thesis. I could not have imagined to fulfill my objectives without having their support. Besides my advisor, I would like to thank my thesis committee co-chair Dr. Jianguo Wu. I feel lucky to be supervised by such an excellent and enthusiastic expert in my field of research. I found him available whenever I face and trouble or had a question about my research. He consistently steered me in the right direction and innovative suggestion. In addition, I would be grateful to my thesis committee Dr. Yirong Lin, Dr. Mahmud Shahriar Hossain for serving on my committee and providing helpful suggestion to improve my thesis. My sincere thanks also goes to my fellow labmates and the CQPSI research group Dr.

The 7th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2021) Beijing, China, Oct.31-Nov.3, 2021 1 Abstract: The conventional automatic drug dispenser can significantly contribute to numerous services such as medicine industries, pharmacy shops and medical assistance by reducing the workload of medical workers and allowing patients to take their timely medication. In this paper, we use a segmentation technique to identify the location of drug pillboxes from the medicine shelves with the automatic counting of spiral pillboxes. The proposed method is a solution to a dynamic segmentation framework for recognizing objects from large datasets. Firstly, the picture is filtered to remove the noise to recognize the boundary line. Following that, projection lines are drawn in the center to divide the segments of pillboxes by utilizing the midpoint formula, which supports smooth counting of line detection among objects. Besides, we applied the...

Circulating Tumor Cells (CTCs) are considered a prognostic marker in pancreatic cancer. In this study we present a new approach for counting CTCs and CTC clusters in patients with pancreatic cancer using the Isoflux TM System with the Hough transform algorithm (Hough-Isoflux TM ). The Hough-Isoflux TM approach is based on the counting of an array of pixels with a nucleus and cytokeratin expression excluding the CD45 signal. Total CTCs including free and CTC clusters were evaluated in healthy donor samples mixed with pancreatic cancer cells (PCCs) and in samples from patients with pancreatic ductal adenocarcinoma (PDAC). The Isoflux TM System with manual counting was used in a blinded manner by three technicians who used Manual-Isoflux TM as a reference. The accuracy of the Hough-Isoflux TM approach for detecting PCC based on counted events was 91.00% [84.50, 93.50] with a PCC recovery rate of 80.75 ± 16.41%. A high correlation between the Hough-Isoflux TM and Manual-Isoflux TM was observed for both free CTCs and for clusters in experimental PCC (R 2 = 0.993 and R 2 = 0.902 respectively). However, the correlation rate was better for free CTCs than for clusters in PDAC patient samples (R 2 = 0.974 and R 2 = 0.790 respectively). In conclusion, the Hough-Isoflux TM approach showed high accuracy for the detection of circulating pancreatic cancer cells. A better correlation rate was observed between Hough-Isoflux TM approach and with the Manual-Isoflux TM for isolated CTCs than for clusters in PDAC patient samples.

Circulating Tumor Cells (CTCs) are considered a prognostic marker in pancreatic cancer. In this study we present a new approach for counting CTCs and CTC clusters in patients with pancreatic cancer using the IsofluxTM System with the Hough transform algorithm (Hough-IsofluxTM). The Hough-IsofluxTM approach is based on the counting of an array of pixels with a nucleus and cytokeratin expression excluding the CD45 signal. Total CTCs including free and CTC clusters were evaluated in healthy donor samples mixed with pancreatic cancer cells (PCCs) and in samples from patients with pancreatic ductal adenocarcinoma (PDAC). The IsofluxTM System with manual counting was used in a blinded manner by three technicians who used Manual-IsofluxTM as a reference. The accuracy of the Hough-IsofluxTM approach for detecting PCC based on counted events was 91.00% [84.50, 93.50] with a PCC recovery rate of 80.75 ± 16.41%. A high correlation between the Hough-IsofluxTM and Manual-IsofluxTM was observed f...

Through the centuries, artists have been inspired by the phenomenon of drowning. Ordinary people, saints and historical figures have been threatened or have died by drowning. Although past research and previous scholarly work has documented very well what has been done in the past in terms of prevention and rescue of drowning, no previous effort has been dedicated to how artists perceived drowning and rescue in the past in terms of art. This article aims to show paintings depicting drowning episodes and rescue interventions from around the world in various periods of time.

This paper proposes a technique for lane boundaries detection and following, which can be used in a driver assistance system. This technique is destined only to painted road with slow curvature. Then, a linear model is used to obtain robust information about vehicle ...

Introduction. Iterative tomographic reconstruction techniques are particularly interesting when the number of acquired projections is not enough to match the Nyquist criteria. These techniques differ in computational time and image quality. Purpose. SIRT, SART and CGLS algorithms have been studied in order to evaluate the application of iterative reconstruction methods to low-dose phase-contrast breast tomography, in the framework of the SYRMA-CT project. The number of iterations has been optimized while reducing the number of projections used during the reconstruction. Materials and methods. The tomographic data set of two large breast tissue samples (9 cm) has been acquired using a monochromatic X-ray beam at 38 keV. Images were acquired in propagationbased phase-contrast mode and a single-material single-distance phase-retrieval algorithm was applied to the projections before reconstruction. The codes applied are part of the ASTRA Tomography Toolbox. The quality of the images was evaluated using the CNR and few fullreference quality indexes. The reconstruction performed with the filter back-projection including all the acquired projections was considered as gold standard. Results. According to full-reference indexes the best quality is achieved when the CNR is similar to the one of the gold standard. The number of iterations can be used to optimize the noise preserving the visibility of the details. Conclusion. Full reference indexes require a gold standard acquired at higher statistics, due to their strong dependence on the noise. The evaluation of a team of radiologists is required to compare full-references quality indexes and diagnostic quality of the reconstructed images. Disclosure. Nothing to declare.

The conventional two-dimensional Rough transform technique is generalized into a projection-based transform method by using the modified Radon transform for estimating a three-dimensional target tracks embedded in a time-sequential set of image frames. The targets of concern are dim, unresolved point targets moving along straight paths across a same field of view. Since the target signal-to-noise is low and the spatial extend of the target is less than a pixel, one must rely on integration over a target track which span over many image frames. Instead of processing the entire 3-D data set, a set of projections are taken using the modified 3-D Radon transform. The projection frames are processed further to extract the track parameters using the Rough transform. This projection-based method not only lowers the data dimensionality but maintains a comparable estimation performance to that of using the entire 3-D data by successfully incorporating all available knowledge obtained from the set of projections. The simulation results are presented for the synthetic and real infrared image sequences containing synthetically generated 3-D target tracks under various signal-to-noise conditions.

When a document is fed to a scanner either mechanically or by a human operator for digitization, it suffers from some degrees of skew or flit. Skew angle detection is an important component of any Optical Character Recognition (OCR) and document analysis system. In this letter we consider skew estimation of Roman script. The method considers the lowermost and uppermost pixels of some selected characters of the text which may be subject to Hough transform for skew angle detection. A fast approach is also proposed which works almost as accurately as Hough transform. Experimental results are presented and compared with results on several other skew detection methods.

Aim of this study is obtain calculable maze path data from a maze image or taken maze photo. Image of maze can be a viewable natural maze or previously produced or pressed maze. After digitalization of maze image, image processing techniques are using to analyzing maze walls or lines. Bird’s eye view correction can be applied if photo is not taken from bird’s eye view. Adjustable threshold value is using to convert image to monochrome. Hough lines algorithm is used to detect maze walls or lines. Detected lines are clustering, concatenating and splitting from intersection points in order. Thus, uniform line segments can be obtained. Finally, these line segments on the maze walls or lines are converting to integer data and saving into two dimensional matrices

Introduction Sugar beet (Beta vulgaris L.) as the second most important world's sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important to efficiently eliminate weeds at early growing stages. The first step of precision weed control is accurate detection of weeds location in the field. This operation can be performed by machine vision techniques. Hough transform is one of the shape feature extraction methods for object tracking in image processing which is basically used to identify lines or other geometrical shapes in an image. Generalized Hough transform (GHT) is a modified version of the Hough transform used not only for geometrical forms, but also for detecting any arbitrary shape. This method is based on a pattern matching principle that uses a set of vectors of feature points (usually object edge points) to a re...