Thresholding

In today’s digital world, image edge detection is widely used in all over the globe. Digital image edge detection is used to detect the sharp changes in brightness of the images and the edge of a digital image. There are many classical techniques used to detect image edge detection like Canny, Sobel, Prewitt and Robert edge detector method. The proposed research work mainly focuses on the study of different edge detection methods which were previously derived and to propose an efficient method of digital image edge detection technique using Discrete Wavelet Transform (DWT). The results were compared with one of the best edge detector method: Canny Edge Detector. In the proposed image edge detection method, the first analytical result showed that an input image after applying the thresholding technique represents a segmentation of image edges and the second analytical results showed the 8- connected components of pixel connectivity in two dimensional images related to their neighbor pixels producing the enhanced edges. To evaluate the performance of the proposed algorithm with the existing algorithms, the two thresholding values were applied as T_low and T_high using hysteresis thresholding method. PSNR and width σ were also computed to detect the efficiency of the proposed algorithm.

Segmentation is a process of dividing an image into distinct regions with the aim to extracts object of interest from the background. The traditional thresholding and clustering segmentation techniques that were widely used are Otsu and Kmeans, respectively. However, the segmentation process becomes more challenging for segmenting natural images. Both Otsu and K-means methods failed to produce good quality of segmented areas under natural environment due to the complex background and non-uniform illumination on the images. Therefore, this paper proposed an improved thresholding-based segmentation with inverse technique (TsTN) that was able to partition natural images. A comparison between Otsu, K-means and TsTN techniques was conducted using colour-based image processes on the quality of the segmented images. The analysis results showed that TsTN has the ability to produce good quality of segmented images. Furthermore, this improved technique was proved to be more accurate than the traditional thresholding and clustering techniques.

Gel Electrophoresis (GE) is one of the most used methods which separate nucleic acid and protein molecules according to electric charge, amount of them, molecule weights and other physical features. GE is used in many fields such as genetic, molecular biology and biochemistry. In this paper, Canny edge detection algorithm based on artificial neural network is used to separate and detect DNA bands automatically. Thus, GE analysis of gel electrophoresis period is realized and many properties of DNA bounds like intensity are extracted from digital images automatically. Keywords: DNA Gel Electrophoresis, Canny Algorithm, Thresholding, Artificial Neural Network.

Der folgende Text ist Ergebnis des Nachdenkens über Schwellen als Grenzphäno-mene, die oft entscheidend sind-und über ihren Sinn für das Menschenleben. Bezüglich meiner mehr als zwanzig Jahren langer Tätigkeit als Mutter, Lehrerin, Lektorin und Katechetin frage ich nach einer möglichen Erziehung zur Fähig-keit, hinter einer Schwelle zu leben, eine Schwelle zu überschreiten, einer mög-lichen Erziehung zur Hoffnung, zur Freiheit und Wahrheit. Nur flüchtig wird die ,Schwellenhaftigkeit" von einigen Gesichtspunkten erwähnt-es geht um einen Versuch, die Schwelle in unserer Sprachkultur zu erörtern. Das Thema sollte aus der Perspektive (alt)griechischer, biblischer, christlicher und philosophischer Kul-tur vertieft werden. Der Zweck ist, das Denken und Reflexion des Phänomens zu fördern, das jeden betrifft, und dadurch die Dynamik der Existenz möglichst zu unterstützen. Jeder Mensch wohnte irgendwo, überschritt die Schwellen, pilgerte durch die (natürliche oder kulturelle) Landschaft, in der es (natürliche oder kulturelle) Schwellen, Scheiden, Raine gibt. Die Schwellenhaftigkeit gehört zum menschli-chen Aufhalten in der Welt. Ohne Schwellen im Sinne von Grenzen, Scheiden, Rai-ne wären wir nicht-es gäbe für uns (wie wir jetzt sind) nichts, was ist, denn was es auch gibt, ist für uns begrenzt, hat seine Schwelle. Bereits im alten Griechenland führte dieses Bewusstwerden zur zweierlei ge-gensätzlichen Mündung in der philosophischen Reflexion bei der Suche nach An-fang.

"In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then to fit the Gaussian mixtures to the histogram of image, the expectation maximization (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms and their corresponding parameterization. Finally, the optimal threshold which is the average of these Gaussian mixture means is chosen. And the experimental results show that the new algorithm performs better.

Keywords: Histogram; Optimization; Thresholding"

1. Our recommendations help –

A. Entrepreneurs to downsize, right size or close their enterprise without government permission.
B. But, all workers who have been laid off must get PF and Severance Pay (as in Europe and Brazil)

2. Therefore we are AGAINST THRESHOLDS. Entrepreneurs and Workers suffer because of TWO THRESHOLDS.
A. SIZE: Units with 100+ workers suffer many restraints to Labour flexibility. This is disagreeable to Entrepreneurs.
B. DURATION: Only workers who have been employed for over 240 days are entitled to benefits. This seriously disadvantages workers.

We are against all thresholds as they:
i. Encourage small enterprises and not big scale, high technology ones
ii. Encourage employment of non-permanent casual workers; this blocks skill development

3. ALL WORKERS ARE ON THE MUSTER and are entitled to all benefits.

4. When worker is retrenched/laid-off, the benefits over and above PF, Bonus also includes SEVERANCE PAY (as in Europe and Brazil). We have allowed for exceptions in “Seasonal Industries”, but there are certain constraints in this sector too.

5. So now no enterprise gains from being
A. Small and Employing below 100 workers
OR
B. Firing workers before 240 days of Employment are over

6. So hiring contract /casual workers now become less attractive.
     
THEREFORE:
A. All workers hired for non-core jobs must come from a contractor’s firm where this job is a core one
B. All contract workers, no matter where they are currently hired to work, are on the Muster of their parent firm

7. On Dispute Resolution we suggest:
A. One Workers’ Council whose office bearers are elected by universal secret ballot BUT only by those inside the company.
B. Trade Unions can function within but the body representing workers will be the Workers’ Council.

8. Also a Three Tier Dispute Resolution Mechanism is suggested.
A. Workers’ Council, whose office bearers will be internal and elected internally as well.
B. Conciliation Committee, which will have labour representatives.
C. Labour Tribunal.

The time taken at each level is fixed without exceptions.

Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries represent the major cause of neurological disability to a clot or hematoma caused by Haemorrhage (ICH) and is the The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion every 100,000 population with deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of structural damage to brain. Accurate segmentation of the haemorrhage. This study is on segment Keywords: Intracranial decomposition; Brain haemorrhage segmentation is the first step before detecting the been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec efficient when there is a large dataset Hssayeni and colleagues multiple slices and made it public. Second, used deep learning methods to perform segmentation and got a dice coefficient of 31% which is good compared to and colleagues [12] propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result with the combination than FCM clustering and active use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the among the three. Arjun Majumdar and colleagues haemorrhage instead of Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries represent the major cause of neurological disability. A traumatic brain injury to a clot or hematoma caused by an accident or any other trauma. (ICH) and is the most common and serious consequence of head injury which can be life The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion every 100,000 population with most of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of ctural damage to brain. Accurate segmentation of the. This study is on segmentation of the brain haemorrhage Intracranial haemorrhage; Discrete wavelet transforms I. RELATED WORK Brain haemorrhage segmentation is the first step before detecting the been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec efficient when there is a large dataset, which is not easily available in case of brain haemorrhage. Hssayeni and colleagues [1][2] have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice coefficient of 31% which is good compared to other deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result with the combination than FCM clustering and active contour methods alone. use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the among the three. Arjun Majumdar and colleagues [8] haemorrhage instead of traditional methods and achieve a Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries traumatic brain injury (TBI) is damage to the brain, secondary an accident or any other trauma. This hematoma is known as an Intracranial most common and serious consequence of head injury which can be life The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion people and there is approximately one radiologist for of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of ctural damage to brain. Accurate segmentation of the haemorrhage is the first step before detecting the brain haemorrhage images using discrete wavelet transforms. iscrete wavelet transforms; Segmentation; RELATED WORK Brain haemorrhage segmentation is the first step before detecting the haemorrhage in the brain. A lot of work has been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec which is not easily available in case of brain haemorrhage. have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result contour methods alone. Tong Duc Phong and colleagues use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the most time [8] use a modified version of U-Net to detect the brain traditional methods and achieve an overall specificity of 98.6% on the small dataset. Brain Haemorrhage Segmentation using Dircrete Wavelet Transform. the terms of the Creative Commons Attribution License; Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries (TBI) is damage to the brain, secondary This hematoma is known as an Intracranial most common and serious consequence of head injury which can be life-threatening. The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls people and there is approximately one radiologist for of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the well-being of the health care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of is the first step before detecting the brain images using discrete wavelet transforms. Thresholding; Wavelet haemorrhage in the brain. A lot of work has been done on the brain haemorrhage detection using methods like Convolutional neural network [2][3][5][11] and other efficient and advanced deep learning techniques. But that is resource intensive. It is also necessary and which is not easily available in case of brain haemorrhage. Murtada D. have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result Tong Duc Phong and colleagues [13] use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception-ResNet and a most time-consuming model Net to detect the brain overall specificity of 98.6% on the small dataset.

A computationaily efficient solution to the problem of minimum error thresholding is derived under the assumption of object and pixel grey level values being normally distributed. The method is applicable in multithreshold selection. Thresholding Minimum error decision rule Classification error Dynamic clustering

This paper proposes a thresholding approach for crack detection in an unmanned aerial vehicle (UAV) based infrastructure inspection system. The proposed algorithm performs recur-sively on the intensity histogram of UAV-taken images to exploit their crack-pixels appearing at the low intensity interval. A quantified criterion of interclass contrast is proposed and employed as an object cost and stop condition for the recursive process. Experiments on different datasets show that our algorithm outper-forms different segmentation approaches to accurately extract crack features of some commercial buildings.

Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as its basis for the Seeded Region Growing method. A novel real time integrated method is developed in this work to locate the segmented region of interest of an image, based on the Region Growing segmentation method along with the Thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity is suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results are performed on different images obtained from an Alpert dataset. A comparative study is performed with the human segmented image, threshold based region growing and the proposed integrated method. The results prove that the proposed integrated method outperforms the region growing method in terms of the recall and F-score. While, it has comparable recall values with that gained by the human segmented images. It was noted that as the image under test has a dark background with the brighter object, the proposed method provides the superior recall value compared to the other methods.

In diagnosis of diseases Ultrasonic devices are frequently used by healthcare professionals. The medical imaging devices namely X-ray, CT/MRI and ultrasound are producing abundant images which are used by medical practitioners in the process of diagnosis . The main problem faced by them is the noise introduced due to the consequence of the coherent nature of the wave transmitted. These noises corrupt the image and often lead to incorrect diagnosis. In general, ECG signals affected by noises such as baseline wandering, power line interference, electromagnetic interference and high frequency noises during data acquisition. In the recent paper we have considered the Discrete Wavelet Transform (DWT) based wavelet Denoising have incorporated using different Thresholding techniques to remove major sources of noises from the acquired ECG signals. The experimental results shows the significant reduction of White Gaussian noise and it retains the ECG signal morphology effectively. Different performance measures were considered to select the appropriate wavelet function and Thresholding rule for efficient noise removal methods such as Mean Square Error (MSE),Peak Signal to Noise Ratio (PSNR) and Percentage Root Mean Square Difference (PRD) . The experimental result shows the db" wavelet and BayesShrink Thresholding rule is optimal for reducing noise in the real time ECG signals.

Image binarization is the process of separation of pixel values into two groups, black as background and white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This paper describes a locally adaptive thresholding technique that removes background by using local mean and standard deviation. Most common and simplest approach to segment an image is using thresholding. In this work we present an efficient implementation for threshoding and give a detailed comparison of Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding algorithm is implemented on medical images. The quality of segmented image is measured by statistical parameters: Jaccard Similarity Coefficient, Peak Signal to Noise Ratio (PSNR).

DETECCIÓN DE CONTORNOS EN IMÁGENES CON PYTHON Y "opencv". El propósito del presente trabajo, es exponer el procedimiento a seguir, para la detección de contornos sobre una imagen con el lenguaje de programación "Python", haciendo uso de la librería "opencv". Así, para empezar, procederemos a importar las librerías y recursos que vamos a emplear en nuestro proyecto: >>> #IMPORTAMOS LIBRERÍAS A USAR. >>> import cv2 >>> import matplotlib.pyplot as plt >>> Haciendo uso conjunto de estas dos librerías, nos disponemos a representar los contornos de una imagen sobre fondo blanco, contenida en una archivo de nombre "hand.png":

In recent years, numerous machinery health monitoring technologies have been developed by the US Navy to aid in the detection and classification of developing machinery faults for various Naval platforms. Existing Naval condition assessment systems such as ICAS (Integrated Condition Assessment System) employ several fault detection and diagnostic technologies ranging from simple thresholding to rule-based algorithms. However, these technologies have not specifically focused on the ability to predict the future condition (prognostics) of a machine based on the current diagnostic state of the machinery and its available operating and failure history data. An advanced prognostic capability is desired because the ability to forecast this future condition enables a higher level of condition-based maintenance for optimally managing total life cycle costs (LCC). A second issue is that a framework does not exist for "plug-and-play" integration of new diagnostic and prognostic technologies into existing Naval platforms. This paper outlines such prognostic enhancements to diagnostic systems (PEDS) using a generic framework for developing interoperable prognostic "modules". Specific prognostic module examples developed for gas turbine engines and gearbox systems are also provided.

Umane e Sociali (DISUS) dell'Università degli Studi di Napoli "L'Orientale" e con fondi di Ricerca Scientifica d'Ateneo (RA 2015, responsabile prof. Lorenzo Bianchi). Proprietà letteraria riservata INDICE Premessa p. 9 GEORGES PIERI: La mise en question du pouvoir dans l'imaginaire du politique: l'exemple antique » 15 ANTONELLA SANNINO: Il potere dell'immaginazione nel De mirabilibus mundi e nel De occulta philosophia » 21 MASSIMILIANO CHIANESE: «Per fabulas et apologos et aenigmata»: apologhi e immaginazione 'politica' in Pietro Pomponazzi » 37 ORESTE TRABUCCO: Percorsi patavini: da imaginatio ad ingenium » 59 MARIASSUNTA PICARDI: Immaginazione e politica in John Dee. Dalla Corona imperiale alla monarchia universale » 71 GUIDO GIGLIONI: Imagination and power in Francis Bacon » 99 MICHÈLE BENAITEAU: L'imagination du pouvoir dans l'historiographie des révoltes du XVII ème : quelques cas italiens et français » 141 PIERRE GUENANCIA: Pascal: les cordes de l'imagination » 185 LORENZO BIANCHI: Il potere dell'immaginazione in Voltaire » 207 JEAN FERRARI: Imagination et pouvoir, de l'utopie au projet » 233 ELENA TAVANI WALTER BENJAMIN: IMMAGINI SULLA SOGLIA DEL POLITICO Nel saggio Esperienza e povertà Walter Benjamin descrive il «nudo uomo» della generazione uscita dal primo conflitto mondiale -«non più ricca, ma più povera di esperienza comunicabile» -come colpito da una miseria del tutto nuova, frutto di uno straordinario sviluppo della tecnica che nell'allestire il campo di «correnti distruttrici» della guerra tanto più mette in rilievo la fragilità e la nudità, fisica, morale, culturale, dell'umano superstite. Si tratta della stessa scena, vista però nella sua opacità e ritratta come in un negativo fotografico, che affiora dall'estetica della guerra di Marinetti, il qualenel manifesto per la guerra coloniale d'Etiopia, citato da Benjamin nella Postilla al saggio sull'opera d'arte -, esalta la bellezza della guerra generatrice di nuove forme, suoni, colori, sensazioni, descrivendo gli esseri umani come un mero materiale a disposizione della tecnica e come realizzato il sogno di una «metallizzazione» del corpo umano. E proprio la nuova umanità massificata, commenta Benjamin, diventa pura fonte di energia che, già sperimentata come forza lavoro all'interno del sistema produttivo del capitalismo industriale, diventa ora una «fiumana umana» spinta «nel letto delle trincee» 1 .

The main objective of this paper is to elaborate how defective fabric parts can be processed using Matlab with image processing techniques. In developing countries like India especially in Tamilnadu, Tirupur the Knitwear capital of the country in three decades yields a major income for the country. The city also employs either directly or indirectly more than 3 lakhs of people and earns almost an income of 12, 000 crores per annum for the country in past three decades [2]. To upgrade this process the fabrics when processed in textiles the fault present on the fabrics can be identified using Matlab with Image processing techniques. This image processing technique is done using Matlab 7.3 and for the taken image, Noise Filtering, Histogram and Thresholding techniques are applied for the image and the output is obtained in this paper. This research thus implements a textile defect detector with system vision methodology in image processing.

This article examines post-socialist transformations by studying material traces and gaps in the Estonian capital. The need for sutures is presented as the consequence of a state of collective liminality, here manifested in the juxtaposition of unfinished projects and in threshold experiences in Tallinn. Relying on empirical examples, literature, architecture, and visual signs, I explore the lack of contextual fit, saturation, and redundancy in this city, concluding that continuous radical changes have increased entropy in the cityscape.

Segmentation technique is one of the main steps in image processing used to distinguish different objects in the image which has been developed in order to make images smooth and easy to evaluate. Many algorithms have been elaborated for gray scale images. However, the problem of segmentation for color images, which convey much more information about objects in the image, has received much less attention of scientific community. While several papers of color image segmentation techniques were published, but those did not emerge. This work presents an extensive survey of algorithms for color image segmentation, a categorization of them according well defined list of attributes, suggestions for their improvements and descriptions of few novel approaches.

Keywords: Segmentation, Color Image Segmentation, Median Filter, Fuzzy logic, Otsu Method and Thresholding.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

All machines, vehicles and buildings are subjected to dynamic forces that cause vibration. Most practical noise and vibration problems are related to resonance phenomena where the operational forces excite one or more modes of vibration. Modes of vibration that lie within the frequency range of the operational dynamic forces always represent potential problems. Mode shapes are the dominant motion of a structure at each of its natural or resonant frequencies. Modes are an inherent property of a structure and do not depend on the forces acting on it. On the other hand, operational deflection shapes do show the effects of forces or loads, and may contain contributions due to several modes of vibration. This project deals with optimization and modal analysis of the upper arm suspension of double wishbone suspension. Upper arm has been modeled using CATIAV5, meshing will be done in HYPERMESH12.0, and ANSYS will be used for post processing. Boundary forces will be calculated. Static analysis will be done which is needed to be done for optimization, low stressed region will be identified and material will be removed from that region. Re-analysis (modal) will be done on the modified model. Once we get desired results, model will be fabricated and tested with FFT analyzer to check for response of the arm. Suspension system of an automobile plays an im1portant role in ensuring the stability of the automobile. Control arm plays major role in independent suspension system. It is generally made of forged steel which has considerable disadvantages such as weight, cost etc.

Binarization methods are applied to document images for discriminating the text from the background based on pure thresholding and filtering combined with image processing algorithms. The proposed binarization procedure consists of five discrete steps in image processing, for different classes of document images. A refinement technique enhances further the image quality. Results on Byzantine historical manuscripts are discussed and potential applications and further research are proposed. The main contribution of this paper is to propose a simple and robust binarization procedure for pre-filtered historical manuscripts images, and simulation results are also presented.

Driver drowsiness represents an important risk on the roads. It is one of the main factors leading to accidents or near-missed accidents. So there is a need to develop a system to detect drowsiness of a driver so that the risk of accidents due to drowsiness can be reduced. The Computer Based System to Detect Sleep of a Driver includes the process for detecting the drowsiness of a driver. It detects driver drowsiness and progressively warns the driver, so that he/she can either correct the behavior or stop driving .Video of the driver is taken as input to the system from this video frames are extracted. Then each extracted frame is processed by the system. First of all, blurring of image is done. Then RGB to HSV conversion of image is done. After conversion of image to HSV color format thresholding is applied. Head movement of driver is tracked through camera and offset is set. If head movement offset is greater than set offset value drowsiness of driver is detected and alarm is generated.

The exploration of oceans and sea beds is being made increasingly possible through the development of Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it must confront the existence of notable challenges. However, an automatic detecting and tracking system is the first and foremost element for an AUV or an aqueous surveillance network. In this paper a method of Kalman filter was presented to solve the problems of objects track in sonar images. Region of object was extracted by threshold segment and morphology process, and the features of invariant moment and area were analysed. Results show that the method presented has the advantages of good robustness, high accuracy and real-time characteristic, and it is efficient in underwater target track based on sonar images and also suited for the purpose of Obstacle avoidance for the AUV to operate in the constrained underwater environment.

The quality control of the agricultural products, which in many cases is through intuitive observation of the visible features of the product, plays a key role in the survival of the agricultural industry. From a long times ago, the qualitative categorization of these products have been performed by trained people who search for products for the specific characteristics. On the other hand, hard and repetitive working in the humankind workers can make them some mistakes in computing quality control errors. Became Hence, nowadays, by entering the machine vision systems into this subject, they turned into a reliable, low-cost and real-time technology. Despite the existence of machine vision systems in this process, there are still major challenges in categorizing agricultural products in terms of quality, size, shape, and examination of defects. Potato is one of the most important agricultural products that is produced and has a high application. Unfortunately, it suffers from various types of diseases and defects. Hence, its quality control has a particular importance. In general, image quality control algorithms for agricultural products such as citrus and potatoes are comprised of two parts of product visualization and defect detection. Machine vision is one of the newest methods in automated potato sorting; this technique consists of two stages including separating the image of potato from the background and then examining the presence of a defect in the chopped potato image. In this chapter, different image segmentation methods have been studied and analyzed which are applied to the potato images as a first step technique for quality inspection of this kind of vegetables.

Image segmentation and edge detection are of great interest in image processing prior to image recognition step. Segmentation process has an important application in Military, Biomedical, space, and environmental applications. In this paper, we applied the spatial domain methods, Thresholding and Edge based methods (Roberts operator, Sobel operator, Prewitt operator, and Laplacian operator). In this research, we found that using Fourier Transform in edge detection applications yields to a bad results and has a serious drawback. Wavelet transform plays a very important role in the image processing analysis, for its fine results when it is used in multi-resolution, multi-scale modeling. Unlike Discrete Cosine transforms or Fourier transforms, wavelet transform offers a natural decomposition of images at multiple resolutions. The resulting representation of wavelet transform provides an attractive tradeoff between spatial and frequency resolution where the human visual system can be be...

The AIRCC's International Journal of Computer Science and Engineering Survey (IJCSES) is devoted to fields of Computer Science and Engineering surveys, tutorials and overviews. The IJCSES is a peer-reviewed scientific journal published in electronic form as well as print form. The journal will publish research surveys, tutorials and expository overviews in computer science and engineering. Articles from supplementary fields are welcome, as long as they are relevant to computer science and engineering.

The aim of this work is to identify  geometrical shape
object  in  an  image.  In  this  paper  the  object  is
considered to be as a square shape. The identification
requires  find  three  characteristics,  which  are  number,
size, and location of the object. To achieve the goal of
this  work  this  paper  presents  an  algorithm  that
combines  from  some  of  statistical  approaches  and
image  analysis  techniques.  This  algorithm  has  been
implemented  to  arrive  at  the  major  objectives  in  this
thesis.  The  algorithm  has  been  evaluated  by  using
simulated data, and yields good results, and then it has
been applied to real data.

Image segmentation is the process of subdividing a digital image into its systematized regions or objects which is useful in image analysis. In this review paper, we carried out an organized survey of many image segmentation techniques which are flexible, cost effective and computationally more efficient. We classify these segmentation methods into three categories: the traditional methods, graph theoretical methods and combination of both traditional and graph theoretical methods. In the second and third category of image segmentation approaches, the image is modeled as a weighted and undirected graph. Normally a pixel or a group of pixels are connected with nodes. The edge weights represent the dissimilarity between the neighborhood pixels. The graph or the image is then divided according to a benchmark designed to model good clusters. Every partition of the nodes or the pixels as output from these algorithms is measured as an object segment in an image representing a graph. Some of the popular algorithms are thresholding, normalized cuts, iterated graph cut, clustering method, watershed transformation, minimum cut, grey graph cut, and minimum spanning treebased segmentation.

Image denoising is an important step in image compression and other image processing algorithms. Hard and soft thresholding algorithms are often used to denoise the images. Recently wavelet transform has been used as a tool to denoise the images. However, there are problems associated with the thresholding algorithms. There is no subjective way to determine the threshold. In this work, we implement a simple Bayesian theory to obtain optimal threshold for such algorithms. MATLAB simulations were performed to validate the working of Bayesian thresholding method.

In digital image processing, binarization (two-level thresholding) is a commonly used technique for image segmentation. It is the process of converting a gray scale image to a binary image. Furthermore, binarization methods are divided into two groups as global binarization and locally adaptive binarization. A number of binarization techniques have been proposed over the years. Bernsen's method is one of locally adaptive binarization methods developed for image segmentation. In this study, Bernsen's locally adaptive binarization method is implemented and then tested for different gray scale images.

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.

Different research algorithms and systems are being generated day by day for a variety of DSP applications for detection and processing of image, speech, audio, acoustic signals etc. Traditionally Fourier transform is used for the analysis of signal but it does not provide analysis of non stationary signal like speech signal. Therefore Gabor introduces another transform for analyzing the non stationary signal in which window technique is considered. In Short Time Fourier Transform (STFT), the signal is divided into small enough segments which are assumed as stationary. For this purpose a finite length window function "w" is chosen. This window's width has to be equal to the signal segment where its stationary is valid. The limitation of STFT is that once the size of the window is fixed then it cannot be changed. Therefore, another transform required which is known as Wavelet transforms (WT). Wavelet transform is an efficient method for analyzing non stationary signals i.e. signals in which frequency components changes with time like speech signals. Discrete wavelet transform is used to denoise the speech signal in which White Gaussian noise is added. Different threshold rules (Heursure, Rigrsure, Minimaxi, Sqtwolog) are used to determine the threshold limit of speech signal with different wavelets (db13 and sym13).

Resonant tunneling diodes (RTDs) have functional versatility and high speed switching capability. The integration of resonant tunneling diodes and MOS transistor makes threshold gates and logics. The design and fabrication of linear threshold gates will be presented based on a monostable bistable transition logic element. Each of its input terminals consist out of a resonant tunnelling diode merged with a transistor device. The circuit models of RTD and MOSFET are simulated in HSPICE. Two input XOR gate is designed and tested.

Skin cancer is one of the major causes of deaths in recent days. Early detection of skin cancer reduces death at higher rate. Ceroscopy is one of the major modalities used in diagnosis of skin lesions. Skin lesions are of different types. Among them the most common types of skin lesion found in human are melanoma, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC).The accurate diagnosis information cannot be obtained by human interpretation. In order to overcome the error due to human interpretation an efficient computerized image analysis system has been developed. The proposed image analysis system consists of preprocessing, lesion segmentation, feature extraction and classification. In classification, different types of classifiers such as support vector machine (SVM), probabilistic neural network (PNN) and adaptive neurofuzzy inference system (ANFIS) are applied to classify the skin cancer types and their performance is compared using the evaluated parameters.