Active Contours and Image Segmentation: The Current State Of the Art

Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours

Association of Data Mining and healthcare domain: Issues and current state of the art

Data mining has been used prosperously in the favorably perceived areas such as e- business, marketing and retail because of which it is now applicable in knowledge discovery in databases (KDD) in many industrial areas and economy. Data mining is mainly gaining its importance and usage in the areas of medicine and public health. In this paper the investigation of present methods of KDD, applying data mining methods for healthcare and public health has been discussed. The problems and difficulties related to data mining and healthcare in practice are also mentioned. In survey, the use of data mining has increased, along with examination of healthcare institutions so that the health policy prepared is the best, perceive disease causes and protect deaths in hospital and discover the dishonest insurance declaration.stabilization of continuous and fed-batch cultivation processes. In the paper are investigated Monod-Wang kinetic model and it singular Monod form. The simpler Monod and Monod-Wang models are restricted forms of Wang-Yerusalimsky model. The Wang-Yerusalimsky kinetic model could be accepted as a common model. A second order sliding mode is investigated and compared with standard sliding mode algorithms. The sliding mode control permits to solve the control problems with smaller quantity of priory information and elimination of parameters and measurements noises

Projecting Active Contours with Diminutive Sequence Optimality

Active contours are widely used in image segmentation. To cope with missing or misleading features in image frames taken in contexts such as spatial and surveillance, researchers have commence various ways to model the preceding of shapes and use the prior to constrict active contours. However, the shape prior is frequently learnt from a large set of annotated data, which is not constantly accessible in practice. In addition, it is often doubted that the existing shapes in the training set will be sufficient to model the new instance in the testing image. In this paper we propose to use the diminutive sequence of image frames to learn the missing contour of the input images. The central median minimization is a simple and effective way to impose the proposed constraint on existing active contour models. Moreover, we extend a fast algorithm to solve the projected model by using the hastened proximal method. The Experiments done using image frames acquired from surveillance, which demonstrated that the proposed method can consistently improve the performance of active contour models and increase the robustness against image defects such as missing boundaries

Query Based Face Retrieval From Automatic Reconstructed Images based on 3D Frontal View - Using EICA

Face recognition systems have been playing a vital role from several decades. Thus, various algorithms for face recognition are developed for various applications like 2018;person identification2019;, 2019;human computer interaction2019;, 2019;security systems2019;. A framework for face recognition with different poses through face reconstruction is being proposed in this paper. In the present work, the system is trained with only a single frontal face with normal illumination and expression. Instead of capturing the image of a person in different poses using camera or video, different views of the 3D face are reconstructed with the help of a 3D face shape model. This automatically increases the size of the training set. This approach outperforms the present 2D techniques with higher recognition rate. This paper refers to the face detection and recognition approach, which primarily focuses on Enhanced Independent Component Analysis(EICA) for the Query Based Face Retrieval and the implementation is done in Scilab. This method detects the static face(cropped photo as input) and also faces from group picture, and these faces are reconstructed using 3D face shape model. Image preprocessing is used inorder to reduce the error rate when there are illuminated images. Scilab2019;s SIVP toolbox is used for image analysis

Prototype centric (PC) software development process model: A machine learning based Hybrid Software Development Model

Here in this paper we propose a Machine learning technique based Hybrid software development process model called prototype centric, in short can refer as PC. The proposed hybrid model works by considering any one or more traditional models as source models. We also conduct empirical study to analyze the performance of the PC over other traditional models that are most frequently quoted in literature

SECURITY ENABLE AND AUTHENTIC FINDING OF FALLING PEACE ATTACKS IN WANET

In straightforward cellular measure, link errors are rather serious, and potency not be far minor-league than bag dropping rate of society assailant thus crowd assailant can camouflage in scrim of unkind convey assets. We trouble in combating a crowd beat and sympathetic in ramification of detecting adventure of choosy folder drops and see virulent node that are culpable for such drops. In our work at the same time as biopsy of folder array losses in reach the web, we watch in definitive even if losses arise with link errors altogether, in other words by corporate action of link errors farther vengeful drop. We cultivate definite conclusion for disclosure of scrupulous carton drops that got to by society assailants. For ensuring of reckoning of correlations, we promote a holomorphic slender authenticator i.e. on populace auditing compose evidence that permits the discoverer to establish sincerity of folder loss science which appear by nodes. This pattern is penetrable preserving, and sustains low transmission again depot spending. Our finding other provides conscientious also socially valid outcome census as impression to cultivate find result

Prediction of Stock Market Index Using Genetic Algorithm

The generation of profitable trading rules for stock market investments is a difficult task but admired problem. First stage is classifying the prone direction of the price for BSE index (India cements stock price index (ICSPI)) futures with several technical indicators using artificial intelligence techniques. And second stage is mining the trading rules to determined conflict among the outputs of the first stage using the evolve learning. We have found trading rule which would have yield the highest return over a certain time period using historical data. These groundwork results suggest that genetic algorithms are promising model yields highest profit than other comparable models and buy-and-sell strategy. Experimental results of buying and selling of trading rules were outstanding. Key words: Data mining, Trading rule, Genetic algorithm, ANN, ICSPI predictio