Pattern recognition (Engineering)

Retinal vascular segmentation is the primary stage of any retinal image analysis system. This vascular segmentation is useful in automated retinal disease detection and in diagnosis system, as it is found that retina indicates early signs of various systemic diseases like hypertension, diabetes, cardiovascular diseases and retinopathy of prematurity. In this, retinal image segmentation plays a vital role in image processing; with this approach in this paper we have proposed an improved method for retinal blood vessel segmentation using morphological image processing. In this proposed work not only the entire retinal graph network is extracted from retina but here we classifies vessels branching and crossing in the whole network by assigning the class of end point, intersection point, splitting point. The results of given method are compared with all existing segmentation systems and here we found that our method outperforms by giving accuracy of 90% in vascular segmentation.

The positron emission tomography (PET) is a tool reference in routine clinical oncology. His applications has concerned the management and therapeutic monitoring, identification and definition of targets for radiotherapy, and quantification. The most frequently radiotracer's used are fluo-deoxyglucose(FDG) and Fluo-choline (18F-choline). In addition to the functional information, PET with algorithms of post treatment could give information on the stage of cell differentiation in HCC. The objective is to determine the stage of cell differentiation in HCC on imaging PET. We have apply the process of imaging post treatment on the image of PET Choline and PET FDG of patient whose stage of cell differentiation is known at histology. The same result as histology were found. The volume of HCC lesions detected using 18F-FDG was slightly better than that using 18F-choline. The capacity of these two radiotracers to detect rate of fixation of HCC depend of the type of lesion. This combinat...

The main objective of this project is cell nuclei segmentation and classification. In pre-processing convert the gray scale and apply the segmentation. In segmentation this project proposes the graph cut algorithm. After segmentation the features are extracts such as texture and etc. In classification this project used the feed forward neural network. The proposed method is very efficient. The feed forward neural network is very efficient for classification.

Fabric detection has an outstanding importance in inspection of fabric quality and defects. It works on principle of spectral estimation technique vision. it gets bonded to locate defected regions accurately. This project represented a most accepted method for patterned fabric defect detection and classification using spectral estimation technique and rough set theory. To extract the Regular pattern from the image of the patterned fabric, here use of Estimating Signal Parameter via Rotational Invariance Technique (ESPRIT) is done. In this technique the defected region i.e. the shape and location of the flawed areas are detected by comparing the pattern image and the source image also the rough set classifier is trained and tested to detect the types of defects in the patterned fabric image. Practically it is observed that this method can successfully be used to analyze & find the defects in patterned fabrics with nearly 96% success rate. This method is result oriented and better improving than previous method.

Garments conventionally address the protective, social and aesthetic needs of individuals but they can also be engineered to carry out a range of specific functions. Besides incorporating the features to meet the specific demands of a particular function, the garment must meet the basic requirements of protection and comfort. Pattern engineering for functional clothing involves applying technical, scientific and mathematical knowledge of patterns to modify and develop patterns with the objective of developing a garment which meets the function specific requirements, is comfortable to wear and contributes to maximize the efficiency and performance of the wearer. This paper explores, through examples, use of pattern engineering for functional garments to achieve these stated objectives. It establishes pattern engineering as the first step of planning a purposeful, efficient and aesthetic functional garment. It shows how the techniques of pattern engineering can be used to find solutions to challenges posed by the anthropometry of the human body and how these techniques are used to generate the blueprint of a functional garment incorporating all functional and aesthetic components.

Garments conventionally address the protective, social and aesthetic needs of individuals but they can also be engineered to carry out a range of specific functions. Besides incorporating the features to meet the specific demands of a particular function, the garment must meet the basic requirements of protection and comfort. Pattern engineering for functional clothing involves applying technical, scientific and mathematical knowledge of patterns to modify and develop patterns with the objective of developing a garment which meets the function specific requirements, is comfortable to wear and contributes to maximize the efficiency and performance of the wearer. This paper explores, through examples, use of pattern engineering for functional garments to achieve these stated objectives. It establishes pattern engineering as the first step of planning a purposeful, efficient and aesthetic functional garment. It shows how the techniques of pattern engineering can be used to find solutions to challenges posed by the anthropometry of the human body and how these techniques are used to generate the blueprint of a functional garment incorporating all functional and aesthetic components.

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Image is a set of objects and extracting these object components from image has been a continuous area of research since very long time. In image object extraction the main emphasis is on extracting each or focused objects from the image. Image object extraction techniques first detect the objects in images and in second step extract the detected objects from the original image. The object extraction techniques commonly use methods like edge detection, segmentation and background subtraction to name a few. With the advancements of research in various other fields of science and technology over the period of time, these techniques have found their application in many different areas i.e. Medical disease detection, satellite object extraction, image retrieval, objects recognition etc. This paper gives brief

Problem statement: Heart localization is an important step in cardiac Magnetic Resonance Images (MRI) analysis. This study aims to locate the moving heart region from MRI sequence of images. Approach: The idea is to use the motion detection techniques to isolate the heart region from the background image and then apply morphological operations to construct a moving heart region mask. The mask is then applied to the MRI image to separate the Region Of Interest (ROI) that includes the heart. The K-means clustering algorithm is applied to the ROI to segment the heart walls. Results: Experimental results have shown that the performance of the proposed technique is superior to other MRI heart segmentation techniques in both complexity and accuracy. Conclusion: The proposed technique can be used as a pre segmentation step in any other future heart segmentation techniques to increase their accuracy through the localization of the moving heart region. The presented technique is fully automated technique and superior compared to other segmentation techniques.

The recent boom of artificial Neural Networks (NN) has shown that NN can provide viable solutions to a variety of problems. However, their complexity and the lack of efficient interpretation of NN architectures (commonly considered black box techniques) has adverse effects on the optimization of each NN architecture. One cannot simply use a generic topology and have the best performance in every application field, since the network topology is commonly fine-tuned to the problem/dataset in question. In this paper, we introduce a novel method of computationally assessing the complexity of the dataset. The NN is treated as an information channel, and thus information theory is used to estimate the optimal number of neurons for each layer, reducing the memory and computational load, while achieving the same, if not greater, accuracy. Experiments using common datasets confirm the theoretical findings, and the derived algorithm seems to improve the performance of the original architecture.

Pattern Recognition and Classification is considered one of the most promising applications in the scientific field of Artificial Neural Networks (ANN). However, regardless of the vast scientific advances in almost every aspect of the technology and mathematics, neural networks still need to be fairly large and complex (i.e., deep), in order to provide robust results. In this article, we propose a novel ANN architecture approach that aims to combine two fairly small Neural Networks based on an introduced probability term of correct classification. Additionally, we present a second ANN, used to reclassify the potentially incorrect results by using the most probable error-free results as additional training data with the predicted labels. The proposed method achieves a rapid decrease in the mean square error compared to other large and complex ANN architectures with a similar execution time. Our approach demonstrates increased effectiveness when applied to various databases, related t...

A lot of approaches, each following a different strategy, have been proposed in the literature to provide AdaBoost with cost-sensitive properties. In the first part of this series of two papers, we have presented these algorithms in a homogeneous notational framework, proposed a clustering scheme for them and performed a thorough theoretical analysis of those approaches with a fully theoretical foundation. The present paper, in order to complete our analysis, is focused on the empirical study of all the algorithms previously presented over a wide range of heterogeneous classification problems. The results of our experiments, confirming the theoretical conclusions, seem to reveal that the simplest approach, just based on cost-sensitive weight initialization, is the one showing the best and soundest results, despite having been recurrently overlooked in the literature.

Segmentation of brain tumor manually consumes more time and it is a challenging task. This paper detects the tumor inside the brain by doing segmentation and extraction of the tumor which is been detected. To prove the efficiency of the detection of brain tumor we have performed a comparative study of two segmentation algorithms namely “watershed segmentation algorithm” and “k-means clustering segmentation algorithm”. After the segmentation process the various morphological operations are applied on the segmented image. The morphological operations are applied to concentrate only on the required tumor part and ignoring the remaining area in the brain. The various thresholding algorithms like “Otsu’s thresholding” and “brute force thresholding” is applied to improve the efficiency of the final output image. Comparative study is made between the segmentation algorithms and the thresholding algorithms used. The further step of this project is to present an analytical method to detect t...

The new imaging techniques for organic body tissue give more details and information about the normal and abnormal tissue that help to distinguish the overlapping in margin of each organ. In this paper a new method of tumor line detection and segmentation is used to separate the abnormal from the normal surrounding tissue to get a real identification of involved and noninvolved area that help the surgeon to distinguish the involved area precisely. The method used in this paper is watershed method and image processing by using MatLab 7.1 to detect the tumor boundaries in MRI and CT image for different cases. Our result in this study is very clear for physician to distinguish the area of tumor for surgical planning.

Over the past decade, different image segmentation algorithms have been developed and employed in segmenting or analyzing brain magnetic resonance imaging (MRI) scans in the clinical applications for the detection of brain tumor. However, accurate detection, compression and transmission of brain tumor data remain parts of the challenging tasks militating against brain tumor telemedicine services due to the complex nature of brain tumor MRI scans. In overcoming this challenge, five different brain tumor segmentation algorithms were developed for this study. The algorithms were developed using MATLAB scripts. The developed algorithms were evaluated using patients' data retrieved from Mayfield website. The result of the comparative performance compression rate efficiency evaluation test carried out shows that the developed hybrid thresholdwatershed segmentation algorithm outperforms others in terms of compression efficiency. The result implies that the usage of the developed hybrid threshold-watershed segmentation algorithm in transmitting brain tumor patients' data transmission over wireless communication system will require limited bandwidth resource.

Image segmentation is a classic subject in the field of image processing and also is a hotspot and focus of image processing techniques. Many image segmentation ways are developed by the researchers so as to create pictures sleek and simple to judge. Since there is no general solution to the image segmentation problem, these techniques often have to be combined with domain knowledge in order to effectively solve an image segmentation problem. Till now, there is no perfect algorithm has been developed that could glance at all the factors present in image segmentation can be avoided. The aim of the researchers is to find out the issues of digital image segmentation that challenges the computer vision and enhance its accuracy and performance by planning new algorithms. In this paper, methodologies like K-Means clustering and watershed segmentation are discussed.

Remote sensing data is an important source of geological information. Several applications of different platforms were studied by many authors. One of the common applications was the geological mapping after applying various techniques of image processing. This paper aims to apply the fuzzy connectedness technique of image segmentation in solving the tasks of lithological characterization and trace various geologic contacts. This goal was achievable by using a new algorithm written by Matlab. The results after running the new code on the pilot area of G. Muwaylhah at Eastern Desert of Egypt were highly matches with those of the visual edge detection and mapping by running the classification algorism of ENVI commercial package.

The watershed segmentation methods are essential methods, to be considered for quick results in image handling and analysis. However, the main problem arises in produced image because it causes excess segmentation and noise. This research is conducted to improve this presented algorithm based on the mathematical morphology and filters to minimize flaws mentioned in that paper. Objective of this research is to find the gaps in the existing literary works. In most cases, themarker based segmentation is best because it marks the part of segment. The working of this proposed algorithm is checked by optimization of the part that is still an area of research.

The main objective of this project is fake currency detection using the image processing. Fake currency detection is a process of finding the forgery currency. After choose the image apply preprocessing. In pre-processing the image to be crop, smooth and adjust. Convert the image into gray color. After conversion apply the image segmentation. The features are extracting and reduce. Finally compare the image into original or forgery.

The three-dimensional (3D) modeling and recognition of 3D objects have been traditionally performed using local features to represent the underlying 3D surface. Extraction of features requires cropping of several local surface patches around detected keypoints. Although an important step, the extraction and representation of such local patches adds to the computational complexity of the algorithms. This paper proposes a novel Keypoints-based Surface Representation (KSR) technique. The proposed technique has the following two characteristics: (1) It does not rely on the computation of features on a small surface patch cropped around a detected keypoint. Rather, it exploits the geometrical relationship between the detected 3D keypoints for local surface representation. (2) KSR is computationally efficient, requiring only seconds to process 3D models with over 50000 points with a MATLAB implementation. Experimental results on the UWA and Stanford 3D models dataset suggest that it can accurately perform pairwise and multiview range image registration (3D modeling). KSR was also tested