Combination Of Shape Features Research Articles

CFNN for Identifying Poisonous Plants

Identification of poisonous plants is a hard challenge for researchers because of the great similarity between poisonous and non- poisonous plants. Traditional methods to identify poisonous plant can be tiresome, therefore, automated poisonous plants identification system is needed. In this work, cascade forward neural network framework is proposed to identify poisonous plants based on their leaves. The proposed system was evaluated on both (poisonous leaves/non-poisonous leaves) which are collected using smart phone and internet. Combination of shape features and statistical features are extracted from leaf then fed to cascade-forward neural network which used TRAINLM function for training. 500 samples of leaf images are used, 250 samples are poisonous, the remaining 250 samples are non-poisonous.300 samples used in training, 200 samples for testing. Our system is achieved an accuracy value of 99.5%.

Распознавание объектов по составляющим их примитивам и отношениям между ними

The paper’s goal is to develop a methodology and algorithm for the recognition of objects in the environment, keeping the quality with an increasing number of objects. For this purpose, the following problems were solved: recognition of the shape features, estimation of relations between features, and matching between the found features and relations and the defined templates (descriptions of complex and simple objects of the real world). A convolutional neural network is used for the shape feature recognition. In order to train it we used artificially generated images with shape features (3D primitive objects) that were randomly placed on the scene with different properties of their surfaces. The set of relations necessary to recognize objects, which can be represented as a combination of shape features, is formed. Testing on photos of real-world objects showed the ability to recognize real-world objects regardless of their type (in cases where different models and modifications are possible). This paper considers an example of outdoor luminaire recognition. The example shows the algorithm's ability not only to detect an object in the image but also to estimate the position of its components. This solution makes it possible to use the algorithm in the task of object manipulation performed by robotic systems.

Convolutional Neural Network as an Image Processing Technique for Classification of Bacilli Tuberculosis Extra Pulmonary (TBEP) Disease

Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting for a long time. Biopsy is one of the techniques used to take the patient's lung fluid and given Ziehl Neelsen chemical dye and then observed using a microscope to determine this TBEP disease. In this research, the TBEP detection process was developed using a classification method, namely CNN with feature extraction and feature selection. The feature uses 5 features where these features are a combination of shape features and texture features with the highest information gain value. From the results of research conducted through the training and testing stages of the classification method using feature selection, the accuracy rate is higher than not using feature selection with a comparison of the feature selection stage increasing 0.6536% for the training process, and 0.8942% for the testing process.

Mental health diagnosis of college students based on facial recognition and neural network

In recent years, college campus incidents caused by mental health problems have been increasing year by year, and college students’ mental health problems have become the focus of attention of schools, society and parents. Based on this, this paper proposes a facial emotion recognition method for college students. By using moving target detection, target classification, target tracking, and a series of image preprocessing techniques, this method achieves intelligent monitoring of the area where college students are located and can automatically alert when a potentially dangerous target is found. Moreover, this method uses a combination of shape features and motion features to select and extract feature quantities. In addition, the method calculates the similarity between the target and candidate target corresponding sub-models, and according to the ability of each feature to distinguish between the target and the background, monitors the student’s mental health in real time and prevents various problems from occurring. Through experimental research, we can see that the model constructed in this paper has good performance.

Multiple Human Body Postures Detection using Kinect

<span lang="EN-US">This paper presents a method to detect multiple human body postures using Kinect sensor. In this study, a combination of shape features and body joint points are used as input features. The Kinect sensor which used infrared camera to produce a depth image is suitable to be used in an environment that has varying lighting conditions. The method for human detection is done by processing the depth image and joint data (skeleton) which able to overcome several problems such as cluttered background, various articulated poses, and change in color and illumination. Then, the body joint coordinates found on the object are used to calculate the body proportion ratio. In the experiment, the average body proportions from three body parts are obtained to verify the suitableness of golden ratio usage in this work. Finally, the measured body proportion is compared with Golden Ratio to determine whether the found object is a real human body or not. This method is tested for various scenarios, where true positive human detection is high for various postures. This method able to detect a human body in low lighting and dark room. The average body proportions obtained from the experiment show that the value is close to the golden ratio value.</span>

AN EFFICIENT APPROACH FOR CONTENT BASED IMAGE RETRIEVAL USING HIERARCHICAL PART-TEMPLATE AND TREE MODELING

Image based content recognition and retrieval is critical in many applications. Existing mechanisms for content based image retrieval lack in terms of performance. In this paper a hierarchical template tree based CBIR system is described. Content in image is represented using a combination of shape features and low level features. Comprehensive feature set definitions proposed enables in achieving better performance. Shape and low level features are considered as templates. Templates of similar categories are further decomposed to form a hierarchical template tree. Query image is converted into a query template and is decomposed. A part template based matching scheme and SVM classifier is used to retrieve visually similar images. Results presented in the paper prove superior performance of proposed technique when compared to recent existing mechanisms in place. An improvement of 10.45% and 9.69% in mean average precision and mean retrieval accuracy is reported using proposed approach.

Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features

Leukemia is a type of cancer which is caused by malignant neoplasms in leukocyte cells. Leukemia disease which can cause death quickly enough for the sufferer is a type of acute lymphocyte leukemia (ALL). In this study, we propose automatic detection of lymphocyte leukemia through classification of lymphocyte cell images obtained from peripheral blood smear single cell. There are two main objectives in this study. The first is to extract featuring cells. The second objective is to classify the lymphocyte cells into two classes, namely normal and abnormal lymphocytes. In conducting this study, we use combination of shape feature and histogram feature, and the classification algorithm is k-nearest Neighbour with k variation is 1, 3, 5, 7, 9, 11, 13, and 15. The best level of accuracy, sensitivity, and specificity in this study are 90%, 90%, and 90%, and they were obtained from combined features of area-perimeter-mean-standard deviation with k=7.

A decision guidance framework for non-traditional machining processes selection

Abstract In order to realize the manufacturing/machining demands thrived by newer, hard and difficult-to-machine materials being utilized in the present day industries, an assortment of non-traditional machining (NTM) processes has been developed over the past few decades. These processes are capable of generating intricate and complex shapes with high degree of accuracy, close dimensional tolerance and better surface finish. In this paper, a decision guidance framework is developed in Visual BASIC 6.0 to help the process engineers in selecting the most appropriate NTM process for a specific work material and shape feature combination. It also assists in identifying the ideal process parameter combinations for the most suitable NTM process. The derived results highly corroborate with the opinions of the experts in the related field, demonstrating the acceptability of the developed system.

The morphometrics of "masculinity" in human faces.

In studies of social inference and human mate preference, a wide but inconsistent array of tools for computing facial masculinity has been devised. Several of these approaches implicitly assumed that the individual expression of sexually dimorphic shape features, which we refer to as maleness, resembles facial shape features perceived as masculine. We outline a morphometric strategy for estimating separately the face shape patterns that underlie perceived masculinity and maleness, and for computing individual scores for these shape patterns. We further show how faces with different degrees of masculinity or maleness can be constructed in a geometric morphometric framework. In an application of these methods to a set of human facial photographs, we found that shape features typically perceived as masculine are wide faces with a wide inter-orbital distance, a wide nose, thin lips, and a large and massive lower face. The individual expressions of this combination of shape features—the masculinity shape scores—were the best predictor of rated masculinity among the compared methods (r = 0.5). The shape features perceived as masculine only partly resembled the average face shape difference between males and females (sexual dimorphism). Discriminant functions and Procrustes distances to the female mean shape were poor predictors of perceived masculinity.

Evolutionary grammars based design framework for product innovation

Innovation and creativity are the key successful factors and a global priority in engineering industries. This paper describes the development of an evolutionary grammars based design framework for product innovation. A new genetic representation of shape grammars is developed in an evolutionary computing environment. The results generated by the genetic algorithm define a new combination of shape features for alternative designs. In this way, traditional shape grammar is extended to an interactive context in which generative and evolutionary computing methods are combined. Both product component design as well as product configuration are supported in this framework. Two important design issues are addressed in this research, 1) Product form exploration, and 2) Product design strategies. To tackle the issues, this research demonstrates the efficient use of evolutionary grammars to support the exploration of innovative product form designs.

Selection of non-traditional machining processes using analytic network process

Non-traditional machining (NTM) processes are now being widely used to generate intricate and accurate shapes in materials, like titanium, stainless steel, high strength temperature resistant (HSTR) alloys, fiber-reinforced composites, ceramics, refractories and other difficult-to-machine alloys having higher strength, hardness, toughness and other diverse material properties. Generation of complex shapes in such materials by the traditional machining processes is experienced to be difficult. For effective utilization of the capabilities of different NTM processes, careful selection of the most suitable process for a given machining application is often required. Selection of the best suited NTM process for a work material and shape feature combination requires the consideration of several criteria. In this paper, an analytic network process (ANP)-based approach is proposed to select the most appropriate NTM process for a given machining application taking into account the interdependency and feedback relationships among various criteria affecting the NTM process selection decision. To avoid the difficult and time consuming mathematical calculations of the ANP, a computer program is also developed in Visual Basic 6.0 with graphical user interface to automate the entire NTM selection decision process. It simply acts as an ANP solver. The observed results from the ANP solver are quite satisfactory and match well with those obtained by the past researchers.

Applications of the MOORA method for decision making in manufacturing environment

To meet the challenges of global competitiveness, manufacturing organizations are now facing the problems of selecting appropriate manufacturing strategies, product and process designs, manufacturing processes and technologies, and machinery and equipment. The selection decisions become more complex as the decision makers in the manufacturing environment have to assess a wide range of alternatives based on a set of conflicting criteria. To aid these selection processes, various multi-objective decision-making (MODM) methods are now available. This paper explores the application of an almost new MODM method, i.e., the multi-objective optimization on the basis of ratio analysis (MOORA) method to solve different decision-making problems as frequently encountered in the real-time manufacturing environment. Six decision-making problems which include selection of (a) an industrial robot, (b) a flexible manufacturing system, (c) a computerized numerical control machine, (d) the most suitable non-traditional machining process for a given work material and shape feature combination, (e) a rapid prototyping process, and (f) an automated inspection system are considered in this paper. In all these cases, the results obtained using the MOORA method almost corroborate with those derived by the past researchers which prove the applicability, potentiality, and flexibility of this method while solving various complex decision-making problems in present day manufacturing environment.

Retrieval of intracranial hemorrhages in computed tomography brain images using binary coherent vector

We investigate the use of a new binary coherent vector approach, integrated in a proposed content-based medical retrieval (CBMIR) system, to retrieve computed tomography (CT) brain images. Five types of hemorrhages consisting of 150 plain axial CT brain images are queried from a database of 2500 normal and abnormal CT brain images. Possible combinations of shape features are portrayed as feature vectors and are evaluated based on precision-recall plots. Solidity, form factor, equivalent circular diameter (ECD), and Hu moment are proposed as identifying features of intracranial hemorrhages in CT brain images. In addition to identifying hemorrhages, the proposed approach significantly improves the CBMIR system performance. This retrieval system can be widely useful due to rapid development in computer vision and computer database management, both of which motivated this application of CBMIR.

A combined TOPSIS-AHP-method-based approach for non-traditional machining processes selection

With the introduction and increased use of newer and harder materials such as titanium, stainless steel, high-strength temperature-resistant (HSTR) alloys, fibre-reinforced composites, and ceramics in the aerospace, nuclear, missile, turbine, automobile, tool, and die-making industries, a different class of machining processes has emerged. Instead of employing the conventional cutting tools, these non-traditional machining (NTM) processes use energy in its direct form to remove materials from the workpiece. Selection of the most suitable NTM process for machining a shape feature on a given work material requires consideration of several factors. A combined method using the ‘technique for order preference by similarity to ideal solution’ (TOPSIS) and an analytical hierarchy process (AHP) is proposed to select the most appropriate NTM process for a specific work material and shape feature combination, while taking into account different attributes affecting the NTM process selection decision. This paper also includes the design and development of a TOPSIS-AHP-method-based expert system that can automate the decision-making process with the help of a graphical user interface and visual aids. The expert system not only segregates the acceptable NTM processes from the list of the available processes, but also ranks them in decreasing order of preference. It also helps the user as a responsible guide to select the best NTM process by incorporating all the possible error-trapping mechanisms.