Search results for: image%20segmentation
1965 Principle Component Analysis on Colon Cancer Detection
Authors: N. K. Caecar Pratiwi, Yunendah Nur Fuadah, Rita Magdalena, R. D. Atmaja, Sofia Saidah, Ocky Tiaramukti
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Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack human’s colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this project is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal / healthy colon condition. This system will classify colon cancer starting from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resize, convert RGB image to grayscale, edge detection and last, histogram equalization. Tests will be done by trying some K-NN input parameter setting. The result of this project is an image processing system that can detect and classify the type of colon cancer with high accuracy and low computation time.Keywords: carcinoma, colorectal cancer, k-nearest neighbor, lymphoma, principle component analysis
Procedia PDF Downloads 1991964 Fabric Drapemeter Development towards the Analysis of Its Behavior in 3-D Design
Authors: Aida Sheeta, M. Nashat Fors, Sherwet El Gholmy, Marwa Issa
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Globalization has raised the customer preferences not only towards the high-quality garments but also the right fitting, comfort and aesthetic apparels. This only can be accomplished by the good interaction between fabric mechanical and physical properties as well as the required style. Consequently, this paper provides an integrated review of the fabric drape terminology because it is considered as an essential feature in which the fabric can form folds with the help of the gravity. Moreover, an instrument has been fabricated in order to analyze the static and dynamic drape behaviors using different fabric types. In addition, the obtained results find out the parameters affecting the drape coefficient using digital image processing for various kind of commercial fabrics. This was found to be an essential first step in order to analyze the behavior of this fabric when it is fabricated in a certain 3-D garment design.Keywords: cloth fitting, fabric drape nodes, garment silhouette, image processing
Procedia PDF Downloads 1791963 Out-of-Plane Bending Properties of Out-of-Autoclave Thermosetting Prepregs during Forming Processes
Authors: Hassan A. Alshahrani, Mehdi H. Hojjati
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In order to predict and model wrinkling which is caused by out of plane deformation due to compressive loading in the plane of the material during composite prepregs forming, it is necessary to quantitatively understand the relative magnitude of the bending stiffness. This study aims to examine the bending properties of out-of-autoclave (OOA) thermosetting prepreg under vertical cantilever test condition. A direct method for characterizing the bending behavior of composite prepregs was developed. The results from direct measurement were compared with results derived from an image-processing procedure that analyses the captured image during the vertical bending test. A numerical simulation was performed using ABAQUS to confirm the bending stiffness value.Keywords: Bending stiffness, out-of-autoclave prepreg, forming process, numerical simulation.
Procedia PDF Downloads 2911962 Investigating the Sloshing Characteristics of a Liquid by Using an Image Processing Method
Authors: Ufuk Tosun, Reza Aghazadeh, Mehmet Bülent Özer
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This study puts forward a method to analyze the sloshing characteristics of liquid in a tuned sloshing absorber system by using image processing tools. Tuned sloshing vibration absorbers have recently attracted researchers’ attention as a seismic load damper in constructions due to its practical and logistical convenience. The absorber is liquid which sloshes and applies a force in opposite phase to the motion of structure. Experimentally characterization of the sloshing behavior can be utilized as means of verifying the results of numerical analysis. It can also be used to identify the accuracy of assumptions related to the motion of the liquid. There are extensive theoretical and experimental studies in the literature related to the dynamical and structural behavior of tuned sloshing dampers. In most of these works there are efforts to estimate the sloshing behavior of the liquid such as free surface motion and total force applied by liquid to the wall of container. For these purposes the use of sensors such as load cells and ultrasonic sensors are prevalent in experimental works. Load cells are only capable of measuring the force and requires conducting tests both with and without liquid to obtain pure sloshing force. Ultrasonic level sensors give point-wise measurements and hence they are not applicable to measure the whole free surface motion. Furthermore, in the case of liquid splashing it may give incorrect data. In this work a method for evaluating the sloshing wave height by using camera records and image processing techniques is presented. In this method the motion of the liquid and its container, made of a transparent material, is recorded by a high speed camera which is aligned to the free surface of the liquid. The video captured by the camera is processed frame by frame by using MATLAB Image Processing toolbox. The process starts with cropping the desired region. By recognizing the regions containing liquid and eliminating noise and liquid splashing, the final picture depicting the free surface of liquid is achieved. This picture then is used to obtain the height of the liquid through the length of container. This process is verified by ultrasonic sensors that measured fluid height on the surface of liquid.Keywords: fluid structure interaction, image processing, sloshing, tuned liquid damper
Procedia PDF Downloads 3381961 A Sustainable Society and Its Order Principles: Implications of Common Grace and the Man as the Image of God
Authors: Wenfu Zheng, Guanghe Zheng
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The discussion on the social sustainability in existing literature is limited to two-dimension epistemology space with only two elements: the human and nature. Using the revelation of the Bible God, the paper adds a moral component to the two-dimension space. With the new variable being introduced, the authors formulate a to three-dimension epistemology space and discuss its implications. Based on the space, the authors explore the hierarchical structure of order principles for a sustainable society. The social order principle system hierarchically consists of three principles: moral, relational, and rational. The justification of every principle is analyzed briefly. The paper concluded that all these order principles are necessary assurance of building a sustainable society.Keywords: common grace, saving grace, sustainable society, the image of God
Procedia PDF Downloads 1801960 The Image of Egypt in CNN, BBC and Al Jazeera News Channels in Terms of Democracy, Economic Status and Stability
Authors: Sarah El Mokadem
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Egypt has been the focus of international media since 2011 revolution and its repercussions. By the end of 2017, President Abdel Fattah El Sisi will have finished his first term of presidency. With an upcoming presidential election, all eyes are returning back to Egypt as there are speculations about whether the current regime will uphold or change points in the constitution determining the years of presidency term and the allowed number or reelections. In this paper, the researcher examines the reports related to Egypt in three international news channels with different ideologies. The research aims to identify the frames used to portray major issues in Egypt like the economic struggle, democracy levels and stability and safety of the country. All available reports from these three channels in 2017 on YouTube were analyzed which is the year before the presidential elections.Keywords: content analysis, Egypt, image building, news channel ideology
Procedia PDF Downloads 2101959 DBN-Based Face Recognition System Using Light Field
Authors: Bing Gu
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Abstract—Most of Conventional facial recognition systems are based on image features, such as LBP, SIFT. Recently some DBN-based 2D facial recognition systems have been proposed. However, we find there are few DBN-based 3D facial recognition system and relative researches. 3D facial images include all the individual biometric information. We can use these information to build more accurate features, So we present our DBN-based face recognition system using Light Field. We can see Light Field as another presentation of 3D image, and Light Field Camera show us a way to receive a Light Field. We use the commercially available Light Field Camera to act as the collector of our face recognition system, and the system receive a state-of-art performance as convenient as conventional 2D face recognition system.Keywords: DBN, face recognition, light field, Lytro
Procedia PDF Downloads 4541958 Determinants of Customer Satisfaction: The case of Abyssinia Bank Customers in Addis Ababa Ethiopia
Authors: Yosef Ferede Bogale
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The purpose of this study was to evaluate the degree of customer satisfaction and the variables influencing it in the instance of the Bank of Abyssinia branches in the districts of Arada and Bole in Addis Ababa. The study was carried out utilizing a mixed research approach and a descriptive and explanatory research design in Addis Ababa, the capital city of Ethiopia. Both primary and secondary data were employed in this investigation. The study's target population consisted of 1000 of the bank's most prestigious clients. With a 93% response rate, 265 respondents from both genders in the active age group had higher levels of education and work experience and were in the active age group. Customers of the case bank under consideration comprised the study's target audience. The respondents, who belonged to both gender groups, were in the active age bracket with superior levels of education and work experience. As a result, this investigation discovered that the degree of client satisfaction was assigned a medium rating. Additionally given a middling rating were the company's image practices, employee competency, technology, and service quality. Further, the results also demonstrate that corporate image, employees’ competency, technology, and service quality all positively and significantly affect customer happiness. This study found that, to varying degrees, company image, technology, competence, and high-quality financial services will all improve consumer happiness. According to this report, banks should monitor customer satisfaction and service quality at least twice a year. This is because there is a growing movement among bank service providers for accountability, and measuring these factors is crucial. This study also recommends that banks make every effort to satisfy consumers' expectations to the highest level.Keywords: customer satisfaction, corporate image, quality service risk, banks
Procedia PDF Downloads 911957 A Neural Approach for the Offline Recognition of the Arabic Handwritten Words of the Algerian Departments
Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda
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In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction
Procedia PDF Downloads 5051956 Calculating Ventricle’s Area Based on Clinical Dementia Rating Values on Coronal MRI Image
Authors: Retno Supriyanti, Ays Rahmadian Subhi, Yogi Ramadhani, Haris B. Widodo
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Alzheimer is one type of disease in the elderly that may occur in the world. The severity of the Alzheimer can be measured using a scale called Clinical Dementia Rating (CDR) based on a doctor's diagnosis of the patient's condition. Currently, diagnosis of Alzheimer often uses MRI machine, to know the condition of part of the brain called Hippocampus and Ventricle. MRI image itself consists of 3 slices, namely Coronal, Sagittal and Axial. In this paper, we discussed the measurement of the area of the ventricle especially in the Coronal slice based on the severity level referring to the CDR value. We use Active Contour method to segment the ventricle’s region, therefore that ventricle’s area can be calculated automatically. The results show that this method can be used for further development in the automatic diagnosis of Alzheimer.Keywords: Alzheimer, CDR, coronal, ventricle, active contour
Procedia PDF Downloads 2551955 Detection of Powdery Mildew Disease in Strawberry Using Image Texture and Supervised Classifiers
Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang
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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors
Procedia PDF Downloads 1141954 Determinants of Customer Satisfaction: The Case of Abyssinia Bank Customers in Addis Ababa Ethiopia
Authors: Yosef Ferede Bogale
Abstract:
The purpose of this study was to evaluate the degree of customer satisfaction and the variables influencing it in the instance of the Bank of Abyssinia branches in the districts of Arada and Bole in Addis Ababa. The study was carried out utilizing a mixed research approach and a descriptive and explanatory research design in Addis Ababa, the capital city of Ethiopia. Both primary and secondary data were employed in this investigation. The study's target population consisted of 1000 of the bank's most prestigious clients. With a 93% response rate, 265 respondents from both genders in the active age group had higher levels of education and work experience and were in the active age group. Customers of the case bank under consideration comprised the study's target audience. The respondents, who belonged to both gender groups, were in the active age bracket with superior levels of education and work experience. As a result, this investigation discovered that the degree of client satisfaction was assigned a medium rating. Additionally given a middling rating were the company's image practices, employee competency, technology, and service quality. Further, the results also demonstrate that corporate image, employees’ competency, technology, and service quality all positively and significantly affect customer happiness. This study found that, to varying degrees, company image, technology, competence, and high-quality financial services will all improve consumer happiness. According to this report, banks should monitor customer satisfaction and service quality at least twice a year. This is because there is a growing movement among bank service providers for accountability, and measuring these factors is crucial. This study also recommends that banks make every effort to satisfy consumers' expectations to the highest level.Keywords: customer satisfaction, corporate image, quality services risk, bank
Procedia PDF Downloads 471953 Heritage, Cultural Events and Promises for Better Future: Media Strategies for Attracting Tourism during the Arab Spring Uprisings
Authors: Eli Avraham
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The Arab Spring was widely covered in the global media and the number of Western tourists traveling to the area began to fall. The goal of this study was to analyze which media strategies marketers in Middle Eastern countries chose to employ in their attempts to repair the negative image of the area in the wake of the Arab Spring. Several studies were published concerning image-restoration strategies of destinations during crises around the globe; however, these strategies were not part of an overarching theory, conceptual framework or model from the fields of crisis communication and image repair. The conceptual framework used in the current study was the ‘multi-step model for altering place image’, which offers three types of strategies: source, message and audience. Three research questions were used: 1.What public relations crisis techniques and advertising campaign components were used? 2. What media policies and relationships with the international media were adopted by Arab officials? 3. Which marketing initiatives (such as cultural and sports events) were promoted? This study is based on qualitative content analysis of four types of data: 1) advertising components (slogans, visuals and text); (2) press interviews with Middle Eastern officials and marketers; (3) official media policy adopted by government decision-maker (e.g. boycotting or arresting newspeople); and (4) marketing initiatives (e.g. organizing heritage festivals and cultural events). The data was located in three channels from December 2010, when the events started, to September 31, 2013: (1) Internet and video-sharing websites: YouTube and Middle Eastern countries' national tourism board websites; (2) News reports from two international media outlets, The New York Times and Ha’aretz; these are considered quality newspapers that focus on foreign news and tend to criticize institutions; (3) Global tourism news websites: eTurbo news and ‘Cities and countries branding’. Using the ‘multi-step model for altering place image,’ the analysis reveals that Middle Eastern marketers and officials used three kinds of strategies to repair their countries' negative image: 1. Source (cooperation and media relations; complying, threatening and blocking the media; and finding alternatives to the traditional media) 2. Message (ignoring, limiting, narrowing or reducing the scale of the crisis; acknowledging the negative effect of an event’s coverage and assuring a better future; promotion of multiple facets, exhibitions and softening the ‘hard’ image; hosting spotlight sporting and cultural events; spinning liabilities into assets; geographic dissociation from the Middle East region; ridicule the existing stereotype) and 3. Audience (changing the target audience by addressing others; emphasizing similarities and relevance to specific target audience). It appears that dealing with their image problems will continue to be a challenge for officials and marketers of Middle Eastern countries until the region stabilizes and its regional conflicts are resolved.Keywords: Arab spring, cultural events, image repair, Middle East, tourism marketing
Procedia PDF Downloads 2721952 Use of Segmentation and Color Adjustment for Skin Tone Classification in Dermatological Images
Authors: Fernando Duarte
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The work aims to evaluate the use of classical image processing methodologies towards skin tone classification in dermatological images. The skin tone is an important attribute when considering several factor for skin cancer diagnosis. Currently, there is a lack of clear methodologies to classify the skin tone based only on the dermatological image. In this work, a recent released dataset with the label for skin tone was used as reference for the evaluation of classical methodologies for segmentation and adjustment of color space for classification of skin tone in dermatological images. It was noticed that even though the classical methodologies can work fine for segmentation and color adjustment, classifying the skin tone without proper control of the aquisition of the sample images ended being very unreliable.Keywords: segmentation, classification, color space, skin tone, Fitzpatrick
Procedia PDF Downloads 71951 Using Scale Invariant Feature Transform Features to Recognize Characters in Natural Scene Images
Authors: Belaynesh Chekol, Numan Çelebi
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The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.Keywords: character recognition, KNN, natural scene image, SIFT
Procedia PDF Downloads 2741950 Simulation of X-Ray Tissue Contrast and Dose Optimisation in Radiological Physics to Improve Medical Imaging Students’ Skills
Authors: Peter J. Riley
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Medical Imaging students must understand the roles of Photo-electric Absorption (PE) and Compton Scatter (CS) interactions in patients to enable optimal X-ray imaging in clinical practice. A simulator has been developed that shows relative interaction probabilities, color bars for patient dose from PE, % penetration to the detector, and obscuring CS as Peak Kilovoltage (kVp) changes. Additionally, an anthropomorphic chest X-ray image shows the relative tissue contrasts and overlying CS-fog at that kVp, which determine the detectability of a lesion in the image. A series of interactive exercises with MCQs evaluate the student's understanding; the simulation has improved student perception of the need to acquire "sufficient" rather than maximal contrast to enable patient dose reduction at higher kVp.Keywords: patient dose optimization, radiological physics, simulation, tissue contrast
Procedia PDF Downloads 801949 Words Spotting in the Images Handwritten Historical Documents
Authors: Issam Ben Jami
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Information retrieval in digital libraries is very important because most famous historical documents occupy a significant value. The word spotting in historical documents is a very difficult notion, because automatic recognition of such documents is naturally cursive, it represents a wide variability in the level scale and translation words in the same documents. We first present a system for the automatic recognition, based on the extraction of interest points words from the image model. The extraction phase of the key points is chosen from the representation of the image as a synthetic description of the shape recognition in a multidimensional space. As a result, we use advanced methods that can find and describe interesting points invariant to scale, rotation and lighting which are linked to local configurations of pixels. We test this approach on documents of the 15th century. Our experiments give important results.Keywords: feature matching, historical documents, pattern recognition, word spotting
Procedia PDF Downloads 2671948 Black-Box-Base Generic Perturbation Generation Method under Salient Graphs
Authors: Dingyang Hu, Dan Liu
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DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.Keywords: adversarial sample, gradient, probability, black box
Procedia PDF Downloads 881947 Scintigraphic Image Coding of Region of Interest Based on SPIHT Algorithm Using Global Thresholding and Huffman Coding
Authors: A. Seddiki, M. Djebbouri, D. Guerchi
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Medical imaging produces human body pictures in digital form. Since these imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. Many current compression schemes provide a very high compression rate but with considerable loss of quality. On the other hand, in some areas in medicine, it may be sufficient to maintain high image quality only in region of interest (ROI). This paper discusses a contribution to the lossless compression in the region of interest of Scintigraphic images based on SPIHT algorithm and global transform thresholding using Huffman coding.Keywords: global thresholding transform, huffman coding, region of interest, SPIHT coding, scintigraphic images
Procedia PDF Downloads 3541946 Edge Enhancement Visual Methodology for Fat Amount and Distribution Assessment in Dry-Cured Ham Slices
Authors: Silvia Grassi, Stefano Schiavon, Ernestina Casiraghi, Cristina Alamprese
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Dry-cured ham is an uncooked meat product particularly appreciated for its peculiar sensory traits among which lipid component plays a key role in defining quality and, consequently, consumers’ acceptability. Usually, fat content and distribution are chemically determined by expensive, time-consuming, and destructive analyses. Moreover, different sensory techniques are applied to assess product conformity to desired standards. In this context, visual systems are getting a foothold in the meat market envisioning more reliable and time-saving assessment of food quality traits. The present work aims at developing a simple but systematic and objective visual methodology to assess the fat amount of dry-cured ham slices, in terms of total, intermuscular and intramuscular fractions. To the aim, 160 slices from 80 PDO dry-cured hams were evaluated by digital image analysis and Soxhlet extraction. RGB images were captured by a flatbed scanner, converted in grey-scale images, and segmented based on intensity histograms as well as on a multi-stage algorithm aimed at edge enhancement. The latter was performed applying the Canny algorithm, which consists of image noise reduction, calculation of the intensity gradient for each image, spurious response removal, actual thresholding on corrected images, and confirmation of strong edge boundaries. The approach allowed for the automatic calculation of total, intermuscular and intramuscular fat fractions as percentages of the total slice area. Linear regression models were run to estimate the relationships between the image analysis results and the chemical data, thus allowing for the prediction of the total, intermuscular and intramuscular fat content by the dry-cured ham images. The goodness of fit of the obtained models was confirmed in terms of coefficient of determination (R²), hypothesis testing and pattern of residuals. Good regression models have been found being 0.73, 0.82, and 0.73 the R2 values for the total fat, the sum of intermuscular and intramuscular fat and the intermuscular fraction, respectively. In conclusion, the edge enhancement visual procedure brought to a good fat segmentation making the simple visual approach for the quantification of the different fat fractions in dry-cured ham slices sufficiently simple, accurate and precise. The presented image analysis approach steers towards the development of instruments that can overcome destructive, tedious and time-consuming chemical determinations. As future perspectives, the results of the proposed image analysis methodology will be compared with those of sensory tests in order to develop a fast grading method of dry-cured hams based on fat distribution. Therefore, the system will be able not only to predict the actual fat content but it will also reflect the visual appearance of samples as perceived by consumers.Keywords: dry-cured ham, edge detection algorithm, fat content, image analysis
Procedia PDF Downloads 1701945 Automated Feature Detection and Matching Algorithms for Breast IR Sequence Images
Authors: Chia-Yen Lee, Hao-Jen Wang, Jhih-Hao Lai
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In recent years, infrared (IR) imaging has been considered as a potential tool to assess the efficacy of chemotherapy and early detection of breast cancer. Regions of tumor growth with high metabolic rate and angiogenesis phenomenon lead to the high temperatures. Observation of differences between the heat maps in long term is useful to help assess the growth of breast cancer cells and detect breast cancer earlier, wherein the multi-time infrared image alignment technology is a necessary step. Representative feature points detection and matching are essential steps toward the good performance of image registration and quantitative analysis. However, there is no clear boundary on the infrared images and the subject's posture are different for each shot. It cannot adhesive markers on a body surface for a very long period, and it is hard to find anatomic fiducial markers on a body surface. In other words, it’s difficult to detect and match features in an IR sequence images. In this study, automated feature detection and matching algorithms with two type of automatic feature points (i.e., vascular branch points and modified Harris corner) are developed respectively. The preliminary results show that the proposed method could identify the representative feature points on the IR breast images successfully of 98% accuracy and the matching results of 93% accuracy.Keywords: Harris corner, infrared image, feature detection, registration, matching
Procedia PDF Downloads 2951944 High Altitude Glacier Surface Mapping in Dhauliganga Basin of Himalayan Environment Using Remote Sensing Technique
Authors: Aayushi Pandey, Manoj Kumar Pandey, Ashutosh Tiwari, Kireet Kumar
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Glaciers play an important role in climate change and are sensitive phenomena of global climate change scenario. Glaciers in Himalayas are unique as they are predominantly valley type and are located in tropical, high altitude regions. These glaciers are often covered with debris which greatly affects ablation rate of glaciers and work as a sensitive indicator of glacier health. The aim of this study is to map high altitude Glacier surface with a focus on glacial lake and debris estimation using different techniques in Nagling glacier of dhauliganga basin in Himalayan region. Different Image Classification techniques i.e. thresholding on different band ratios and supervised classification using maximum likelihood classifier (MLC) have been used on high resolution sentinel 2A level 1c satellite imagery of 14 October 2017.Here Near Infrared (NIR)/Shortwave Infrared (SWIR) ratio image was used to extract the glaciated classes (Snow, Ice, Ice Mixed Debris) from other non-glaciated terrain classes. SWIR/BLUE Ratio Image was used to map valley rock and Debris while Green/NIR ratio image was found most suitable for mapping Glacial Lake. Accuracy assessment was performed using high resolution (3 meters) Planetscope Imagery using 60 stratified random points. The overall accuracy of MLC was 85 % while the accuracy of Band Ratios was 96.66 %. According to Band Ratio technique total areal extent of glaciated classes (Snow, Ice ,IMD) in Nagling glacier was 10.70 km2 nearly 38.07% of study area comprising of 30.87 % Snow covered area, 3.93% Ice and 3.27 % IMD covered area. Non-glaciated classes (vegetation, glacial lake, debris and valley rock) covered 61.93 % of the total area out of which valley rock is dominant with 33.83% coverage followed by debris covering 27.7 % of the area in nagling glacier. Glacial lake and Debris were accurately mapped using Band ratio technique Hence, Band Ratio approach appears to be useful for the mapping of debris covered glacier in Himalayan Region.Keywords: band ratio, Dhauliganga basin, glacier mapping, Himalayan region, maximum likelihood classifier (MLC), Sentinel-2 satellite image
Procedia PDF Downloads 2191943 The Intersection/Union Region Computation for Drosophila Brain Images Using Encoding Schemes Based on Multi-Core CPUs
Authors: Ming-Yang Guo, Cheng-Xian Wu, Wei-Xiang Chen, Chun-Yuan Lin, Yen-Jen Lin, Ann-Shyn Chiang
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With more and more Drosophila Driver and Neuron images, it is an important work to find the similarity relationships among them as the functional inference. There is a general problem that how to find a Drosophila Driver image, which can cover a set of Drosophila Driver/Neuron images. In order to solve this problem, the intersection/union region for a set of images should be computed at first, then a comparison work is used to calculate the similarities between the region and other images. In this paper, three encoding schemes, namely Integer, Boolean, Decimal, are proposed to encode each image as a one-dimensional structure. Then, the intersection/union region from these images can be computed by using the compare operations, Boolean operators and lookup table method. Finally, the comparison work is done as the union region computation, and the similarity score can be calculated by the definition of Tanimoto coefficient. The above methods for the region computation are also implemented in the multi-core CPUs environment with the OpenMP. From the experimental results, in the encoding phase, the performance by the Boolean scheme is the best than that by others; in the region computation phase, the performance by Decimal is the best when the number of images is large. The speedup ratio can achieve 12 based on 16 CPUs. This work was supported by the Ministry of Science and Technology under the grant MOST 106-2221-E-182-070.Keywords: Drosophila driver image, Drosophila neuron images, intersection/union computation, parallel processing, OpenMP
Procedia PDF Downloads 2291942 Inspection of Railway Track Fastening Elements Using Artificial Vision
Authors: Abdelkrim Belhaoua, Jean-Pierre Radoux
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In France, the railway network is one of the main transport infrastructures and is the second largest European network. Therefore, railway inspection is an important task in railway maintenance to ensure safety for passengers using significant means in personal and technical facilities. Artificial vision has recently been applied to several railway applications due to its potential to improve the efficiency and accuracy when analyzing large databases of acquired images. In this paper, we present a vision system able to detect fastening elements based on artificial vision approach. This system acquires railway images using a CCD camera installed under a control carriage. These images are stitched together before having processed. Experimental results are presented to show that the proposed method is robust for detection fasteners in a complex environment.Keywords: computer vision, image processing, railway inspection, image stitching, fastener recognition, neural network
Procedia PDF Downloads 4401941 A Visualization Classification Method for Identifying the Decayed Citrus Fruit Infected by Fungi Based on Hyperspectral Imaging
Authors: Jiangbo Li, Wenqian Huang
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Early detection of fungal infection in citrus fruit is one of the major problems in the postharvest commercialization process. The automatic and nondestructive detection of infected fruits is still a challenge for the citrus industry. At present, the visual inspection of rotten citrus fruits is commonly performed by workers through the ultraviolet induction fluorescence technology or manual sorting in citrus packinghouses to remove fruit subject with fungal infection. However, the former entails a number of problems because exposing people to this kind of lighting is potentially hazardous to human health, and the latter is very inefficient. Orange is used as a research object. This study would focus on this problem and proposed an effective method based on Vis-NIR hyperspectral imaging in the wavelength range of 400-1000 nm with a spectroscopic resolution of 2.8 nm. In this work, three normalization approaches are applied prior to analysis to reduce the effect of sample curvature on spectral profiles, and it is found that mean normalization was the most effective pretreatment for decreasing spectral variability due to curvature. Then, principal component analysis (PCA) was applied to a dataset composing of average spectra from decayed and normal tissue to reduce the dimensionality of data and observe the ability of Vis-NIR hyper-spectra to discriminate data from two classes. In this case, it was observed that normal and decayed spectra were separable along the resultant first principal component (PC1) axis. Subsequently, five wavelengths (band) centered at 577, 702, 751, 808, and 923 nm were selected as the characteristic wavelengths by analyzing the loadings of PC1. A multispectral combination image was generated based on five selected characteristic wavelength images. Based on the obtained multispectral combination image, the intensity slicing pseudocolor image processing method is used to generate a 2-D visual classification image that would enhance the contrast between normal and decayed tissue. Finally, an image segmentation algorithm for detection of decayed fruit was developed based on the pseudocolor image coupled with a simple thresholding method. For the investigated 238 independent set samples including infected fruits infected by Penicillium digitatum and normal fruits, the total success rate is 100% and 97.5%, respectively, and, the proposed algorithm also used to identify the orange infected by penicillium italicum with a 100% identification accuracy, indicating that the proposed multispectral algorithm here is an effective method and it is potential to be applied in citrus industry.Keywords: citrus fruit, early rotten, fungal infection, hyperspectral imaging
Procedia PDF Downloads 2881940 DenseNet and Autoencoder Architecture for COVID-19 Chest X-Ray Image Classification and Improved U-Net Lung X-Ray Segmentation
Authors: Jonathan Gong
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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning
Procedia PDF Downloads 1211939 Employee Branding: An Exploratory Study Applied to Nurses in an Organization
Authors: Pawan Hinge, Priya Gupta
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Due to cutting edge competitions between organizations and war for talent, the workforce as an asset is gaining significance. The employees are considered as the brand ambassadors of an organization, and their interactions with the clients and customers might impact directly or indirectly on the overall value of the organization. Especially, organizations in the healthcare industry the value of an organization in the perception of their employees can be one of the revenue generating and talent retention strategy. In such context, it is essential to understand that the brand awareness among employees can effect on employer brand image and brand value since the brand ambassadors are the interface between organization and customers and clients. In this exploratory study, we have adopted both quantitative and qualitative approaches for data analysis. Our study shows existing variation among nurses working in different business units of the same organization in terms of their customer interface or interactions and brand awareness.Keywords: brand awareness, brand image, brand value, customer interface
Procedia PDF Downloads 2741938 Video Club as a Pedagogical Tool to Shift Teachers’ Image of the Child
Authors: Allison Tucker, Carolyn Clarke, Erin Keith
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Introduction: In education, the determination to uncover privileged practices requires critical reflection to be placed at the center of both pre-service and in-service teacher education. Confronting deficit thinking about children’s abilities and shifting to holding an image of the child as capable and competent is necessary for teachers to engage in responsive pedagogy that meets children where they are in their learning and builds on strengths. This paper explores the ways in which early elementary teachers' perceptions of the assets of children might shift through the pedagogical use of video clubs. Video club is a pedagogical practice whereby teachers record and view short videos with the intended purpose of deepening their practices. The use of video club as a learning tool has been an extensively documented practice. In this study, a video club is used to watch short recordings of playing children to identify the assets of their students. Methodology: The study on which this paper is based asks the question: What are the ways in which teachers’ image of the child and teaching practices evolve through the use of video club focused on the strengths of children demonstrated during play? Using critical reflection, it aims to identify and describe participants’ experiences of examining their personally held image of the child through the pedagogical tool video club, and how that image influences their practices, specifically in implementing play pedagogy. Teachers enrolled in a graduate-level play pedagogy course record and watch videos of their own students as a means to notice and reflect on the learning that happens during play. Using a co-constructed viewing protocol, teachers identify student strengths and consider their pedagogical responses. Video club provides a framework for teachers to critically reflect in action, return to the video to rewatch the children or themselves and discuss their noticings with colleagues. Critical reflection occurs when there is focused attention on identifying the ways in which actions perpetuate or challenge issues of inherent power in education. When the image of the child held by the teacher is from a deficit position and is influenced by hegemonic dimensions of practice, critical reflection is essential in naming and addressing power imbalances, biases, and practices that are harmful to children and become barriers to their thriving. The data is comprised of teacher reflections, analyzed using phenomenology. Phenomenology seeks to understand and appreciate how individuals make sense of their experiences. Teacher reflections are individually read, and researchers determine pools of meaning. Categories are identified by each researcher, after which commonalities are named through a recursive process of returning to the data until no more themes emerge or saturation is reached. Findings: The final analysis and interpretation of the data are forthcoming. However, emergent analysis of the data collected using teacher reflections reveals the ways in which the use of video club grew teachers’ awareness of their image of the child. It shows video club as a promising pedagogical tool when used with in-service teachers to prompt opportunities for play and to challenge deficit thinking about children and their abilities to thrive in learning.Keywords: asset-based teaching, critical reflection, image of the child, video club
Procedia PDF Downloads 951937 Music Note Detection and Dictionary Generation from Music Sheet Using Image Processing Techniques
Authors: Muhammad Ammar, Talha Ali, Abdul Basit, Bakhtawar Rajput, Zobia Sohail
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Music note detection is an area of study for the past few years and has its own influence in music file generation from sheet music. We proposed a method to detect music notes on sheet music using basic thresholding and blob detection. Subsequently, we created a notes dictionary using a semi-supervised learning approach. After notes detection, for each test image, the new symbols are added to the dictionary. This makes the notes detection semi-automatic. The experiments are done on images from a dataset and also on the captured images. The developed approach showed almost 100% accuracy on the dataset images, whereas varying results have been seen on captured images.Keywords: music note, sheet music, optical music recognition, blob detection, thresholding, dictionary generation
Procedia PDF Downloads 1691936 Kernel-Based Double Nearest Proportion Feature Extraction for Hyperspectral Image Classification
Authors: Hung-Sheng Lin, Cheng-Hsuan Li
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Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE.Keywords: feature extraction, kernel method, double nearest proportion feature extraction, kernel double nearest feature extraction
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