Search results for: breast cancer detection.

Authors: Samar Krimi, Kaïs Ouni, Noureddine Ellouze

Abstract:

The method described in this paper deals with the problems of T-wave detection in an ECG. Determining the position of a T-wave is complicated due to the low amplitude, the ambiguous and changing form of the complex. A wavelet transform approach handles these complications therefore a method based on this concept was developed. In this way we developed a detection method that is able to detect T-waves with a sensitivity of 93% and a correct-detection ratio of 93% even with a serious amount of baseline drift and noise.

Keywords: ECG, Modulus Maxima Wavelet Transform, Performance, T-wave detection

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Authors: Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang, Zhang Mengxia, Guo Aimin, Wang Wei

Abstract:

Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.

Keywords: CT image, Curvelet transform, Small pulmonary nodules, Support vector machines, Texture extraction.

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Authors: Ş. Çiftcioğlu, E. Efe

Abstract:

In recent years, cancer has been at the top of diseases that cause death in children. Adequate and balanced nutrition plays an important role in the treatment of cancer. Cancer and cancer treatment is affecting food intake, absorption and metabolism, causing nutritional disorders. Appropriate nutrition is very important for the cancerous child to feel well before, during and after the treatment. There are various difficulties in feeding children with cancer. These are the cancer-related factors. Other factors are environmental and behavioral. As health professionals who spend more time with children in the hospital, nurses should be able to support the children on nutrition and help them to have balanced nutrition. This study aimed to evaluate the importance of nursing approaches in the nutrition of children with cancer. This article is planned as a review article by searching the literature on this field. Anorexia may develop due to psychogenic causes or chemotherapeutic agents or accompanying infections and nutrient uptake may be reduced.  In addition, stomatitis, mucositis, taste and odor changes in the mouth, the feeling of nausea, vomiting and diarrhea can also reduce oral intake and result in significant losses in the energy deficit. In assessing the nutritional status of children with cancer, determining weight loss and good nutrition is essential anamnesis of a child.  Some anthropometric measurements and biochemical tests should be used to evaluate the nutrition of the child. The nutritional status of pediatric cancer patients has been studied for a long time and malnutrition, in particular under nutrition, in this population has long been recognized. Yet, its management remains variable with many malnourished children going unrecognized and consequently untreated. Nutritional support is important to pediatric cancer patients and should be integrated into the overall treatment of these children.

Keywords: Cancer treatment, children, complication, nutrition, nursing approaches.

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Authors: Masrour Dowlatabadi, Jalil Shirazi

Abstract:

In this paper, an improved edge detection algorithm based on fuzzy combination of mathematical morphology and wavelet transform is proposed. The combined method is proposed to overcome the limitation of wavelet based edge detection and mathematical morphology based edge detection in noisy images. Experimental results show superiority of the proposed method, as compared to the traditional Prewitt, wavelet based and morphology based edge detection methods. The proposed method is an effective edge detection method for noisy image and keeps clear and continuous edges.

Keywords: Edge detection, Wavelet transform, Mathematical morphology, Fuzzy logic.

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Authors: Deng Shiwei

Abstract:

The users are now expecting higher level of DSP(Digital Signal Processing) software quality than ever before. Prevention and detection of defect are critical elements of software quality assurance. In this paper, principles and rules for prevention and detection of defect are suggested, which are not universal guidelines, but are useful for both novice and experienced DSP software developers.

Keywords: defect detection, defect prevention, DSP-software, software development, software testing.

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Authors: Zhang Xiaodong, Tang Zixin, Liu Xin

Abstract:

In order to avoid self-collision of space manipulators during operation process, a real-time detection method is proposed in this paper. The manipulator is fitted into a cylinder-enveloping surface, and then, a kind of detection algorithm of collision between cylinders is analyzed. The collision model of space manipulator self-links can be detected by using this algorithm in real-time detection during the operation process. To ensure security of the operation, a safety threshold is designed. The simulation and experiment results verify the effectiveness of the proposed algorithm for a 7-DOF space manipulator.

Keywords: Space manipulator, Collision detection, Self-collision, the real-time collision detection.

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Authors: Medjram Sofiane, Babahenini Mohamed Chaouki, Mohamed Benali Yamina

Abstract:

Skin detection is an important task for computer vision systems. A good method of skin detection means a good and successful result of the system. The colour is a good descriptor for image segmentation and classification; it allows detecting skin colour in the images. The lighting changes and the objects that have a colour similar than skin colour make the operation of skin detection difficult. In this paper, we proposed a method using the YCbCr colour space for skin detection and lighting effects elimination, then we use the information of texture to eliminate the false regions detected by the YCbCr skin model.

Keywords: Skin detection, YCbCr, GLCM, Texture, Human skin.

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Authors: Zeynep R. Ege, Aydin Akan, Faik N. Oktar, Betul Karademir, Oguzhan Gunduz

Abstract:

Early detection of cancer could save human life and quality in insidious cases by advanced biomedical imaging techniques. Designing targeted detection system is necessary in order to protect of healthy cells. Electrospun nanofibers are efficient and targetable nanocarriers which have important properties such as nanometric diameter, mechanical properties, elasticity, porosity and surface area to volume ratio. In the present study, indocyanine green (ICG) organic dye was stabilized and encapsulated in polymer matrix which polyethylene oxide (PEO) and chitosan (CHI) multilayer nanofibers via co-axial electrospinning method at one step. The co-axial electrospun nanofibers were characterized as morphological (SEM), molecular (FT-IR), and entrapment efficiency of Indocyanine Green (ICG) (confocal imaging). Controlled release profile of PEO/CHI/ICG nanofiber was also evaluated up to 40 hours.

Keywords: Chitosan, coaxial electrospinning, controlled releasing, indocyanine green, nanoprobe, polyethylene oxide.

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Authors: Sulaiman Al Amro, Ali Alkhalifah

Abstract:

The growing number of computer viruses and the detection of zero day malware have been the concern for security researchers for a large period of time. Existing antivirus products (AVs) rely on detecting virus signatures which do not provide a full solution to the problems associated with these viruses. The use of logic formulae to model the behaviour of viruses is one of the most encouraging recent developments in virus research, which provides alternatives to classic virus detection methods. In this paper, we proposed a comparative study about different virus detection techniques. This paper provides the advantages and drawbacks of different detection techniques. Different techniques will be used in this paper to provide a discussion about what technique is more effective to detect computer viruses.

Keywords: Computer viruses, virus detection, signature-based, behaviour-based, heuristic-based.

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Authors: M. Ranjeeth, S. Anuradha

Abstract:

Spectrum sensing is the main feature of cognitive radio technology. Spectrum sensing gives an idea of detecting the presence of the primary users in a licensed spectrum. In this paper we compare the theoretical results of detection probability of different fading environments like Rayleigh, Rician, Nakagami-m fading channels with the simulation results using energy detection based spectrum sensing. The numerical results are plotted as Pf Vs Pd for different SNR values, fading parameters. It is observed that Nakagami fading channel performance is better than other fading channels by using energy detection in spectrum sensing. A MATLAB simulation test bench has been implemented to know the performance of energy detection in different fading channel environment.

Keywords: Spectrum sensing, Energy detection, fading channels, Probability of detection, probability of false alarm.

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Authors: Hyung-Keun Jee, Sung-Uk Jung, Jang-Hee Yoo

Abstract:

To increase reliability of face recognition system, the system must be able to distinguish real face from a copy of face such as a photograph. In this paper, we propose a fast and memory efficient method of live face detection for embedded face recognition system, based on the analysis of the movement of the eyes. We detect eyes in sequential input images and calculate variation of each eye region to determine whether the input face is a real face or not. Experimental results show that the proposed approach is competitive and promising for live face detection.

Keywords: Liveness Detection, Eye detection, SQI.

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Authors: F. Matusek, G. Pujolle, R. Reda

Abstract:

Shadow detection is still considered as one of the potential challenges for intelligent automated video surveillance systems. A pre requisite for reliable and accurate detection and tracking is the correct shadow detection and classification. In such a landscape of conditions, privacy issues add more and more complexity and require reliable shadow detection. In this work the intertwining between security, accuracy, reliability and privacy is analyzed and, accordingly, a novel architecture for Privacy Enhancing Video Surveillance (PEVS) is introduced. Shadow detection and masking are dealt with through the combination of two different approaches simultaneously. This results in a unique privacy enhancement, without affecting security. Subsequently, the methodology was employed successfully in a large-scale wireless video surveillance system; privacy relevant information was stored and encrypted on the unit, without transferring it over an un-trusted network.

Keywords: Video Surveillance, Intelligent Video Surveillance, Physical Security, WSSU, Privacy, Shadow Detection.

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Authors: Dewan Md. Farid, Nguyen Huu Hoa, Jerome Darmont, Nouria Harbi, Mohammad Zahidur Rahman

Abstract:

In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive naïve Bayesian tree (NBTree), which induces a hybrid of decision tree and naïve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and dynamic large intrusion detection dataset, the detection accuracy of naïve Bayesian classifier does not scale up as well as decision tree. It has been successfully tested in other problem domains that naïve Bayesian tree improves the classification rates in large dataset. In naïve Bayesian tree nodes contain and split as regular decision-trees, but the leaves contain naïve Bayesian classifiers. The experimental results on KDD99 benchmark network intrusion detection dataset demonstrate that this new approach scales up the detection rates for different attack types and reduces false positives in network intrusion detection.

Keywords: Detection rates, false positives, network intrusiondetection, naïve Bayesian tree.

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Authors: Se Ho Choi, Jong Pil Yun, Boyeul Seo, YoungSu Park, Sang Woo Kim

Abstract:

This paper presents a real-time defect detection algorithm for high-speed steel bar in coil. Because the target speed is very high, proposed algorithm should process quickly the large volumes of image for real-time processing. Therefore, defect detection algorithm should satisfy two conflicting requirements of reducing the processing time and improving the efficiency of defect detection. To enhance performance of detection, edge preserving method is suggested for noise reduction of target image. Finally, experiment results show that the proposed algorithm guarantees the condition of the real-time processing and accuracy of detection.

Keywords: Defect detection, edge preserving filter, real-time image processing, surface inspection.

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Authors: A. A. Shafie, Fadhlan Hafiz, M. H. Ali

Abstract:

Motion detection is very important in image processing. One way of detecting motion is using optical flow. Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. The method used for finding the optical flow in this project is assuming that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. This technique is later used in developing software for motion detection which has the capability to carry out four types of motion detection. The motion detection software presented in this project also can highlight motion region, count motion level as well as counting object numbers. Many objects such as vehicles and human from video streams can be recognized by applying optical flow technique.

Keywords: Background modeling, Motion detection, Optical flow, Velocity smoothness constant, motion trajectories.

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Authors: Sharareh Sharifi, Pargol Ghavam Mostafavi, Ali Mashinchian Moradi, Mohammad Hadi Givianrad, Hassan Niknejad

Abstract:

Marine organisms such as soft coral, sponge, ascidians, and tunicate containing rich source of natural compound have been studied in last decades because of their special chemical compounds with anticancer properties. The aim of this study was to investigate anti-cancer property of ethyl acetate extracted from marine sea pen Virgularia gustaviana found from Persian Gulf coastal (Bandar Abbas). The extraction processes were carried out with ethyl acetate for five days. Thin layer chromatography (TLC) and high-performance liquid chromatography (HPLC) were used for qualitative identification of crude extract. The viability of HeLa and MDA-Mb-231 cancer cells was investigated using MTT assay at the concentration of 25, 50, and a 100 µl/ml of ethyl acetate is extracted. The crude extract of Virgularia gustaviana demonstrated ten fractions with different Retention factor (Rf) by TLC and Retention time (Rt) evaluated by HPLC. The crude extract dose-dependently decreased cancer cell viability compared to control group. According to the results, the ethyl acetate extracted from Virgularia gustaviana inhibits the growth of cancer cells, an effect which needs to be further investigated in the future studies.

Keywords: Virgularia gustaviana, Cembrane Diterpene, anti-cancer, HeLa cancer Cell, MDA-Md-231 Cancer cell.

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Authors: Esyin Chew, Haydn Blackey

Abstract:

There are increasingly plagiarism offences for students in higher education in the digital educational world. On the other hand, various and competitive online assessment and plagiarism detection tools are available in the market. Taking the University of Glamorgan as a case study, this paper describes and introduces an institutional journey on electronic plagiarism detection to inform the initial experience of an innovative tool and method which could be further explored in the future research. The comparative study and system workflow for e-plagiarism detection tool are discussed. Benefits for both academics and students are also presented. Electronic plagiarism detection tools brought great benefits to both academics and students in Glamorgan. On the other hand, the debates raised in such initial experience are discussed.

Keywords: Educational Technology, Plagiarism detection, Turnitin

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Authors: Begol, Moslem, Maghooli, Keivan

Abstract:

Image Edge Detection is one of the most important parts of image processing. In this paper, by fuzzy technique, a new method is used to improve digital image edge detection. In this method, a 3x3 mask is employed to process each pixel by means of vicinity. Each pixel is considered a fuzzy input and by examining fuzzy rules in its vicinity, the edge pixel is specified and by utilizing calculation algorithms in image processing, edges are displayed more clearly. This method shows significant improvement compared to different edge detection methods (e.g. Sobel, Canny).

Keywords: Fuzzy Systems, Edge Detection, Fuzzy edgedetection

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Authors: T. S. Chou, K. K. Yen, J. Luo

Abstract:

The network traffic data provided for the design of intrusion detection always are large with ineffective information and enclose limited and ambiguous information about users- activities. We study the problems and propose a two phases approach in our intrusion detection design. In the first phase, we develop a correlation-based feature selection algorithm to remove the worthless information from the original high dimensional database. Next, we design an intrusion detection method to solve the problems of uncertainty caused by limited and ambiguous information. In the experiments, we choose six UCI databases and DARPA KDD99 intrusion detection data set as our evaluation tools. Empirical studies indicate that our feature selection algorithm is capable of reducing the size of data set. Our intrusion detection method achieves a better performance than those of participating intrusion detectors.

Keywords: Intrusion detection, feature selection, k-nearest neighbors, fuzzy clustering, Dempster-Shafer theory

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Authors: Chi-Shan Chen, Mao-Chou Hsu, Feng-Chuan Pan

Abstract:

The incidence of oral cancer in Taiwan increased year by year. It replaced the nasopharyngeal as the top incurrence among head and neck cancers since 1994. Early examination and earlier identification for earlier treatment is the most effective medical treatment for these cancers. Although the government fully subsidized the expenses with tremendous promotion program for oral cancer screening, the citizen-s participation remained low. Purpose of this study is to understand the factors affecting the citizens- behavior intensions of taking an oral cancer screening. Based on the Theory of Planned Behavior, this study adopted four distinctive variables in explaining the captioned behavior intentions.700 questionnaires were dispatched with 500 valid responses or 71.4% returned by the citizens with an age 30 or above from the eastern counties of Taiwan. Test results has shown that attitude toward, subjective norms of, and perceived behavioral control over the oral cancer screening varied from some demographic factors to another. The study proofed that attitude toward, subjective norms of, and perceived behavioral control over the oral cancer screening had positive impacts on the corresponding behavior intention. The test concluded that the theory of planned behavior was appropriate as a theoretical framework in explaining the influencing factors of intentions of taking oral cancer screening. This study suggested the healthcare professional should provide high accessibility of screening services other than just delivering knowledge on oral cancer to promote the citizens- intentions of taking the captioned screening. This research also provided a practical implication to the healthcare professionals when formulating and implementing promotion instruments for lifting the screening rate of oral cancer.

Keywords: Theory of planned behavior, oral cancer, cancer screening

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Authors: Marjan Kuchaki Rafsanjani, Ali Movaghar, Faroukh Koroupi

Abstract:

As mobile ad hoc networks (MANET) have different characteristics from wired networks and even from standard wireless networks, there are new challenges related to security issues that need to be addressed. Due to its unique features such as open nature, lack of infrastructure and central management, node mobility and change of dynamic topology, prevention methods from attacks on them are not enough. Therefore intrusion detection is one of the possible ways in recognizing a possible attack before the system could be penetrated. All in all, techniques for intrusion detection in old wireless networks are not suitable for MANET. In this paper, we classify the architecture for Intrusion detection systems that have so far been introduced for MANETs, and then existing intrusion detection techniques in MANET presented and compared. We then indicate important future research directions.

Keywords: Intrusion Detection System(IDS), Misbehavingnodes, Mobile Ad Hoc Network(MANET), Security.

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Authors: Fereshteh Mahdavi, Maizatul Akmar Ismail, Noorhidawati Abdullah

Abstract:

Currently WWW is the first solution for scholars in finding information. But, analyzing and interpreting this volume of information will lead to researchers overload in pursuing their research. Trend detection in scientific publication retrieval systems helps scholars to find relevant, new and popular special areas by visualizing the trend of input topic. However, there are few researches on trend detection in scientific corpora while their proposed models do not appear to be suitable. Previous works lack of an appropriate representation scheme for research topics. This paper describes a method that combines Semantic Web and ontology to support advance search functions such as trend detection in the context of scholarly Semantic Web system (SSWeb).

Keywords: Trend, Semi-Automatic Trend Detection, Ontology, Semantic Trend Detection.

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Authors: Padmakumar S., Vivek Agarwal, Kallol Roy

Abstract:

A model based fault detection and diagnosis technique for DC motor is proposed in this paper. Fault detection using Kalman filter and its different variants are compared. Only incipient faults are considered for the study. The Kalman Filter iterations and all the related computations required for fault detection and fault confirmation are presented. A second order linear state space model of DC motor is used for this work. A comparative assessment of the estimates computed from four different observers and their relative performance is evaluated.

Keywords: DC motor model, Fault detection and diagnosis Kalman Filter, Unscented Kalman Filter

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Authors: Shigenori Togashi, Kei Takenaka

Abstract:

We developed new processes which can collect and detect rapidly airborne pathogens such as the avian flu virus for the pandemic prevention. The fluorescence antibody technique is known as one of high-sensitive detection methods for viruses, but this needs up to a few hours to bind sufficient fluorescence dyes to viruses for detection. In this paper, we developed a mist-labeling can detect substitution viruses in a short time to improve the binding rate of fluorescent dyes and substitution viruses by the micro reaction process. Moreover, we developed the rapid detection system with the above “mist labeling”. The detection system set with a sampling bag collecting patient’s breath and a cartridge can detect automatically pathogens within 10 minutes.

Keywords: Viruses, Sampler, Mist, Detection, Fluorescent dyes, Microreaction.

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Authors: Mounira RouaÐùnia, Noureddine Doghmane

Abstract:

In this paper we consider the problem of change detection and non stationary signals tracking. Using parametric estimation of signals based on least square lattice adaptive filters we consider for change detection statistical parametric methods using likelihood ratio and hypothesis tests. In order to track signals dynamics, we introduce a compensation procedure in the adaptive estimation. This will improve the adaptive estimation performances and fasten it-s convergence after changes detection.

Keywords: Change detection, Hypothesis test, likelihood ratioleast square lattice adaptive filters.

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Authors: Amir Vahid Dastjerdi, Kamalrulnizam Abu Bakar

Abstract:

The first generation of Mobile Agents based Intrusion Detection System just had two components namely data collection and single centralized analyzer. The disadvantage of this type of intrusion detection is if connection to the analyzer fails, the entire system will become useless. In this work, we propose novel hybrid model for Mobile Agent based Distributed Intrusion Detection System to overcome the current problem. The proposed model has new features such as robustness, capability of detecting intrusion against the IDS itself and capability of updating itself to detect new pattern of intrusions. In addition, our proposed model is also capable of tackling some of the weaknesses of centralized Intrusion Detection System models.

Keywords: Distributed Intrusion Detection System, Mobile Agents, Network Security.

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Authors: Ferenc Peter Pach, Janos Abonyi

Abstract:

This paper focuses on the data-driven generation of fuzzy IF...THEN rules. The resulted fuzzy rule base can be applied to build a classifier, a model used for prediction, or it can be applied to form a decision support system. Among the wide range of possible approaches, the decision tree and the association rule based algorithms are overviewed, and two new approaches are presented based on the a priori fuzzy clustering based partitioning of the continuous input variables. An application study is also presented, where the developed methods are tested on the well known Wisconsin Breast Cancer classification problem.

Keywords:

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Authors: Joffroy Beauquier, Yongjie Hu

Abstract:

The intrusion detection problem has been frequently studied, but intrusion detection methods are often based on a single point of view, which always limits the results. In this paper, we introduce a new intrusion detection model based on the combination of different current methods. First we use a notion of distance to unify the different methods. Second we combine these methods using the Pearson correlation coefficients, which measure the relationship between two methods, and we obtain a combined distance. If the combined distance is greater than a predetermined threshold, an intrusion is detected. We have implemented and tested the combination model with two different public data sets: the data set of masquerade detection collected by Schonlau & al., and the data set of program behaviors from the University of New Mexico. The results of the experiments prove that the combination model has better performances.

Keywords: Intrusion detection, combination, distance, Pearson correlation coefficients.

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Authors: M A Hannan, A. Hussain, S. A. Samad, K. A. Ishak, A. Mohamed

Abstract:

This paper presents a general trainable framework for fast and robust upright human face and non-human object detection and verification in static images. To enhance the performance of the detection process, the technique we develop is based on the combination of fast neural network (FNN) and classical neural network (CNN). In FNN, a useful correlation is exploited to sustain high level of detection accuracy between input image and the weight of the hidden neurons. This is to enable the use of Fourier transform that significantly speed up the time detection. The combination of CNN is responsible to verify the face region. A bootstrap algorithm is used to collect non human object, which adds the false detection to the training process of the human and non-human object. Experimental results on test images with both simple and complex background demonstrate that the proposed method has obtained high detection rate and low false positive rate in detecting both human face and non-human object.

Keywords: Algorithm, detection of human and non-human object, FNN, CNN, Image training.

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Authors: Yin-Ching Keung, Kit-Lun Yick

Abstract:

The issue of bra fitting has persisted throughout history despite advancements in molded bra cups. To gain a deeper understanding of the interaction between the breast and bra pattern, this study combines the art of traditional bra patternmaking with 3D body scanning technology. By employing a 2D bra pattern drafting method and analyzing the effect of body shape on the desired bra cup shape, the study focuses on the differentiation of the lower cup among bras designed for flat and round body-shaped breasts. The results shed light on the impact of body shape on bra fit and provide valuable insights for further research and improvements in bra design, pattern drafting, and fit. The integration of 3D body scanning technology enhances the accuracy and precision of measurements, allowing for a more comprehensive analysis of the unique contours and dimensions of the breast and body. Ultimately, the study aims to provide individuals with different body shapes a more comfortable and well-fitted bra-wearing experience, contributing to the ongoing efforts to alleviate the longstanding problem of bra fitting.

Keywords: Breast shapes, bra fitting, 3D body scanning, bra patternmaking.

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