Laroussi Hammouda, Hassen Mekki, Khaled Kaaniche, M. Chtourou
{"title":"Image-based visual servoing dealing with constraints","authors":"Laroussi Hammouda, Hassen Mekki, Khaled Kaaniche, M. Chtourou","doi":"10.1109/IPAS.2016.7880132","DOIUrl":"https://doi.org/10.1109/IPAS.2016.7880132","url":null,"abstract":"This paper proposes a new approach to achieve real-time robotic control using image-based visual servoing in the presence of constraints. The proposed method deals with visibility constraints and occultation avoidance using potential fields. Our approach aims to improve the real-time performance of the visual servoing scheme. In fact, the proposed technique is more efficient in terms of computation time and doesn't require neither trajectory planning nor pose estimation step. Experimental results are presented to validate our approach and to demonstrate its efficiency.","PeriodicalId":283737,"journal":{"name":"2016 International Image Processing, Applications and Systems (IPAS)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128445590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A self-constructing neuro-fuzzy classifier for breast cancer diagnosis using swarm intelligence","authors":"M. Elloumi, M. Krid, D. Masmoudi","doi":"10.1109/IPAS.2016.7880145","DOIUrl":"https://doi.org/10.1109/IPAS.2016.7880145","url":null,"abstract":"In this paper, a self-constructing neuro-fuzzy (SCNF) classifier optimized by swarm intelligence technique is proposed for breast cancer diagnosis. The first step in the design is the definition of the fuzzy network structure. Accordingly, a rule generation approach with self-constructing property is proposed. Based on similarity measures, the given input-output patterns are organized into clusters. Then, membership functions are generated roughly to form a fuzzy rule base. To achieve accurate learning, particle swarm optimization (PSO) algorithm is used to adjust consequent and antecedent parameters of the obtained rules. Accordingly, a weighted function is constructed to design the objective function of the PSO, which takes into account the specificity, the sensitivity and the total classification accuracy of the proposed SCNF classifier. The proposed SCNF classifier is evaluated on the widely used Wisconsin breast cancer dataset (WBCD) for breast cancer diagnosis. Experimental results confirm that the proposed model is able to detect breast cancer with a classification accuracy of more than 99%. A comparative study has been elaborated confirming the best performance of the proposed classifier.","PeriodicalId":283737,"journal":{"name":"2016 International Image Processing, Applications and Systems (IPAS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128471062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"High-level features for automatic skin lesions neural network based classification","authors":"Wiem Abbes, D. Sellami","doi":"10.1109/IPAS.2016.7880148","DOIUrl":"https://doi.org/10.1109/IPAS.2016.7880148","url":null,"abstract":"Melanoma is the most dangerous form of skin cancer. It can be developed from pigmented cells of the skin and can grow and spread swiftly to other organs (metastasis). An early diagnosis increases the chance of cure. In the past three decades, the increase in the incidence of melanoma has given rise to more accurate methods of analysis. Feature extraction is a critical step in melanoma decision support systems. Early dermatoscopic rules (ABCD rule, 7-point checklist, Menzies method and CASH algorithm), used by experts are generally low level features. In this paper, we consider several dermatoscopic rules for automatic detection of melanoma in order to generate new high level features allowing semantic analysis. Such extracted features are based on shape characterization and color and texture features. A neural network classifier is used for decision making. Experimental results indicate that semantic analysis is a useful method for discrimination of melanocytic skin tumors with good accuracy. The proposed method yields a good sensitivity of 92% and a specificity of 95% on a database of 206 skin lesion images. A comparative study with recent previous works illustrates that our approach outperforms in terms of accuracy and specificity.","PeriodicalId":283737,"journal":{"name":"2016 International Image Processing, Applications and Systems (IPAS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121189575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aicha Rima Cheniti, H. Besbes, Joseph Haggège, C. Sintes
{"title":"Measurement simulation of gas pressure by ultrasound in a two phase flow fluid","authors":"Aicha Rima Cheniti, H. Besbes, Joseph Haggège, C. Sintes","doi":"10.1109/IPAS.2016.7880141","DOIUrl":"https://doi.org/10.1109/IPAS.2016.7880141","url":null,"abstract":"The adequate use of ultrasound permits the measurement of carbon dioxide pressure in a two-phase flow fluid. The proposed method relies on the measurement of radial mixture velocities acquired from ultrasound data by Doppler effect. The gas pressure is calculated knowing the measured radial velocities using Drift flux model and Young-Laplace equations The modeling of system response consists on the determination of elemental acoustic signals through radial mixture velocities and volumetric gas fraction calculated at two positions of the measurement domain. The summation of these signals gives the total responses. The frequency spectra of these signals permits the calculation of the radial mixture velocities and consequently estimate the gas pressure through the mean mixture pressure.","PeriodicalId":283737,"journal":{"name":"2016 International Image Processing, Applications and Systems (IPAS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117286204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hanene Sahli, A. B. Slama, A. Zaafouri, M. Sayadi, Rathwen Rachdi
{"title":"Automated detection of current fetal head in ultrasound sequences","authors":"Hanene Sahli, A. B. Slama, A. Zaafouri, M. Sayadi, Rathwen Rachdi","doi":"10.1109/IPAS.2016.7880142","DOIUrl":"https://doi.org/10.1109/IPAS.2016.7880142","url":null,"abstract":"Accurate diagnostic and prognostic of fetus detects is an important challenge based on fetal head formation to supply much critical information that requires more attention in evaluating the abnormal heads. One of the fundamental problems currently faced, is how to limit the low signal to noise ratio with respect to the complexity of small fetal head ultrasound images dimension. This paper deals with a fully automatic detection system of subsequent fetal head composition from ultrasound images. In the preprocessing task, two filters have been used for speckle noise reducing. Using the Hough transform technique, fetal head structure detection is achieved, giving 97% as segmentation accuracy. Experimental results are analyzed using five ultrasound sequences that illustrate the effectiveness and the accuracy of the proposed method for a factual diagnostic of fetal heads.","PeriodicalId":283737,"journal":{"name":"2016 International Image Processing, Applications and Systems (IPAS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128919538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Image fusion models and techniques at pixel level","authors":"Yosra Ben Salem, K. Hamrouni, B. Solaiman","doi":"10.1109/IPAS.2016.7880115","DOIUrl":"https://doi.org/10.1109/IPAS.2016.7880115","url":null,"abstract":"Information fusion consists in combining information in order to maximize the relevant information and reduce the redundancy. It is widely used in many fields, especially in image processing, for analyzing situations. Since the first use of the information fusion concept, many approaches have been introduced to define a processing model to merge information. Three basic approaches are used in information fusion: the JDL Model which is the first one used, the Intelligence Cycle Model and the DFD (Data — Features — Decision) Model. According to the field of application and the type of the information manipulated, the processing model is different. In image processing, various techniques and methods are used to perform image fusion. Many techniques are most used in research studies: PCA (Principal Component Analyses), Wavelet transform,… In this paper we present a general overview of the basic models and techniques used in image fusion.","PeriodicalId":283737,"journal":{"name":"2016 International Image Processing, Applications and Systems (IPAS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131502788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Type P63 digitized color images performs better identification for ovarian reproductive tissue analysis","authors":"T. S. Sazzad, L. Armstrong, A. K. Tripathy","doi":"10.1109/IPAS.2016.7880069","DOIUrl":"https://doi.org/10.1109/IPAS.2016.7880069","url":null,"abstract":"For pathology routine examination microscopic biopsy slides are considered as a most viable choice to analyze and identity ovarian reproductive tissues. Experts require substantial amount of time to process the biopsy slides under the microscope. Electronic modalities are not suitable to analyze smaller tissues especially ovarian reproductive tissues. To reduce the time and effort it would be a better choice to incorporate a computer based approach. In this paper existing research work related review has been carried out and a new modified approach has been presented. The proposed study results indicate improved and an acceptable accuracy rate in comparison to manual microscopic analysis and other existing computer based approaches.","PeriodicalId":283737,"journal":{"name":"2016 International Image Processing, Applications and Systems (IPAS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132531198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the efficiency of OSEM algorithm for tomographic lung CT images reconstruction","authors":"Hamida Romdhane, M. A. Cherni, Dorra Ben Sallem","doi":"10.1109/IPAS.2016.7880131","DOIUrl":"https://doi.org/10.1109/IPAS.2016.7880131","url":null,"abstract":"Sometimes, computed tomography (CT) examinations need to be repeated. This may generate adverse effects on patients. To avoid it, an efficient reconstruction technique should be applied. This paper presents a qualitative and quantitative comparative study of four iterative algorithms (Algebraic Reconstruction Technique (ART), Maximum Likelihood Expectation Maximization (MLEM), Ordered-subsets expectation maximization (OSEM) and Simultaneous algebraic reconstruction technique (SART)). The four techniques are applied on a ‘dicom’ lung computed tomography image. Qualitatively, we can not differentiate between the reconstructed images. They are almost the same for all the methods. But, qualitatively, the best performance was observed with OSEM algorithm which provides the best quality image according to practically all the computed evaluation criteria. Moreover, OSEM insures this best performance in shorter processing time ranging from two to three times less compared to the other methods.","PeriodicalId":283737,"journal":{"name":"2016 International Image Processing, Applications and Systems (IPAS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127460023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Design and evaluation of optimized router pipeline stages for network on chip","authors":"Bouraoui Chemli, A. Zitouni","doi":"10.1109/IPAS.2016.7880067","DOIUrl":"https://doi.org/10.1109/IPAS.2016.7880067","url":null,"abstract":"In the past few years, Network on chip (NoC) is presented as the best communication architecture for complex chip. Unlike conventional bus, NoC allows many cores to communicate concurrently, provides more scalability and enhance the system performances. For this reason, we propose a flexible router for NoC architecture. The proposed router implements a minimal routing algorithm to avoid deadlocks and a priority based arbiter to ensure the quality of service (QoS) improvement. In this paper, we optimize a previous version of our router design and present an efficient approach to reduce the dependency between the pipeline stages for NoC architectures. This work aims to reduce the hardware complexity and enhance the system performances. In order to evaluate our design performances we compared it with other popular works from the literature.","PeriodicalId":283737,"journal":{"name":"2016 International Image Processing, Applications and Systems (IPAS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130754763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Identifying and classifying cancerous cells based on the Ki67 detector","authors":"H. Seddik, Bechir Saidani","doi":"10.1109/IPAS.2016.7880070","DOIUrl":"https://doi.org/10.1109/IPAS.2016.7880070","url":null,"abstract":"The image processing arose from the idea of the necessity to replace the human observer by a machine. The interest of this paper is to replace the medical image by information interpretable. Usually, experts have manually performed to count the cell nuclei biopsy samples, one by one. This method ensures that accuracy is achieved in the final diagnosis delivered by pathologists, but the time until the patient is notified can vary from weeks to months depending on the laboratory resources. Cancer developing speed is also a limiting factor, so the sooner the disease is discovered the better and quicker the patient can start with the treatment or preparations for surgery can be arranged. Promptness in cancer recognition increases the chances to overcome this illness that affects every year more and more men as the world population's life expectancy increases. So, for this reason, it has proposed an automatic method. To return the more reliable and fast diagnosis, we applied a method based on tools and algorithms. The chain of this processing is begun with the segmentation to separate the various constituent zones the image. Secondly, we have the step of detecting the edges of the prostatic cells as well their center. Finally, we have the step of counting where we are going to find a score for the diagnosis.","PeriodicalId":283737,"journal":{"name":"2016 International Image Processing, Applications and Systems (IPAS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125062784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}