During last years, images and videos have become widely used in many daily applications. Indeed, they can come from cameras, smartphones, social networks of from medical devices. Generally, these images and videos are...
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ISBN:
(纸本)9781450352819
During last years, images and videos have become widely used in many daily applications. Indeed, they can come from cameras, smartphones, social networks of from medical devices. Generally, these images and videos are used for illustrating people or objects (cars, trains, planes, etc.) in many situations such as airports, train stations, public areas, sport events, hospitals, etc. Thus, image and video processingalgorithms have got increasing importance, they are required from various computer visions applications such as motion tracking, real time event detection, database (images and videos) indexation and medical computer aided diagnosis methods. In this paper, we propose a cloud platform that integrates the above-mentioned methods, which are generally developed with popular open source image and video processing libraries (OpenCV(1), OpenGL(2), ITK3, VTK4, etc.). Theses modules are automatically integrated and configured in the cloud application. Thus, the platform users will have access to different computer vision techniques without the need to download, install and configure the corresponding software. Each guest can select the required application, load its data and get the output results in a safe and simple way. The cloud platform can handle the variety of Operating systems and programming languages (C++, Java, Python, etc.). Experimentations were conducted within two kinds of applications. The first represents medical methods such as image segmentation in MR images, 3D image reconstruction from 2D radiographs, left ventricle segmentation and tracking from 2D echocardiography. The second kind of applications is related to video processing such as face, people and cars tracking, and abnormal event detection in crowd videos.
Single-image blind deconvolution is one of the most challenging fields in imageprocessing which restores a sharp image from its blurred *** blind deconvolution algorithms have made significant ***,the restoration of ...
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ISBN:
(纸本)9781538629185
Single-image blind deconvolution is one of the most challenging fields in imageprocessing which restores a sharp image from its blurred *** blind deconvolution algorithms have made significant ***,the restoration of blurred images with little scale edges and periodic textures is still a hard *** solve this problem,this paper proposes a new normalized sparse regularization blind deconvolution algorithm,which uses a gradient saliency map to prohibit the image small structures on image blurry kernel ***,salient detection is performed to select the important area which conforms with the human vision system and generates a binary mask to screen out useful ***,the normalized sparse regularization blind deconvolution method is applied to obtain accurate blur kernel and recover the sharp ***,the experiment results show that the algorithm can effectively deblur the degraded image on different scenarios.
Surveillance is very essential for the safety of power substation. The detection of whether wearing safety helmets or not for perambulatory workers is the key component of overall intelligent surveillance system in po...
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ISBN:
(纸本)9781538604908
Surveillance is very essential for the safety of power substation. The detection of whether wearing safety helmets or not for perambulatory workers is the key component of overall intelligent surveillance system in power substation. In this paper, a novel and practical safety helmet detection framework based on computer vision, machine learning and imageprocessing is proposed. In order to ascertain motion objects in power substation, the ViBe background modelling algorithm is employed. Moreover, based on the result of motion objects segmentation, real-time human classification framework C4 is applied to locate pedestrian in power substation accurately and quickly. Finally, according to the result of pedestrian detection, the safety helmet wearing detection is implemented using the head location, the color space transformation and the color feature discrimination. Extensive compelling experimental results in power substation illustrate the efficiency and effectiveness of the proposed framework.
High performance computing (HPC) systems underwent a significant increase in their processing capabilities. Modern HPC systems combine large numbers of homogeneous and heterogeneous computing resources. Scalability is...
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ISBN:
(纸本)9781538659304
High performance computing (HPC) systems underwent a significant increase in their processing capabilities. Modern HPC systems combine large numbers of homogeneous and heterogeneous computing resources. Scalability is, therefore, an essential aspect of scientific applications to efficiently exploit the massive parallelism of modern HPC systems. This work introduces an efficient version of the parallel spin-image algorithm (PSIA), called EPSIA. The PSIA is a parallel version of the spin-image algorithm (SIA). The (P)SIA is used in various domains, such as 3D object recognition, categorization, and 3D face recognition. EPSIA refers to the extended version of the PSIA that integrates various well-known dynamic loop scheduling (DLS) techniques. The present work: (1) Proposes EPSIA, a novel flexible version of PSIA;(2) Showcases the benefits of applying DLS techniques for optimizing the performance of the PSIA;(3) Assesses the performance of the proposed EPSIA by conducting several scalability experiments. The performance results are promising and show that using well-known DLS techniques, the performance of the EPSIA outperforms the performance of the PSIA by a factor of 1.2 and 2 for homogeneous and heterogeneous computing resources, respectively.
In this paper, we put forward an interval type-2 fuzzy neural network (IT2FNN) to deal with control issues of nonlinear systems with uncertainties. The fuzzy rules of the IT2FNN use interval type-2 triangular fuzzy se...
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ISBN:
(纸本)9781509060344
In this paper, we put forward an interval type-2 fuzzy neural network (IT2FNN) to deal with control issues of nonlinear systems with uncertainties. The fuzzy rules of the IT2FNN use interval type-2 triangular fuzzy sets to account for antecedent parts and adopt crisp numbers for the corresponding consequents. To effectively cope with uncertainties in the systems, a sliding-mode-control theory-based approach with new parameter learning rules is proposed to update the IT2FNN. The overall stability of the proposed methodology is also proved by using appropriate Lyapunov functions. Finally, the proposed method is applied to control the angular position of an inverted pendulum system. Simulation results indicate that, compared to a conventional proportional-derivative controller, the IT2FNN with the proposed learning rules can eliminate the uncertainties in performance and efficiently track the angle trajectory as desired.
Self-training plays an important role in sports exercise. However, if not under the instruction of a coach, it would be ineffective for most amateurs or inexperienced players to exercise on their own. Therefore, estab...
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ISBN:
(纸本)9781538608401
Self-training plays an important role in sports exercise. However, if not under the instruction of a coach, it would be ineffective for most amateurs or inexperienced players to exercise on their own. Therefore, establishing computer-assisted training systems for sports exercise is a recently emerging topic. In this paper, we propose a billiard self-training system, which aims at improving billiard players' performance by utilizing intelligent glasses as a wearable camera and displayer. The proposed system is able to automatically analyze user-captured images of the billiard table from multiple views and display the ball configurations on a virtual top-view table. Enriched visual presentation can be provided to give the practitioner a further sight into the game. The experiments conducted on sixteen sets of different ball configurations show promising results.
Based on the three elements model of pneumatic muscle actuators(PMA), this paper proposed a T-S fuzzy logic control with genetic algorithm optimization and achieved the trajectory tracking control of PMA. To guarantee...
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Based on the three elements model of pneumatic muscle actuators(PMA), this paper proposed a T-S fuzzy logic control with genetic algorithm optimization and achieved the trajectory tracking control of PMA. To guarantee the stability of control system, the Lyapunov direct method was used. And the LMI Toolbox of Matlab was used in this paper to solve linear matrix inequalities(LMls) and calculate the state feedback gains. Finally, the results of experiment demonstrated that, T-S fuzzy logic control with genetic algorithm optimization can achieve desired control performance, which overcome the chattering of trajectory tracking, reduced tracking error effectively and improved the accuracy of control.
An algorithm capable of computing the robot position by evaluating measurements of frame to frame intensity differences was extended to be able to detect outliers in the measurements to exclude them from the evaluatio...
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An algorithm capable of computing the robot position by evaluating measurements of frame to frame intensity differences was extended to be able to detect outliers in the measurements to exclude them from the evaluation to perform the positioning, with the aim of improving its robustness in irregular terrain scenes, such as consisting of flat surfaces with stones on them. The images are taken by a camera firmly attached to the robot, tilted downwards, looking at the planetary surface. A measurement is detected as an outlier only if its intensity difference and linear intensity gradients can not be described by motion compensation. According to the experimental results, this modification reduced the positioning error by a factor of one third in difficult terrain, maintaining its positioning error, which resulted in an average of 1.8%, within a range of 0.15% and 2.5% of distance traveled, similar to those achieved by state of the art algorithms successfully used in robots here on earth and on Mars.
Prostate cancer is becoming a threat to humanity. Today, the diagnosis of diseases is still be realized mostly by manual methods. Nevertheless, this traditional process is inefficient and not accurate. Its precision d...
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ISBN:
(纸本)9781538631751
Prostate cancer is becoming a threat to humanity. Today, the diagnosis of diseases is still be realized mostly by manual methods. Nevertheless, this traditional process is inefficient and not accurate. Its precision depends on the operator's expertise. Thus, applying machine learning algorithms for malignant cells detection and counting remains a significant purpose in medical image analysis research. In this paper, we apply a modified ACO algorithm to measure the rate of cell growth of cancer's patient automatically due to segmentation and counting process. The proposed method was applied on several medical images obtained from MRI-guided prostate biopsies. The robustness of this idea was showed by comparison with hand-labeled obtained segmentation results.
Nowadays, the individuals identification is a problem in many private company, but also in governmental and public order entities. Currently, there are multiple biometric methods, each with different advantages. A mod...
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ISBN:
(纸本)9781538692349;9781538692332
Nowadays, the individuals identification is a problem in many private company, but also in governmental and public order entities. Currently, there are multiple biometric methods, each with different advantages. A modern biometric method, which has several advantages, especially in terms of security against forgery, is finger vein identification. In the present work we have proposed and developed multi-core algorithms for the identification of people using finger veins, based on the SIFT displacement method, which currently has reached the highest efficiency performance in the state-of-the-art in the finger vein identification. The highest performance was reached with the hierarchical multi-core version, which uses two different type of threads, one is in charge of the query management and the second one in charge of the query processing with the database.
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