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Conference

International Conference Sensing and Imaging 

About: International Conference Sensing and Imaging is an academic conference. The conference publishes majorly in the area(s): Iterative reconstruction & Reconstruction algorithm. Over the lifetime, 56 publications have been published by the conference receiving 82 citations.

Papers published on a yearly basis

Papers
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Book ChapterDOI
17 Jun 2018
TL;DR: It is decided to design and apply diffuse mathematical procedures and tools to reduce subjectivity and uncertainty in decision-making, creating work algorithms for this policy, which includes multifactorial weights and analysis with measurement indicators that they allow tangible and reliable results.
Abstract: Every day organizations pay more attention to Human Resources Management, because this human factor is preponderant in the results of it. An important policy is the Performance Evaluation (ED), since it allows the control and monitoring of management indicators, both individual and by process. To analyze the results, decision making in many organizations is done in a subjective manner and in consequence it brings serious problems to them. Taking into account this problem, it is decided to design and apply diffuse mathematical procedures and tools to reduce subjectivity and uncertainty in decision-making, creating work algorithms for this policy, which includes multifactorial weights and analysis with measurement indicators that they allow tangible and reliable results. Statistical techniques (ANOVA) are also used to establish relationships between work groups and learn about best practices.

31 citations

Book ChapterDOI
05 Jun 2017
TL;DR: It is observed from the steady-state analysis that all the measured parameters from aneurysmal aorta model were higher than those obtained from the aortA model of normal subject, which could help the surgeons in assessing the severity of aorticAneurysms.
Abstract: The steady and transient flow simulations in an aorta model of normal subject were carried through computational fluid dynamic (CFD) technique. The steady- and transient-state computational fluid dynamic models of patient-specific aortic aneurysm were developed. The computed tomographic (CT) image data was used to generate the geometry of aortic models. The laminar flow was considered for simulating the flow of blood. The haemodynamic parameters like wall pressure, wall shear stress (WSS) and velocity distribution were estimated from the models. The obtained results depicted that the flow in the aorta model of normal subject was stable and aneurysmal aorta model became unstable. It is observed from the steady-state analysis that all the measured parameters from aneurysmal aorta model were higher than those obtained from the aorta model of normal subject. These measured parameters from this study could help the surgeons in assessing the severity of aortic aneurysms.

7 citations

Book ChapterDOI
Chen Yang1, Xiaohong Chen1, Lei Liu1, Tingting Liu1, Shuang Geng1 
17 Jun 2018
TL;DR: A hybrid recommendation approach that combines social behaviors, the genres of movies and existing collaborative filtering algorithms to perform movie recommendation is put forward.
Abstract: With the increasing demand for personalized recommendation, traditional collaborative filtering cannot satisfy users’ needs. Social behaviors such as tags, comments and likes are becoming more and more popular among the recommender system users, and are attracting the attentions of the researchers in this domain. The behavior characteristics can be integrated with traditional interest community and some content features. In this paper, we put forward a hybrid recommendation approach that combines social behaviors, the genres of movies and existing collaborative filtering algorithms to perform movie recommendation. The experiments with MovieLens dataset show the advantage of our proposed method comparing to the benchmark method in terms of recommendation accuracy.

7 citations

Book ChapterDOI
05 Jun 2017
TL;DR: This paper presents an efficient new hybrid object proposal method, which gets the initial proposal by computing multiple hierarchical segmentations using super pixels and then ranks the proposal according to region score – which is defined as number of contours wholly enclosed in the proposed region, passing only the top object proposal for the post-classification.
Abstract: Object detection in natural images is evolving, with enormous commercial achievements, becoming relatively common in every industry Modern research in this area is progressing in many directions, with numerous different techniques being proposed to achieve state-of-the-art detection performance Recent object detection methods use two steps to detect high-quality objects: first, it generates a set of object proposals as accurate as possible, and then these proposals are passed to object classifier for post-classification This paper presents an efficient new hybrid object proposal method, which gets the initial proposal by computing multiple hierarchical segmentations using super pixels and then ranks the proposal according to region score – which is defined as number of contours wholly enclosed in the proposed region, passing only the top object proposal for the post-classification Passing few object proposals in the object detection pipeline for post-classification speeds up the object detection process This paper demonstrates that our method results in high-quality class-independent object locations, with mean average best overlap of 0833 at 1500 locations, resulting in a superior detection rate in object detection tasks at relatively fast speeds – as compared to object detection methods using selective search – and greatly reduces the false-positive rate

6 citations

Book ChapterDOI
05 Jun 2017
TL;DR: In this paper, a semi-empirical method to generate seamless mosaicking of multi-strip airborne hyperspectral images is presented, which can efficiently remove the illumination gradient in both single image and between multi-scene images.
Abstract: The needs of high-precision earth observation have led to the development of high-resolution and high-dimensionality RS data and greatly promoted the standard for processing and application of airborne hyperspectral images The varying brightness gradients of the airborne images cause problems in generating “seamless” mosaic for hyperspectral surveys, which severely affect the radiometric consistencies for subsequent analyses We present a semiempirical method to generate seamless mosaicking of multi-strip airborne hyperspectral images and introduce the model principle as well as the calculation process in detail The experimental results based on HyMap images in Lop Nor area show that this method can efficiently remove the illumination gradient in both single image and between multi-scene images Moreover, the MNF-transformed images and spectrum from overlap were chosen to assess the model; the results show that the Hapke-based model can be used to improve the airborne hyperspectral mosaicking effect and have great potential to subsequent quantitative applications

4 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
201822
201734