DNN based phrase boundary detection using knowledge-based features and feature representations from CNN

Author(s):  
Pavan J Kumar ◽  
Chiranjeevi Yarra ◽  
Prasanta Kumar Ghosh
1996 ◽  
Vol 23 (6) ◽  
pp. 662-668 ◽  
Author(s):  
Mark W. Groch ◽  
William D. Erwin ◽  
Paul H. Murphy ◽  
Amjad Ali ◽  
Warren Moore ◽  
...  

Author(s):  
Z. Fu ◽  
A. Y. C. Nee

Abstract Concurrent (or simultaneous) engineering has recently been proposed as a potential means to improve the product development practice. It requires the product life-cycle aspects such as manufacturing requirements to be considered during the stages of designing a part so that the design feedback on the manufacturability, assemblability and so on can be provided to the designers. To support this purpose needs the integration of geometric models, analysis and synthesis tools as well as domain knowledge while the design is in progress. In the past few years, the concept of features has received significant attentions in the context of design and manufacturing automation. However, the application of features is currently limited, mainly due to the domain-dependent nature of features. A crucial problem has been the interpretation of multiple feature viewpoints, particularly, the conversion among feature representations, that is to support the reasoning about feature-based representations of a product design from a specific perspective and their interpretation. In this paper, important engineering perspectives and related feature-based representations supporting concurrent design and manufacturing have been identified. A methodology of interpreting different feature representations has been proposed based on a coupling between grammatical formalism and knowledge-based inference. A case study of applying this methodology to the conversion from design features based representation into representations suitable for machining process planning is reported.


Author(s):  
Yang Hee Yee ◽  
◽  
Chun Kee Jeon ◽  
Sang-Rok Oh ◽  
Mignon-Park ◽  
...  

A Cardiac function is evaluated quantitatively by analyzing a shape change of the heart wall boundaries in angiographic images. To begin with, a boundary detection of end systolic left ventricle (ESLV) and end diastolic left ventricle (EDLV) is essential for the quantitative analysis of the cardiac function. Conventional methods for the boundary detection are almost semi-automatic, and a knowledgeable human operator’s intervention is still required. Manual tracing of the boundaries is currently used for subsequent analysis and diagnosis. However, these methods do not cut excessive time, labor, and subjectivity associated with manual intervention by a human operator. Generally, EDLV images have noncontiguous and ambiguous edge signal on some boundary regions. In this paper, we propose a new method for an automated detection of left ventricle (LV) boundaries in noncontiguous and ambiguous EDLV images. The proposed boundary detection scheme is based on a priori knowledge information and is divided into two steps. The first step is to detect EDLV boundary using ESLV boundary. The second step is to correct the detected EDLV boundary using the left ventricle (LV) shape information. We compared the proposed method with the manual method to detect the EDLV boundary. And through the experiments of the proposed method, we verified the usefulness of this method.


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