A new indexing technique to retrieve images using integration of colour-size, texture and shape features

2015 ◽  
Vol 2 (3) ◽  
pp. 280
Author(s):  
N. Puviarasan ◽  
R. Bhavani
Keyword(s):  
Shape Features ◽  
Indexing Technique

A Study on Nuclei Shape Features at the Classification of Glioma Disease Stage Using CNN

2020 ◽  
Vol 140 (12) ◽  
pp. 1367-1368
Author(s):  
Daisuke Saito ◽  
Hiroharu Kawanaka ◽  
V. B. Surya Prasath ◽  
Bruce J. Aronow
Keyword(s):  
Disease Stage ◽  
Shape Features

scholarly journals Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Standards Of Care ◽  
Shape Features ◽  
Selection Methods ◽  
Segmentation Framework ◽  
Vessel Tortuosity ◽  
Benign Nodules ◽  
Degradation Evaluation

Abstract Objective The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

Coupling Deep Textural and Shape Features for Sketch Recognition

2020 ◽  
Author(s):  
Qi Jia ◽  
Xin Fan ◽  
Meiyu Yu ◽  
Yuqing Liu ◽  
Dingrong Wang ◽  
...  
Keyword(s):  
Shape Features ◽  
Sketch Recognition

scholarly journals A novel p-harmonic descent approach applied to fluid dynamic shape optimization

2021 ◽  
Author(s):  
Peter Marvin Müller ◽  
Niklas Kühl ◽  
Martin Siebenborn ◽  
Klaus Deckelnick ◽  
Michael Hinze ◽  
...  
Keyword(s):  
Shape Optimization ◽  
Large Deformations ◽  
Fluid Dynamic ◽  
Shape Optimisation ◽  
Shape Features ◽  
Floating Bodies ◽  
Novel Method ◽  
Sharp Corners ◽  
Dynamic Shape

AbstractWe introduce a novel method for the implementation of shape optimization for non-parameterized shapes in fluid dynamics applications, where we propose to use the shape derivative to determine deformation fields with the help of the $$p-$$ p - Laplacian for $$p > 2$$ p > 2 . This approach is closely related to the computation of steepest descent directions of the shape functional in the $$W^{1,\infty }-$$ W 1 , ∞ - topology and refers to the recent publication Deckelnick et al. (A novel $$W^{1,\infty}$$ W 1 , ∞ approach to shape optimisation with Lipschitz domains, 2021), where this idea is proposed. Our approach is demonstrated for shape optimization related to drag-minimal free floating bodies. The method is validated against existing approaches with respect to convergence of the optimization algorithm, the obtained shape, and regarding the quality of the computational grid after large deformations. Our numerical results strongly indicate that shape optimization related to the $$W^{1,\infty }$$ W 1 , ∞ -topology—though numerically more demanding—seems to be superior over the classical approaches invoking Hilbert space methods, concerning the convergence, the obtained shapes and the mesh quality after large deformations, in particular when the optimal shape features sharp corners.

scholarly journals Exploring International and Inter-Sector Differences of Social Enterprises in the UK and India

2021 ◽  
Vol 13 (11) ◽  
pp. 5870
Author(s):  
Philipp Kruse
Keyword(s):  
Social Entrepreneurship ◽  
Social Enterprise ◽  
Social Enterprises ◽  
Future Research ◽  
Shape Features ◽  
Qualitative And Quantitative ◽  
Clear Cut ◽  
The One ◽  
Definition Of ◽  
The Uk

Social Entrepreneurship (SE) describes a new entrepreneurial form combining the generation of financial and social value. In recent years, research interest in SE increased in various disciplines with a particular focus on the characteristics of social enterprises. Whereas a clear-cut definition of SE is yet to be found, there is evidence that culture and economy affect and shape features of SE activity. In addition, sector-dependent differences are supposed. Building on Institutional Theory and employing a mixed qualitative and quantitative approach, this study sheds light on the existence of international and inter-sector differences by examining 161 UK and Indian social enterprises. A content analysis and analyses of variance were employed and yielded similarities as well as several significant differences on an international and inter-sector level, e.g., regarding innovativeness and the generation of revenue. The current study contributes to a more nuanced picture of the SE landscape by comparing social enterprise characteristics in a developed and a developing country on the one hand and different sectors on the other hand. Furthermore, I highlight the benefits of jointly applying qualitative and quantitative methodologies. Future research should pay more attention to the innate heterogeneity among social enterprises and further consolidate and extend these findings.

Nature-inspired hybrid deep learning for race detection by face shape features

2020 ◽  
Vol 13 (3) ◽  
pp. 365-388
Author(s):  
Asha Sukumaran ◽  
Thomas Brindha
Keyword(s):  
Feature Extraction ◽  
Hybrid Algorithm ◽  
Shape Features ◽  
Error Performance ◽  
Color Feature ◽  
Content Type ◽  
Dragonfly Algorithm ◽  
Face Shape ◽  
Race Detection ◽  
Better Than

PurposeThe humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.Design/methodology/approachThis paper intends to propose a new race detection model using face shape features. The proposed model includes two key phases, namely. (a) feature extraction (b) detection. The feature extraction is the initial stage, where the face color and shape based features get mined. Specifically, maximally stable extremal regions (MSER) and speeded-up robust transform (SURF) are extracted under shape features and dense color feature are extracted as color feature. Since, the extracted features are huge in dimensions; they are alleviated under principle component analysis (PCA) approach, which is the strongest model for solving “curse of dimensionality”. Then, the dimensional reduced features are subjected to deep belief neural network (DBN), where the race gets detected. Further, to make the proposed framework more effective with respect to prediction, the weight of DBN is fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm (LMUDA), which is the conceptual hybridization of lion algorithm (LA) and dragonfly algorithm (DA).FindingsThe performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error performance. Moreover, LMUDA attains high accuracy at 100th iteration with 90% of training, which is 11.1, 8.8, 5.5 and 3.3% better than the performance when learning percentage (LP) = 50%, 60%, 70%, and 80%, respectively. More particularly, the performance of proposed DBN + LMUDA is 22.2, 12.5 and 33.3% better than the traditional classifiers DCNN, DBN and LDA, respectively.Originality/valueThis paper achieves the objective detecting the human races from the faces. Particularly, MSER feature and SURF features are extracted under shape features and dense color feature are extracted as color feature. As a novelty, to make the race detection more accurate, the weight of DBN is fine tuned with a new hybrid algorithm referred as LMUDA, which is the conceptual hybridization of LA and DA, respectively.

Articulated 3D model matching using multi-scale histograms of shape features for customized additive manufacturing

2021 ◽  
Vol 132 ◽  
pp. 103520
Author(s):  
Xin Lin ◽  
Kunpeng Zhu ◽  
Min Zhou ◽  
Jerry Ying Hsi Fuh ◽  
Qing-guo Wang
Keyword(s):  
Additive Manufacturing ◽  
3D Model ◽  
Shape Features ◽  
Model Matching ◽  
Multi Scale
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