scholarly journals Video Background Segmentation Using Adaptive Background Models

2009 ◽  
pp. 623-632 ◽  
Author(s):  
Xiaoyu Wu ◽  
Yangsheng Wang ◽  
Jituo Li
Keyword(s):  
Background Segmentation ◽  
Background Models

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

scholarly journals SLFS: Semi-supervised Light-field Foreground-background Segmentation

2021 ◽  
Author(s):  
Maryam Hamad ◽  
Caroline Conti ◽  
Ana Maria de Almeida ◽  
Paulo Nunes ◽  
Luis Ducla Soares
Keyword(s):  
Light Field ◽  
Background Segmentation

scholarly journals AxiSEM: broadband 3-D seismic wavefields in axisymmetric media

Solid Earth ◽  
2014 ◽  
Vol 5 (1) ◽  
pp. 425-445 ◽  
Author(s):  
T. Nissen-Meyer ◽  
M. van Driel ◽  
S. C. Stähler ◽  
K. Hosseini ◽  
S. Hempel ◽  
...  
Keyword(s):  
Normal Modes ◽  
Spectral Element ◽  
Multipole Expansion ◽  
Small Scale ◽  
Computational Domain ◽  
Diverse Range ◽  
Earthquake Sources ◽  
Third Dimension ◽  
Computational Resources ◽  
Background Models

Abstract. We present a methodology to compute 3-D global seismic wavefields for realistic earthquake sources in visco-elastic anisotropic media, covering applications across the observable seismic frequency band with moderate computational resources. This is accommodated by mandating axisymmetric background models that allow for a multipole expansion such that only a 2-D computational domain is needed, whereas the azimuthal third dimension is computed analytically on the fly. This dimensional collapse opens doors for storing space–time wavefields on disk that can be used to compute Fréchet sensitivity kernels for waveform tomography. We use the corresponding publicly available AxiSEM (www.axisem.info) open-source spectral-element code, demonstrate its excellent scalability on supercomputers, a diverse range of applications ranging from normal modes to small-scale lowermost mantle structures, tomographic models, and comparison with observed data, and discuss further avenues to pursue with this methodology.

Are Social Background Models Time-Bound?

1986 ◽  
Vol 80 (3) ◽  
pp. 957-967 ◽  
Author(s):  
S. Sidney Ulmer
Keyword(s):  
Supreme Court ◽  
Social Background ◽  
Research Note ◽  
Background Model ◽  
Confounding Variable ◽  
Background Models

In this research note I seek to determine whether a significantly predicting social background model for analyzing the votes of Supreme Court justices is time-bound. I argue that an affirmative result poses serious questions for past uses of such models, none of which has controlled for the possibility that time is a confounding variable. A model that significantly predicted the votes of the justices in the Court's 1903–1968 terms was constructed. Analysis with this model for two periods—from 1903 to 1935, and from 1936 to 1968—established that the model was not timeneutral. Appropriate theoretical implications are drawn.

scholarly journals The Principles of Visual Perception Within The Context of Visual Illusions

2020 ◽  
Vol 8 (18) ◽  
Keyword(s):  
Visual Perception ◽  
Semantic Knowledge ◽  
Visual Illusions ◽  
Complex Information ◽  
Phenomenological Experience ◽  
Background Segmentation ◽  
Visual Phenomena ◽  
And Function ◽  
Effective Use ◽  
The Brain

In the transformation of the low-level, ambiguous retinal signal into a vivid and meaningful phenomenological experience, certain aspects are as essential as the input coming from the external environment. The semantic knowledge stored in memory, figure-background segmentation, grouping principles, and current mood and expectations of the person are equally important. Visual illusions, which might be described as the discrepancy between the objective properties of the external world and their subjective representations, is a common feature of the visual perception that provides meaningful insights with regards to the structure and function of the complex information processor in the brain. In this context, visual illusions are the end results of the optimization strategies that allow the effective use of limited neuronal and metabolic resources, and thus reflect the natural working principles while coping with these limitations, rather than restrictions inflicted upon the system. In this review, we present a compilation of illusions and summarize the key principles of visual perception on the basis of these visual phenomena. In the final section, we also discuss a number of recent topics within the context of Bayesian inference and psychopathology, illusions and alpha brain oscillations and time perception to describe the current directions in the field. Keywords Visual perception, visual illusions, visual system

Neural Network-based Pest Detection with K-Means Segmentation: Impact of Improved Dragonfly Algorithm

2021 ◽  
pp. 2150040
Author(s):  
Madhuri Devi Chodey ◽  
C Noorullah Shariff
Keyword(s):  
Neural Network ◽  
Video Processing ◽  
Agricultural Science ◽  
Video Frame ◽  
Learning Technology ◽  
Moving Frames ◽  
Dragonfly Algorithm ◽  
Detection And Identification ◽  
Background Segmentation ◽  
Pest Detection

Pest detection and identification of diseases in agricultural crops is essential to ensure good product since it is the major challenge in the field of agriculture. Therefore, effective measures should be taken to fight the infestation to minimise the use of pesticides. The techniques of image analysis are extensively applied to agricultural science that provides maximum protection to crops. This might obviously lead to better crop management and production. However, automatic pest detection with machine learning technology is still in the infant stage. Hence, the video processing-based pest detection framework is constructed in this work by following six major phases, viz. (a) Video Frame Acquisition, (b) Pre-processing, (c) Object Tracking, (d) Foreground and Background Segmentation, (e) Feature Extraction, and (f) Classification. Initially, the moving frames of videos are pre-processed, and the movement of the object is tracked with the aid of the foreground and background segmentation approach via K-Means clustering. From the segmented image, a new feature evaluation termed as Distributed Intensity-based LBP features (DI-LBP) along with edges and colour are extracted. Further, the features are subjected to a classification process, where an optimised Neural Network (NN) is used. As a novelty, the training of NN will be carried out using a new Dragonfly with New Levy Update (D-NU) algorithm via updating the weight. Finally, the performance of the proposed model is analysed over other conventional models with respect to certain performance measures for both video and image datasets.

Sign in / Sign up

Export Citation Format

Share Document