Segmenting the periocular region using a hierarchical graphical model fed by texture / shape information and geometrical constraints

2014 ◽  
Author(s):  
Hugo Proenca ◽  
Joao C. Neves ◽  
Gil Santos
Keyword(s):  
Graphical Model ◽  
Shape Information ◽  
Geometrical Constraints ◽  
Periocular Region

Plastic Deformation in Microtomed Sections

1971 ◽  
Vol 29 ◽  
pp. 458-459
Author(s):  
J. Temple Black
Keyword(s):  
Plastic Deformation ◽  
Plastic Strain ◽  
Applied Stress ◽  
Elastic Strain ◽  
High Strain ◽  
Tool Material ◽  
Cutting Edge ◽  
Material Structure ◽  
Geometrical Constraints

The output of the ultramicrotomy process with its high strain levels is dependent upon the input, ie., the nature of the material being machined. Apart from the geometrical constraints offered by the rake and clearance faces of the tool, each material is free to deform in whatever manner necessary to satisfy its material structure and interatomic constraints. Noncrystalline materials appear to survive the process undamaged when observed in the TEM. As has been demonstrated however microtomed plastics do in fact suffer damage to the top and bottom surfaces of the section regardless of the sharpness of the cutting edge or the tool material. The energy required to seperate the section from the block is not easily propogated through the section because the material is amorphous in nature and has no preferred crystalline planes upon which defects can move large distances to relieve the applied stress. Thus, the cutting stresses are supported elastically in the internal or bulk and plastically in the surfaces. The elastic strain can be recovered while the plastic strain is not reversible and will remain in the section after cutting is complete.

Improvement of Automatic Lip Extraction Process Using Shape Information of Lips

2010 ◽  
Vol 130 (5) ◽  
pp. 873-881 ◽  
Author(s):  
Yoshiyuki Sato ◽  
Junichi Narita ◽  
Yoichi Kageyama ◽  
Makoto Nishida
Keyword(s):  
Extraction Process ◽  
Shape Information

DEVELOPMENT OF A METHOD FOR DESIGNING INFORMATION SYSTEMS FOR ENERGY COMPANIES

2020 ◽  
Vol 5 (333) ◽  
pp. 53-57
Author(s):  
T.B. Aldongar ◽  
◽  
F.U. Malikova ◽  
G.B. Issayeva ◽  
B.R. Absatarova ◽  
...  
Keyword(s):  
Information Systems ◽  
Business Processes ◽  
Design Research ◽  
Graphical Model ◽  
Formal Model ◽  
Workflow Management ◽  
Research Process ◽  
Information Models ◽  
The Creation ◽  
Concept Of Information

The creation of information models requires the use of known methods and the development of new methods of formalizing the pre-design research process. The modeling process consists of four stages: data collection on the object of management - pre-project research; creation of a graphical model of business processes taking place in the enterprise; development of a formal model of business processes; business research by optimizing the formal model. To support the creation of workflow management services and systems, the complex offers methodologies, standards and specialized software that make up the developer's tools. This can be ensured only by modern automated methods based on information systems. It is important that the information collected is structured to meet the needs of potential users and stored in a form that allows the use of modern access technologies. Before discussing the effectiveness of FIM, it should be noted that the basic concept of information itself is still not the same. In a pragmatic way, it is a set of messages in the form of an important document for the system. Information can be evaluated not only by volume, but also by various parameters, the most important of which are: timeliness, relevance, value, aging, accuracy, etc. in addition, the information may be clear, probable and accurate. The methods of its reception and processing are different in each case.

A Probabilistic-Graphical-Model Based Approach for Representing Lineages in Uncertain Data

2011 ◽  
Vol 34 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Kun YUE ◽  
Wei-Yi LIU ◽  
Yun-Lei ZHU ◽  
Wei ZHANG
Keyword(s):  
Graphical Model ◽  
Uncertain Data ◽  
Probabilistic Graphical Model ◽  
Model Based

Surgical Anatomy of the Ocular Adnexa

2012 ◽  
Author(s):  
David Jordan ◽  
Louise Mawn ◽  
Richard L. Anderson
Keyword(s):  
Head And Neck ◽  
Plastic Surgery ◽  
Surgical Approach ◽  
Neck Surgery ◽  
Surgical Anatomy ◽  
Head And Neck Surgery ◽  
Anatomical Features ◽  
Ocular Adnexa ◽  
Periocular Region ◽  
Orbital Region

Surgical Anatomy of the Ocular Adnexa is a beautifully and thoughtfully illustrated anatomical text that provides the ophthalmic surgeon or any surgeon working in the eyelid/orbital region with detailed yet concise, easy to read and understand descriptions of the anatomy in any particular region of the eyelid, orbit or nasolacrimal system. Throughout the text are clinical pearls and vignettes to help the reader appreciate why certain anatomical features are important to understand. Key anatomical concepts are highlighted and easy to visualize with real cadaver photos as well as the artists rendition of the same region. This book: - Develops a thorough understanding of the anatomy in the eyelid, orbit, nasolacriaml and periocular regions. - Fosters an appreciation of how knowledge of the anatomy leads to a better understanding of the pathophysiology of various disease processes involving the eyelid, orbit, nasolacrimal and periocular region. - Conveys the importance of anatomy in the surgical approach to various disease processes in the eyelid, orbit, nasolacrimal and periocular regions. This second edition will be an invaluable guidel to all those working in the eyelid, orbital, and nasolacrimal areas including residents, fellows and staff in ophthalmology, otolaryngology/head and neck surgery, plastic surgery and neurosurgeons working in and around the orbit.

Psychological networks in clinical populations: investigating the consequences of Berkson's bias

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jill de Ron ◽  
Eiko I. Fried ◽  
Sacha Epskamp
Keyword(s):  
Network Structure ◽  
Binary Data ◽  
Ordinal Data ◽  
Graphical Model ◽  
Rating Scale ◽  
Network Models ◽  
Simulation Studies ◽  
Potential Threat ◽  
Sum Score ◽  
Research Populations

Abstract Background In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. Methods In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. Results The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items. Conclusion Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.

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