'Visual features' diaporamas de présentation

Visualization

1.07k views • 86 slides

Learning and Inference in Vision: from Features to Scene Understanding

Learning and Inference in Vision: from Features to Scene Understanding. Jonathan Huang, Tomasz Malisiewicz MLD Student Research Symposium, 2009. Sky. Bridge. Sign. Trees. Car. Road. Huge datasets. PASCAL Visual Objects Challenge (VOC) dataset. ~15000 annotated images,

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From Frequent Itemsets to Semantically Meaningful Visual Patterns

Advisor ? Dr. Koh Jia-Ling Speaker ? Tu Yi-Lang Date ? 2008.05.29. From Frequent Itemsets to Semantically Meaningful Visual Patterns. Junsong Yuan, Ying Wu, Ming Yang (KDD 2007). Introduction.

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Perception

Perception. Ref: Banich, Ch. 6 pp. 185-198. Update. Error in table from last lecture: Language lateralisation - sodium amytal study. Test 1 coming up - details tomorrow. The Visual Perception Hierarchy. Initial cortical processing: Primary visual cortex (V1)

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Accounting for the relative importance of objects in image retrieval

BMVC 2010 Sung Ju Hwang and Kristen Grauman University of Texas at Austin. Accounting for the relative importance of objects in image retrieval. Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search

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Enhancement of textual images classification using their global and local visual content

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Lip Feature Extraction Using Red Exclusion

Lip Feature Extraction Using Red Exclusion. Trent W. Lewis and David M.W. Powers Flinders University of SA VIP2000. Overview. Context Lip Feature Extraction Related Work (greyscale, horizontal edges, red and hue colour spaces) Red Exclusion AVSR: Results and Issues Summary. Context.

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Different Pathways , Different Processes

Different Pathways , Different Processes. Retinocollicular vs. Retinostriate. Recall that 10% of optic nerve gets routed through the Superior Colliculus (SC) What does it do? SC contributes to control of eye movements (saccade vector maps) SC and Pulvinar contribute to orienting of attention.

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Kaggle: Whale Challenge

Kaggle: Whale Challenge. ??? jang@cs.nthu.edu.tw http://www.cs.nthu.edu.tw/~jang ?????????? ???? ?????. Whale Challenge. Problem definition Identify the existence of whales from sensor recordings Characteristics: Imbalance data Some recordings are hardly recognizable by non-experts.

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Lumber Grades

FW1035 Lecture 13 Bowyer et al, Chapter 13. Lumber Grades. Grading is typically by: Strength 1. Visual Estimation 2. Measured Appearance Specialty product needs. Hardwood Lumber Grading. Factory Lumber Lengths from 4 to 16 feet in one foot increments, random widths.

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Visual Features for Content-based Medical Image Retrieval

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Text Features

Text Features. IDOE Standard 8.2.1- Compare and contrast the features and elements of consumer materials to gain meaning from documents. Why?!. Features help you to understand how an informational text is organized! Cool, huh? ?. The Text Body:.

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PENN S TATE

Spring 2009 Project Descriptions. ME 546 - Designing Product Families - IE 546. Timothy W. Simpson Professor of Mechanical & Industrial Engineering and Engineering Design The Pennsylvania State University University Park, PA 16802 USA phone: (814) 863-7136 email: tws8@psu.edu

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Its History, Influence, and how it fares against competition

The i Pad. Its History, Influence, and how it fares against competition. By Paul John. Outline. History Messagepad Penlite iPad Gen 1 Gen 2 Gen 3 Interface App AppStore Microsoft Surface vs Ipad 4. A Bit Of History.

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Irony

Irony. The difference between what is said and what is meant. The difference between what is intended/expected and what the actual result is. Eg. . Dramatic Irony. When the reader knows more than the characters. Eg. .

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Sherlock HOLMES

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Features of speech

Features of speech. Use as many of these as possible. Visual features Smile Eye contact Gesture C onfident stance Movement Use of props PowerPoint P ictures Video. Verbal features Use of pronoun ( you, we, us ,they ) Simile Instruction Humour Linking phrases to show structure

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Department of Psychology & The Human Computer Interaction Program

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Correlative Multi-Label Multi-Instance Image Annotation

Overview. Correlative Multi-Label Multi-Instance Image Annotation Xiangyang Xue, Wei Zhang, Jie Zhang, Bin Wu, Jianping Fan, and Yao Lu Fudan University, China & UNC-Charlotte.

197 views • 1 slides