Segmentation Free Spotting of Cuneiform using Part Structured - PowerPoint PPT Presentation

Segmentation Free Spotting of Cuneiform using Part Structured Models Heidelberg University, Bartosz Bogacz Smith College, Nicholas Howe Heidelberg University, Hubert Mara

Cuneiform Script ● More than 3,000 years of history ● Evolved from a pictographic to a syllabic script c ● More than 500,000 clay tablets ● Only few Assyriologists

Cuneiform Script ● Cuneiform is a writing system used by at least 7 different languages ● Written by impressing a rectangular stylus in wet clay ● Our approach models geometric patterns instead of language

Goal ● Only few tablets are transliterated ● Transliterations can be incomplete and subjective ● Provide a mechanism for searching by graphical query

Different Sources 3D Scans Retro-digitized Born-digital Unification of sources requires a common geometrical representation

Extracting Wedges ● We model wedges as triangles with arms ● Find possible candidate wedges by finding cycles ● Prune this set of candidates using modeling constraints – No overlapping wedges – Sizes and angles are within sane bounds – Prioritize bigger wedges

Extracting Wedges ● We re-formulate this constraint satisfaction task as an optimizing assignment task ● This enables us an efficient O(n^3) solution ● The set of strokes is being assigned to a set of candidate wedges

Optimal Assignment

Wedge Features ● We want to represent extracted wedges as feature vectors ● Intersections and endpoints are most salient points in wedges ● Model wedges using these keypoints

Keypoint Model ● Feature vector is a concatenation of the keypoints in our wedge model – Wedge-head intersections – Wedge-arm endpoints

Keypoint Model ● Features are compared by Euclidean distance ● Our new approach reorders points using optimal assignment

Part-structured Spotting • Model characters as wedges connected by tree of fmexible links • Align query to candidates by deforming links • Probability of a match is wedge similarities plus amount of link deformation

Generalized Distance Transform GDT Query T arget • Trades ofg between wedge similarity and distance

Part Structured Match Demo Query T arget

Sample Results

Evaluation ● Symbol spotting task with 40 query symbols of various lengths ● We compare against Rothacker et al. HMM Latin word spotting – No elevation data to evaluate their approach for cuneiform spotting – We rasterize our data to make it available for their method

Evaluation ● Dataset are two cuneiform tablets with 500 identifiable characters ● Tablets are only incompletely labeled, precluding an automated evaluation ● Retrieval results are checked by an expert for false positives

Evaluation

Query Results

Summary ● Fast and optimizing method for cuneiform wedge detection ● Native and accurate feature representation of cuneiform wedges ● Fast symbol spotting of cuneiform characters

Part-Structured Spotting vs. T emplate Matching Query T arget Part-structured: T emplate: Approximate match Matches only part everywhere

Segmentation Free Spotting of Cuneiform using Part Structured - PowerPoint PPT Presentation

Segmentation Free Spotting of Cuneiform using Part Structured Models Heidelberg University, Bartosz Bogacz Smith College, Nicholas Howe Heidelberg University, Hubert Mara Cuneiform Script More than 3,000 years of history Evolved from a

CUNEIFORM http://www.etoyoc.com/yoda/papers/ tcl2019.Cuneiform_Slides.pdf PRELUDE Cuneiform

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Part 1 : Image Segmentation Anne Vialard LaBRI, Universit de Bordeaux Contents Introduction

VIDEO SIGNALS Segmentation WHAT IS SEGMENTATION WHAT IS SEGMENTATION Segmentation is a

Segmentation Bottom-up Segmentation Semantic / instance segmentation Many Slides from L.

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The Universal Declaration of Human Rights written in the ancient Akkadian cuneiform script.

M2-01a Noah and his sons M3-01 M1-86 M1- M2-58a M3-02 M1-56 M3-03 Cuneiform Library

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Segmentation Free Spotting of Cuneiform using Part Structured - PowerPoint PPT Presentation

Segmentation Free Spotting of Cuneiform using Part Structured Models Heidelberg University, Bartosz Bogacz Smith College, Nicholas Howe Heidelberg University, Hubert Mara Cuneiform Script More than 3,000 years of history Evolved from a

CUNEIFORM http://www.etoyoc.com/yoda/papers/ tcl2019.Cuneiform_Slides.pdf PRELUDE Cuneiform

ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource

Birdwatching Spotting Scopes April, 2020 GENERAL FEATURES OF BIRDWATCHING SPOTTING SCOPES

Pixel-Level Im Image Understanding wit ith Semantic Segmentation and Panoptic Segmentation

Segmentation of an Image Using Free Distributive Lattices Jan Pavl k Brno University of

Target or tactical June, 2020 spotting scopes TARGET OR TACTICAL SPOTTING SCOPES Target or

Angled Spotting Scopes March, 2020 ANGLED SPOTTING SCOPES FOR HUNTING Appropriate for hunting

Part 1 : Image Segmentation Anne Vialard LaBRI, Universit de Bordeaux Contents Introduction

VIDEO SIGNALS Segmentation WHAT IS SEGMENTATION WHAT IS SEGMENTATION Segmentation is a

Segmentation Bottom-up Segmentation Semantic / instance segmentation Many Slides from L.

Cuneiform writing had over 560 signs Egyptian hieroglyphics were simpler and used only 20 or 30

FREE GRADIENT DISCONTINUITY AND IMAGE SEGMENTATION Franco Tomarelli Politecnico di Milano

Segmentation Segmentation Segmentation Define the accurate boundaries of all objects in an image

2. Early Writing Systems Cuneiform, early Linear Scripts Origins of the

The Universal Declaration of Human Rights written in the ancient Akkadian cuneiform script.

M2-01a Noah and his sons M3-01 M1-86 M1- M2-58a M3-02 M1-56 M3-03 Cuneiform Library

Semantic Segmentation / Instance Segmentation Based on Deep learning Yiding Liu 2018.12.08

Segmentation using Segmentation using Bayesian Decision Theory Bayesian Decision Theory

Lecture 2 Annotation tools &amp; Segmentation Summary of Part 1 Annotation theory

Free Trade Agreements a global perspective March 2015 Agenda Part 1 Introduction by

Segmentation by discrete watersheds Part 1: Watershed cuts Jean Cousty Four-Day Course on

Part 4 Segmentation, Targeting &amp; Positioning (STP) Resource Person MATHISHA

Discriminative Keyword Spotting Joseph Keshet, The Hebrew University David Grangier, IDIAP

Image Segmentation using Seg3D Segmentation From Clinical Scans RA RA LA RV LA LV RV LV

Lecture 2 Annotation tools & Segmentation Summary of Part 1 Annotation theory

Part 4 Segmentation, Targeting & Positioning (STP) Resource Person MATHISHA