RECOGNITION OF TEOTIHUACAN POTSHERDS
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1 RECOGNITION OF TEOTIHUACAN POTSHERDS Tepalcatl project: Early Postdoc.Mobility SNSF Edgar Roman-Rangel. UniGe. Stephane Marchand-Maillet. UniGe. Diego Jimenez-Badillo. INAH, Mexico.
2 OUTLINE Introduction Current data Research work Challenges
3 OUTLINE Introduction Current data Research work Challenges
![Images from the project Urban Archaeology by INAH Aztec deities. Image from [1].](/docs-images/76/73792925/images/4-3.jpg "[1] Jimenez-Badillo et al.")
4 Introduction: Motivation Analyzing ceramics, and potsherds in particular. One of the most abundant and studied objects in Archaeology. Images from the project Urban Archaeology by INAH Aztec deities. Image from [1]. [1] Jimenez-Badillo et al. A Computer Platform to Analyze the Shape of Museum Objects in 3D. IWVA, 2015.
5 Introduction: Motivation Analyzing ceramics, and potsherds in particular. One of the most abundant and studied objects in Archaeology. Images from the project Urban Archaeology by INAH Information about daily practices of ancient groups. Aztec deities. Image from [1]. [1] Jimenez-Badillo et al. A Computer Platform to Analyze the Shape of Museum Objects in 3D. IWVA, 2015.
6 Introduction: Motivation Analyzing ceramics, and potsherds in particular. One of the most abundant and studied objects in Archaeology. Images from the project Urban Archaeology by INAH Information about daily practices of ancient groups. Thousands of potsherds in a common excavation site. Aztec deities. Image from [1]. [1] Jimenez-Badillo et al. A Computer Platform to Analyze the Shape of Museum Objects in 3D. IWVA, 2015.
7 Introduction: Challenges Manual analysis: tedious. Storage vs further use. Examples of size of potsherds. Image from [2]. Small potsherds (~5 to 20%) of a complete piece. Diagnostic? 3D scanning. [2] Roman-Rangel et al. Categorization of Aztec Potsherds using 3D Local Descriptors. ACCV, [3] Jimenez-Badillo and Ruiz-Correa. Beyond 3D scanning. CAA, 2014.
![Image from [2]. Small potsherds (~5 to 20%) of a complete piece.](/docs-images/76/73792925/images/8-2.jpg "Diagnostic? 3D scanning. Scanning process. Image from [3].")
![[2] Roman-Rangel et al.](/docs-images/76/73792925/images/8-3.jpg "Categorization of Aztec Potsherds using 3D Local Descriptors.")
8 Introduction: Challenges Manual analysis: tedious. Storage vs further use. Examples of size of potsherds. Image from [2]. Small potsherds (~5 to 20%) of a complete piece. Diagnostic? 3D scanning. Scanning process. Image from [3]. [2] Roman-Rangel et al. Categorization of Aztec Potsherds using 3D Local Descriptors. ACCV, [3] Jimenez-Badillo and Ruiz-Correa. Beyond 3D scanning. CAA, 2014.
![Introduction: Motivation Can we help here? Aztec deities. Image from [1].](/docs-images/76/73792925/images/9-1.jpg "[1] Jimenez-Badillo et al.")
9 Introduction: Motivation Can we help here? Aztec deities. Image from [1]. [1] Jimenez-Badillo et al. A Computer Platform to Analyze the Shape of Museum Objects in 3D. IWVA, 2015.
10 OUTLINE Introduction Current data Research work Challenges
11 Current Data: Source 3D scans of potsherds from Teotihuacan.
12 Current Data: Source 3D scans of potsherds from Teotihuacan.
13 Current Data: Source 3D scans of potsherds from Teotihuacan. Teotihuacan: city of gods (where men can become gods). Developed between 1st and 7th century C.E. Most important cultural, commercial, religious, and political center at the time. Aztecs claimed to descend from them. Mainly known through oral traditions (not official records).
![Current Data: Examples Plate Pot Bowl Crater Censer Vase Vase support Image from [4]. Image from [5].](/docs-images/76/73792925/images/14-1.jpg "Curved Highly curved Curved with slight border Curved with clear border Convex Flat [4] Roman-Rangel and")
14 Current Data: Examples Plate Pot Bowl Crater Censer Vase Vase support Image from [4]. Image from [5]. Curved Highly curved Curved with slight border Curved with clear border Convex Flat [4] Roman-Rangel and Jimenez-Badillo. Similarity Analysis of Archaeological Potsherds using 3D surfaces. MCPR, [5] Roman-Rangel et al., Classification and Retrieval of Archaeological Potsherds Using Histograms of Spherical Orientations. JOCCH, 2016.
15 Current Data: Dataset 67 diagnostic potsherds: Plate Pot Bowl Crater Censer Vase Vase support 140 non-diagnostic potsherds: Curved Highly curved Curved with slight border Curved with clear border Convex Flat
16 OUTLINE Introduction Current data Research work Challenges
17 Research Work: Scanning. Description. Similarity Analysis. Retrieval and classification. [4] Roman-Rangel and Jimenez-Badillo. Similarity Analysis of Archaeological Potsherds using 3D surfaces. MCPR, 2015.
18 Research Work: Scanning. Evaluation of local 3D shape descriptors e.g., SISI, LD-SIFT, 3DSC, SHOT Description. Similarity Analysis. Retrieval and classification. [4] Roman-Rangel and Jimenez-Badillo. Similarity Analysis of Archaeological Potsherds using 3D surfaces. MCPR, 2015.
19 Research Work: Scanning. Evaluation of local 3D shape descriptors e.g., SISI, LD-SIFT, 3DSC, SHOT Description. Similarity Analysis. Retrieval and classification. [4] Roman-Rangel and Jimenez-Badillo. Similarity Analysis of Archaeological Potsherds using 3D surfaces. MCPR, 2015.
20 Research Work: Retrieval and classification. [5] Roman-Rangel et al., Classification and Retrieval of Archaeological Potsherds Using Histograms of Spherical Orientations. JOCCH, [6] Jimenez-Badillo and Roman-Rangel. Application of the Bag of Words Model for Analyzing Archaeological Potsherds. CAA, 2014.
21 Research Work: Retrieval and classification. HoSO descriptor [5]. [5] Roman-Rangel et al., Classification and Retrieval of Archaeological Potsherds Using Histograms of Spherical Orientations. JOCCH, [6] Jimenez-Badillo and Roman-Rangel. Application of the Bag of Words Model for Analyzing Archaeological Potsherds. CAA, 2014.
22 Research Work: Retrieval and classification. HoSO descriptor [5]. [5] Roman-Rangel et al., Classification and Retrieval of Archaeological Potsherds Using Histograms of Spherical Orientations. JOCCH, [6] Jimenez-Badillo and Roman-Rangel. Application of the Bag of Words Model for Analyzing Archaeological Potsherds. CAA, 2014.
23 Research Work: Retrieval and classification. HoSO descriptor [5]. [5] Roman-Rangel et al., Classification and Retrieval of Archaeological Potsherds Using Histograms of Spherical Orientations. JOCCH, [6] Jimenez-Badillo and Roman-Rangel. Application of the Bag of Words Model for Analyzing Archaeological Potsherds. CAA, 2014.
![[5] Roman-Rangel et al.](/docs-images/76/73792925/images/24-3.jpg ", Classification and Retrieval of")
![[6] Jimenez-Badillo and Roman-Rangel.](/docs-images/76/73792925/images/24-5.jpg "Application of the Bag of Words Model for")
24 Research Work: Retrieval and classification. HoSO descriptor [5]. [5] Roman-Rangel et al., Classification and Retrieval of Archaeological Potsherds Using Histograms of Spherical Orientations. JOCCH, [6] Jimenez-Badillo and Roman-Rangel. Application of the Bag of Words Model for Analyzing Archaeological Potsherds. CAA, 2014.
25 Research Work: Outcomes Initial dataset of potsherds.
26 Research Work: Outcomes Initial dataset of potsherds. Interest of archaeologists.
27 Research Work: Outcomes Initial dataset of potsherds. Interest of archaeologists. Initial results. Similarity analysis of potsherds. Improved classification and retrieval performance. Diagnostic potsherds are easier. Larger potsherds are easier.
28 Research Work: Outcomes Initial dataset of potsherds. Interest of archaeologists. Initial results. Similarity analysis of potsherds. Improved classification and retrieval performance. Diagnostic potsherds are easier. Larger potsherds are easier. New research questions and challenges.
29 OUTLINE Introduction Current data Research work Challenges
30 Challenges: Future Work Enlarge the dataset (learning methods).
31 Challenges: Future Work Enlarge the dataset (learning methods). Incorporate other features: color, material, motifs, etc.
32 Challenges: Future Work Enlarge the dataset (learning methods). Incorporate other features: color, material, motifs, etc. Design/develop actual browsing tool.
33 Challenges: Future Work Enlarge the dataset (learning methods). Incorporate other features: color, material, motifs, etc. Design/develop actual browsing tool. Beyond visual analysis (documentation).
34 Challenges: Future Work Enlarge the dataset (learning methods). Incorporate other features: color, material, motifs, etc. Design/develop actual browsing tool. Beyond visual analysis (documentation). Test on newly discovered examples.
35 Challenges: Future Work Enlarge the dataset (learning methods). Incorporate other features: color, material, motifs, etc. Design/develop actual browsing tool. Beyond visual analysis (documentation). Test on newly discovered examples. Disseminate.
36 Thank you. Q&A cvml.unige.ch/edgar
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