Automated classification of archaeological ceramic materials by means of texture measures

Hein, Irmgard; Rojas-Domínguez, Alfonso; Ornelas, Manuel; D’Ercole, G.; Peloschek, Lisa

doi:10.1016/j.jasrep.2017.12.032

Cited by 19 publications

(24 citation statements)

References 13 publications

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“…La tabla 3 muestra la mediana y la varianza del porcentaje de clasicación de los 33 experimentos independientes para cada conguración, así como los porcentajes obtenidos por descriptores del estado del arte [15]. De los resultados obtenidos, se puede observar que en este experimento, los descriptores generados por GE obtuvieron un mejor desempeño.…”

Section: Resultsunclassified

“…Instancia del problema. Se usó la base de datos utilizada en el trabajo [15], la cual está formada por imágenes digitales de texturas de piezas arqueológicas de la cultura egipcia. Esta base de datos está dividida en dos clases: Arcilla del Nilo con 22 imágenes y Arcilla Marl con 47 imágenes.…”

Section: Metodologíaunclassified

“…Filtro de Gabor con orientación de 135 grados Aver Filtro de promediado con una ventana de agrupado de tamaño 5x5 [15] 0.7391 * Filtros de Gabor [15] 0.6812 * Descriptores de Haralick [15] 0.7101 * *El trabajo no reporta varianza Tabla 4. Ejemplos de los mejores descriptores generados por las cuatro conguraciones de GE y sus respectivos desempeños.…”

Section: Símbolos Terminales Descripciónunclassified

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Una comparativa de diferentes parámetros en la construcción evolutiva de descriptores para la clasifcacióncación de imágenes de texturas de piezas arqueológicas

Falcón¹,

Ornelas-Rodríguez²,

Sotelo-Figueroa³

et al. 2018

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Resumen. En el presente trabajo se muestran los resultados de los descriptores construidos mediante una metodología basada en Gramática Evolutiva. Se hace la comparación de dos parámetros de la propuesta, uno es el uso de los canales de color y la escala de grises en el proceso de construcción de los descriptores, lo cual se llevó a cabo utilizando dos gramáticas tipo Backus-Naur en el proceso evolutivo. La primera gramática usa los canales de color rojo, azul, verde y la escala de grises, mientras que la segunda gramática solo usa la escala de grises. El otro parámetro es la forma de representar los vectores de características de las imágenes procesadas con los descriptores construidos, comparando dos alternativas: el histograma y los estadísticos del histograma. Los descriptores construidos fueron aplicados a la clasicación de imágenes de texturas de piezas arqueológicas procedentes de la cultura egipcia. Se muestran los resultados obtenidos, los cuales fueron analizados estadísticamente.Palabras clave: clasicación de imágenes, construcción de descriptores, gramática evolutiva.Abstract. This work shows results about descriptors constructed by a methodology based in Grammatical Evolution. A comparison of two 83

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Section: Resultsunclassified

Section: Metodologíaunclassified

Section: Símbolos Terminales Descripciónunclassified

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Una comparativa de diferentes parámetros en la construcción evolutiva de descriptores para la clasifcacióncación de imágenes de texturas de piezas arqueológicas

Falcón¹,

Ornelas-Rodríguez²,

Sotelo-Figueroa³

et al. 2018

RCS

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“…Archaeologists have done this with artefacts for statistical and morphometric analyses (e.g. Aprile, Castellano, & Eramo, ; Hein, Rojas‐Domínguez, Ornelas, D’Ercole, & Peloschek, ; Hofmann, Marschallinger, Unterwurzacher, & Zobl, ; Lamotte & Masson, ) and have used similar methods to study site formation processes (e.g. Sanger, ).…”

Section: Future Directionsmentioning

confidence: 99%

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

Davis

2018

Archaeological Prospection

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Object-based image analysis (OBIA) is a method of assessing remote sensing data that uses morphometric and spectral parameters simultaneously to identify features in remote sensing imagery. Over the past 10-15 years, OBIA methods have been introduced to detect archaeological features. Improvements in accuracy have been attained by using a greater number of morphometric variables and multiple scales of analysis. This article highlights the developments that have occurred in the application of OBIA within archaeology and argues that OBIA is both a useful and necessary tool for archaeological research. Additionally, I discuss future research paths using this method. Some of the suggestions put forth here include: pushing for multifaceted research designs utilizing OBIA and manual interpretation, using OBIA methods for directly studying landscape settlement patterns, and increasing data sharing of methods between researchers. KEYWORDS automated feature extraction, landscape analysis, machine learning, object-based image analysis, pattern recognition, remote sensing

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“…MI encompasses statistical classifiers, semi-automated analysis, deep learning, machine learning, and other methods of systematically parsing through image data to extract information [14,16,25]. While a majority of this work has focused on the development of algorithms for detecting archaeological deposits from landscape-scale satellite and aerial imagery, and airborne laser scanning (ALS) [6,12,26], research has laser scanning (ALS) [6,12,26], research has also focused on analyzing individual materials and smaller-scale phenomena [4,20,22,23,[27][28][29]. In the past several years, alone, there has been an explosion of machine learning research with archaeological remote sensing foci around the world (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Geographic Disparity in Machine Intelligence Approaches for Archaeological Remote Sensing Research

Davis

2020

Remote Sensing

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A vast majority of the archaeological record, globally, is understudied and increasingly threatened by climate change, economic and political instability, and violent conflict. Archaeological data are crucial for understanding the past, and as such, documentation of this information is imperative. The development of machine intelligence approaches (including machine learning, artificial intelligence, and other automated processes) has resulted in massive gains in archaeological knowledge, as such computational methods have expedited the rate of archaeological survey and discovery via remote sensing instruments. Nevertheless, the progression of automated computational approaches is limited by distinct geographic imbalances in where these techniques are developed and applied. Here, I investigate the degree of this disparity and some potential reasons for this imbalance. Analyses from Web of Science and Microsoft Academic searches reveal that there is a substantial difference between the Global North and South in the output of machine intelligence remote sensing archaeology literature. There are also regional imbalances. I argue that one solution is to increase collaborations between research institutions in addition to data sharing efforts.

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Automated classification of archaeological ceramic materials by means of texture measures

Cited by 19 publications

References 13 publications

Una comparativa de diferentes parámetros en la construcción evolutiva de descriptores para la clasifcacióncación de imágenes de texturas de piezas arqueológicas

Una comparativa de diferentes parámetros en la construcción evolutiva de descriptores para la clasifcacióncación de imágenes de texturas de piezas arqueológicas

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

Geographic Disparity in Machine Intelligence Approaches for Archaeological Remote Sensing Research

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