Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Geostatistics For Environmental Scientists

An Introduction to Statistical Learning (with Applications in R), Gareth James,Daniela Witten,Trevor Hastie and Robert Tibshirani著, Springer, 2013年8月, 430pp., 価格 59.99〓, ISBN 978-1-4614-7137-0

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On the evaluation of soil erosion models:Are we doing enough?

Fallacies about the nature of biases have shadowed a proper cognitive understanding of biases and their sources, which in turn lead to ways that minimize their impact. Six such fallacies are presented: it is an ethical issue, only applies to "bad apples", experts are impartial and immune, technology eliminates bias, blind spot, and the illusion of control. Then, eight sources of bias are discussed and conceptualized within three categories: (A) factors that relate to the specific case and analysis, which include the data, reference materials, and contextual information, (B) factors that relate to the specific person doing the analysis, which include past experience base rates, organizational factors, education and training, and personal factors, and lastly, (C) cognitive architecture and human nature that impacts all of us. These factors can impact what the data are (e.g., how data are sampled and collected, or what is considered as noise and therefore disregarded), the actual results (e.g., decisions on testing strategies, how analysis is conducted, and when to stop testing), and the conclusions (e.g., interpretation of the results). The paper concludes with specific measures that can minimize these biases.

Cognitive and Human Factors in Expert Decision Making: Six Fallacies and the Eight Sources of Bias

Statistical relationships between soil colour and soil attributes in semiarid areas

Estimation of small-scale soil erosion in laboratory experiments with Structure from Motion photogrammetry

Low-Cost Smartphone-Based Photogrammetry for the Analysis of Cranial Deformation in Infants

Smartphone-based photogrammetric 3D modelling assessment by comparison with radiological medical imaging for cranial deformation analysis

Determining the correct color is essential for proper cultural heritage documentation and cataloging. However, the methodology used in most cases limits the results since it is based either on perceptual procedures or on the application of color profiles in digital processing software. The objective of this study is to establish a rigorous procedure, from the colorimetric point of view, for the characterization of cameras, following different polynomial models. Once the camera is characterized, users obtain output images in the sRGB space that is independent of the sensor of the camera. In this article we report on pyColorimetry software that was developed and tested taking into account the recommendations of the Commission Internationale de l'Eclairage (CIE). This software allows users to control the entire digital image processing and the colorimetric data workflow, including the rigorous processing of raw data. We applied the methodology on a picture targeting Levantine rock art motifs in Remigia Cave (Spain) that is considered part of a UNESCO World Heritage Site. Three polynomial models were tested for the transformation between color spaces. The outcomes obtained were satisfactory and promising, especially with RAW files. The best results were obtained with a second-order polynomial model, achieving residuals below three CIELAB units. We highlight several factors that must be taken into account, such as the geometry of the shot and the light conditions, which are determining factors for the correct characterization of a digital camera.

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Ángel Marqués-Mateu

Papers

Statistical relationships between soil colour and soil attributes in semiarid areas

Estimation of small-scale soil erosion in laboratory experiments with Structure from Motion photogrammetry

Low-Cost Smartphone-Based Photogrammetry for the Analysis of Cranial Deformation in Infants

Smartphone-based photogrammetric 3D modelling assessment by comparison with radiological medical imaging for cranial deformation analysis

Camera characterization for improving color archaeological documentation