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

Generalized Linear Models (2nd ed.)

Morphometric tools for landmark data: geometry and biology

This paper presents an overview of significant advances made in the emerging field of nature-inspired computing (NIC) with a focus on the physics- and biology-based approaches and algorithms. A parallel development in the past two decades has been the emergence of the field of computational intelligence (CI) consisting primarily of the three fields of neural networks, evolutionary computing and fuzzy logic. It is observed that NIC and CI intersect. The authors advocate and foresee more cross-fertilisation of the two emerging fields.

/pdf/nature-inspired-computing-an-overview-and-some-future-4cvoerlwh4.pdf

Nature Inspired Computing: An Overview and Some Future Directions

Shape optimization of free-form steel space-frame roof structures with complex geometries using evolutionary computing

Polygonal surface models are typically used in three dimensional 3D visualizations and simulations. They are obtained by laser scanners, computer vision systems or medical imaging devices to model highly detailed object surfaces. Surface mesh simplification aims to reduce the number of faces used in a 3D model while keeping the overall shape, boundaries and volume. In this work, we propose to deal with the 3D open model mesh simplification problem from an evolutionary multi-objective viewpoint. The quality of a solution is defined by two conflicting objectives: the accuracy and the simplicity of the model. We adapted the Non-Dominated Sorting Genetic Algorithm II NSGA-II and the Multi-Objective Evolutionary Algorithm Based on Decomposition MOEA/D to tackle the problem. We compare their performance with two classic approaches and two single-objective implementations. The comparison has been carried out using six different datasets from six corresponding real-world objects. Experimental results have demonstrated that NSGA-II and MOEA/D performs similarly and obtain the best solutions for the studied problem.

/pdf/evolutionary-multi-objective-optimization-for-mesh-kol0dpv4vw.pdf

Evolutionary multi-objective optimization for mesh simplification of 3D open models

Computer vision and soft computing for automatic skull-face overlay in craniofacial superimposition

Craniofacial superimposition involves the process of overlaying a skull with a number of ante-mortem images of an individual and the analysis of their morphological correspondence. Within the craniofacial superimposition process, the skull-face overlay stage focuses on achieving the best possible overlay of the skull and a single ante-mortem image of a missing person. This technique has been commonly applied following a nonautomatic trial-and-error approach. Automatic skull-face overlay methods have been developed obtaining promising results. In this paper, we present two new variants that are an extension of existing 3-D–2-D methods to automatically superimpose a skull 3-D model on a facial photograph. We have modeled the imprecision related to the facial soft tissue depth between corresponding pairs of cranial and facial landmarks which typically guide the automatic approaches. As an illustration of the model’s performance, the soft tissue distances associated to studies for Mediterranean population have been considered for dealing with this landmark matching uncertainty. Hence, we directly incorporate the corresponding landmark spatial relationships within the automatic skull-face overlay procedure. We have tested the performance of our proposal on 18 skull-face overlay instances from a ground truth data set obtaining valuable results. The current proposal is thus the first automatic skull-face overlay method evaluated in a reliable and unbiased way.

Modeling Facial Soft Tissue Thickness for Automatic Skull-Face Overlay

An experimental study on fuzzy distances for skull–face overlay in craniofacial superimposition

One of the most important tasks in forensic anthropology is human identification Over the past decades, forensic anthropologists have focused on improving techniques to increase the accuracy of identification Following a thorough examination of unidentified human remains, the investigator chooses a specific identification technique to be applied, depending on the availability of ante mortem and post mortem data Craniofacial superimposition is a forensic process in which photographs of a missing person are compared with a skull in order to determine whether is the individual depicted and the skeletal remains are the same person After more than one century of development, craniofacial superimposition has become an interdisciplinary research field where computer science has acquired a key role as a complement of forensic sciences Moreover, the availability of new digital equipment has resulted in a significant advance in the applicability of this forensic identification technique In this paper, we review a semi-automatic method devised to assist the forensic anthropologist in the identification process using craniofacial superimposition The technique is based on a three-stage methodology The first two are performed automatically by soft computing techniques However, the final decision corresponds to the forensic expert The performance of the proposed method is illustrated using several real-world identification cases

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1626_Campomanes-et-al-JAIHC-forenses-on-line-first.pdf

B. Rosario Campomanes-Álvarez

Papers

Evolutionary multi-objective optimization for mesh simplification of 3D open models

Computer vision and soft computing for automatic skull-face overlay in craniofacial superimposition

Modeling Facial Soft Tissue Thickness for Automatic Skull-Face Overlay

An experimental study on fuzzy distances for skull–face overlay in craniofacial superimposition

Computer-based craniofacial superimposition in forensic identification using soft computing