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Chapter and Conference Paper
Orthogonal Local Image Descriptors with Convolutional Autoencoders
This work proposes the use of deep learning architectures, and in particular Convolutional Autencoders (CAE’s), to incorporate an explicit component of orthogonality to the computation of local image descript...
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Chapter and Conference Paper
Assessing Deep Learning Architectures for Visualizing Maya Hieroglyphs
This work extends the use of the non-parametric dimensionality reduction method t-SNE [11] to unseen data. Specifically, we use retrieval experiments to assess quantitatively the performance of several existing m...
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Chapter and Conference Paper
Rotation Invariant Local Shape Descriptors for Classification of Archaeological 3D Models
We introduce a method for estimation of rotation invariant local shape descriptors for 3D models. This method follows a successful idea commonly used to obtain rotation invariant descriptors in 2D images, and ...
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Chapter and Conference Paper
Transferring Neural Representations for Low-Dimensional Indexing of Maya Hieroglyphic Art
We analyze the performance of deep neural architectures for extracting shape representations of binary images, and for generating low-dimensional representations of them. In particular, we focus on indexing bi...
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Chapter and Conference Paper
Similarity Analysis of Archaeological Potsherds Using 3D Surfaces
This work presents a new methodology for efficient scanning and analysis of 3D shapes representing archaeological potsherds, which is based on single-view 3D scanning. More precisely, this work presents an ana...
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Chapter and Conference Paper
HOOSC128: A More Robust Local Shape Descriptor
This work introduces a new formulation of the Histogram-of-Orientations Shape-Context (HOOSC) descriptor [9], which has shorter dimensionality and higher degree of scale and rotation invariance with respect to...
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Chapter and Conference Paper
Evaluating Shape Descriptors for Detection of Maya Hieroglyphs
In this work we address the problem of detecting instances of complex shapes in binary images. We investigated the effects of combining DoG and Harris-Laplace interest points with SIFT and HOOSC descriptors. A...
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Chapter and Conference Paper
Stopwords Detection in Bag-of-Visual-Words: The Case of Retrieving Maya Hieroglyphs
We present a method for automatic detection of stopwords in visual vocabularies that is based upon the entropy of each visual word. We propose a specific formulation to compute the entropy as the core of this ...
