Carlos Hinojosa

Carlos Hinojosa
Industrial University of Santander | UIS · School of Systems Engineering and Informatics

PhD(c) in Computer Science
Computer scientist working on computer vision and computational imaging. Currently looking for job opportunities.

About

28
Publications
2,311
Reads
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208
Citations
Citations since 2016
27 Research Items
207 Citations
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20162017201820192020202120220102030405060
20162017201820192020202120220102030405060

Publications

Publications (28)
Chapter
Full-text available
The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition. In this paper, we propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline. Our framework parameterizes the camera lens to successful...
Article
Full-text available
Recent advances in deep learning led to several algorithms for the accurate diagnosis of pneumonia from chest X-rays. However, these models require large training medical datasets, which are sparse, isolated, and generally private. Furthermore, these models in medical imaging are known to over-fit to a particular data domain source, i.e., these alg...
Preprint
Full-text available
The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition. In this paper, we propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline. Our framework parameterizes the camera lens to successful...
Article
Full-text available
In recent years, compressive spectral imaging (CSI) has emerged as a new acquisition technique that acquires coded projections of the spectral scene, reducing considerably storage and transmission costs. Among several CSI devices, the single-pixel camera (SPC) architecture excels due to its low implementation cost when acquiring a large number of s...
Conference Paper
Full-text available
The widespread use of always-connected digital cameras in our everyday life has led to increasing concerns about the users' privacy and security. How to develop privacy-preserving computer vision systems? In particular, we want to prevent the camera from obtaining detailed visual data that may contain private information. However, we also want the...
Article
Full-text available
Accurate unsupervised classification of hyperspectral images (HSIs) is challenging and has drawn widespread attention in remote sensing due to its inherent complexity. Although significant efforts have been made to develop a variety of methods, most of them rely on supervised strategies. Subspace clustering methods, such as Sparse Subspace Clusteri...
Article
The unsupervised classification of hyperspectral images (HSIs) draws attention in the remote sensing community due to its inherent complexity and the lack of labeled data. Among unsupervised methods, sparse subspace clustering (SSC) achieves high clustering accuracy by constructing a sparse affinity matrix. However, SSC has limitations when cluster...
Preprint
Full-text available
Accurate land cover segmentation of spectral images is challenging and has drawn widespread attention in remote sensing due to its inherent complexity. Although significant efforts have been made for developing a variety of methods, most of them rely on supervised strategies. Subspace clustering methods, such as Sparse Subspace Clustering (SSC), ha...
Preprint
Full-text available
Accurate land cover segmentation of spectral images is challenging and has drawn widespread attention in remote sensing due to its inherent complexity. Although significant efforts have been made for developing a variety of methods, most of them rely on supervised strategies. Subspace clustering methods, such as Sparse Subspace Clustering (SSC), ha...
Article
Full-text available
The accurate segmentation of remotely sensed hyperspectral images has widespread attention in the Earth observation and remote sensing communities. In the past decade, most of the efforts focus on the development of different supervised methods for hyperspectral image classification. Recently, the computer vision community is developing unsupervise...
Conference Paper
In a CSI classification task, it is necessary to a reconstruction process in most cases. In contrast, an approach for classification tasks of SPC avoiding the reconstruction process is presented with an accuracy of 97.72%.
Article
Full-text available
Imaging spectroscopy collects the spectral information of a scene by sensing all the spatial information across the electromagnetic wavelengths and are useful for applications in surveillance, agriculture, and medicine, etc. In contrast, compressive spectral imaging (CSI) systems capture compressed projections of the scene, which are then used to r...
Article
The low spatial resolution of hyperspectral (HS) images generally limits the classification accuracy. Therefore, different multiresolution data fusion techniques have been proposed in the literature. In this paper, a method for supervised classification of spectral images from data fusion measurements is proposed. Specifically, the proposed approac...
Article
Full-text available
Compressive spectral imaging (CSI) acquires compressed observations of a spectral scene by applying different coding patterns at each spatial location and then performing a spectral-wise integration. Relying on compressive sensing, spectral image reconstruction is achieved by using nonlinear and relatively expensive optimization-based algorithms. I...
Conference Paper
Full-text available
This paper proposes a classification method that fuses superpixels-segmentation information from an RGB image with a hyperspectral image without estimating the high spatial-spectral resolution cube. This methodology improves the classification accuracy while boosting the performance.
Conference Paper
Full-text available
This paper proposes a spectral image clustering approach that uses a 3-D Gaussian filter to incorporate the spatial information of the scene in the Sparse Subspace Clustering model obtaining a more accurate representation coefficient matrix.
Conference Paper
Full-text available
This paper proposes a new hyperspectral image subspace clustering framework which adds a total variation denoising constraint in order to improve the similarity between data points from the same subspace.
Article
Full-text available
The snapshot colored compressive spectral imager (SCCSI) is a recent compressive spectral imaging (CSI) architecture that senses the spatial and spectral information of a scene in a single snapshot by means of a colored mosaic FPA detector and a dispersive element. Commonly, CSI architectures allow multiple snapshot acquisition, yielding improved r...
Conference Paper
Full-text available
Compressive spectral imaging (CSI) captures coded and dispersed projections of the spatio-spectral source rather than direct measurements of the voxels. Using the coded projections, an l1 minimization reconstruction algorithm is then used to reconstruct the underlying scene. An architecture known as the snapshot colored compressive spectral imager...

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Projects

Projects (2)
Project
Medical imaging Convolutional Neural networks Transfer learning
Project
Design a supervised classification system using spectral images acquired by merging a single pixel architecture and complementary information from a high spatial resolution intensity sensor.