Research expertise:
- Spectral imaging.
- Color Imaging.
- Color vision deficiencies.
- Non-invasive techniques for the study of art paintings and other art pieces.
- High Dynamic Range imaging.
- Polarimetric imaging.
- Multispectral illuminant invariants.
- Digital Image Processing and analysis.
- Digital Image segmentation and classification.
- Computer simulations and real world experiments.
- Imaging systems design and optimization.
- Spectral reflectance estimation.
- Eye tracking.
- Visual attention models.
- Color Imaging.
- Color vision deficiencies.
- Non-invasive techniques for the study of art paintings and other art pieces.
- High Dynamic Range imaging.
- Polarimetric imaging.
- Multispectral illuminant invariants.
- Digital Image Processing and analysis.
- Digital Image segmentation and classification.
- Computer simulations and real world experiments.
- Imaging systems design and optimization.
- Spectral reflectance estimation.
- Eye tracking.
- Visual attention models.
Editor & Scientific reviewer in journals:
- Guest Editor in Journal Sensors, special Issue "Color and Spectral Sensors".
- Guest Editor in Journal Computation, special Issue "Applications of Computation in Multispectral and Hyperspectral Imaging Systems".
- Guest Editor in Jpurnal of Imaging, special Issue "Hyperspectral imaging and its applications".
- JOSA. Optical Society of America.
- Applied Optics. Optical Society of America.
- Optic Express. Optical Society of America.
- Optic Letters. Optical Society of America.
- Sensor.
- SPIE Optical Engineering.
- Color Research and Application. Wiley.
- Guest Editor in Journal Computation, special Issue "Applications of Computation in Multispectral and Hyperspectral Imaging Systems".
- Guest Editor in Jpurnal of Imaging, special Issue "Hyperspectral imaging and its applications".
- JOSA. Optical Society of America.
- Applied Optics. Optical Society of America.
- Optic Express. Optical Society of America.
- Optic Letters. Optical Society of America.
- Sensor.
- SPIE Optical Engineering.
- Color Research and Application. Wiley.
Member of congress committee:- Member of local committee in congress Light and Color in Nature. Granada, 2016.
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Study of the effect of passive and active aids for Color Vision Deficiency (CVD) observers:
Under construction, sorry for the inconvenience. New information will be available soon.
Hyperspectral imaging for conservation, restoration and analysis of cultural heritage and works of art:
Digital imaging techniques are widely used in the examination of cultural heritage objects such as paintings, documents, plasterworks, posters, maps, etc. All these items are subject to the action of degradation agents as well as the intervention of conservation and restoration proffesionals. Studying in deep these pieces we can detect degaded or restored areas, detect materials such as pigments, study, model and predict the appearance of conservation products such as varnishes, binders or consolidants, simulate the color appearance under different illuminants, etc. Summing up, spectral imaging opens a huge amount of possibilities for the accurate study of culturalheritage. Currently we are working together with various departments of the University of Granada like the department of Analitical Chemistry in the Faculty of Science or the department of Painting in the Faculty of Art. We are studying art paintings, paper documents, plasterworks, varnishes, pigments, binders, consolidants, inks, etc. We are designing and developping new capturing and processing techniques to overcome the limitations of current spectral imaging frameworks, as well as new techniques and metrics to evaluate the effect of different agents on the appearance of the heritage items.
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Visual saliency detection through multispectral imaging:
Visual saliency detection consists on identifying what regions in a scene catch the observers attention upon inspection. If we could mimic what human observers look at when they are assigned a given task (e.g. surveillance, traffic monitoring, etc), we could use automatic systems to assist and enhance the performance on those tasks. Many visual attention models use color (RGB) images to identify salient regions. However this is still an open problem. In our lab, we are studying whether increasing the spectral information captured by the imaging systems (i.e. moving from RGB to multispectral), actually helps improving saliency detection. A database of 8-channels multispetral images including information within the visible and near-infrared spectral ranges (from 400 nm to 1000 nm), has been built and annotated. Nowadays, devices like eyetrackers allow us to accurately measure those positions where the eye fixations occur when looking at an image. We have performed psychophysical experiments using them in order to retrieve the heat maps for each of the images. These heat maps serve as ground truth for the assessments of the visual saliency models applied to both RGB and multispectral images. This way we can directly compare whether adding spectral information increases the performance.
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Object segmentation and material classification in Multispectral HDR polarimetric imaging:
Together with my research group at the Color Imaging Lab in Granada, and in collaboration with professors from the Horiuchi & Hirai's laboratory, in the Department of Imaging Sciences of the Faculty of Creative Engineering at the Graduate School of Science and Engineering of the University of Chiba in Japan, I have proposed a new workflow for the segmentation and material classification of objects present in multispectral HDR polarimetric images of indoor scenes. The capture was done using a rotating Liquid Crystal Tunable Filter, and also a pre-processing step to correct the images from misalignments and radiometric calibration.
The resulting multispectral HDR polarimetric image cubes were used for object segmentation based on an new image processing pipeline designed by us. Such pipeline cosist in some steps such as RGB image rendering, highlights removal, and meashift among others. Also a material classification pipeline has been proposed as an evolution of the previous one proposed by Chiba University staff. This a metal/dielectric classification based in the Degree of Linear Polarization (DoLP) map around the highlight areas. A more robust method based in a DoLP ratio instead of directly assessing the DoLP surface curvature has been proposed and tested to perform more robustly than the previously proposed method. |
High resolution hyperspectral imaging of effect coatings:
Under construction, sorry for the inconvenience. New information will be available soon.
A graphical summary of my PhD. thesis objectives presented in a workshop for PhD. students in Physics and Space Sciences at University of Granada:
High Dynamic Range imaging:
One of the main topics of my PhD. is the study of High Dynamic Range Imaging techniques used to improve the dynamic range of common, consumer use imaging systems. These systems are based in CCD/CMOS image sensors that can be used for monochrome, color or multispectral imaging.
Together with my research group, I developed an algorithm, to estimate the bracketing sets needed to capture the full dynamic range of an HDR scene, without the need of any prior knowledge of information about image content. Therefore we call the method blind. Besides, it is applicable to any camera, whatever the shape of its Camera Response Function (CRF) is. Linear or not. For this reason we call it universal. The method works on-line, as the exposure times are estimated during the capturing process and not after some previous image acquisition. It is also minimal, as every single shot taken is used to compose the final HDR radiance map of the scene, and the number of shots to cover the full dynamic range of the scene is also minimal. However, the method can be easily tuned so that the user can decide if it is more crucial to have a minimum bracketing set (shorter capturing time) or a higher signal to noise ration (SNR). A Journal article and a congress communication have been published about this method. |
Spectral Imaging:
One of the main research interests in the Color Imaging Laboratory (my research group), is spectral imaging. We work on designing spectral imaging systems that overcome current devices' limitations (size, cost, time of capture, uncontrolled conditions, etc). We work with spectral capturing devices such as systems based on Liquid Crystal Tunable Filters (LCTF), Bragg grating hyperspectral imagers, filter wheel high speed cameras, multispectral line-scan sensors, etc.
We design and optimize the systems for general or specific spectral imaging applications. We develop tools to use them and improve their limitations. We mostly work in Matlab programming environment for image capture and data processing and analisys. One of the cut-edge technologies we work with is that of Transverse Field Detectors (TFD). They are CMOS-based filter-less multispectral silicon image sensors. They are still in a prototype stage of development, and we proposed an imaging system combined with Multispectral Filter Arrays, to obtain up to 36 spectral bands images in a quick capture. A journal article as well as some congress communications have been published about this system. We also collaborate in international research projects with European companies to develop a system for high resolution hyperspectral imaging of gonio-chromatic effect coatings for car paints. |
High Dynamic Range Multispectral Polarimetric imaging:
There exist many applications in which the polarization state of light is important. These applications range from the study of natural phenomena that polarize light (like skylight, halos, rainbows, etc), to the material classification studying the Degree of Polarization around the highlights of objects.
We are creating a database of High Dynamic Range Multispectral Polarimetric images of outdoors scenes for making it publicly available. Such images are captured using a monochrome camera together with a filter wheel of 8 slots. We use 5 spectral channels in the visible range, 2 in the Near Infrared range and 1 channel in between visible and Near Infrared. Besides a linear polarizer is placed in front of the camera rotating along the optical axis. We capture differently exposed images for each spectral band and for each of the four polarization angles: 0º, 45º, 90º and 35º. This way we can calculate the Degree Of Linear Polarization map (DOLP map) according to the Stokes parameters. |
High Dynamic Range Polarimetric Image registration:
Many authors have proposed solutions for eliminating ghosting effects when building high dynamic range (HDR) images from several differently exposed low dynamic range (LDR) images. Registration techniques such as key points retrieval and warping work fine when we are dealing with monochrome or RGB LDR images. However when we already compose the HDR images, the common techniques fail to register the images, as the range of pixel values for a HDR image is pretty high. We proposed a pre-processing step that enables us to use these common registration techniques with HDR images. This way we can align properly several HDR images of the same scene, such as those captured with different polarization angles.
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Multispectral high dynamic range radiance map generation from sensor responses:
When capturing images of scenes where the dynamic range is too high for a single shot, we take several shots with different exposure settings and then compose a high dynamic range image with them. This step is mapping from sensor responses space to a space where each pixel's value is proportional to the amount of light coming from the scene. We need to take care of some factors that hold us back from getting accurate radiance-proportional values in the resulting HDR image, like veiling glare. We performed an absolute radiometric calibration of our camera with help of a Spectroradiometer. We compose the Camera Response Function mapping sensor responses (using known exposure times) to integrated radiance pixel-wise. We are studying the limits in which the veiling glare allows us to get useful radiometric information pixel-wise from the images.
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Infrared imaging:
Multispectral imaging is usually assumed to be within the visible range of the spectrum. However, adding some spectral bands in our imaging system which lie in the infrared region, could improve the performance in certain applications. It is demonstrated, that vegetation can be detected and segmented in images by computing the vegetation index, which is a ratio between two spectral bands. One of them in the near infrared portion of the spectrum.