Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2018). Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images. In International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing (Vol. 721).
Abstract: This paper proposes a novel approach to use cross-spectral
images to achieve a better performance with the proposed Adaptive Harris
corner detector comparing its obtained results with those achieved
with images of the visible spectra. The images of urban, field, old-building
and country category were used for the experiments, given the variety of
the textures present in these images, with which the complexity of the
proposal is much more challenging for its verification. It is a new scope,
which means improving the detection of characteristic points using crossspectral
images (NIR, G, B) and applying pruning techniques, the combination
of channels for this fusion is the one that generates the largest
variance based on the intensity of the merged pixels, therefore, it is that
which maximizes the entropy in the resulting Cross-spectral images.
Harris is one of the most widely used corner detection algorithm, so
any improvement in its efficiency is an important contribution in the
field of computer vision. The experiments conclude that the inclusion of
a (NIR) channel in the image as a result of the combination of the spectra,
greatly improves the corner detection due to better entropy of the
resulting image after the fusion, Therefore the fusion process applied to
the images improves the results obtained in subsequent processes such as
identification of objects or patterns, classification and/or segmentation.
|
|
Xavier Soria, & Angel D. Sappa. (2018). Improving Edge Detection in RGB Images by Adding NIR Channel. In 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) (pp. 266–273).
|
Xavier Soria, Angel D. Sappa, & Riad Hammoud. (2018). Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059.Vol. 18(Issue 7).
Abstract: Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
|
Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2018). Cross-spectral image dehaze through a dense stacked conditional GAN based approach. In 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) (pp. 358–364).
Abstract: This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.
|
A. Amato, F. Lumbreras, & Angel D. Sappa. (2014). A general-purpose crowdsourcing platform for mobile devices. In Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014 (Vol. 3, pp. 211–215). Lisbon, Portugal: IEEE.
Abstract: This paper presents details of a general purpose micro-taskon-demand platform based on the crowdsourcing philosophy. This platformwas specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquityand iii) embedded sensors.The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks.Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and task- solver).Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way.Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications.Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform.
|
P. Ricaurte, C. Chilán, C. A. Aguilera-Carrasco, B. X. Vintimilla, & Angel D. Sappa. (2014). Performance Evaluation of Feature Point Descriptors in the Infrared Domain. In Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2013 (Vol. 1, pp. 545–550). IEEE.
Abstract: This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered.
|
N. Onkarappa, Cristhian A. Aguilera, B. X. Vintimilla, & Angel D. Sappa. (2014). Cross-spectral Stereo Correspondence using Dense Flow Fields. In Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014 (Vol. 3, pp. 613–617). IEEE.
Abstract: This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach.
|
Ricaurte P, Chilán C, Cristhian A. Aguilera, Boris X. Vintimilla, & Angel D. Sappa. (2014). Feature Point Descriptors: Infrared and Visible Spectra. Sensors Journal, Vol. 14, pp. 3690–3701.
Abstract: This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.
|
Cristhian A. Aguilera, Angel D. Sappa, & R. Toledo. (2015). LGHD: A feature descriptor for matching across non-linear intensity variations. In IEEE International Conference on, Quebec City, QC, 2015 (pp. 178–181). Quebec City, QC, Canada: IEEE.
Abstract: This paper presents a new feature descriptor suitable to the task of matching features points between images with nonlinear intensity variations. This includes image pairs with significant illuminations changes, multi-modal image pairs and multi-spectral image pairs. The proposed method describes the neighbourhood of feature points combining frequency and spatial information using multi-scale and multi-oriented Log- Gabor filters. Experimental results show the validity of the proposed approach and also the improvements with respect to the state of the art.
|
M. Oliveira, L. Seabra Lopes, G. Hyun Lim, S. Hamidreza Kasaei, Angel D. Sappa, & A. Tomé. (2015). Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015 (pp. 2488–2495). Hamburg, Germany: IEEE.
Abstract: In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline.
|
