A Multiple-Expert Binarization Framework for Multispectral Images

In this work, a multiple-expert binarization framework for multispectral images is proposed. The framework is based on a constrained subspace selection limited to the spectral bands combined with state-of-the-art gray-level binarization methods. The framework uses a binarization wrapper to enhance the performance of the gray-level binarization. Nonlinear preprocessing of the individual spectral bands is used to enhance the textual information. An evolutionary optimizer is considered to obtain the optimal and some suboptimal 3-band subspaces from which an ensemble of experts is then formed. The framework is applied to a ground truth multispectral dataset with promising results. In addition, a generalization to the cross-validation approach is developed that not only evaluates generalizability of the framework, it also provides a practical instance of the selected experts that could be then applied to unseen inputs despite the small size of the given ground truth dataset.Comment: 12 pages, 8 figures, 6 tables. Presented at ICDAR'1

LEARNING-FREE DEEP FEATURES FOR MULTISPECTRAL PALM-PRINT CLASSIFICATION

The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods

Visual image processing in various representation spaces for documentary preservation

This thesis establishes an advanced image processing framework for the enhancement and restoration of historical document images (HDI) in both intensity (gray-scale or color) and multispectral (MS) representation spaces. It provides three major contributions: 1) the binarization of gray-scale HDI; 2) the visual quality restoration of MS HDI; and 3) automatic reference data (RD) estimation for HDI binarization. HDI binarization is one of the enhancement techniques that produces bi-level information which is easy to handle using methods of analysis (OCR, for instance) and is less computationally costly to process than 256 levels of grey or color images. Restoring the visual quality of HDI in an MS representation space enhances their legibility, which is not possible with conventional intensity-based restoration methods, and HDI legibility is the main concern of historians and librarians wishing to transfer knowledge and revive ancient cultural heritage. The use of MS imaging systems is a new and attractive research trend in the field of numerical processing of cultural heritage documents. In this thesis, these systems are also used for automatically estimating more accurate RD to be used for the evaluation of HDI binarization algorithms in order to track the level of human performance. Our first contribution, which is a new adaptive method of intensity-based binarization, is defined at the outset. Since degradation is present over document images, binarization methods must be adapted to handle degradation phenomena locally. Unfortunately, these methods are not effective, as they are not able to capture weak text strokes, which results in a deterioration of the performance of character recognition engines. The proposed approach first detects a subset of the most probable text pixels, which are used to locally estimate the parameters of the two classes of pixels (text and background), and then performs a simple maximum likelihood (ML) to locally classify the remaining pixels based on their class membership. To the best of our knowledge, this is the first time local parameter estimation and classification in an ML framework has been introduced for HDI binarization with promising results. A limitation of this method in the case with as the intensity-based methods of enhancement is that they are not effective in dealing with severely degraded HDI. Developing more advanced methods based on MS information would be a promising alternative avenue of research. In the second contribution, a novel approach to the visual restoration of HDI is defined. The approach is aimed at providing end users (historians, librarians, etc..) with better HDI visualization, specifically; it aims to restore them from degradations, while keeping the original appearance of the HDI intact. Practically, this problem cannot be solved by conventional intensity-based restoration methods. To cope with these limitations, MS imaging is used to produce additional spectral images in the invisible light (infrared and ultraviolet) range, which gives greater contrast to objects in the documents. The inpainting-based variational framework proposed here for HDI restoration involves isolating the degradation phenomena in the infrared spectral images, and then inpainting them in the visible spectral images. The final color image to visualize is therefore reconstructed from the restored visible spectral images. To the best of our knowledge, this is the first time the inpainting technique has been introduced for MS HDI. The experimental results are promising, and our objective, in collaboration with the BAnQ (Bibliothèque et Archives nationales de Québec), is to push heritage documents into the public domain and build an intelligent engine for accessing them. It is useful to note that the proposed model can be extended to other MS-based image processing tasks. Our third contribution is presented, which is to consider a new problem of RD (reference data) estimation, in order to show the importance of working with MS images rather than gray-scale or color images. RDs are mandatory for comparing different binarization algorithms, and they are usually generated by an expert. However, an expert’s RD is always subject to mislabeling and judgment errors, especially in the case of degraded data in restricted representation spaces (gray-scale or color images). In the proposed method, multiple RD generated by several experts are used in combination with MS HDI to estimate new, more accurate RD. The idea is to include the agreement of experts about labels and the multivariate data fidelity in a single Bayesian classification framework to estimate the a posteriori probability of new labels forming the final estimated RD. Our experiments show that estimated RD are more accurate than an expert’s RD. To the best of our knowledge, no similar work to combine binary data and multivariate data for the estimation of RD has been conducted

Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001