308 research outputs found

    Introduction to FERET Database and Facial Recognition using Local Binary Patterns

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    Face recognition includes two basic testing procedures. It includes both facial identification and verification. Face identification involves the process of giving the unknown fresh face to the system and ask it if it can recognize the person from already available database (of multiple images) in the system. For verification purposes, biometric signatures are stored on smart card in advance before delivering those to the authenticated masses. These persons swipe their biometric card in the card reader and give system their fresh signature. Now, the system, in turn, will compare both the newly given signature by the claimed person with the signature stored in the biometric card. On the basis of the compared result, either the claimed person is considered authenticated or not

    Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system

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    Remotely sensed hyperspectral image classification is a very challenging task due to the spatial correlation of the spectral signature and the high cost of true sample labeling. In light of this, the collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. In this paper, both these paradigms contribute to the definition of a spectral-relational classification methodology for imagery data. We propose a novel algorithm to assign a class to each pixel of a sparsely labeled hyperspectral image. It integrates the spectral information and the spatial correlation through an ensemble system. For every pixel of a hyperspectral image, spatial neighborhoods are constructed and used to build application-specific relational features. Classification is performed with an ensemble comprising a classifier learned by considering the available spectral information (associated with the pixel) and the classifiers learned by considering the extracted spatio-relational information (associated with the spatial neighborhoods). The more reliable labels predicted by the ensemble are fed back to the labeled part of the image. Experimental results highlight the importance of the spectral-relational strategy for the accurate transductive classification of hyperspectral images and they validate the proposed algorithm

    Ensemble of convolutional neural networks to improve animal audio classification

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    Abstract In this work, we present an ensemble for automated audio classification that fuses different types of features extracted from audio files. These features are evaluated, compared, and fused with the goal of producing better classification accuracy than other state-of-the-art approaches without ad hoc parameter optimization. We present an ensemble of classifiers that performs competitively on different types of animal audio datasets using the same set of classifiers and parameter settings. To produce this general-purpose ensemble, we ran a large number of experiments that fine-tuned pretrained convolutional neural networks (CNNs) for different audio classification tasks (bird, bat, and whale audio datasets). Six different CNNs were tested, compared, and combined. Moreover, a further CNN, trained from scratch, was tested and combined with the fine-tuned CNNs. To the best of our knowledge, this is the largest study on CNNs in animal audio classification. Our results show that several CNNs can be fine-tuned and fused for robust and generalizable audio classification. Finally, the ensemble of CNNs is combined with handcrafted texture descriptors obtained from spectrograms for further improvement of performance. The MATLAB code used in our experiments will be provided to other researchers for future comparisons at https://github.com/LorisNanni

    A New Optical Density Granulometry-Based Descriptor for the Classification of Prostate Histological Images Using Shallow and Deep Gaussian Processes

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    [EN] Background and objective Prostate cancer is one of the most common male tumors. The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to histopathological image classification. Here we introduce a novel family of morphological descriptors which, extracted in the appropriate image space and combined with shallow and deep Gaussian process based classifiers, improves early prostate cancer diagnosis. Method We decompose the acquired RGB image in its RGB and optical density hematoxylin and eosin components. Then, we define two novel granulometry-based descriptors which work in both, RGB and optical density, spaces but perform better when used on the latter. In this space they clearly encapsulate knowledge used by pathologists to identify cancer lesions. The obtained features become the inputs to shallow and deep Gaussian process classifiers which achieve an accurate prediction of cancer. Results We have used a real and unique dataset. The dataset is composed of 60 Whole Slide Images. For a five fold cross validation, shallow and deep Gaussian Processes obtain area under ROC curve values higher than 0.98. They outperform current state of the art patch based shallow classifiers and are very competitive to the best performing deep learning method. Models were also compared on 17 Whole Slide test Images using the FROC curve. With the cost of one false positive, the best performing method, the one layer Gaussian process, identifies 83.87% (sensitivity) of all annotated cancer in the Whole Slide Image. This result corroborates the quality of the extracted features, no more than a layer is needed to achieve excellent generalization results. Conclusion Two new descriptors to extract morphological features from histological images have been proposed. They collect very relevant information for cancer detection. From these descriptors, shallow and deep Gaussian Processes are capable of extracting the complex structure of prostate histological images. The new space/descriptor/classifier paradigm outperforms state-of-art shallow classifiers. Furthermore, despite being much simpler, it is competitive to state-of-art CNN architectures both on the proposed SICAPv1 database and on an external databaseThis work was supported by the Ministerio de Economia y Competitividad through project DPI2016-77869. The Titan V used for this research was donated by the NVIDIA CorporationEsteban, AE.; López-Pérez, M.; Colomer, A.; Sales, MA.; Molina, R.; Naranjo Ornedo, V. (2019). A New Optical Density Granulometry-Based Descriptor for the Classification of Prostate Histological Images Using Shallow and Deep Gaussian Processes. Computer Methods and Programs in Biomedicine. 178:303-317. https://doi.org/10.1016/j.cmpb.2019.07.003S30331717

    Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

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    Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presentedSiirretty Doriast
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