Capsule Networks for Object Detection in UAV Imagery

Recent advances in Convolutional Neural Networks (CNNs) have attracted great attention in remote sensing due to their high capability to model high-level semantic content of Remote Sensing (RS) images. However, CNNs do not explicitly retain the relative position of objects in an image and, thus, the effectiveness of the obtained features is limited in the framework of the complex object detection problems. To address this problem, in this paper we introduce Capsule Networks (CapsNets) for object detection in Unmanned Aerial Vehicle-acquired images. Unlike CNNs, CapsNets extract and exploit the information content about objects’ relative position across several layers, which enables parsing crowded scenes with overlapping objects. Experimental results obtained on two datasets for car and solar panel detection problems show that CapsNets provide similar object detection accuracies when compared to state-of-the-art deep models with significantly reduced computational time. This is due to the fact that CapsNets emphasize dynamic routine instead of the depth.EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEart

Encoding Motion Cues for Pedestrian Path Prediction in Dense Crowd Scenarios

Pedestrian path prediction is an emerging topic in the crowd visual analysis domain, notwithstanding its practical importance in many respects. To date, the few contributions in the literature proposed quite straightforward approaches, and only a few of them have taken into account the interaction between pedestrians as a paramount cue in forecasting their potential walking preferences in a given scene. Moreover, the typical trend was to evaluate the proposed algorithms on sparse scenarios. To cope with more realistic cases, in this paper, we present an efficient approach for pedestrian path prediction in densely crowded scenes. The proposed approach initiates by extracting motion features related to the target pedestrian and his/her neighbors. Second, in order to further increase the representativeness of the extracted motion cues, an autoencoder feature learning model is considered, whose outcome finally feeds a Gaussian process regression prediction model to infer the potential future trajectories of the target pedestrians given their walking records in the scene. Experimental results demonstrate that our framework scores plausible results and outperforms traditional methods in the literature

Recovering the Sight to blind People in indoor Environments with smart Technologies

The methodologies presented in this thesis address the problem of blind people rehabilitation through assistive technologies. In overall terms, the basic and principal needs that a blind individual might be concerned with can be confined to two components, namely (i) navigation/ obstacle avoidance, and (ii) object recognition. Having a close look at the literature, it seems clear that the former category has been devoted the biggest concern with respect to the latter one. Moreover, the few contributions on the second concern tend to approach the recognition task on a single predefined class of objects. Furthermore, both needs, to the best of our knowledge, have not been embedded into a single prototype. In this respect, we put forth in this thesis two main contributions. The first and main one tackles the issue of object recognition for the blind, in which we propose a ‘coarse recognition’ approach that proceeds by detecting objects in bulk rather than focusing on a single class. Thus, the underlying insight of the coarse recognition is to list the bunch of objects that likely exist in a camera-shot image (acquired by the blind individual with an opportune interface, e.g., voice recognition synthesis-based support), regardless of their position in the scene. It thus trades the computational time with object information details as to lessen the processing constraints. As for the second contribution, we further incorporate the recognition algorithm, along with an implemented navigation system that is supplied with a laser-based obstacle avoidance module. Evaluated on image datasets acquired in indoor environments, the recognition schemes have exhibited, with little to mild disparities with respect to one another, interesting results in terms of either recognition rates or processing gap. On the other hand, the navigation system has been assessed in an indoor site and has revealed plausible performance and flexibility with respect to the usual blind people’s mobility speed. A thorough experimental analysis is hereby provided alongside laying the foundations for potential future research lines, including object recognition in outdoor environments

Sparse modeling of the land use classification problem

In this paper, we present a fusion method contextualized within a land use classification framework. At first, feature vectors are extracted from all the color channels of the given test image. Then, the generated vectors are recovered over a bunch of training feature vectors extracted from training images. The resulting reconstruction residuals feed a fusion mechanism to further compose a final residual that serves for inferring the final decision of the class pertaining to the test image. Validated on a benchmark dataset, the presented method shows to promote drastic improvements over using only one single spectral channel. Furthermore, encouraging gains have been recorded with respect to reference works

Sparse coding joint decision rule for ear print recognition

Human ear recognition has been promoted as a profitable biometric over the past few years. With respect to other modalities, such as the face and iris, that have undergone a significant investigation in the literature, ear pattern is relatively still uncommon. We put forth a sparse coding-induced decision-making for ear recognition. It jointly involves the reconstruction residuals and the respective reconstruction coefficients pertaining to the input features (co-occurrence of adjacent local binary patterns) for a further fusion. We particularly show that combining both components (i.e., the residuals as well as the coefficients) yields better outcomes than the case when either of them is deemed singly. The proposed method has been evaluated on two benchmark datasets, namely IITD1 (125 subject) and IITD2 (221 subjects). The recognition rates of the suggested scheme amount for 99.5% and 98.95% for both datasets, respectively, which suggest that our method decently stands out against reference state-of-the-art methodologies. Furthermore, experiments conclude that the presented scheme manifests a promising robustness under large-scale occlusion scenarios

Person re-identification by order-induced metric fusion

This paper presents a novel two-pronged framework for person re-identification. Its idea articulates over the fact that distinct descriptors manifest different ranking scores for the same probe pattern. Thus, if conveniently fused, the descriptors in hand are ought to compensate each other, leading to significant improvements. In this respect, this paper proposes a learning-free weighting method that penalizes and averages the re-identification estimates (e.g., distances) pointed out by different descriptors according to their confidence in evidencing the correct match, to a given probe person, among a given gallery. We particularly show that tangible improvements can be attained with respect to utilizing each descriptor individually. Moreover, we consider a confidence measure mechanism that treats the mutual pairwise distances within the gallery, in order to raise the scores obtained at the fusion stage, and we show that interesting improvements can be achieved. We evaluate the proposed framework on four benchmark datasets and advance late works by large margins

Person re-identification by order-induced metric fusion

This paper presents a novel two-pronged framework for person re-identification. Its idea articulates over the fact that distinct descriptors manifest different ranking scores for the same probe pattern. Thus, if conveniently fused, the descriptors in hand are ought to compensate each other, leading to significant improvements. In this respect, this paper proposes a learning-free weighting method that penalizes and averages the re-identification estimates (e.g., distances) pointed out by different descriptors according to their confidence in evidencing the correct match, to a given probe person, among a given gallery. We particularly show that tangible improvements can be attained with respect to utilizing each descriptor individually. Moreover, we consider a confidence measure mechanism that treats the mutual pairwise distances within the gallery, in order to raise the scores obtained at the fusion stage, and we show that interesting improvements can be achieved. We evaluate the proposed framework on four benchmark datasets and advance late works by large margins. \ua9 2017 Elsevier B.V

Heart sounds analysis using wavelets responses and support vector machines

Over the last decade, computerized heart screening techniques have been increasingly receiving attention. In general, one can say that such techniques can be categorized as: with, or without the so-called Electrocardiogram (ECG) signal. Considering this latter strategy, we devote this paper with the intention to design an algorithm that provides with heart sounds known as Phonocardiograms (PGC) investigation for further definition of the present pathology if any. A novel algorithm for heart sounds segmentation is also presented. The decision making is accomplished by means of support vector machines (SVM) classifier which is fed by characteristic features extracted from PCGs basing on wavelet filter banks coefficients so that PCG signals are classified into five classes: normal heart sound (NHS), aortic stenosis (AS), aortic insufficiency (Al) mitral stenosis (MS), and mitral insufficiency (MI). The SVM was trained on a low-dimensional feature space, and tested on relatively a big dataset in order to show its generalization capability

Date Fruit Sorting Based on Deep Learning and Discriminant Correlation Analysis

Date fruit is among the major crops in the middle-east region, where millions of tons are harvested every year. Date is a healthy fruit, which involves sugars, minerals and vitamins. In addition, it helps preventing human body from several diseases such as cancer and heart diseases. Date sorting is a fundamental step in the date industry. However, manually conducting such an operation, by human labors, is expensive and time-consuming. In this paper, we propose a method for classifying the type of date fruit by incorporating supervised and unsupervised deep networks. Specifically, we use discriminant correlation analysis (DCA) algorithm to fuse features learned from convolution neural networks (VGG-F) and an unsupervised network called PCANet. DCA jointly performs feature fusion and dimensionality reduction with a low computational complexity. To carry out experiments, we introduce a new benchmark dataset of date fruit images from 20 date varieties. Our benchmark is, to the best of our knowledge, the largest one in terms of number of varieties. Note that the dataset is publicly available at https://unsat.000webhostapp.com/dataset . Experimental results demonstrate the utility of DCA as well as the complementarity of the fused features. It has also been shown the effectiveness of the proposed method compared to several relevant methods

A comprehensive review of hybrid models for solar radiation forecasting

Solar radiation components assessment is a highly required parameter for solar energy applications. Due to the non-stationary behavior of solar radiation parameters and variety of atmosphere conditions, stand-alone forecasting models are insufficient for providing accurate estimation in some cases. In this respect, different hybrid models have been proposed in recent years to overcome the limitations of single models and boost the forecasting precision. In this paper, acomprehensive literature review of the recent trends in hybrid model techniques for solar radiation components assessment is presented. The main objective behind this study is to present a comparative study between different hybrid models, explore their application, and identify promising and potential models for solar radiation application assessment. The performance ranking of each hybrid model is complicated due the diversity of the data length and scale, forecasting horizon, performance metrics, time step and climate condition. Overall, the presented study provides preliminary guidelines for a complete view of the hybrid models and tools that can be used in order to improve solar radiation assessment