The PASCAL Visual Object Classes Challenge

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Remote Sensing And Image Interpretation

On account of environmental concerns, the fate and adverse effects of plastics have attracted considerable interest in the past few years. Recent studies have indicated the potential for fragmentation of plastic materials into nanoparticles, i.e., "nanoplastics," and their possible accumulation in the environment. Nanoparticles can show markedly different chemical and physical properties than their bulk material form. Therefore possible risks and hazards to the environment need to be considered and addressed. However, the fate and effect of nanoplastics in the (aquatic) environment has so far been little explored. In this review, we aim to provide an overview of the literature on this emerging topic, with an emphasis on the reported impacts of nanoplastics on human health, including the challenges involved in detecting plastics in a biological environment. We first discuss the possible sources of nanoplastics and their fates and effects in the environment and then describe the possible entry routes of these particles into the human body, as well as their uptake mechanisms at the cellular level. Since the potential risks of environmental nanoplastics to humans have not yet been extensively studied, we focus on studies demonstrating cell responses induced by polystyrene nanoparticles. In particular, the influence of particle size and surface chemistry are discussed, in order to understand the possible risks of nanoplastics for humans and provide recommendations for future studies.

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Emergence of Nanoplastic in the Environment and Possible Impact on Human Health

Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .

Deep learning classifiers for hyperspectral imaging: A review

Over the past three decades, significant developments have been made in hyperspectral imaging due to which it has emerged as an effective tool in numerous civil, environmental, and military applications. Modern sensor technologies are capable of covering large surfaces of earth with exceptional spatial, spectral, and temporal resolutions. Due to these features, hyperspectral imaging has been effectively used in numerous remote sensing applications requiring estimation of physical parameters of many complex surfaces and identification of visually similar materials having fine spectral signatures. In the recent years, ground based hyperspectral imaging has gained immense interest in the research on electronic imaging for food inspection, forensic science, medical surgery and diagnosis, and military applications. This review focuses on the fundamentals of hyperspectral image analysis and its modern applications such as food quality and safety assessment, medical diagnosis and image guided surgery, forensic document examination, defense and homeland security, remote sensing applications such as precision agriculture and water resource management and material identification and mapping of artworks. Moreover, recent research on the use of hyperspectral imaging for examination of forgery detection in questioned documents, aided by deep learning, is also presented. This review can be a useful baseline for future research in hyperspectral image analysis.

Modern Trends in Hyperspectral Image Analysis: A Review

Deep learning is revolutionizing the already rapidly developing field of computer vision The convolutional neural network (CNN) is a state-of-the-art deep learning tool that learns high level features directly from a huge dataset of labeled images In document image processing, ink analysis allows for determination of ink age and forgery and identification of pen or writer The spectral information of inks in hyperspectral document images provides valuable information about the underlying material and thus helps in identification and discrimination of inks based on their unique spectral signatures even if they have the same color Ink mismatch detection is a key step in document forgery detection Although various ink mismatch detection techniques are available in the recent literature, there is a constant need for development of more accurate and effective methods to empower automated document forgery detection A state-of-the-art deep learning method for ink mismatch detection in hyperspectral document images is proposed The spectral responses of ink pixels are extracted from a hyperspectral document image, reshaped to a CNN-friendly image format and fed to the CNN for classification The proposed method effectively identifies different ink types in a hyperspectral document image for forgery detection and achieves an overall accuracy of 982% for blue and 88% for black inks, which is the highest accuracy among the latest techniques of ink mismatch detection on the UWA Writing Ink Hyperspectral Images (WIHSI) database and differentiates between the highest number of inks mixed in unbalanced proportions in a hyperspectral document image Furthermore, a detailed discussion on selection of appropriate CNN architecture and classification results are presented in this paper along with comparison with the former methods of ink mismatch detection This research opens a new window for research on automated forgery detection in hyperspectral document images using deep learning

Deep learning for automated forgery detection in hyperspectral document images

Multispectral imaging allows for analysis of images in multiple spectral bands. Over the past three decades, airborne and satellite multispectral imaging have been the focus of extensive research in remote sensing. In the recent years, ground based multispectral imaging has gained an immense amount of interest in the fields ranging from computer vision and medical imaging to art, archaeology and computational forensics. The rich information content in multispectral images allows forensic experts to examine the chemical composition of forensic traces. Due to its rapid, non-contact and non-destructive characteristics, multispectral imaging is an effective tool for visualization, age estimation, detection and identification of forensic traces in document images. Ink mismatch is a key indicator of forgery in a document. Inks of different materials exhibit different spectral signature even if they have the same color. Multispectral analysis of questioned documents images allows identification and discrimination of visually similar inks. In this paper, an efficient automatic ink mismatch detection technique is proposed which uses Fuzzy C-Means Clustering to divide the spectral responses of ink pixels in handwritten notes into different clusters which relate to the unique inks used in the document. Sauvola's local thresholding technique is employed to efficiently segment foreground text from the document image. Furthermore, feature selection is used to optimize the performance of the proposed method. The presented method provides better ink discrimination results than state-of-the-art methods when tested on publicly available UWA Writing Inks Dataset.

Automated Forgery Detection in Multispectral Document Images Using Fuzzy Clustering

Hyperspectral imaging helps in identifying patterns and objects in an observed hyperspectral scene on the basis of their unique spectral signatures; such identification is otherwise difficult using regular imaging Recently, ink mismatch detection analysis based on hyperspectral imaging has shown enormous potential in distinguishing visually similar inks Such analysis provides significant information to forensic document examiners to determine the authenticity of the questioned documents However, a major challenge still exists in disproportionate ink mismatch detection because it is inherently an unbalanced clustering problem The presented approach deals with ink mismatch detection in unbalanced clusters by using hyperspectral unmixing scheme It identifies the spectral signatures (endmembers) of the inks and their corresponding proportions (abundances) Our results show that HySime outperforms other methods in signal subspace estimation Hyperspectral unmixing is done by using minimum volume enclosing simplex algorithm Efficacy of the purposed approach is demonstrated by successfully distinguishing varying disproportionate ink datasets generated from UWA database and results are compared with existing state of the art methods in hyperspectral ink mismatch detection field We expect that these finding will further encourage the use of hyperspectral imaging in document analysis, particularly towards automated questioned document examination

Towards Automated Ink Mismatch Detection in Hyperspectral Document Images

This paper presents the implementation details of a proposed solution to the Emotion Recognition in the Wild 2017 Challenge, in the category of group-level emotion recognition. The objective of this sub-challenge is to classify a group's emotion as Positive, Neutral or Negative. Our proposed approach incorporates both image context and facial information extracted from an image for classification. We use Convolutional Neural Networks (CNNs) to predict facial emotions from detected faces present in an image. Predicted facial emotions are combined with scene-context information extracted by another CNN using fully connected neural network layers. Various techniques are explored by combining and training these two Deep Neural Network models in order to perform group-level emotion recognition. We evaluate our approach on the Group Affective Database 2.0 provided with the challenge. Experimental evaluations show promising performance improvements, resulting in approximately 37% improvement over the competition's baseline model on the validation dataset.

Asad Abbas

Papers

Modern Trends in Hyperspectral Image Analysis: A Review

Deep learning for automated forgery detection in hyperspectral document images

Automated Forgery Detection in Multispectral Document Images Using Fuzzy Clustering

Towards Automated Ink Mismatch Detection in Hyperspectral Document Images

Group emotion recognition in the wild by combining deep neural networks for facial expression classification and scene-context analysis