The PASCAL Visual Object Classes Challenge

IEEE Transactions on Pattern Analysis and Machine Intelligence

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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.

/pdf/emergence-of-nanoplastic-in-the-environment-and-possible-y3p26c4dn5.pdf

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

Robust and accurate detection of text in natural scene images and document images is a very challenging and common research problem. Over the past few decades, a variety of algorithms for text detection in images have been developed but there is still need for more robust and accurate text detection methods. In this work, we have proposed an accurate and robust text detection framework in which canny edge detection, maximally stable extremal regions and geometric filtering are employed in combination to efficiently collect and filter letter candidates in an image. Subsequently, individual letter patches are grouped to detect text sequences, which are then fragmented into isolated word patches. Finally, optical character recognition is employed to digitize the word patches. The proposed algorithm is tested on images representing different scenarios ranging from documents to natural scenes. Promising results have been reported which prove the accuracy and robustness of the proposed framework and encourage its practical implementation in real world applications.

Automated Latin Text Detection in Document Images and Natural Scene Images based on Connected Component Analysis

Image stabilisation aims to compensate and smoothen the effects of undesired trembling motion of cameras mounted on non-static platforms. It becomes quite a challenging task in the case of moving platforms, such as ground vehicles, unmanned aerial vehicles, and handheld devices. Many satisfactory solutions to the image stabilisation problem are proposed in the recent literature, but most of these methods are not adaptable for handling a wide range of intentional motions with minimum lag, especially in real-time scenarios. In this study, the authors propose an online two-dimensional image stabilisation technique based on dual motion vector integration, which is a novel adaptive motion smoothing technique that employs an average length of motion vectors to estimate the intentional motion. The overall computational cost of the proposed system is significantly reduced by employing frame-shaking judgment that only allows processing of jittering frames. Promising experimental results have been obtained on challenging videos obtained from hand-held and vehicle-mounted cameras which demonstrate the robustness and effectiveness of the proposed technique against feature point mismatching and presence of moving objects within the scene at a frame rate of 30 frames per second.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2018.5368

Robust and computationally efficient online image stabilisation framework based on adaptive dual motion vector integration

With the advancements in precision farming, crop sensing is gaining importance for timely crop health management. Leaf water content (LWC) is key component to determine vegetation health and nourishment. Timely estimation of LWC could save us from hazardous damage by pre-planning: drought stress on plants, irrigation and prediction of woodland fire. The retrieval of LWC from visible to shortwave infrared (VSWIR: 0.39 to 2.5 µm) mid- and thermal-infrared (MIR and TIR: 2.50 to 14.0 µm) windows of electromagnetic spectrum has been investigated using different statistical algorithms. Deep learning is modernizing the fast growing field of machine learning and image processing. The convolutional neural network (CNN) is ultramodern technique of deep learning that learns and extracts features directly from data. This research is focused on the extraction of different features of different plant species by using CNN for Regression. The modeled CNN architecture automatically detects prominent features to estimate LWC in plant species from its reflectance spectra, recorded for varying amount of LWC. Previous methods applied on same dataset yielded accuracy of 93% and Root Mean Square Error (RMSE) of 7.1, however, CNN resulted in better and swift results with an accuracy of 98.4% and RMSE of 4.183. This study helps in identifying the important spectral regions for quantifying water stresses in vegetation. The outcomes of this study can enable the future space missions to foresee water content of different plant species on the basis of their spectral signatures for illustrating vegetation stresses.

Convolutional Neural Network based Regression for Leaf Water Content Estimation

. The adoption of neural-symbolic hybrid approaches in visual intelligence is essential to progress toward seamless high-level understanding and reasoning about visual scenes. In this direction, Scene Graph Generation (SGG) is a promising and challenging task, which involves the prediction of objects, their attributes and pairwise visual relationships in a visual scene to create a structured, symbolic scene representation, known as a scene graph, which is utilized in downstream visual reasoning to perform a desired task, such as image captioning, visual question answering, image retrieval, multimedia event processing or image synthesis. The crowdsourced training datasets used for this purpose are highly imbalanced and it is nearly impossible to collect and collate training samples for every visual concept or visual relationship due to a huge number of possible combinations of objects and relationship predicates. Leveraging commonsense knowledge is a natural solution to augment the data-driven approaches with external knowledge to enhance the expressiveness and autonomy of visual understanding and reasoning frameworks. In this paper, we proposed a neural-symbolic visual understanding and reasoning framework based on commonsense knowledge enrichment. Deep neural network-based techniques are used for object detection and multi-modal pairwise relationship prediction to generate a scene graph of an image, which is followed by rule-based algorithms to refine and enrich the scene graph using commonsense knowledge. The commonsense knowledge is extracted from a heterogeneous knowledge graph in the form of related facts and background information about the scene graph elements. The enriched scene graphs are then leveraged in downstream visual reasoning pipelines. We performed comprehensive evaluation of the proposed framework using the common datasets and standard evaluation metrics. As a result of commonsense knowledge enrichment, the relationship recall scores R@100 and mR@100 increased from 36.5 and 11.7 to 39.1 and 12.6 respectively on the Visual Genome (VG) dataset and similar results were observed for the COCO dataset. The proposed framework outperformed the state-of-the-art methods in terms of R@K and mR@K on the standard split of VG. We incorporated scene graph-based image captioning and image generation models as downstream tasks of SGG with knowledge enrichment. With the use of enriched scene graphs, SPICE and CIDEr scores obtained by the image captioning model increased from 20.7 and 115.3 to 23.8 and 131.4 respectively, and the proposed approach outperformed the state-of-the-art scene graph-based image captioning techniques in terms of SPICE and CIDEr scores and achieved comparable performance in terms of BLEU, ROGUE and METEOR scores. The qualitative results of image generation showed that the enriched scene graphs result in more realistic images in which the semantic concepts in the input scene graph can be more clearly observed. The encouraging results validate the effectiveness of knowledge enrichment in scene graphs using heterogeneous knowledge graphs. The source code is available at https://github.com/jaleedkhan/neusire.

NeuSIRE: Neural-Symbolic Image Representation and Enrichment for Visual Understanding and Reasoning

Figure 1: Giant bilateral sacral Tarlov cysts (cysts have been marked along their span with arrows while the associated nerve roots have been encircled). Bukhari SS. Giant symptomatic bilateral sacral Tarlov cysts – a case report.

Muhammad Jaleed Khan

Papers

Automated Latin Text Detection in Document Images and Natural Scene Images based on Connected Component Analysis

Robust and computationally efficient online image stabilisation framework based on adaptive dual motion vector integration

Convolutional Neural Network based Regression for Leaf Water Content Estimation

NeuSIRE: Neural-Symbolic Image Representation and Enrichment for Visual Understanding and Reasoning

Giant Symptomatic Bilateral Sacral Tarlov Cysts - A Case Report