15 research outputs found
Quantifying Seagrass Distribution in Coastal Water With Deep Learning Models
Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations
Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI
Brain network analysis is a useful approach to studying human brain disorders
because it can distinguish patients from healthy people by detecting abnormal
connections. Due to the complementary information from multiple modal
neuroimages, multimodal fusion technology has a lot of potential for improving
prediction performance. However, effective fusion of multimodal medical images
to achieve complementarity is still a challenging problem. In this paper, a
novel hierarchical structural-functional connectivity fusing (HSCF) model is
proposed to construct brain structural-functional connectivity matrices and
predict abnormal brain connections based on functional magnetic resonance
imaging (fMRI) and diffusion tensor imaging (DTI). Specifically, the prior
knowledge is incorporated into the separators for disentangling each modality
of information by the graph convolutional networks (GCN). And a disentangled
cosine distance loss is devised to ensure the disentanglement's effectiveness.
Moreover, the hierarchical representation fusion module is designed to
effectively maximize the combination of relevant and effective features between
modalities, which makes the generated structural-functional connectivity more
robust and discriminative in the cognitive disease analysis. Results from a
wide range of tests performed on the public Alzheimer's Disease Neuroimaging
Initiative (ADNI) database show that the proposed model performs better than
competing approaches in terms of classification evaluation. In general, the
proposed HSCF model is a promising model for generating brain
structural-functional connectivities and identifying abnormal brain connections
as cognitive disease progresses.Comment: 4 figure
Bag of ARSRG Words (BoAW)
In recent years researchers have worked to understand image contents in computer vision. In particular, the bag of visual words (BoVW) model, which describes images in terms of a frequency histogram of visual words, is the most adopted paradigm. The main drawback is the lack of information about location and the relationships between features. For this purpose, we propose a new paradigm called bag of ARSRG (attributed relational SIFT (scale-invariant feature transform) regions graph) words (BoAW). A digital image is described as a vector in terms of a frequency histogram of graphs. Adopting a set of steps, the images are mapped into a vector space passing through a graph transformation. BoAW is evaluated in an image classification context on standard datasets and its effectiveness is demonstrated through experimental results compared with well-known competitors
SynDrone -- Multi-modal UAV Dataset for Urban Scenarios
The development of computer vision algorithms for Unmanned Aerial Vehicles
(UAVs) imagery heavily relies on the availability of annotated high-resolution
aerial data. However, the scarcity of large-scale real datasets with
pixel-level annotations poses a significant challenge to researchers as the
limited number of images in existing datasets hinders the effectiveness of deep
learning models that require a large amount of training data. In this paper, we
propose a multimodal synthetic dataset containing both images and 3D data taken
at multiple flying heights to address these limitations. In addition to
object-level annotations, the provided data also include pixel-level labeling
in 28 classes, enabling exploration of the potential advantages in tasks like
semantic segmentation. In total, our dataset contains 72k labeled samples that
allow for effective training of deep architectures showing promising results in
synthetic-to-real adaptation. The dataset will be made publicly available to
support the development of novel computer vision methods targeting UAV
applications.Comment: Accepted at ICCV Workshops, downloadable dataset with CC-BY license,
8 pages, 4 figures, 8 table
Semantically Derived Geometric Constraints for {MVS} Reconstruction of Textureless Areas
Conventional multi-view stereo (MVS) approaches based on photo-consistency measures are generally robust, yet often fail in calculating valid depth pixel estimates in low textured areas of the scene. In this study, a novel approach is proposed to tackle this challenge by leveraging semantic priors into a PatchMatch-based MVS in order to increase confidence and support depth and normal map estimation. Semantic class labels on image pixels are used to impose class-specific geometric constraints during multiview stereo, optimising the depth estimation on weakly supported, textureless areas, commonly present in urban scenarios of building facades, indoor scenes, or aerial datasets. Detecting dominant shapes, e.g., planes, with RANSAC, an adjusted cost function is introduced that combines and weighs both photometric and semantic scores propagating, thus, more accurate depth estimates. Being adaptive, it fills in apparent information gaps and smoothing local roughness in problematic regions while at the same time preserves important details. Experiments on benchmark and custom datasets demonstrate the effectiveness of the presented approach
Flood Detection Using Multi-Modal and Multi-Temporal Images: A Comparative Study
Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that occurred in November 2000 in Gloucester, UK. Experimental results show that the domain adaptation-based approach, semi-supervised domain adaptation (SSDA) with 20 labeled data samples, achieved slightly better values of the area under the precision-recall (PR) curve (AUC) of 0.9173 and F1 score of 0.8846 than those by traditional machine approaches. However, SSDA required much less labor for ground-truth labeling and should be recommended in practice
Recommended from our members
Adaptive robust blind watermarking scheme improved by entropy-based SVM and optimized quantum genetic algorithm
With the intensive study of machine learning in digital watermarking, its ability to balance the robustness and transparency of watermarking technology has attracted researchers’ attention. Therefore, quantum genetic algorithm, which serves as an intelligent optimized scheme combined with biological genetic mechanism and quantum computing, is widely used in various fields. In this study, an adaptive robust blind watermarking algorithm by means of optimized quantum genetics (OQGA) and entropy classification-based SVM (support vector machine) is proposed. The host image was divided into two parts according to the odd and even rows of the host image. One part was transformed by DCT (discrete cosine transform), and then the embedding intensity and position were separately trained by entropy-based SVM and OQGA; the other part was by DWT (discrete wavelet transform), in which the key fusion was achieved by an ergodic matrix to embed the watermark. Simulation results indicate the proposed algorithm ensures the watermark scheme transparency as well as having better resistance to common attacks such as lossy JPEG compression, image darken, Gaussian low-pass filtering, contrast decreasing, salt-pepper noise, and geometric attacks such as rotation and cropping.
Received
22 May 2019
Revised
18 Aug 2019
Accepted
17 Sep 2019
Published
28 Oct 201
Biometric Systems
Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study