Sparse Coral Classification Using Deep Convolutional Neural Networks

Autonomous repair of deep-sea coral reefs is a recent proposed idea to support the oceans ecosystem in which is vital for commercial fishing, tourism and other species. This idea can be operated through using many small autonomous underwater vehicles (AUVs) and swarm intelligence techniques to locate and replace chunks of coral which have been broken off, thus enabling re-growth and maintaining the habitat. The aim of this project is developing machine vision algorithms to enable an underwater robot to locate a coral reef and a chunk of coral on the seabed and prompt the robot to pick it up. Although there is no literature on this particular problem, related work on fish counting may give some insight into the problem. The technical challenges are principally due to the potential lack of clarity of the water and platform stabilization as well as spurious artifacts (rocks, fish, and crabs). We present an efficient sparse classification for coral species using supervised deep learning method called Convolutional Neural Networks (CNNs). We compute Weber Local Descriptor (WLD), Phase Congruency (PC), and Zero Component Analysis (ZCA) Whitening to extract shape and texture feature descriptors, which are employed to be supplementary channels (feature-based maps) besides basic spatial color channels (spatial-based maps) of coral input image, we also experiment state-of-art preprocessing underwater algorithms for image enhancement and color normalization and color conversion adjustment. Our proposed coral classification method is developed under MATLAB platform, and evaluated by two different coral datasets (University of California San Diego's Moorea Labeled Corals, and Heriot-Watt University's Atlantic Deep Sea).Comment: Thesis Submitted for the Degree of MSc Erasmus Mundus in Vision and Robotics (VIBOT 2014

Automatic Classification of Bright Retinal Lesions via Deep Network Features

The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods.Comment: Preprint submitted to Journal of Medical Imaging | SPIE (Tue, Jul 28, 2017

Detecting and avoiding frontal obstacles from monocular camera for micro unmanned aerial vehicles

In literature, several approaches are trying to make the UAVs fly autonomously i.e., by extracting perspective cues such as straight lines. However, it is only available in well-defined human made environments, in addition to many other cues which require enough texture information. Our main target is to detect and avoid frontal obstacles from a monocular camera using a quad rotor Ar.Drone 2 by exploiting optical flow as a motion parallax, the drone is permitted to fly at a speed of 1 m/s and an altitude ranging from 1 to 4 meters above the ground level. In general, detecting and avoiding frontal obstacle is a quite challenging problem because optical flow has some limitation which should be taken into account i.e. lighting conditions and aperture problem

Sparse Coral Classification Using Deep Convolutional Neural Networks

Autonomous repair of deep-sea coral reefs is a recent proposed idea to support the oceans ecosystem in which is vital for commercial fishing, tourism and other species. This idea can be operated through using many small autonomous underwater vehicles (AUVs) and swarm intelligence techniques to locate and replace chunks of coral which have been broken off, thus enabling re-growth and maintaining the habitat. The aim of this project is developing machine vision algorithms to enable an underwater robot to locate a coral reef and a chunk of coral on the seabed and prompt the robot to pick it up. Although there is no literature on this particular problem, related work on fish counting may give some insight into the problem. The technical challenges are principally due to the potential lack of clarity of the water and platform stabilization as well as spurious artifacts (rocks, fish, and crabs). We present an efficient sparse classification for coral species using supervised deep learning method called Convolutional Neural Networks (CNNs). We compute Weber Local Descriptor (WLD), Phase Congruency (PC), and Zero Component Analysis (ZCA) Whitening to extract shape and texture feature descriptors, which are employed to be supplementary channels (feature-based maps) besides basic spatial color channels (spatial-based maps) of coral input image, we also experiment state-of-art preprocessing underwater algorithms for image enhancement and color normalization and color conversion adjustment. Our proposed coral classification method is developed under MATLAB platform, and evaluated by two different coral datasets (University of California San Diego's Moorea Labeled Corals, and Heriot-Watt University's Atlantic Deep Sea)

Association of Helicobacter pylori infection and severity of coronary artery atherosclerosis in patients with suspected coronary artery disease

Background: There is a strong correlation between Helicobacter pylori (HP) infection and coronary artery disease (CAD). There is also a strong correlation between HP infection and the severity of coronary artery atherosclerosis in patients with CAD. Our study determined the association of HP infection and severity of coronary artery atherosclerosis in patients with suspected CAD. Methods: A prospective study of 100 individuals who had coronary angiography for coronary atherosclerosis was conducted. Body mass index (BMI), blood pressure, blood cholesterol, blood glucose, leukocyte count, hemoglobin, and urea breath test were all done on the patients. Coronary angiograms were graded based on vascular and angiographic severity scores. Results: Triglyceride, (TG), Low Density Lipoprotein (LDL), C- Reactive Protein (CRP), Erythrocyte Sedimentation Rate (ESR), vessel score, and angiographic severity score all showed high correlations with Gensini score. There was a substantial association between vessel score and TG, LDL and angiographic severity score. It was found that angiographic severity score has a substantial positive link to a person's BMI; LDL; CRP; ESR, and vessel score. Conclusion: Although HP infection has been linked to an increased risk of coronary artery disease (CAD), established risk variables outweigh their potential impact

Security and forensics exploration of learning-based image coding

Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG AI) and Joint Video Experts Team's (JVET) deep neural networks (DNN) based video coding. These codecs in fact represent a new type of media format. As a dire consequence, traditional media security and forensic techniques will no longer be of use. This paper proposes an initial study on the effectiveness of traditional watermarking on two state-of-the-art learning based image coding. Results indicate that traditional watermarking methods are no longer effective. We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy

Security and Forensics Exploration of Learning-based Image Coding

Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG-AI) and MJoint Video Experts Team's (JVET) deep neural networks (DNN) based video coding. These codecs in fact represent a new type of media format. As a dire consequence, traditional media security and forensic techniques will no longer be of use. This paper proposes an initial study on the effectiveness of traditional watermarking on two state-of-the-art learning based image coding. Results indicate that traditional watermarking methods are no longer effective. We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy

Responses of Satellite Chlorophyll-a to the Extreme Sea Surface Temperatures over the Arabian and Omani Gulf

peer reviewedExtreme events such as Marine Heat Waves (MHWs) and Low Chlorophyll-a (LChl-a) in the ocean have devastating impacts on the marine environment, particularly when they occur simultaneously (i.e., the compound of MHWs and LChl-a events). In this study, we investigate the spatiotemporal variability of MHWs and LChl-a events in the Arabian and Omani Gulf. For this purpose, we used satellite-based high-resolution observations of SST (0.05° × 0.05°; from 1982 to 2020) and chlorophyll-a concentration data (0.04° × 0.04°; from 1998 to 2020). Hourly air temperature, wind, and heat flux components from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) were used to explain the link between these extreme events and atmospheric forcings. Moreover, our results revealed that the annual frequency of MHW and LChl-a is related to the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The results revealed an average SST warming trend of about 0.44 ± 0.06 °C/decade and 0.32 ± 0.04 °C/decade for the Arabian Gulf (AG) and the Gulf of Oman (OG), respectively. This warming rate was accompanied by MHW frequency and duration trends of 0.97 events/decade and 2.3 days/decade, respectively, for the entire study region from 1982 to 2020. The highest annual MHW frequencies were recorded in 2010 (6 events) and 2020 (5 events) associated with LChl-a frequency values of 4 and 2, respectively. La Niña events in 1999, 2010, 2011, and 2020 were associated with higher frequencies of MHW and LChl-a. The positive phase of IOD coincides with high MHW frequency in 2018 and 2019. The longest compound MHW and LChl-a event with a duration of 42 days was recorded in 2020 at OG. This extreme compound event was associated with wind stress reduction. Our results provide initial insights into the spatiotemporal variability of the compound MHW and LChl-a events that occurred in the AG and OG

TRAIT : a trusted media distribution framework

Trusted distribution and consumption of media content has become a challenging issue, especially with the advancement of machine learning-based techniques such as deep fake. To address such challenges, this paper proposes a new metadata schema which is embedded within a larger framework that facilitates trusted media distribution. This schema is realised through a distributed media blockchain core in conjunction with algorithms to detect media modifications. Such a framework is expected to improve trust in media consumption, ensuring media integrity, authenticity and provenance

Wavelet-Based Reflection Symmetry Detection via Textural and Color Histograms: Algorithm and Results

The proposed algorithm detects globally the symmetry axes inside an image plane. The main steps are as follows: We firstly extract edge features using Log-Gabor filters with different scales and orientations. Afterwards, we use the edge characteristics associated with the textural and color information as symmetrical weights for voting triangulation. In the end, we construct a polar-based voting histogram based on the accumulation of the symmetry contribution (local texture and color information), in order to find the maximum peaks presenting as candidates of the primary symmetry axes