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Studying fish recognition has important realistic and theoretical significance to aquaculture and marine biology. Fish recognition is challenging problem because of distortion, overlap and occlusion of digital images. Previous researchers have done a lot of work on fish recognition, but the classification accuracy may be not high enough. Classification and recognition methods based on convolutional...
Predicting the performance of an underwater acoustic network (UAN) is a challenging task due to the spatiotemporal variability of the links and its complicated dependence on multiple factors. We present a machine-learning model based on logistic regression (LogR) to capture the spatio-temporal variation in the performance of a UAN. The model captures the effect of environmental factors such as wind...
Side scan sonar (SSS) is a vital sensor for autonomous underwater vehicles (AUVs) to do ocean survey. Many methods have been proposed to carry out SSS image segmentation, among which machine learning algorithms provide outstanding performance. Machine learning algorithms like support vector machine (SVM) and convolutional neural networks (CNN) are the most used. When SVM is used to do pixel-level...
In this work we develop and demonstrate a probabilistic generative model for phytoplankton communities. The proposed model takes counts of a set of phytoplankton taxa in a timeseries as its training data, and models communities by learning sparse co-occurrence structure between the taxa. Our model is probabilistic, where communities are represented by probability distributions over the species, and...
This paper proposes and implements a convolutional neural network (CNN) that maps images from a camera to an error signal to guide and control an autonomous underwater vehicle into the entrance of a docking station. The paper proposes to use an external positioning system synchronized with the vehicle to obtain a dataset of images matched with the position and orientation of the vehicle. By using...
Finding mines in Sonar imagery is a significant problem with a great deal of relevance for seafaring military and commercial endeavors. Unfortunately, the lack of enormous Sonar image data sets has prevented automatic target recognition (ATR) algorithms from some of the same advances seen in other computer vision fields. Namely, the boom in convolutional neural nets (CNNs) which have been able to...
Explosive naval mines pose a threat to ocean and sea faring vessels, both military and civilian. This work applies deep neural network (DNN) methods to the problem of detecting minelike objects (MLO) on the seafloor in side-scan sonar imagery. We explored how the DNN depth, memory requirements, calculation requirements, and training data distribution affect detection efficacy. A visualization technique...
In this paper, a joint synchronization and Doppler scaling factor estimation algorithm has been proposed for underwater acoustic communications. The training sequence, which consists of two Zadoff-Chu (ZC) sequences being conjugate with each other, is utilized to synchronize and estimate Doppler scaling factor, time delay and carrier frequency offsets (CFOs). ZC sequences are well designed to show...
This paper show how neural networks, configured for regression, can be used to learn the relationships between Inertial Motion Unit (IMU) data collected on a robotic platform and the robot's commanded system state. By learning how the IMU data relates to commanded robot state we can use the neural network to predict what commands have been issued to the robot. By comparing the prediction to the actual...
Monitoring the vast expanse of oceans presents a daunting challenge: how to take sufficient high-quality measurements with a relatively small group of specialized scientists and limited resources. Despite advances in remote sensing and autonomous technology, the oceans remain under-explored and under-sampled. One solution for addressing this challenge is to rely on citizen scientists, people outside...
Climate changes in the Polar Regions are accelerating and scientists are trying to understand both the patterns and resulting impacts. Specific regions of the Arctic and Antarctic are exhibiting greater changes than others, creating areas of interest (hot spots) for polar research. The Polar Interdisciplinary Coordinated Education (Polar ICE, see www.polar-ice.org) project, funded by the National...
This paper presents a performance comparison of several state-of-the-art visual feature extraction algorithms when applied in a poorly-structured environment as found on the planet Mars. So far, no systematic evaluation of feature extraction algorithms in extraterrestrial environments is available. The algorithms in this paper are evaluated using the Devon Island dataset which is said to have one...
In this paper, we address interesting questions about how feng shui influences house price from a data perspective. First, is feng shui likely to influence house price? Second, how do different feng shui features, e.g., house shape, master bedroom location, and other interior room arrangements, influence the price? Third, can we automatically diagnose the feng shui problems of a house? From a dataset...
Artificial Neural Networks are a widely used computing system implemented for a wide variety of tasks and problems. A common application of such networks is classification problems. However, a significant amount of this research focuses on one and two-dimensional information, such as vectorized data and images. There is limited research performed on three-dimensional media such as video clips. This...
In recent years, various approaches have been investigated towards blind image quality assessment (IQA) with high accuracy and low complexity. In this paper we develop a pre-saliency map based blind IQA method, which takes advantage of saliency information in prior of quality prediction for performance enhancement by two steps. 1) We split the image into patches and design a convolution neural network...
Motor imagery (MI) based on brain computer interfaces (BCIs) have been widely applied for upper limb motor rehabilitation. Due to the fact that a large number of disabled people need to restore or improve walking ability, it is also important to investigate the use of MI-based BCIs for lower limb motor rehabilitation. The brain activity of lower limb MI is more difficult to detect because of low reliability...
Recently, deep learning has enjoyed a great deal of success for computer vision problems due to its capability to model highly complex tasks, such as image classification, object detection, face recognition, among many others. Although these neural networks are nowadays very powerful, there is a huge amount of parameters (i.e. the model) that need to be learned and require considerable storage space...
In this work, we propose to derive the attribute specific similarity score for a pair of images using an existing parent deep model. As an example, given two facial images, we derive a similarity score for attributes like gender and complexion using an existing face recognition model. It is not always feasible to train a new model for each attribute, as training of deep neural network based model...
Kotenseki is a collection of classical and ancient Japanese literature. It is comprised of image books that express Japanese stories by using comic drawings of different characters, such as humans, nature, and animals. To effectively store them for posterity, a search system is important. We propose an efficient CBIR system to assist the users in easily accessing the information and have an enjoyable...
Collaborative filtering is widely used in recommender systems. When training data are extremely sparse, neighbor selection methods work ineffectively. To address this issue, this paper proposes a distributed representation model that represents users as low-dimensional vectors for neighbor selection by considering the chronological order of users' ratings. Experiments show that the proposed method...
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