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In this paper, we present an approach for producing side scan sonar image mosaicking under a robust SLAM scheme. A Pose Graph based SLAM algorithm is used to perform a correction over the sensor trajectory for enabling image registration, using observation constraints extracted from the images. However, due to the operational context, the available odometry data carries a high degree of uncertainty...
In this paper, we deal with the most challenging task of recovering the 3D human pose from just a single monocular image, that may be a synthetic image or a real internet image. The retrieval and reconstruction of the articulated 3D pose, both are prerequisites for the analysis of the people in images/videos. We address both tasks together and propose an efficient framework for search & retrieval...
To deal with the rigid template matching problem in real-world scenarios, we propose a novel iterative feature-pair updating framework which is also robust to high levels of outliers, such as background changing, complex nonrigid deformation and partial occlusion. Given a pair of template image and target image, we first extract a set of corresponding feature-pairs as candidates. Then, we propose...
Given the significant industrial growth of demand for virtual reality (VR), 360º video streaming is one of the most important VR applications that require cost-optimal solutions to achieve widespread proliferation of VR technology. Because of its inherent variability of data-intensive content types and its tiled-based encoding and streaming, 360º video requires new encoding ladders in adaptive streaming...
Feature selection is the process of selecting a subset of relevant features from the larger set of collected features. As the amount of available data grows with technology, feature selection becomes a more important part of the system-design process. In real-world applications, there are several costs associated with the collection, processing, and storage of data. Given that these costs can vary...
Feature selection, as a fundamental component of building robust models, plays an important role in many machine learning and data mining tasks. Since acquiring labeled data is particularly expensive in both time and effort, unsupervised feature selection on unlabeled data has recently gained considerable attention. Without label information, unsupervised feature selection needs alternative criteria...
Real-time processing of sensor data collected from moving vehicles is crucial to the development of active navigational systems. In this paper we develop a fast solution to the task of mosaic construction from aerial images, that allows the embedded generation of a faithful representation of the surveyed area. Most current solutions are off-line (i.e. all data is first collected and then processed...
The success of deep neural networks usually relies on a large number of labeled training samples, which unfortunately are not easy to obtain in practice. Unsupervised domain adaptation focuses on the problem where there is no labeled data in the target domain. In this paper, we propose a novel deep unsupervised domain adaptation method that learns transferable features. Different from most existing...
It is well recognized that air quality inference is of great importance for environmental protection. However, due to the limited monitoring stations and various impact factors, e.g., meteorology, traffic volume and human mobility, inference of air quality index (AQI) could be a difficult task. Recently, with the development of new ways for collecting and integrating urban, mobile, and public service...
Given the soaring amount of data being generated daily, graph mining tasks are becoming increasingly challenging, leading to tremendous demand for summarization techniques. Feature selection is a representative approach that simplifies a dataset by choosing features that are relevant to a specific task, such as classification, prediction, and anomaly detection. Although it can be viewed as a way to...
We propose an action parsing algorithm to parse a video sequence containing an unknown number of actions into its action segments. We argue that context information, particularly the temporal information about other actions in the video sequence, is valuable for action segmentation. The proposed parsing algorithm temporally segments the video sequence into action segments. The optimal temporal segmentation...
Identification of the correct medicinal plants that goes in to the preparation of a medicine is very important in ayurvedic medicinal industry. The main features required to identify a medicinal plant is its leaf shape, colour and texture. Colour and texture from both sides of the leaf contain deterministic parameters to identify the species. This paper explores feature vectors from both the front...
Microseismic monitoring technology has broad application prospects in large-scale civil engineering safety monitoring fields, such as tunnels. However, due to the lack of a perfect localization method, the microseismic monitoring system has the large localization error at present. To solve this problem, firstly, we preprocess the chaotic microseismic signal, using power spectrum analysis method to...
The goal of the semantic object correspondence problem is to compute dense association maps for a pair of images such that the same object parts get matched for very different appearing object instances. Our method builds on the recent findings that deep convolutional neural networks (DCNNs) implicitly learn a latent model of object parts even when trained for classification. We also leverage a key...
The visual and automatic classification of vehicles plays an important role in the Transport Area. Besides of security issues, the monitoring of the type of traffic in streets and highways, as well the traffic dynamics over time, allows the optimization of use and of resources related to such public infrastructure. In this work we propose a novel method, called 2D-DBM, for robust and efficient automatic...
Feature selection is a process of selecting a subset of features that is highly distinguishable from the data set to obtain better or at least equivalent success rates. Artificial Bee Colony (ABC) Algorithm is a intelligence algorithm that model the behavior of honey bees in the nature of food seeking behavior and has been developed to produce a solution at continuous space. BitABC is a bitwise operator...
Deep Neural Networks (DNN) are the dominant technique widely used in English and Chinese speech recognition currently. However, Tibetan speech recognition research starts late and mainly uses Hidden Markov Model (HMM). In this paper, We show a better method of replacing Gaussian Mixture Models (GMM) by DNN to Tibetan Lhasa dialect speech recognition system. The system contains seven layers of features...
Cuckoo Search is a recent nature-inspired metaheuristic algorithm, inspired by the cuckoo birds' aggressive strategy to breeding. The Cuckoo Search algorithm iteratively uses a Lévy flight random walk to explore a search space. The Lévy flight mechanism takes sudden turns of 90 degrees and consequently the Cuckoo's Search strategy does not carefully search around the cuckoos' nest, and hence it suffers...
In this paper, an effective approach to Simultaneous Localization and Mapping (SLAM) based on RGB-D images is presented toward autonomous operation of a Leg/Arm Composite Mobile Robot (LACMR), in which depth information and its effective features are utilized sufficiently so as to overcome some malpractice in conventional methods and enhance the performance of SLAM. Our scheme can be narrated as follows...
The interactive image segmentation model allows users to iteratively add new inputs for refinement until a satisfactory result is finally obtained. Therefore, an ideal interactive segmentation model should learn to capture the user's intention with minimal interaction. However, existing models fail to fully utilize the valuable user input information in the segmentation refinement process and thus...
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