Most of current clustering methods are designed for general purpose other than a specific color pixel classification use. Color Line model representation emerged as the ultimate method for clustering pixels using RGB ...
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ISBN:
(纸本)9781509045204
Most of current clustering methods are designed for general purpose other than a specific color pixel classification use. Color Line model representation emerged as the ultimate method for clustering pixels using RGB color components. However, this method is strongly sensitive to the adjustment of input parameters, which cannot conform to the frequent change of image structures and compositions. In this paper, we address this problem by introducing a hemisphere-grid based method for RGB pixel classification. Our method minimizes the reliance on user provided parameters as well as it can dynamically estimate the proper number of clusters. The properly clustering results prove the robustness and advantages of our method in classifying color pixels for unfamiliar input images.
Near duplicate image detection needs the matching of a bit altered images to the original image. This will help in the detection of forged images. A great deal of effort has been dedicated to visual applications that ...
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ISBN:
(纸本)9781509025534
Near duplicate image detection needs the matching of a bit altered images to the original image. This will help in the detection of forged images. A great deal of effort has been dedicated to visual applications that need efficient image similarity metrics and signature. Digital images can be easily edited and manipulated owing to the great functionality of imageprocessing software. This leads to the challenge of matching somewhat altered images to their originals, which is termed as near duplicate image detection. This paper discusses the literature reviewed on the development of several image matching algorithms. This paper encompasses 2 sections. Section 1 is the introduction. Section 2 discusses the literature reviewed on the development of image matching algorithms.
In this paper, a novel approach for night image enhancement is proposed. The algorithm improves the visuality of the night image by adjusting the background data of the value component (V) in HSV space. We firstly obt...
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ISBN:
(纸本)9781509007691
In this paper, a novel approach for night image enhancement is proposed. The algorithm improves the visuality of the night image by adjusting the background data of the value component (V) in HSV space. We firstly obtain background image from V, then increase the dynamic range of the background image by optimized transfer function. Finally, we can restore the enhancement V from enhanced background image. The experimental results demonstrate that the proposed approach can improve the perceptual quality, and have a good effect for night image by comparing with other algorithms.
Current practice in convolutional neural networks (CNN) remains largely bottom-up and the role of top-down process in CNN for pattern analysis and visual inference is not very clear. In this paper, we propose a new me...
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ISBN:
(纸本)9783319464930;9783319464923
Current practice in convolutional neural networks (CNN) remains largely bottom-up and the role of top-down process in CNN for pattern analysis and visual inference is not very clear. In this paper, we propose a new method for structured labeling by developing convolutional pseudoprior (ConvPP) on the ground-truth labels. Our method has several interesting properties: (1) compared with classic machine learning algorithms like CRFs and Structural SVM, ConvPP automatically learns rich convolutional kernels to capture both short-and long-range contexts;(2) compared with cascade classifiers like Auto-Context, ConvPP avoids the iterative steps of learning a series of discriminative classifiers and automatically learns contextual configurations;(3) compared with recent efforts combining CNN models with CRFs and RNNs, ConvPP learns convolution in the labeling space with improved modeling capability and less manual specification;(4) compared with Bayesian models like MRFs, ConvPP capitalizes on the rich representation power of convolution by automatically learning priors built on convolutional filters. We accomplish our task using pseudo-likelihood approximation to the prior under a novel fixed-point network structure that facilitates an end-to-end learning process. We show state-of-the-art results on sequential labeling and image labeling benchmarks.
The proceedings contain 24 *** special focus in this conference is on Short Papers, Big Data Applications and Principles, Data centered Smart Applications and ADBIS Doctoral *** topics include: Towards automated perfo...
ISBN:
(纸本)9783319440651
The proceedings contain 24 *** special focus in this conference is on Short Papers, Big Data Applications and Principles, Data centered Smart Applications and ADBIS Doctoral *** topics include: Towards automated performance optimization of BPMN business processes;pixel-based analysis of information dashboard attributes;towards adaptive distributed top-k query processing;basis functions as pivots in space of users preferences;towards semi-structured JSON big data;skyline algorithms on streams of multidimensional data;canonical data model for data warehouse;a quality-based query rewriting algorithm for data integration, towards spatial crowdsourcing in vehicular networks using mobile agents;shift of imageprocessing technologies to column-oriented databases;influence of parallelism property of streaming engines on their performance;reducing big data by means of context-aware tailoring;feature ranking and selection for big data sets;a bagged associative classifier for big data frameworks;a new parallel approximate subspace clustering algorithm;smart modeling for lightweight mobile application development methods;an implementation method of an information credibility calculation system for emergency such as natural disasters;model capsules for research and engineering networks;usage of aspect-oriented programming in adaptive application structure;short-term user behaviour changes modelling and framework for managing distinct versions of data in relational databases.
Rail transportation systems, which are commonly used in today's world, should be inspected at certain intervals for possible accidents. During the rail inspection, the physical vibration on rail lines causes a blu...
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Rail transportation systems, which are commonly used in today's world, should be inspected at certain intervals for possible accidents. During the rail inspection, the physical vibration on rail lines causes a blurring effect on the images. Doing deblurring automatically requires information of blurring rates and specifying the parameters accordingly for deblurring. With this purpose, a test equipment which can move on rail lines and a camera system for the detection of blurring and deblurring is integrated with Inertial Measurement Unit (IMU) is promoted in this study. Then, with Attitude and Heading Reference System (AHRS) algorithm, the effect of blurring at the moment of the vibration is examined, point spread function (PSF) value is chosen dynamically and deblurring is achieved. In order to increase the accuracy rates of detection algorithms, a pre-treatment method is proposed for detecting the blur effect and removing it.
In modern aerial Intelligence, Surveillance and Reconnaissance operations, precise 3D information becomes inevitable for increased situation awareness. In particular, object geometries represented by texturized digita...
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ISBN:
(数字)9781510603790
ISBN:
(纸本)9781510603783;9781510603790
In modern aerial Intelligence, Surveillance and Reconnaissance operations, precise 3D information becomes inevitable for increased situation awareness. In particular, object geometries represented by texturized digital surface models constitute an alternative to a pure evaluation of radiometric measurements. Besides the 3D data's level of detail aspect, its availability is time-relevant in order to make quick decisions. Expanding the concept of our preceding remote sensing platform developed together with OHB System AG and Geosystems GmbH, in this paper we present an airborne multi-sensor system based on a motor glider equipped with two wing pods;one carries the sensors, whereas the second pod downlinks sensor data to a connected ground control station by using the Aerial Reconnaissance Data System of OHB. An uplink is created to receive remote commands from the manned mobile ground control station, which on its part processes and evaluates incoming sensor data. The system allows the integration of efficient imageprocessing and machine learning algorithms. In this work, we introduce a near real-time approach for the acquisition of a texturized 3D data model with the help of an airborne laser scanner and four high-resolution multi-spectral (RGB, near-infrared) cameras. image sequences from nadir and off-nadir cameras permit to generate dense point clouds and to texturize also facades of buildings. The ground control station distributes processed 3D data over a linked geoinformation system with web capabilities to off-site decision-makers. As the accurate acquisition of sensor data requires boresight calibrated sensors, we additionally examine the first steps of a camera calibration workflow.
Nowadays, high dynamic range imaging techniques are often applied for outdoor applications that may encounter scenes with an extremely high dynamic range. In such scenes, a halo can be produced by gradient reversal, a...
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ISBN:
(纸本)9781467395564
Nowadays, high dynamic range imaging techniques are often applied for outdoor applications that may encounter scenes with an extremely high dynamic range. In such scenes, a halo can be produced by gradient reversal, and color information is easily damaged. In this paper, we propose an exposure fusion algorithm that does not suffer from any artifacts in extremely high dynamic range scenes. Using the HSV color model, gradient reversal is suppressed in the Value component, and color information is preserved in the other components. Experimental results show that these two artifacts are effectively avoided, and all of the scene's details are preserved.
The development in computing power highlights some forgotten algorithms, which were neglected because of their complexity and slowness on early computers. One example is the Wavelet-Transformation Profilometry (WTP) o...
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The development in computing power highlights some forgotten algorithms, which were neglected because of their complexity and slowness on early computers. One example is the Wavelet-Transformation Profilometry (WTP) of which successful application is demonstrated in the paper. WTP is a high level signal processing method using orthogonal algorithms for huge datasets. The high performance in quality and running speed makes the described method suitable for medical imageprocessing applications.
The proceedings contain 80 papers. The special focus in this conference is on Computer and Communication Technologies. The topics include: Human gait recognition using gait flow image and extension neural network;impr...
ISBN:
(纸本)9788132225225
The proceedings contain 80 papers. The special focus in this conference is on Computer and Communication Technologies. The topics include: Human gait recognition using gait flow image and extension neural network;improved topology preserving maps for wireless sensor networks through d-VCS;real-time processing and analysis for activity classification to enhance wearable wireless ECG;adaptive video quality throttling based on network bandwidth for virtual classroom systems;an enhanced security pattern for wireless sensor network;computational intelligence-based parametrization on force-field modeling for silicon cluster using ASBO;natural language-based self-learning feedback analysis system;adaptive filter design for extraction of fetus ECG signal;linear and non-linear buckling testing on aluminium structures;a performance analysis of openstack open-source solution for IAAS cloud computing;the application of sub-pattern approach in 2d shape recognition and retrieval;moderator intuitionistic fuzzy sets and application in medical diagnosis;an empirical comparative study of novel clustering algorithms for class imbalance learning;analysis of student feedback by ranking the polarities;seizure onset detection by analyzing long-duration eeg signals;prototype of a coconut harvesting robot with visual feedback;wireless personal area network and PSO-based home security system;a framework for ranking reviews using ranked voting method;dynamic multiuser scheduling with interference mitigation in sc-FDMA-based communication systems;adaptive MAC for bursty traffic in wireless sensor networks and a minimal subset of features using correlation feature selection model for intrusion detection system.
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