Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of objects, automatic navigation, medicine and other application fields. Successful implementation of higher level image analy...
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ISBN:
(纸本)9781509063451
Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of objects, automatic navigation, medicine and other application fields. Successful implementation of higher level image analysis tasks, however, is conditioned by reliable detection of characteristic image local regions termed keypoints. A large number of keypoint detection algorithms has been proposed and verified. The main part of this work is devoted to description of an original keypoint detection algorithm that incorporates depth information computed from stereovision cameras or other depth sensing devices. It was shown that filtering out keypoints that are context dependent, e.g. located on object boundaries can improve the matching performance of the keypoints which is the basis for object recognition tasks. This improvement was shown quantitatively by comparing the proposed algorithm to the widely accepted SIFT keypoint detector algorithm. Our study is motivated by a development of a system aimed at aiding the visually impaired in space perception and object identification.
The proceedings contain 105 papers. The special focus in this conference is on Intelligent systems Design and Applications. The topics include: An innovative approach to manage heterogeneous information using relation...
ISBN:
(纸本)9783319534794
The proceedings contain 105 papers. The special focus in this conference is on Intelligent systems Design and Applications. The topics include: An innovative approach to manage heterogeneous information using relational database systems;estimating the number of clusters as a pre-processing step to unsupervised learning;agglomerative and divisive approaches to unsupervised learning in gestalt clusters;improving imputation accuracy in ordinal data using classification;three case studies using agglomerative clustering;a robust and optimally pruned extreme learning machine;investigating the effect of combining text clustering with classification on improving spam email detection;radial basis function neural networks for datasets with missing values;diversification strategies in differential evolution algorithm to solve the protein structure prediction problem;using cluster barycenters for the generalized traveling salesman problem;on pollution attacks in fully connected P2P networks using trusted peers;certification under uncertainties of control methods for multisource elevators;robust and reliable bionic optimization of nonlinear control problems;human detection using biological signals in camera images with privacy aware;nuclei malignancy analysis based on an adaptive bottom-hat filter;test suite prioritization using nature inspired meta-heuristic algorithms;the improvement of an image compression approach using Weber-Fechner law;a minimal rare substructures-based model for graph database indexing;multibiometrics enhancement using quality measurement in score level fusion;effects of random sampling on SVM hyper-parameter tuning and training a spiking neural network to generate online handwriting movements.
As a powerful technique, sparse coding was adopted by several researchers in different approaches. Particularly in imageprocessing, it has attracted a considerable attention. It can be widely used for representation,...
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As a powerful technique, sparse coding was adopted by several researchers in different approaches. Particularly in imageprocessing, it has attracted a considerable attention. It can be widely used for representation, compression, denoising and separation of all type of signal. Recent works have confirmed that the use of a predefined dictionary is less efficient than a dictionary from training data. According to this idea, this paper proposes a new technique based on wavelet network to create a dictionary to ameliorate the representation and classification of image using sparse coding technique.
The proceedings contain 51 papers. The special focus in this conference is on Computational Intelligence. The topics include: Towards Real-Time Fleet-Event-Handling for the Dynamic Vehicle Routing Problem;performance ...
ISBN:
(纸本)9789897582745
The proceedings contain 51 papers. The special focus in this conference is on Computational Intelligence. The topics include: Towards Real-Time Fleet-Event-Handling for the Dynamic Vehicle Routing Problem;performance of Complex-Valued Multilayer Perceptrons Largely Depends on Learning Methods;efficient Implementation of Self-Organizing Map for Sparse Input Data;a Fuzzy Logic Approach to Improve Phone Segmentation A Case Study of the Dutch Language;the Behavior of Deep Statistical Comparison Approach for Different Criteria of Comparing Distributions;determining Firing Strengths Through a Novel Similarity Measure to Enhance Uncertainty Handling in Non-singleton Fuzzy Logic systems;enhanced Symbolic Regression Through Local Variable Transformations;towards the Enrichment of Arabic WordNet with Big Corpora;R-FCN Object Detection Ensemble based on Object Resolution and image Quality;Depth Value Pre-processing for Accurate Transfer Learning based RGB-D Object Recognition;Self-learning Smart Cameras Harnessing the Generalization Capability of XCS;entorhinal Grid Cells May Facilitate Pattern Separation in the Hippocampus;higher Order Neural Units for Efficient Adaptive Control of Weakly Nonlinear systems;CNN Patch–Based Voting for Fingerprint Liveness Detection;environment Recognition based on images using Bag-of-Words;emotion Recognition from Speech using Representation Learning in Extreme Learning Machines;neural Network Inverse Model for Quality Monitoring Application to a High Quality Lackering Process;parallelization of Real-time Control algorithms on Multi-core Architectures using Ant Colony Optimization;ant Colony Optimization Approaches for the Tree t-Spanner Problem;hierarchy Influenced Differential Evolution: A Motor Operation Inspired Approach;towards a Better Understanding of Deep Neural Networks Representations using Deep Generative Networks;comparing Small Population Genetic algorithms over Changing Landscapes.
As one kind of popular application in computer vision, image clustering has attracted many attentions. Some machine learning algorithms have been widely employed, such as K-Means, Non-negative Matrix Factorization (NM...
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As one kind of popular application in computer vision, image clustering has attracted many attentions. Some machine learning algorithms have been widely employed, such as K-Means, Non-negative Matrix Factorization (NMF), Graph regularized Non-negative Matrix Factorization (GNMF) and Locally Consistent Concept Factorization (LCCF). These methods possess respective strength and weakness. The common problem existing in these clustering algorithms is that they only use one kind of feature. However, different kinds of features complement each other and can be used to improve performance results. In this paper, in order to make use of the complementarity between different features, we propose an image representation method based on multi-features. Clustering results on several benchmark image data sets show that the proposed scheme outperforms some classical methods.
Dermatological Diseases are one of the biggest medical issues in 21st century due to it's highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseas...
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ISBN:
(纸本)9781538619599
Dermatological Diseases are one of the biggest medical issues in 21st century due to it's highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with imageprocessing tools to form a better structure, leading to higher accuracy of 95.3%.
Shadow Detection and removal is the process of enhance the computer vision applications including image segmentation, object recognition, object tracking etc. Detection and Removal of shadow from the images and videos...
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ISBN:
(纸本)9781509047611
Shadow Detection and removal is the process of enhance the computer vision applications including image segmentation, object recognition, object tracking etc. Detection and Removal of shadow from the images and videos can reduce the undesirable outcomes in the computer vision applications and algorithms. The prime objective of this survey paper is to analyze the performance of various currently used shadow detection techniques. In this paper we have discussed the techniques for detecting and removing shadow from the still images and video sequences. The scope of discussed shadow detection and removal techniques is limited to different scenarios: (i) Shadow detection for Indoor and Outdoor scenes, (ii) Shadow detection using fixed or moving camera, (iii) Shadow detection of umbra and penumbra shadows etc.
Multichannel synthetic aperture radar (MSAR) systems are essential for applications such as ground moving target indication (GMTI), interferometric SAR (InSAR), and high-resolution wide-swath (HRWS) imaging. In this p...
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Multichannel synthetic aperture radar (MSAR) systems are essential for applications such as ground moving target indication (GMTI), interferometric SAR (InSAR), and high-resolution wide-swath (HRWS) imaging. In this paper, we analyze and compare MSAR image reconstruction algorithms. Previously, image reconstruction for MSAR has relied heavily on frequency domain matched filtering. Time domain image reconstruction algorithms have several attractive qualities, but their use has been limited due to a high computational burden. In this paper, we utilize digital beamforming and the phase center approximation to develop a fast time domain (fast factorized back-projection, FFBP) algorithm for MSAR. We present two FFBP implementations for MSAR and perform a comparative study between MSAR imaging algorithms. The numerical results confirm the feasibility of the proposed FFBP algorithms for MSAR.
Mainly when applied in the underwater environment, sonar simulation requires great computational effort due to the complexity of acoustic physics. Simulation of sonar operation allows evaluating algorithms and control...
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Mainly when applied in the underwater environment, sonar simulation requires great computational effort due to the complexity of acoustic physics. Simulation of sonar operation allows evaluating algorithms and control systems without going to the real underwater environment;that reduces the costs and risks of in-field experiments. This paper tackles with the problem of real-time underwater imaging sonar simulation by using the OpenGL shading language chain on GPU. Our proposed system is able to simulate two main types of acoustic devices: mechanical scanning imaging sonars and forward-looking sonars. The underwater scenario simulation is performed based on three frameworks: (i) OpenSceneGraph reproduces the ocean visual effects, (ii) Gazebo deals with physical forces, and (iii) the Robot Construction Kit controls the sonar in underwater environments. Our system exploits the rasterization pipeline in order to simulate the sonar devices, which are simulated by means of three parameters: the pulse distance, the echo intensity and the sonar field-of-view, being all calculated over observable objects shapes in the 3D rendered scene. Sonar-intrinsic operational parameters, speckle noise and object material properties are also considered as part of the acoustic image. Our evaluation demonstrated that the proposed system is able to operate close to or faster than the real-world devices. Also, our method generates visually realistic sonar images when compared with real-world sonar images of the same scenes. (C) 2017 Elsevier Ltd. All rights reserved.
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