image segmentation is a key component in many computer vision systems, and it is recovering a prominent spot in the literature as methods improve and overcome their limitations. The outputs of most recent algorithms a...
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ISBN:
(纸本)9781467388511
image segmentation is a key component in many computer vision systems, and it is recovering a prominent spot in the literature as methods improve and overcome their limitations. The outputs of most recent algorithms are in the form of a hierarchical segmentation, which provides segmentation at different scales in a single tree-like structure. Commonly, these hierarchical methods start from some lowlevel features, and are not aware of the scale information of the different regions in them. As such, one might need to work on many different levels of the hierarchy to find the objects in the scene. This work tries to modify the existing hierarchical algorithm by improving their alignment, that is, by trying to modify the depth of the regions in the tree to better couple depth and scale. To do so, we first train a regressor to predict the scale of regions using mid-level features. We then define the anchor slice as the set of regions that better balance between over-segmentation and undersegmentation. The output of our method is an improved hierarchy, re-aligned by the anchor slice. To demonstrate the power of our method, we perform comprehensive experiments, which show that our method, as a post-processing step, can significantly improve the quality of the hierarchical segmentation representations, and ease the usage of hierarchical image segmentation to high-level vision tasks such as object segmentation. We also prove that the improvement generalizes well across different algorithms and datasets, with a low computational cost.1.
Three-dimensional cone-beam (CB) imaging using a multi-axis floor-mounted (or ceiling-mounted) C-arm system has become an important tool in interventional radiology. This success motivates new developments to improve ...
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ISBN:
(纸本)9781510600188
Three-dimensional cone-beam (CB) imaging using a multi-axis floor-mounted (or ceiling-mounted) C-arm system has become an important tool in interventional radiology. This success motivates new developments to improve image quality. One direction in which advancement is sought is the data acquisition geometry and related CB artifacts. Currently, data acquisition is performed using the circular short-scan trajectory, which yields limited axial coverage and also provides incomplete data for accurate reconstruction. To improve the image quality, as well as to increase the coverage in the longitudinal direction of the patient, we recently introduced the ellipse line -ellipse trajectory and showed that this trajectory provides full R-line coverage within the field-of-view, which is a key property for accurate reconstruction from truncated data. An R-line is any segment of line that connects two source positions. Here, we examine how the application of asymmetrical variations to the definition of the ELE trajectory impacts the R-line coverage. This question is significant to understand how much flexibility can be used in the implementation of the ELE trajectory, particularly to adapt the scan to patient anatomy and imaging task of interest. Two types of asymmetrical variations, called axial and angular variations, are investigated.
The proceedings contain 79 papers. The topics discussed include: moving object detection and shadow removal in video surveillance;a literature review on phishing crime, prevention review and investigation of gap;a hyp...
ISBN:
(纸本)9781509032976
The proceedings contain 79 papers. The topics discussed include: moving object detection and shadow removal in video surveillance;a literature review on phishing crime, prevention review and investigation of gap;a hyperspectral imaging technology based method for identifying the variety of Mengding Mountain tea;hearthstone deck-construction with a utility system;computational complexity of imageprocessingalgorithms for an intelligent mobile enabled tongue diagnosis scheme;in the digital future: revitalizing information management systems in afghan settings through not only SQL (MongoDB) technology;solving exercise generation problems by diversity oriented meta-heuristics;and modeling of unmanned aerial vehicle deliveries in populated urban areas for risk management.
Due to limitations in conventional medical imaging and the restrictions imposed by both the anatomy and the surgical approach in pancreatic cancer, there is a need for methods to support intraoperative imaging in orde...
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ISBN:
(纸本)9783319396873;9783319396866
Due to limitations in conventional medical imaging and the restrictions imposed by both the anatomy and the surgical approach in pancreatic cancer, there is a need for methods to support intraoperative imaging in order to improve their accurate anatomical localization and the characterization of their nature. Laparoscopic ultrasounds (LUS) images and endoscopic videos can be used to extract useful information during the surgical procedures. A fast approach for acquiring an estimation of the tumor positioning and size through laparoscopic ultrasounds images has been developed. Based on the surgical video, endoscope 3D tracking is achieved by means of a Shape-from-Motion technique. Intraoperative imaging algorithms' validation has been carried out in an ex vivo porcine model and results have shown the viability of exploiting them for structures characterization and their 3D reconstruction.
Dictionary learning is used in signal, image, audio and video processing applications to represent signals by a sparse set of atoms where sparse representations are managed for the problems of compression, denoising, ...
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Dictionary learning is used in signal, image, audio and video processing applications to represent signals by a sparse set of atoms where sparse representations are managed for the problems of compression, denoising, feature extraction and data classification. In many machine learning applications, classifier ensembles are shown to be superior than their single classifier counterparts. In this paper, we propose to use dictionary learning as a base classifier in ensemble learning methods and introduce Random Subspace Dictionary Learning (RDL) and Bagging Dictionary Learning (BDL) algorithms by learning ensembles of dictionaries for each class using feature/instance subspaces. The experimental results show that the ensemble based dictionary learning methods outperform the single dictionary learning (DL), Support Vector Machines (SVM), and SVM based ensemble classifiers.
Stereo matching techniques aim at reconstructing depth information from a pair of images. The use of stereo matching algorithms in embedded systems is very challenging due to the complexity of state-of-the-art algorit...
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Stereo matching techniques aim at reconstructing depth information from a pair of images. The use of stereo matching algorithms in embedded systems is very challenging due to the complexity of state-of-the-art algorithms. Most stereo matching algorithms are made of three different parts; the cost construction, the cost aggregation and the disparity selection. This paper focuses on comparing the efficiency of different cost construction methods implemented on a Digital Signal Processor (DSP) C6678 platform. Three cost construction algorithms based on census, Sum of Absolute Differences (SAD) and Mutual Information (MI) have been compared in terms of output quality and execution time. Each method has its own specificity discussed in this paper. The SAD is the simplest one and is used as reference in this paper. The census has a good output quality, and the MI is faster.
Two-phase liquid-gas flow occurs in many safety systems of nuclear reactors as well as in reactor cores. To further improve both safety and commercial performance of nuclear reactors, it is important to improve numeri...
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ISBN:
(纸本)9780791850053
Two-phase liquid-gas flow occurs in many safety systems of nuclear reactors as well as in reactor cores. To further improve both safety and commercial performance of nuclear reactors, it is important to improve numerical codes and deepen the understanding of two-phase flow with experiments on gas behaviour in liquids. Among several available measurement methods, ultrasound based methods are affordable and easy to use even for high pressure/temperature flows in non-transparent pipes. Ultrasound Reflector Recognition and Tracking Technique (URRTT) has been developed as a new technique. It uses an ultrasound transducer, which emits ultrasound beam into the liquid with gas bubbles. The phase interface reflects the beam and because of that, the phase interface can be recognised in the reflected signal and the distance (from the transducer) can be calculated. The core of this technique is the tracking algorithm that can separate data of different bubbles from each other and obtain their one dimensional trajectories along the measurement line. Trajectories measured simultaneously by more transducers (at different positions or from different directions) can be combined. That means trajectory of the bubble interface from one transducer can be connected to trajectory from a different transducer and by doing so, a secondary data can be obtained using the information that those trajectories belong to the same bubble. As an example, the average two dimensional velocity between two parallel measurement lines can be obtained. Another example is the measurement of the bubble size using one measurement line with two oppositely oriented transducers. Experiments have been conducted to prove the concept of URRTT. Results have been validated to data obtained by the imageprocessing of footage taken by a high speed camera. The results obtained by URRTT can be of high value since each detected bubble is measured individually and thus, difference in the bubble behaviour based on the size,
In this paper the application for generation of HDR image based on two consecutive images (underexposed and overexposed) for Android mobile operating system is presented. The implemented software preserves a lot of im...
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ISBN:
(纸本)9781509026616
In this paper the application for generation of HDR image based on two consecutive images (underexposed and overexposed) for Android mobile operating system is presented. The implemented software preserves a lot of image details and maintains a low execution time. These features are particularly important for pictures taken using mobile devices in emergency situations. Such photos may constitute evidence that a threat occurred, was properly recognized, or someone committed a crime. HDR images can be also used in mobile systems for supporting pedestrians or drivers. Obtained results indicate on a high effectiveness of the presented solution.
The proceedings contain 72 papers. The special focus in this conference is on Soft Computing Techniques for Advanced Computing, algorithms, Advanced imageprocessing Methodologies and Next Generation Optical systems. ...
ISBN:
(纸本)9788132225379
The proceedings contain 72 papers. The special focus in this conference is on Soft Computing Techniques for Advanced Computing, algorithms, Advanced imageprocessing Methodologies and Next Generation Optical systems. The topics include: Fuzzy logic based UPFC controller for voltage stability and reactive control of a stand-alone hybrid system;APSO based weighting matrices selection of LQR applied to tracking control of SIMO system;application of fuzzy soft multi sets in decision-making problems;plant leaf recognition using ridge filter and curvelet transform with neuro-fuzzy classifier;recognition of repetition and prolongation in stuttered speech using Ann;hybrid model with fusion approach to enhance the efficiency of keystroke dynamics authentication;multi-label classifier for emotion recognition from music;a study on cloud based SOA suite for electronic healthcare records integration;protecting the apache hadoop clusters with hadoop authentication process using kerberos;image analysis for efficient surface defect detection of orange fruits;local Gabor wavelet-based feature extraction and evaluation;human interaction recognition using improved spatio-temporal features;common coupled fixed point results in fuzzy metric spaces using JCLR property;comparative analysis on optic cup and optic disc segmentation for glaucoma diagnosis;an improved reversible data hiding technique based on histogram Bin shifting;a novel approach towards detection and identification of stages of breast cancer;implementation and comparative study of image fusion methods in frequency domain and iris recognition using textural edgeness features.
The paper presents analysis and comparison of complexity of selected algorithms of motion estimation used in video compression. With the use of own, highly optimized software implementation the authors explored comple...
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ISBN:
(纸本)9781467395564
The paper presents analysis and comparison of complexity of selected algorithms of motion estimation used in video compression. With the use of own, highly optimized software implementation the authors explored complexity of the methods applied in the framework of the new High Efficiency Video Compression (HEVC) technology. The influence of many different factors on motion estimation complexity has been deeply studied, including the implementation technique, type of the algorithm, number of processor threads used, kind of metric of blocks similarity. The results obtained allowed to formulate guidelines and conclusions that may be useful for future implementation of motion estimation algorithms in context of HEVC encoders.
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