Showing 1-25 of 4,478 resultsfor "Index Terms":Confidence Map
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Inferential Clustering Approach for Microarray Experiments with Replicated Measurements
Cluster analysis has proven to be a useful tool for investigating the association structure among genes in a microarray data set. There is a rich literature on cluster analysis and various techniques have been developed. Such analyses heavily depend on an appropriate (dis)similarity measure. In this paper, we introduce a general clustering approach based on the confidence interval inferential meth...Show More
Feature Augmentation for Learning Confidence Measure in Stereo Matching
Confidence estimation is essential for refining stereo matching results through a post-processing step. This problem has recently been studied using a learning-based approach, which demonstrates a substantial improvement on conventional simple non-learning based methods. However, the formulation of learning-based methods that individually estimates the confidence of each pixel disregards spatial c...Show More
Color-based road detection and its evaluation on the KITTI road benchmark
2014 IEEE Intelligent Vehicles Symposium Proceedings
Year: 2014 | Conference Paper |
Cited by: Papers (50)
Road detection is one of the key issues of scene understanding for Advanced Driving Assistance Systems (ADAS). Recent approaches has addressed this issue through the use of different kinds of sensors, features and algorithms. KITTI-ROAD benchmark has provided an open-access dataset and standard evaluation mean for road area detection. In this paper, we propose an improved road detection algorithm ...Show More
Estimating the 2D Static Map Based on Moving Stereo Camera
Shadi M. Saleh;Sinan A. Khwandah;Wolfram Hardt;Marcus Hilbrich;Pavlos I. Lazaridis
2018 24th International Conference on Automation and Computing (ICAC)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
Perception is an essential procedure for intelligent vehicles where the safety issue is the most critical one. Usually, the perceptual approach is constructed based on measurements received from multiple sensors such as (Radar, and LiDAR) in order to model the immediate driving environment for autonomous vehicles navigation. These sensors are often limited and uncertain in providing visual informa...Show More
Fusion of ALOS-2/PALSAR-2 and Sentinel-1 Polarimetric Imagery with Explainable Neural Network-based Confidence Level Estimation for Glacier Surface Classification
2023 8th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Glacier monitoring and classification play a vital role in understanding the impact of climate change. Remote sensing imagery, particularly synthetic aperture radar (SAR) data, offers valuable information for glacier surface classification due to its all-weather and day-and-night imaging capability. This paper proposes an approach that combines dual-pol ALOS-2/PALSAR-2 and dual-pol Sentinel-1 pola...Show More
Distributed collaborative situation-map making for disaster response
Interacting with Computers
Cited by: Papers (2)
A situation map that shows the overview of a disaster situation serves as a valuable tool for disaster response teams. It helps them to orientate their location and to make disaster response decisions. It is, however, a complicated task to rapidly generate a complete and comprehensive situation map of a disaster area, particularly due to the centralized organization of disaster management and the ...Show More
Unified Confidence Estimation Networks for Robust Stereo Matching
We present a deep architecture that estimates a stereo confidence, which is essential for improving the accuracy of stereo matching algorithms. In contrast to existing methods based on deep convolutional neural networks (CNNs) that rely on only one of the matching cost volume or estimated disparity map, our network estimates the stereo confidence by using the two heterogeneous inputs simultaneousl...Show More
Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization
Mobile robots build on accurate, real-time mapping with onboard range sensors to achieve autonomous navigation over rough terrain. Existing approaches often rely on absolute localization based on tracking of external geometric or visual features. To circumvent the reliability issues of these approaches, we propose a novel terrain mapping method, which bases on proprioceptive localization from kine...Show More
Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion
Runmin Cong;Jianjun Lei;Changqing Zhang;Qingming Huang;Xiaochun Cao;Chunping Hou
IEEE Signal Processing Letters
Cited by: Papers (198)
Stereoscopic perception is an important part of human visual system that allows the brain to perceive depth. However, depth information has not been well explored in existing saliency detection models. In this letter, a novel saliency detection method for stereoscopic images is proposed. First, we propose a measure to evaluate the reliability of depth map, and use it to reduce the influence of poo...Show More
The Effect of Color Scales on Climate Scientists’ Objective and Subjective Performance in Spatial Data Analysis Tasks
Aritra Dasgupta;Jorge Poco;Bernice Rogowitz;Kyungsik Han;Enrico Bertini;Cláudio T. Silva
Geographical maps encoded with rainbow color scales are widely used by climate scientists. Despite a plethora of evidence from the visualization and vision sciences literature about the shortcomings of the rainbow color scale, they continue to be preferred over perceptually optimal alternatives. To study and analyze this mismatch between theory and practice, we present a web-based user study that ...Show More
A contour extraction method of coke optical texture based on edge confidence map
2012 24th Chinese Control and Decision Conference (CCDC)
Year: 2012 | Conference Paper |
Contour information is an important feature in recognition of coke microscopic optical texture. In view of the characteristic of coke microscopic, a contour extraction method, which is based on joint edge confidence map and dual threshold, is proposed. At first, an edge confidence map with gradient magnitude and orientation is built, then non-maximum suppression is employed to obtain local externa...Show More
People-background segmentation with unequal error cost
2012 19th IEEE International Conference on Image Processing
Year: 2012 | Conference Paper |
Cited by: Papers (2)
We address the problem of segmenting a video in two classes of different semantic value, namely background and people, with the goal of guaranteeing that no people (or body parts) are classified as background. Body parts classified as background are given a higher classification error cost (segmentation with bias on background), as opposed to traditional approaches focused on people detection. To ...Show More
Deep stereo confidence prediction for depth estimation
Sunok Kim;Dongbo Min;Bumsub Ham;Seungryong Kim;Kwanghoon Sohn
2017 IEEE International Conference on Image Processing (ICIP)
Year: 2017 | Conference Paper |
Cited by: Papers (21)
We present a novel method that predicts a confidence to improve the accuracy of an estimated depth map in stereo matching. In contrast to existing learning based approaches relying on hand-crafted confidence features, we cast this problem into a convolutional neural network, learned using both a matching cost volume and its associated disparity map. As the size of the matching cost volume varies d...Show More
Finding correct semantic correspondences between heterogeneous ontologies is one of the most challenging problems in the area of semantic web technologies. As manually constructing such mappings is not feasible in realistic scenarios, a number of automatic matching tools have been developed that propose mappings based on general heuristics. As these heuristics often produce incorrect results, a ma...Show More
Real-time image recovery using temporal image fusion
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2013 | Conference Paper |
Cited by: Papers (1)
In computer vision systems an unpredictable image corruption can have significant impact on its usability. Image recovery methods for partial image damage, in particular in moving scenarios, can be crucial for recovering corrupted images. In these situations, image fusion techniques can be successfully applied to congregate information taken at different instants and from different points-of-view ...Show More
Efficient stereo matching based on a new confidence metric
2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)
Year: 2012 | Conference Paper |
In this paper, we propose a new confidence metric for efficient stereo matching. To measure the confidence of a stereo match, we refer to the curvatures around the two minimum costs of a cost curve, the size of aggregation kernel, and the occlusion information. Using the proposed confidence metric, we then design a weighted median filter, in order to refine the initially estimated disparities with...Show More
Fast Depth Prediction and Obstacle Avoidance on a Monocular Drone Using Probabilistic Convolutional Neural Network
Xin Yang;Jingyu Chen;Yuanjie Dang;Hongcheng Luo;Yuesheng Tang;Chunyuan Liao;Peng Chen;Kwang-Ting Cheng
Recent studies employ advanced deep convolutional neural networks (CNNs) for monocular depth perception, which can hardly run efficiently on small drones that rely on low/middle-grade GPU(e.g. TX2 and 1050Ti) for computation. In addition, the methods which can effectively and efficiently produce probabilistic depth prediction with a measure of model confidence have not been well studied. The lack ...Show More
Simultaneous Localization and Mapping: A Pseudolinear Kalman Filter (PLKF) Approach
Chandima Dedduwa Pathiranage;Keigo Watanabe;Buddhika Jayasekara;Kiyotaka Izumi
2008 4th International Conference on Information and Automation for Sustainability
Year: 2008 | Conference Paper |
Cited by: Papers (4)
This paper describes an improved solution to the simultaneous localization and mapping (SLAM) problem based on pseudolinear models. Accurate estimation of vehicle and landmark states is one of the key issues for successful mobile robot navigation if the configuration of the environment and initial robot location are unknown. A state estimator which can be designed to use the nonlinearity as it is ...Show More
Real time confidence estimation and multi-scale camouflage object detection
2023 IEEE 6th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)
Year: 2023 | Conference Paper |
Due to the fact that objects with camouflage blend seamlessly into their surroundings, exhibiting a exceptional resemblance in terms of texture, color, shape, etc., and possessing boundaries that are indistinguishable from their environment, the detection of such camouflaged objects becomes significantly more challenging. In this research, we introduce a novel network called CRNet, which incorpora...Show More
Robust Object Segmentation using Adaptive Thresholding
2007 IEEE International Conference on Image Processing
Year: 2007 | Conference Paper |
This paper proposes a robust object segmentation algorithm that tackles problems that arise in environments in which the foreground and background colours are similar and there is light reflection in the shadow areas. The proposed algorithm bases its efficiency on a novel RGB colour detection process with adaptive threshold and edge detection, which are combined in order to obtain a foreground map...Show More
Adversarial Confidence Estimation Networks for Robust Stereo Matching
Stereo matching aiming to perceive the 3-D geometry of a scene facilitates numerous computer vision tasks used in advanced driver assistance systems (ADAS). Although numerous methods have been proposed for this task by leveraging deep convolutional neural networks (CNNs), stereo matching still remains an unsolved problem due to its inherent matching ambiguities. To overcome these limitations, we p...Show More
In the urban environment or under complex traffic situation, conventional localization methods like GPS, dead reckoning or SLAM are not precise enough for autonomous driving. So high-precision localization for intelligent vehicles becomes a hot problem. Among all the high-precision localization methods, the category based on sensor maps outperforms others because of its accuracy and real-time prop...Show More
Sparse representation to localize objects in underwater acoustic images
2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT)
Year: 2015 | Conference Paper |
Cited by: Papers (3)
Screening of underwater images for the presence of submerged objects and phenomena like submarine volcanoes is based on the spotting and localization of objects present in underwater. This paper proposes a dictionary based approach in which a dictionary is created from various sub-images of the target image. The dictionary of sub-images is created for multiple scales. The coefficients are computed...Show More
Anatomical Feature-Based Lung Ultrasound Image Quality Assessment Using Deep Convolutional Neural Network
Surya M. Ravishankar;Ryosuke Tsumura;John W. Hardin;Beatrice Hoffmann;Ziming Zhang;Haichong K. Zhang
2021 IEEE International Ultrasonics Symposium (IUS)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Lung ultrasound (LUS) has been used for point-of-care diagnosis of respiratory diseases including COVID-19, with advantages such as low cost, safety, absence of radiation, and portability. The scanning procedure and assessment of LUS are highly operator-dependent, and the appearance of LUS images varies with the probe's position, orientation, and contact force. Karamalis et al. introduced the conc...Show More
Probabilistic Method for Mapping & 3D SLAM of an off-road Terrain with Four Wheeled Robot
2022 5th International Conference on Advances in Science and Technology (ICAST)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Mobile robots use onboard range sensors and accurate, real-time mapping to perform autonomous navigation in challenging terrain. Absolute localisation based on the tracking of exterior geometric or visual cues is frequently used in existing techniques. To overcome the dependability issues with current methods, we propose a novel method for terrain mapping that only uses interoceptive clustering fr...Show More
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