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In 3D object recognition, local feature-based recognition is known to be robust against occlusion and clutter. Local feature estimation requires feature correspondences, including feature extraction and matching. Feature extraction is normally a two-stage process that estimates keypoints and keypoint descriptors, and existing studies show repeatability to be a good indicator of keypoint feature detector...
Research efforts have been devoted to extraction and visualization of vortices in an unsteady (turbulent) flow. Characterizing the behaviors of the flow, vortices are identifiable as regions using a vortex detector known as the lambda2-criterion. Isosurface visualization renders vortex regions based on a chosen isovalue. However, it is highly challenging to choose one isovalue suitable for visualizing...
Estimation of density and direction of crowd flow from surveillance video has attracted much research attention recently in the area of computer vision. Crowd density in a video sequence can be considered as a global entity to estimate the direction of interest of the crowd. Though there are methods to compute the density and direction, a combined approach will give more insights into the problem...
This paper introduced a novel affine, rotation invariant filtering feature matching model based on RGB and geometric-based images. The proposed model was implemented based on summation of epipolar distance of angles, scale estimation based on intensity gradients, and normal vector estimation by using geometric data. Since of former ordering, our method is affine and rotation invariant which provides...
Cyclostationary feature (CSF) detection plays an important role in spectrum sensing for cognitive radio systems, since it has low requirements on a-priori knowledge about the primary user signals and high robustness to noise and interferences. Existing CSF detecting technologies depend on full-size Fast Fourier Transform (FFT), which leads to high implementation cost in case of high carrier frequency...
Real-time human detection is a challenging task due to appearance variance, occlusion and rapidly changing content; therefore it requires efficient hardware and optimized software. This paper presents a real-time human detection scheme on a Raspberry Pi. An efficient algorithm for human detection is proposed by processing regions of interest (ROI) based upon foreground estimation. Different number...
Keypoint features detection from measurements enables efficient localization and map estimation through the compact representation and recognition of locations. The keypoint detector FALKO has been proposed to detect stable points in laser scans for localization and mapping tasks. In this paper, we present novel loop closure methods based on FALKO keypoints and compare their performance in online...
Detecting and localizing insulator plays a vital role in any power line monitoring system. In this work, we present a novel method for rotation invariant insulator detection. Rotation invariance is achieved by an efficient approach for estimating rotation angle of all insulator of an image. Sliding window based local directional pattern (LDP) feature is extracted from the image and support vector...
In this paper we focus on the problem of pedestrian detection in low visibility conditions, with infrared cameras. Widely applied, tracking is essential for driving assistance applications, providing support for removing false positives and forcing the detection of border line true positives. We propose a multiple feature and temporal based pedestrian detector for far-infrared images. Our model benefits...
Human detection in RGB-D images is an important yet very challenging task in computer vision. In this paper, we propose a novel human detection approach in RGB-D images, which integrates ROI (region-of-interest) generation, depth-size relationship estimation and a human detector. Our approach has the following advantages: 1) ROI generation and depth-size relationship estimation take full advantage...
The detection of heartbeat is an important and challenging issue for health care. This work proposes to estimate the QRS complex parameters based on the maximum-likelihood (ML) principle. To this goal, a new signal model and its Bayesian framework are studied. Detectors or estimators based on the Bayesian framework are considered to be optimal in the statistical signal processing point of view. To...
Aiming at the problem how to express relevant relationship between multiple targets, we propose an approach based on the tracking-by-detection (TBD) strategy, where detections from the HOG classifier are regarded as image evidence. Focusing on the issue of localization uncertainty, data association based on greedy heuristics is executed iteratively to retrieve from the erroneous candidate locations...
Humans are increasingly cooperating with machinery/robots in a high number of domains and under uncontrolled conditions. When persons are interacting with machinery, they are exposed to distraction/fatigue, which can lead to dangerous situations. The evaluation of individual's attention and fatigue levels is highly needed in such situations. This is an important measurement to avoid the interaction...
The paper considers the composite detection problem where both detection and parameter estimation are of primary interest. Based on a Neyman-Pearson type of formulation, our goal is to find the joint detector and estimator that minimizes a decision-dependent Bayesian estimation risk subject to the detection error probability constraints. The optimal joint solution not only yields lower Bayesian estimation...
Region of interest (ROI) generation used to extract candidate windows which are likely to have pedestrians is an important step in pedestrian detection systems. In this paper, we propose a novel algorithm to reduce the number of candidate windows extracted as ROI to reduce the computation time of pedestrian detection. In our proposed method, we take advantage of the temporal correlation between the...
Text information in natural scene images plays an important role in many computer vision applications, such as license plate recognition, scene text recognition, and assistive text reading for visually impaired people. Unlike the existing Thai text localization methods, which use connected component analysis and rule-based techniques to locate text position, in this paper, we present a new way to...
This paper presents an orientation estimate scheme using monocular camera and inertial measurement units (IMUs). Unlike the traditional wearable orientation estimation methods, our proposed approach combines both of these two modalities in a novel pattern. Firstly, two visual correspondences between consecutive frames are selected that not only meet the requirement of descriptor similarity constraint,...
New generation autonomous vehicles use different data fusion techniques to solve the Simultaneous Localization And Mapping (SLAM) problems in urban terrains. However, the majority of the implementations uses high-cost sensors like LIDAR to obtain a high accuracy map. In this paper, we present a method to solve this problem using sequences of stereo images. Our approach uses Local Intensity Order Pattern...
Eye gaze is either the gaze direction or the point of gaze of an eye relative to the head to determine a person's line of sight or point of fixation. Gaze based input is processed to initiate different commands remotely in computing systems to provide ease of use, low cost, speedy and safer interaction. For detecting gaze basically the position of a user's pupil and glint is determined and then mapped...
We propose a primary visual cortex inspired oriented edge feature for object pose estimation. The neural feedback like feature is based on a Center-Surround Contrast excitation and a k-Winner-Take-AU inhibition, to extract different orientations of edge response from an image patch. To compute local descriptor, we model each oriented edge response with a PDF distribution, before concatenating their...
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