Accurate three-dimensional pose recognition from monocular images using template matched filtering

Picos, Kenia; Díaz-Ramírez, Víctor H.; Kober, Vitaly; Montemayor, Antonio S.; Pantrigo, Juan José

doi:10.1117/1.oe.55.6.063102

Cited by 37 publications

(5 citation statements)

References 17 publications

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“…The additive noise denoted by (x) is given by a zero-mean Gaussian distribution process. Moreover, Γ in (1) is a transformation matrix that involves the appearance modifications of the target [13] related to scaling S and rotation R (with , , orientation parameters). Hence, Γ = S R is related to the space Π ∈ R 3 .…”

Section: Object Recognition With Correlationmentioning

confidence: 99%

“…Template matching based on correlation filters can be used to solve 3D pose estimation of rigid objects [13]. Moreover, the pose estimation problem can be modeled as a search problem, in which the goal is to find the reference target view that gives the best match between the actual view of the target in the scene [14].…”

Section: Introductionmentioning

confidence: 99%

“…The design and construction of correlation filters is a high-dimensional complex problem, thus finding an optimal solution will require a very large search space. This strategy presented a narrow exploration space using a local search algorithm [13]. In this strategy it was assumed that the object would appear with smooth pose transitions through the video frames.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

Picos

Orozco-Rosas

Díaz-Ramírez

et al. 2018

Mathematical Problems in Engineering

Self Cite

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In this paper, we propose an evolutionary correlation filtering approach for solving pose estimation in noncontinuous video sequences. The proposed algorithm computes the linear correlation between the input scene containing a target in an unknown environment and a bank of matched filters constructed from multiple views of the target and estimates of statistical parameters of the scene. An evolutionary approach for finding the optimal filter that produces the highest matching score in the correlator is implemented. The parameters of the filter bank evolve through generations to refine the quality of pose estimation. The obtained results demonstrate the robustness of the proposed algorithm in challenging image conditions such as noise, cluttered background, abrupt pose changes, and motion blur. The performance of the proposed algorithm yields high accuracy in terms of objective metrics for pose estimation in noncontinuous video sequences.

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Section: Object Recognition With Correlationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

Picos

Orozco-Rosas

Díaz-Ramírez

et al. 2018

Mathematical Problems in Engineering

Self Cite

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show abstract

“…Another approach based on template matching filters has been proposed to solve 3D pose of an object: by generating a set of synthetic images of 3D model of the object as reference templates, a high matching score when the input and reference images are very similar. Given a known 3D model of target, this approach estimates its locations and orientation parameters by maximizing frequency response between the input and the current reference images [9,10]. The input image is globally processed instead of processing only local feature, and it yields high accuracy of 3D pose estimation in comparison with the existing approaches based on segmentation in a challenging environment.…”

Section: Introductionmentioning

confidence: 99%

Elliptic shape prior for object 2D-3D pose estimation using circular feature

Cui

Chen

Gong

et al. 2020

EURASIP J. Adv. Signal Process.

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Many objects in real world have circular feature. It is a difficult task to obtain the 2D-3D pose estimation using circular feature in challenging scenarios. This paper proposes a method to incorporate elliptic shape prior for object pose estimation using a level set method. The relationship between the projection of the circular feature of a 3D object and the signed distance function corresponding to it is analyzed to yield a 2D elliptic shape prior. The method employs the combination of the grayscale histogram, the intensities of edge, and the smoothness distribution as main image feature descriptors that define the image statistical measure model. The elliptic shape prior combined with the image statistical measure energy model drives the elliptic shape contour to the projection of the circular feature of the 3D object with the current pose into the image plane. These works effectively reduce the impacts of the challenging scenarios on the pose estimate results. In addition, the method utilizes particle filters that take into account the motion dynamics of the object among scene frames, and this work provides the robust method for object 2D-3D pose estimation using circular feature in a challenging environment. Various numerical experiments are illustrated to show the performance and advantages of the proposed method.

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“…Building dense 3D maps of environments is an important task for mobile robotics, with applications in navigation, manipulation, semantic mapping, face recognition [9,10,11,12,13,14,15,16,17], and in augmented reality applications, surveillance systems, medical applications [18,19,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

Accurate reconstruction of the 3D indoor environment map with a RGB-D camera based on multiple ICP

Ruchay

Dorofeev

Kober³

2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

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In this paper, we propose a new method for 3D map reconstruction using the Kinect sensor based on multiple ICP. The Kinect sensor provides RGB images as well as depth images. Since the depth and RGB color images are captured by one Kinect sensor with multiple views, each depth image should be related to the color image. After matching of the images (registration), point-to-point corresponding between two depth images is found, and they can be combined and represented in the 3D space. In order to obtain a dense 3D map of the 3D indoor environment, we design an algorithm to combine information from multiple views of the Kinect sensor. First, features extracted from color and depth images are used to localize them in a 3D scene. Next, Iterative Closest Point (ICP) algorithm is used to align all frames. As a result, a new frame is added to the dense 3D model. However, the spatial distribution and resolution of depth data affect to the performance of 3D scene reconstruction system based on ICP. In this paper we automatically divide the depth data into sub-clouds with similar resolution, to align them separately, and unify in the entire points cloud. This method is called the multiple ICP. The presented computer simulation results show an improvement in accuracy of 3D map reconstruction using real data.

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Accurate three-dimensional pose recognition from monocular images using template matched filtering

Cited by 37 publications

References 17 publications

Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

Elliptic shape prior for object 2D-3D pose estimation using circular feature

Accurate reconstruction of the 3D indoor environment map with a RGB-D camera based on multiple ICP

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