KEYWORDS: Image processing algorithms and systems, Imaging systems, 3D metrology, Computer simulations, Cameras, Clouds, 3D modeling, Reconstruction algorithms, Data modeling, 3D image processing
The volume parameter is the basic content of an object morphology analysis. In this article, we propose a new fast object volume calculation algorithm from the point cloud. The proposed algorithm is based on the voxel representation of the point cloud and slice method. The accuracy and speed of the proposed object volume calculation algorithm on real data are compared to that of the state-of-the-art algorithms.
KEYWORDS: Clouds, 3D modeling, Animal model studies, Reconstruction algorithms, Process modeling, Principal component analysis, Data modeling, Visualization, Shape analysis
In this article, we proposed novel fast pose normalization algorithms for the point cloud. The first step deals with the detection of the ground plane of the scene in point cloud. Starting from downsampling point cloud by point cloud filtering and the normal vector of ground plane detected. The next step deals with the 3-D segmentation in point cloud, wherein we delete the ground plane. Then we used the algorithm of axis-aligned bounding box so that it sets the pose and dimensions of a box surrounding the given point cloud. Because we are computing an axis-aligned bounding box, the orientation of the box is just the identity orientation of the calculated unit normal vector to the plane. The algorithm of the axis-aligned bounding box is basically equivalent to taking the min/max of each coordinate. Moreover, we calculate the geometric center of the point cloud after pose normalization.
In this article, we proposed a fast algorithm of symmetry detection 3D model. First, we used the PCA algorithm for initial symmetry detection. Then, using exhaustive search of symmetry planes passing through the center of gravity about the initial symmetry plane, we determined the optimal symmetry plane with the help of the modified Hausdorff metric. The accuracy and speed of the proposed symmetry detection algorithm on real and synthetic data are compared to the PCA algorithm.
The computation of geodesic paths and distances is a common task in many computer graphics applications, for instance obtaining 3D model measurements. Geodesic distance computation is usually performed by exact surface algorithms. However, surface reconstruction is a rather time-consuming process and does not always guarantee to get a good result in the case of missing data or cloud distortions. In this article, we propose a new fast approximate geodesic distance algorithm on the point cloud. Computer simulation results for the proposed algorithm in terms of accuracy and speed of computation are presented and discussed.
A novel algorithm for analysis and classification of breast abnormalities in digital mammography based on a deep convolutional neural network is proposed. Simplified neural network architectures such as MobileNetV2, InceptionResNetV2, Xception, and ResNetV2 are intensively studied for this task. In order to improve the accuracy of detection and classification of breast abnormalities on real data an efficient training algorithm based on augmentation technique is suggested. The performance of the proposed algorithm for analysis and classification of breast abnormalities on real data is discussed and compared to that of the state-of-the-art algorithms.
In this paper, 3D face recognition based on a deep convolutional neural network in autonomous mobile systems is associated with a large size of neural models and extremely high computational complexity of classification procedures owing to the large network depth. To solve this problem, we use compression and pruning algorithms. Since these algorithms decrease the recognition accuracy, we propose an efficient retraining of neural models in such a way to approach the recognition accuracy to very large modern models of neural networks. The performance of the proposed neural models using compression and pruning is compared in terms of face recognition accuracy and compression rate.
KEYWORDS: Reconstruction algorithms, 3D modeling, Sensors, RGB color model, Clouds, Cameras, 3D image processing, Image registration, 3D image reconstruction
In this paper, we propose a new algorithm for dense 3D object reconstruction using a RGB-D sensor at high rate. In order to obtain a dense shape recovery of a 3D object, an efficient merging of the current and incoming point clouds obtained with the Iterative Closest Point is suggested. As a result, incoming frames are aligned to the dense 3D model. The accuracy of the proposed 3D object reconstruction algorithm on real data is compared to that of the estate-of-the-art reconstruction algorithms.
In this paper, we first estimate the accuracy of 3D facial surface reconstruction from real RGB-D depth maps using various depth filtering algorithms. Next, a new 3D face recognition algorithm using deep convolutional neural network is proposed. With the help of 3D face augmentation techniques different facial expressions from a single 3D face scan are synthesized and used for network learning. The performance of the proposed algorithm is compared in terms of 3D face recognition metrics and processing time with that of common 3D face recognition algorithms.
KEYWORDS: Denoising, Reconstruction algorithms, Digital filtering, Magnetorheological finishing, 3D modeling, Image filtering, Data modeling, Nonlinear filtering, RGB color model, Clouds
In this paper, we estimate the accuracy of 3D object reconstruction using depth filtering and data from a RGB-D sensor. Depth filtering algorithms carry out inpainting and upsampling for defective depth maps from a RGB-D sensor. In order to improve the accuracy of 3D object reconstruction, an efficient and fast method of depth filtering is designed. Various methods of depth filtering are tested and compared with respect to the reconstruction accuracy using real data. The presented results show an improvement in the accuracy of 3D object reconstruction using depth filtering from a RGB-D sensor.
In this paper, we propose an algorithm for the detection of local features in depth maps. The local features can be utilized for determination of special points for Iterative Closest Point (ICP) algorithms. The proposed algorithm employs a novel approach based a cascade mechanism, which can be applied for several 3D keypoint detection algorithms. Computer simulation and experimental results obtained with the proposed algorithm in real-life scenes are presented and compared with those obtained with state-of-the-art algorithms in terms of detection efficiency, accuracy, and speed of processing. The results show an improvement in the accuracy of 3D object reconstruction using the proposed algorithm followed by ICP algorithms.
In this paper, we reconstruct 3D object shape using multiple Kinect sensors. First, we capture RGB-D data from Kinect sensors and estimate intrinsic parameters of each Kinect sensor. Second, calibration procedure is utilized to provide an initial rough estimation of the sensor poses. Next, extrinsic parameters are estimated using an initial rigid transformation matrix in the Iterative Closest Point (ICP) algorithm. Finally, a fusion of calibrated data from Kinect sensors is performed. Experimental reconstruction results using Kinect V2 sensors are presented and analyzed in terms of the reconstruction accuracy.
This presentation deals with restoration of the image corrupted by impulsive noise using a novel cascade switching algorithm. The algorithm processes iteratively the observed degraded image changing adaptively parameters of the switching filter. With the help of computer simulation, we show that the proposed algorithm is able to effectively remove impulse noise from a highly contaminated image. The performance of the proposed algorithm is compared with that of common successful algorithms in terms of image restoration metrics.
An algorithm for tracking of multiple objects in video based on time-adjustable adaptive composite correlation filtering is proposed. For each frame a bank of composite correlation filters are designed in such a manner to provide invariance to pose, occlusion, clutter, and illumination changes. The filters are synthesized with the help of an iterative algorithm, which optimizes the discrimination capability for each object. The filters are adapted to the objects changes online using information from the current and past scene frames. Results obtained with the proposed algorithm using real-life scenes are presented and compared with those obtained with state-of-the-art tracking methods in terms of detection efficiency, tracking accuracy, and speed of processing.
In this paper, we propose a perceptual image hash algorithm based on cascade algorithm, which can be applied in image authentication, retrieval, and indexing. Image perceptual hash uses for image retrieval in sense of human perception against distortions caused by compression, noise, common signal processing and geometrical modifications. The main disadvantage of perceptual hash is high time expenses. In the proposed cascade algorithm of image retrieval initializes with short hashes, and then a full hash is applied to the processed results. Computer simulation results show that the proposed hash algorithm yields a good performance in terms of robustness, discriminability, and time expenses.
This paper proposes a novel algorithm for restoring images corrupted with clusters of impulse noise. The noise clusters often occur when the probability of impulse noise is very high. The proposed noise removal algorithm consists of detection of bulky impulse noise in three color channels with local order statistics followed by removal of the detected clusters by means of vector median filtering. With the help of computer simulation we show that the proposed algorithm is able to effectively remove clustered impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics with that of common successful algorithms.
This paper deals with impulse noise removal from color video. The proposed noise removal algorithm employs a switching filtering for denoising of color video; that is, detection of corrupted pixels by means of a novel morphological filtering followed by removal of the detected pixels on the base of estimation of uncorrupted pixels in the previous scenes. With the help of computer simulation we show that the proposed algorithm is able to well remove impulse noise in color video. The performance of the proposed algorithm is compared in terms of image restoration metrics with that of common successful algorithms.
This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs two classical approaches for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of local rank filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise and clustered impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics with that of common successful algorithms.
In this work, a correlation-based algorithm consisting of a set of adaptive filters for recognition of occluded objects in still and dynamic scenes in the presence of additive noise is proposed. The designed algorithm is adaptive to the input scene, which may contain different fragments of the target, false objects, and background to be rejected. The algorithm output is high correlation peaks corresponding to pieces of the target in scenes. The proposed algorithm uses a bank of composite optimum filters. The performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics.
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