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Occlusion, rotation and other factors affect human motion structure because of the incomplete acquired image sequence, resulting in poor performance of non-rigid three-dimensional (3D) motion pose reconstruction. A non-rigid 3D reconstruction and high-precision correction method for motion pose are studied in this paper. A non-rigid imaging model is designed to obtain 3D moving images. According to the frame difference and morphological processing, the background of image is separated and denoised. Combined with motion analysis, 3D motion pose features are extracted as identification of non-rigid 3D motion error actions in a hybrid Convolution Neural Network-Hidden Markov Model to train the correction coefficients, which are used to adjust the pose in 3D motion reconstruction and realise correction. Experimental results show that this method has high precision reconstruction and correction of non-rigid 3D motion pose. [ABSTRACT FROM AUTHOR]

• A novel non-rigid registration method for medical images is proposed by introducing the image field into Demons algorithm. • Introduce the orientation field for registration and make use of the directional information. • There is no extraction of feature points and manual intervention in the proposed method. • Experiments on BrainWeb and FIRE databases show the effectiveness of the proposed registration method for medical images. In medical imaging application, non-rigid registration is an important step that requires both accuracy and efficiency to align the deformed query image with the reference image. For non-rigid registration, Demons is generally used as an effective algorithm. However, since the computational model of Demons uses only the gradient information, the directional information of the image has not been fully utilized. In this paper, we proposed a novel non-rigid registration method for processing medical images by introducing the image field to the Demons algorithm. As direction information is very important for spatial transformation in registration and the image fields contain the direction of the image, by combing image fields with the traditional model based registration algorithm, we introduce the orientation field directly to the model for estimating the distortion, and thus make a better use of the direction information of images and simplify the deformation model. Different performance measures are used to evaluate the qualitative measures of the proposed scheme. Experiments on brain images and fundus images show the improvement of the proposed algorithm compared to the other existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

We introduce a new transformation estimation algorithm using the L2E estimator and apply it to non-rigid registration for building robust sparse and dense correspondences. In the sparse point case, our method iteratively recovers the point correspondence and estimates the transformation between two point sets. Feature descriptors such as shape context are used to establish rough correspondence. We then estimate the transformation using our robust algorithm. This enables us to deal with the noise and outliers which arise in the correspondence step. The transformation is specified in a functional space, more specifically a reproducing kernel Hilbert space. In the dense point case for nonrigid image registration, our approach consists of matching both sparsely and densely sampled SIFT features, and it has particular advantages in handling significant scale changes and rotations. The experimental results show that our approach greatly outperforms state-of-the-art methods, particularly when the data contains severe outliers. [ABSTRACT FROM PUBLISHER]

Content-based 3D object retrieval has become an active topic in many research communities. In this paper, we propose a novel visual similarity-based 3D shape retrieval method (CM-BOF) using Clock Matching and Bag-of-Features. Specifically, pose normalization is first applied to each object to generate its canonical pose, and then the normalized object is represented by a set of depth-buffer images captured on the vertices of a given geodesic sphere. Afterwards, each image is described as a word histogram obtained by the vector quantization of the image's salient local features. Finally, an efficient multi-view shape matching scheme (i.e., Clock Matching) is employed to measure the dissimilarity between two models. When applying the CM-BOF method in non-rigid 3D shape retrieval, multidimensional scaling (MDS) should be utilized before pose normalization to calculate the canonical form for each object. This paper also investigates several critical issues for the CM-BOF method, including the influence of the number of views, codebook, training data, and distance function. Experimental results on five commonly used benchmarks demonstrate that: (1) In contrast to the traditional Bag-of-Features, the time-consuming clustering is not necessary for the codebook construction of the CM-BOF approach; (2) Our methods are superior or comparable to the state of the art in applications of both rigid and non-rigid 3D shape retrieval. [ABSTRACT FROM AUTHOR]