Competing Fronts for Coarse–to–Fine Surface Reconstruction

In this study, we investigated how shapes are classified based on local and global features by four representative convolutional neural networks (CNNs), i.e., AlexNet, VGG, ResNet and Inception. While the local features are based on simple components, such as orientation of line segment, the global features are based on the whole object, such as whether an object has a hole. For example, solid triangles and solid squares are differentiated by local features, solid circles and rings are differentiated by a global feature. Two sets of experiments were performed in this research. In the first experiment, we examined how the four CNNs pre-trained on ImageNet (with transfer learning) learned to differentiate the regular shapes (equilateral-triangles, squares, circles and rings). Our results showed that the pre-trained CNNs exhibited faster learning rates in the tasks discriminating the local features than in the tasks discriminating the global feature. However, the transfer learning of discriminating the global feature in regular shapes were better generalized to irregular shapes than the transfer learning of discriminating local features. In the second experiment, the CNNs were trained from scratch (with random weights initialization) to discriminate local and global features in regular and irregular shapes. Different from the transfer learning, the CNNs exhibited faster learning rates in discriminating the global feature than the local features. Similar to transfer learning, the CNNs exhibited excellent learning generalization to discriminating the global feature of irregular shapes, but poor learning generalization to discriminating the local features in the irregular shapes. The overarching goal of this research is to create a paradigm and benchmark to directly compare how the CNNs and primate visual systems process geometrical invariants. In contrast to the ImageNet approach which employs natural images to train CNNs, we employed the “ShapeNet” approach which features geometrical shapes with well-defined properties. The ShapeNet approach will not only help elucidate the strengths and limitations of CNN computation, but also provide insights into visual information processing in the primates.

This paper presents a general Surface Reconstruction framework which encapsulates the uncertainty of the sam-pled data, making no assumption on the shape of the surface to be reconstructed. Starting from the input points(either points clouds or multiple range images), an Estimated Existence Function (EEF) is built which modelsthe space in which the desired surface could exist and, by the extraction of EEF critical points, the surface isreconstructed. The nal goal is the development of a generic framework able to adapt the result to different kindsof additional information coming from multiple sensors. Categories and Subject Descriptors (according to ACM CCS) : I.3.3 [Computer Graphics]: Shape Modeling, Uncer-tain data, Multi-sensor Data Fusion 1. Introduction 3D scanning devices are becoming more and more availableand affordable. Thanks to modern acquisition technologies,heterogeneous data can be acquired from multiple acquisi-tion sensors, which often incorporate information about un-certainty of the data sampling process. Surface reconstruc-tion techniques designed over a specic sensor often takeinto account uncertainty during the reconstruction process,but they are limited to work with a single device. On thecontrary, general techniques that can process data comingfrom different sensors usually disregard much part of sensor-specic information, and seldom take into account uncer-tainty.The basic concept of our approach is

My name is Demetri Terzopoulos and my co-chair, John Platt, and I would like to welcome you to the panel on Physically-Based Modeling -- Past, Present and Future. I'll start by introducing the panelists; the affiliations you see listed on the screen are somewhat out of date.

Physically-based modeling: past, present, and future

We investigated the application of multiresolution global and local texture features to reduce false-positive detection in a computerized mass detection program. One hundred and sixty-eight digitized mammograms were randomly and equally divided into training and test groups. From these mammograms, two datasets were formed. The first dataset (manual) contained four regions of interest (ROIs) selected manually from each of the mammograms. One of the four ROIs contained a biopsy-proven mass and the other three contained normal parenchyma, including dense, mixed dense/fatty, and fatty tissues. The second dataset (hybrid) contained the manually extracted mass ROIs, along with normal tissue ROIs extracted by an automated Density-Weighted Contrast Enhancement (DWCE) algorithm as false-positive detections. A wavelet transform was used to decompose an ROI into several scales. Global texture features were derived from the low-pass coefficients in the wavelet transformed images. Local texture features were calculated from the suspicious object and the peripheral subregions. Linear discriminant models using effective features selected from the global, local, or combined feature spaces were established to maximize the separation between masses and normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classifier performance. The classification accuracy using global features were comparable to that using local features. With both global and local features, the average area, Az, under the test ROC curve, reached 0.92 for the manual dataset and 0.96 for the hybrid dataset, demonstrating statistically significant improvement over those obtained with global or local features alone. The results indicated the effectiveness of the combined global and local features in the classification of masses and normal tissue for false-positive reduction.

Human perception of the face involves the observation of both coarse (global) and detailed (local) features of the face to identify and categorize a person. Face categorization involves finding common visual cues, such as gender, race and age, which could be used as a precursor to a face recognition system to improve recognition rates. In this paper, we investigate the fusion of both global and local features for gender classification. Global features are obtained using the principal component analysis (PCA) and discrete cosine transformation (DCT) approaches. A spatial local binary pattern (LBP) approach augmented with a two-dimensional DCT approach has been used to find the local features. The performance of the proposed approach has been investigated through extensive experiments performed on FERET database. The proposed approach gives a recognition accuracy of 98.16% on FERET database. Comparisons with some of the existing techniques have shown a marked reduction in number of features used per image to produce results more efficiently and without loss of accuracy for gender classification.

In reverse engineering, the point cloud data processing is the key, it will directly affect the quality and efficiency of the subsequent reconstruction of the surface. However, the amount of data obtained by three-dimensional laser scanning is often larger and redundant, which will increase the difficulty of the surface reconstruction, therefore, the point cloud data is needed to streamline. Depth analysis of the efficiency and accuracy of curvature grid method, a secondary streamlined algorithm is proposed, which take use of non-uniform grid streamline law based on octree to streamline to the point cloud, and re-use curvature streamline method based on B-spline surface fitting to streamline it. This method, whose streamline ratio is relatively high, can effectively retain the feature point cloud data, providing a great convenience for the follow-up of reverse engineering.

This study analyzes the effectiveness of the global (the whole face) and local (regions of eyes, nose, and mouth) features for face recognition. Features describing human faces are encoded in local ternary patterns. The two-class support vector machine is used as the supervised learning algorithm for training recognition models. In the recognition process, recognition modes based on the global features and local features are cascaded. For identifying a face image, the local features are used iteratively for filtering out candidates that can not be clearly identified by the global features, until the one with highest possibility is concluded. The experimental results show that cascading the recognition models of global and local features obtains better classification accuracy than the single classification process.

Point cloud completion using multiscale feature fusion and cross-regional attention

For the existing 3D point cloud model classification method PointNet only extracts global feature information, but for the problem of insufficient local feature information extraction, a network that can take into account the combination of global features and local features is proposed, so as to realize 3D point Classification of cloud models. This paper proposes a classification network based on local location information. First, the point cloud model is sampled in multiple groups to obtain the center point of each group of samples, and then the radius is set to obtain its spherical neighborhood, and the points in the neighborhood and their center points are used as local location features. In information fusion, the acquired local information features are used as high-dimensional features of the neighborhood space through feature mapping. By analogy, multiple sets of sampled local features are integrated to realize the classification of the point cloud model. The experimental results show that the classification network can achieve a classification accuracy of 92.5% on the ModelNet40 data set, which is higher than the current mainstream algorithm.

Sentence similarity is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering. However, a variety of linguistic expressions and ambiguities of words in sentences make it difficult to measure sentence similarity. Many studies show that using local features or global features of a sentence will produce satisfactory sentence representations that can be utilized to measure sentence similarity. Local features reflect the relationships of adjacent words for each sentence and the sequence information of a sentence are usually expressed by global features. However, local features lack abilities to capture sequence information while a small amount of extracted global features is not enough to produce sentence representations with good qualities. In this paper, we propose A Hybrid Model combining Local and Global features into a Siamese Network (HM-LGSN) for sentence similarity calculation. We first propose a new convolution neural network architecture called group convolution neural network to extract the most representative local features (or word semantic features). Then we combine these new features with pre-trained embeddings of words as input to the Bidirectional Gated Recurrent Units to extract global features of sentences. Finally, we select the global features to form sentence representations and calculate sentence similarity through Manhattan distance. The experimental results on SICK, MSRVID, STS-B datasets show that the accuracy of our proposed model is significantly improved by combining local features and global features.

Surface reconstruction via cooperative evolutions

Using deformable models to register medical images can result in problems of initialization of deformable models and robustness and accuracy of matching of inter-subject anatomical variability. To tackle these problems, a novel model is proposed in this paper by compounding local invariant features and global deformable geometry. This model has four steps. First, a set of highly-repeatable and highly-robust local invariant features, called Key Features Model (KFM), are extracted by an effective matching strategy. Second, local features can be matched more accurately through the KFM for the purpose of initializing a global deformable model. Third, the positional relationship between the KFM and the global deformable model can be used to precisely pinpoint all landmarks after initialization. And fourth, the final pose of the global deformable model is determined by an iterative process with a lower time cost. Through the practical experiments, the paper finds three important conclusions. First, it proves that the KFM can detect the matching feature points well. Second, the precision of landmark locations adjusted by the modeled relationship between KFM and global deformable model is greatly improved. Third, regarding the fitting accuracy and efficiency, by observation from the practical experiments, it is found that the proposed method can improve % of the fitting accuracy and reduce around 50% of the computational time compared with state-of-the-art methods.

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which combines both global and local discriminative features. In this method, global features are extracted from whole face images by Fourier transform and local features are extracted from some spatially partitioned image patches by Gabor wavelet transform. After this, multiple classifiers are obtained by applying Fisher Discriminant Analysis on global Fourier features and local patches of Gabor features. All these classifiers are combined to form a hierarchical ensemble by sum rule. We evaluated the proposed method using Face Recognition Grand Challenge (FRGC) experimental protocols and database known as the largest data sets available. Experimental results on FRGC version 2.0 data set have shown that the proposed method achieves a verification rate of 86%, while the best reported was 76%.

This mixed methods study sought to extend what is known about preservice teachers’ [PSTs’] enactment of the core practice of reading and responding to students’ writing by examining their reading of elementary students’ writing at the start and end of writing-focused methods coursework. PSTs’ reading of students’ writing involves analysis of children’s writing strengths and needs and undergirds their teaching of writing, influencing their feedback and instructional decision-making. Participants included 115 elementary PSTs. Their identifications of local and global text features on a beginning- and end-of-semester assessment task were examined. Overall, PSTs showcased differences in their reading of children’s writing at the two time points, displaying a lessened focus on local features (e.g., spelling, punctuation) and a greater focus on global features (e.g., content, structure) at the end of the semester. This trend was especially apparent in their identification of children’s instructional needs. Moreover, PSTs’ reading of children’s informative and persuasive writing tended to be similar to their reading of children’s narrative writing at both time points. The results offer insight into the text features PSTs make available for use in their subsequent responding and suggest the importance of highlighting global text features among children’s instructional needs.