Current trends in free form editing motivate the development of a novel editing paradigm for CAD models beyond traditional CAD editing of mechanical parts. To this end, we need robust and efficient 3D mesh deformation techniques such as 3D structural morphing. In this paper, we present a feature-based approach to 3D morphing of arbitrary genus-0 polyhedral objects that is appropriate for CAD ed...

Motivated by the intuition that critical step of localizing a 2D image in corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose first feature-based dense framework for addressing image-to-point registration problem, dubbed CorrI2P, which consists three modules, i.e., feature embedding, symmetric overlapping region detection, and pose estimation through estab...

background and objective: one way to promote education quality is to evaluate faculty activities by students, departmental heads, and the dean. the degree of correspondence between students' and departmental heads' evaluation shows how honest they are in their assessment. the current paper intends to find the truth.     materials and methods : questionnaires were used to conduct the s...

In this paper, we present a robust algorithm to capture rapid human motion with self-occlusion. Instead of predicting the position of each human feature, the interest-region of full body is estimated. Then candidate features are extracted through the overall search in the interest-region. To establish the correspondence between candidate features and actual features, an adaptive Bayes classifie...

We present a method for estimating classi cation performance of a model-based synthetic aperture radar (SAR) automatic target recognition (ATR) system. Target classi cation is performed by comparing a feature vector extracted from a measured SAR image chip with a feature vector predicted from a hypothesized target class and pose. The feature vectors are matched using a Bayes likelihood metric t...

Finding visual correspondence between local features is key to many computer vision problems. While defining features with larger contextual scales usually implies greater discriminativeness, it could also lead to less spatial accuracy of the features. We propose AutoScaler, a scale-attention network to explicitly optimize this trade-off in visual correspondence tasks. Our architecture consists...

To maintain stable object representations as our eyes or the objects themselves move, the visual system must determine how newly sampled information relates to existing object representations. To solve this correspondence problem, the visual system uses not only spatiotemporal information (e.g., the spatial and temporal proximity between elements), but also feature information (e.g., the simila...

Deep Convolutional Neural Networks (CNNs) have gained great success in image classification and object detection. In these fields, the outputs of all layers of CNNs are usually considered as a high dimensional feature vector extracted from an input image and the correspondence between finer level feature vectors and concepts that the input image contains is all-important. However, fewer studies...

In this thesis graph spectral methods and kernel methods are combined together for the tasks of rigid and non-rigid feature correspondence matching and consistent labelling. The thesis is divided into five chapters. In Chapter 1 we give a brief introduction and an outline of the thesis. In Chapter 2 we review the main techniques in the literature which are related to the developments in this th...