We present an algorithm for registration between a large-scale point cloud and a close-proximity scanned point cloud, providing a localization solution that is fully independent of prior information about the initial positions of the two point cloud coordinate systems. The algorithm, denoted LORAX, selects super-points-local subsets of points-and describes the geometric structure of each with a low-dimensional descriptor. These descriptors are then used to infer potential matching regions for an efficient coarse registration process, followed by a fine-tuning stage. The set of super-points is selected by covering the point clouds with overlapping spheres, and then filtering out those of low-quality or nonsalient regions. The descriptors are computed using state-of-the-art unsupervised machine learning, utilizing the technology of deep neural network based auto-encoders. Abstract This novel framework provides a strong alternative to the common practice of using manually designed key-point descriptors for coarse point cloud registration. Utilizing super-points instead of key-points allows the available geometrical data to be better exploited to find the correct transformation. Encoding local 3D geometric structures using a deep neural network auto-encoder instead of traditional descriptors continues the trend seen in other computer vision applications and indeed leads to superior results. The algorithm is tested on challenging point cloud registration datasets, and its advantages over previous approaches as well as its robustness to density changes, noise, and missing data are shown.

We present a two-step approach for modeling facial variations and class likelihoods over time. Unlike traditional approaches, we explicitly model the temporal domain that is often available, for example, in consumer photos or surveillance systems. Our combined approach draws upon the concepts of manifold transformation and semi-supervised graph-based propagation to simultaneously recognize faces across entire photo collections. Experiments on two datasets demonstrate improved face recognition accuracy.

Video extrapolation is the task of extending a video beyond its original field of view. Extrapolating video in a manner that is consistent with the original video and visually pleasing is difficult. In this work we aim at very wide video extrapolation which increases the complexity of the task. Some video extrapolation methods simplify the task by using a rough color extrapolation. A recent approach focuses on artifact avoidance and run time reduction using foveated video extrapolation, but fails to preserve the structure of the scene. This paper introduces a multi-scale method which combines a coarse to fine approach with foveated video extrapolation. Foveated video extrapolation reduces the effective number of pixels that need to be extrapolated, making the extrapolation less time consuming and less prone to artifacts. The coarse to fine approach better preserves the structure of the scene while preserving finer details near the domain of the input video. The combined method gains improvement both visually and in processing time.

Computer vision attention processes assign variable-hypothesized importance to different parts of the visual input and direct the allocation of computational resources. This nonuniform allocation might help accelerate the image analysis process. This paper proposes a new bottom-up attention mechanism. Rather than taking the traditional approach, which tries to model human attention, we propose a validated stochastic model to estimate the probability that an image part is of interest. We refer to this probability as saliency and thus specify saliency in a mathematically well-defined sense. The model quantifies several intuitive observations, such as the greater likelihood of correspondence between visually similar image regions and the likelihood that only a few of interesting objects will be present in the scene. The latter observation, which implies that such objects are (relaxed) global exceptions, replaces the traditional preference for local contrast. The algorithm starts with a rough preattentive segmentation and then uses a graphical model approximation to efficiently reveal which segments are more likely to be of interest. Experiments on natural scenes containing a variety of objects demonstrate the proposed method and show its advantages over previous approaches.

A visual search is required when applying a recognition process on a scene containing multiple objects. In such cases, we would like to avoid an exhaustive sequential search. This work proposes a dynamic visual search framework based mainly on innerscene similarity. Given a number of candidates (e.g., subimages), we hypothesize is that more visually similar candidates are more likely to have the same identity. We use this assumption for determining the order of attention. Both deterministic and stochastic approaches, relying on this hypothesis, are considered. Under the deterministic approach, we suggest a measure similar to Kolmogorov's epsilon-covering that quantifies the difficulty of a search task. We show that this measure bounds the performance of all search algorithms and suggest a simple algorithm that meets this bound. Under the stochastic approach, we model the identity of the candidates as a set of correlated random variables and derive a search procedure based on linear estimation. Several experiments are presented in which the statistical characteristics, search algorithm, and bound are evaluated and verified.