Relationship Between Two Generalized Images for Discrete and Differential Camera Motions

The recent popularity of catadioptic and multi-camera imaging systems indicates a need to create formal models for general, non-perspective camera geometries. Development of algorithmic tools for interpreting images from a generalized camera model will lead to a better understanding of how to design camera systems for particular tasks. Here we define the corollary to epi-polar constraints for standard cameras - the relationship between two images of a scene taken by generalized cameras from viewpoints related by discrete or differential motions

Embedding Images in non-Flat Spaces

Multi-dimensional scaling is an analysis tool which transforms pairwise distances between points to an embedding of points in space which are consistent with those distances. Two recent techniques in statistical patter recognition, locally linear embedding (LLE) and Isomap, give a mechanism for finding the structure underlying point sets for which comparisons or distances are only meaningful between nearby points. We give a direct method to extend the embedding algorithm to new topologies, finding the optimal embedding of points whose geodesic distance on a surface mathes the given pairwise distance measurements. Surfaces considered include spheres, cylinders, tori, and their higher dimensional corollaries. We give examples of sets of images that come from spaces with these topologies. Using these embedding techniques, we compute pose estimates for thousands of images of an object without knowing the object model or finding corresponding points

Extrinsic Calibration of a Camera and Laser Range Finder

We describes theoretical and experimental results for the extrinsic calibration of sensor platform consisting of a camera and a laser range finder. The proposed technique requires the system to observe a planar pattern in several poses, and the constraints are based upon data captured simultaneously from the camera and the laser range finder. The planar pattern surface and the laser scanline on the planar pattern are related, so these data constrain the relative position and orientation of the camera and laser range finder. The calibration procedure starts with a closed-from solution, which provides initial conditions for a subsequent nonlinear refinement. We present the results from both computer simulated data and an implementation on a B21rT M Mobile Robot from iRobot Corporation, using a Sony firewire digital camera and SICK PLS laser scanner

Extrinsic Auto-calibration of a Camera and Laser Range Finder

This paper describes theoretical and experimental results for the auto-calibration of sensor platform consisting of a camera and a laser range finder. Real-world use of autonomous sensor platforms often requires the recalibration of sensors without an explicit calibration object. The constraints are based upon data captured simultaneously from the camera and the laser range finder while the sensor plat-form undergoes an arbitrary motion. The rigid motions of both sensors are related, so these data constrain the relative position and orientation of the camera and laser range finder. We introduce the mathematical constraints for auto-calibration techniques based upon both discrete and differential motions, and present simulated experimental results, and results from a implementation on a B21rT M Mobile Robot from iRobot Corporation. This framework could also encompass extrinsic calibration with GPS, inertial, infrared, and ultrasonic sensors

Hotels-50K: A Global Hotel Recognition Dataset

Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain

Localized and Configurable Topology Control in Lossy Wireless Sensor Networks

Recent empirical studies revealed that multi-hop wireless networks like wireless sensor networks and 802.11 mesh networks are inherently lossy. This finding introduces important new challenges for topology control. Existing topology control schemes often aim at maintaining network connectivity that cannot guarantee satisfactory path quality and communication performance when underlying links are lossy. In this paper, we present a localized algorithm, called Configurable Topology Control (CTC), that can configure a network topology to different provable quality levels (quantified by worst-case dilation bounds in terms of expected total number of transmisssions) required by applications. Each node running CTC computes its transmission power solely based on the link quality information collected within its local neighborhood and does not assume that the neighbor locations or communication ranges are known. Our simulations based on a realistic radio model of Mica2 motes show that CTC yields configurable communication performance and outperforms existing topology control algorithms that do not account for lossy links