Research /

"Everything should be made as simple as possible, but not simpler."

Albert Einstein

A Unified Model for Near and Remote Sensing

We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead and ground-level images in an end-to-end trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map.

Scott Workman, Menghua Zhai, David J. Crandall, Nathan Jacobs

ICCV 2017

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Understanding and Mapping Natural Beauty

We demonstrate that quantitative measures of scenicness can benefit semantic image understanding, content-aware image processing, and a novel application of cross-view mapping, where the sparsity of ground-level images can be addressed by incorporating unlabeled overhead images in the training and prediction steps.

Scott Workman, Richard Souvenir, Nathan Jacobs

ICCV 2017

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Predicting Ground-Level Scene Layout from Aerial Imagery

We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (noisy) semantic features automatically extracted from co-located ground imagery. Our network architecture takes an aerial image as input, extracts features using a convolutional neural network, and then applies an adaptive transformation to map these features into the ground-level perspective.

Menghua Zhai, Zachary Bessinger, Scott Workman, Nathan Jacobs

CVPR 2017

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Horizon Lines in the Wild

We introduce a large, realistic evaluation dataset, Horizon Lines in the Wild (HLW), containing natural images with labeled horizon lines. Using this dataset, we investigate the application of convolutional neural networks for directly estimating the horizon line, without requiring any explicit geometric constraints or other special cues.

Scott Workman, Menghua Zhai, Nathan Jacobs

BMVC 2016

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Cloudmaps from Static Ground-View Video

Cloud shadows dramatically affect the appearance of outdoor scenes. We describe three approaches that use video of cloud shadows to estimate a cloudmap, a spatio-temporal function that represents the clouds passing over the scene.

Nathan Jacobs, Scott Workman, Richard Souvenir

IVC 2016

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Detecting Vanishing Points using Global Image Context in a Non-Manhattan World

We introduce a novel vanishing point detection algorithm that obtains state-of-the-art performance on three benchmark datasets. The main innovation in our method is the use of global image context to sample possible horizon lines, followed by a novel discrete-continuous procedure to score each horizon line by choosing the optimal vanishing points for the line.

Menghua Zhai, Scott Workman, Nathan Jacobs

CVPR 2016

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Analyzing Human Appearance as a Cue for Dating Images

Given an image, we propose to use the appearance of people in the scene to estimate when the picture was taken.

Tawfiq Salem, Scott Workman, Menghua Zhai, Nathan Jacobs

WACV 2016

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A Fast Method for Estimating Transient Scene Attributes

We describe a fast method for estimating transient scene attributes for a single image. Using our method, we explore applications to webcam imagery, including: 1) supporting automatic browsing and querying of large archives of webcam images, 2) constructing maps of transient attributes from webcam imagery, and 3) geolocalizing webcams.

Ryan Baltenberger, Menghua Zhai, Connor Greenwell, Scott Workman, Nathan Jacobs

WACV 2016

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Sky Segmentation in the Wild: An Empirical Study

This paper presents the results of a large-scale empirical evaluation of the performance of three state-of-the-art approaches on a new dataset, which consists of roughly 100k images captured “in the wild”.

Radu P Mihail, Scott Workman, Zach Bessinger, Nathan Jacobs

WACV 2016

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Wide-Area Image Geolocalization with Aerial Reference Imagery

We learn a joint feature representation for aerial and ground-level imagery and apply this representation to the problem of cross-view image geolocalization.

Scott Workman, Richard Souvenir, Nathan Jacobs

ICCV 2015

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DeepFocal: A Method for Direct Focal Length Estimation

We introduce a method for directly estimating the focal length of a camera from a single image using a deep convolutional neural network.

Scott Workman, Connor Greenwell, Menghua Zhai, Ryan Baltenberger, Nathan Jacobs

ICIP 2015

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Face2GPS: Estimating Geographic Location from Facial Features

We propose a data-driven approach to solving the problem of image geo-localization using an image of a face.

Mohammad T. Islam, Scott Workman, Nathan Jacobs

ICIP 2015

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On the Location Dependence of Convolutional Neural Network Features

We show that features extracted from deep convolutional neural networks are useful for problems in geospatial image analysis.

Scott Workman, Nathan Jacobs

EARTHVISION 2015

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Scene Shape Estimation from Multiple Partly Cloudy Days

We introduce methods for estimating scene geometry in a distributed camera network using videos from partly cloudy days.

Scott Workman, Richard Souvenir, Nathan Jacobs

CVIU 2015

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A Pot of Gold: Rainbows as a Calibration Cue

We derive constraints and demonstrate methods that allow rainbows to be used for camera geolocalization, calibration and rainbow-specific image editing.

Scott Workman, R. P. Mihail, Nathan Jacobs

ECCV 2014

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Exploring the Geo-Dependence of Human Face Appearance

Our work explores the little-studied dependence of facial appearance on geographic location. To support this effort, we constructed GeoFaces, a large dataset of geotagged face images.

Mohammad T. Islam, Scott Workman, Hui Wu, Richard Souvenir, Nathan Jacobs

WACV 2014

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Scene Geometry from Several Partly Cloudy Days

We describe new methods for estimating the geometry of an outdoor scene using video from multiple partly cloudy days.

Nathan Jacobs, Scott Workman, Richard Souvenir

ICDSC 2013

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Cloud Motion as a Calibration Cue

We propose cloud motion as a natural scene cue that enables geometric calibration of static outdoor cameras.

Nathan Jacobs, Mohammad T. Islam, Scott Workman

CVPR 2013

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