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Datasets

We collected about 700K images in 70K street segments from Google Street View for pavement condition assessment. The images are from NYC and the condition annotation is from NYC Open Data website.

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We collected around 5 thousand images containing shadows from a wide variety of scenes and photo types. Annotations in the form of shadow binary masks are provided along with the actual images. The shadow label annotations for the 4K images in the training set are the result of applying our proposed label recovery method to reduce label noise. Whereas, the testing images were carefully annotated manually to produce precise shadow masks.

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Eye movements and image descriptions were collected on 1,000 images from the PASCAL VOC dataset and 104 images from the SUN09 dataset (183.2MB). It also includes 20 object detectors for the PASCAL and 22 object detectors for the SUN09.

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Eight types of two-person interactions were collected using the Microsoft Kinect sensor (3.3GB). We collect eight interactions: approaching, departing, pushing, kicking, punching, exchanging objects, hugging, and shaking hands from seven participants and 21 pairs of two-actor sets. Each frame contains color image, depth map, and 3-dimensional coordinates of 15 joints from each person.

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90 images from the SUN09 Dataset. Object segmentations by human subjects for all 90 images are provided as part of SUN09. Clutter rankings done by 15 human subjects are provided (21.5MB).

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Codes

Higher-order Surface Matching and Registration

Topology Cuts for Image Segmentation

Computer Vision Lab - Documents