Abstract: Systems and methods to predict bidding behavior are described. The system identifies a listing that includes listing information that describes an item that is being auctioned on a network-based marketplace. The system further identifies bid classification information based on the number of bids received for the item. Finally, the system predicts whether no more bids are expected to be received for the item based on the classification information.

Abstract: A system receives images of objects. The system identifies a category for each of the objects, and extracts features from the images. The features relate to a quality of the image. The features of the images are stored in a database according to the category of each object, such that each set of features is associated with its corresponding image. The system displays the images on a network-based publication system, and receives data relating to the displayed images. The data is analyzed, and the images are ranked as a function of the analysis. The system redisplays the images on the network-based publication system as a function of the ranking of the images.

Abstract: Systems and methods to predict bidding behaviour are described. The system identifies a listing that includes listing information that describes an item that is being auctioned on a network-based marketplace. The system further identifies bid classification information based on the number of bids received for the item. Finally, the system predicts whether no more bids are expected to be received for the item based on the classification information.

Abstract: A method of registering a document comprises, with a processor (150), defining (block 505) a plurality of clusters in an image of a template document (300) by assigning each of a number of feature points of an image of a template document to a cluster with the closest mean, with the processor (150), refining (block 510) a correspondence set of the feature points between the image of the template document (300) and the image of the target document (400) using a histogram of Euclidian distances, and with the processor (150), eliminating (block 515) outliers within a correspondence set of the feature points between the image of the template document (300) and an image of a target document (400) by generating a hypothesis and evaluating the hypothesis a number of iterations, in which the image of the target document (400) is captured by an imaging device (110) from a physical document.

Abstract: A system receives images of objects. The system identifies a category for each of the objects, and extracts features from the images. The features relate to a quality of the image. The features of the images are stored in a database according to the category of each object, such that each set of features is associated with its corresponding image. The system displays the images on a network-based publication system, and receives data relating to the displayed images. The data is analyzed, and the images are ranked as a function of the analysis. The system redisplays the images on the network-based publication system as a function of the ranking of the images.

Abstract: A method of registering a document comprises, with a processor (150), defining (block 505) a plurality of clusters in an image of a template document (300) by assigning each of a number of feature points of an image of a template document to a cluster with the closest mean, with the processor (150), refining (block 510) a correspondence set of the feature points between the image of the template document (300) and the image of the target document (400) using a histogram of Euclidian distances, and with the processor (150), eliminating (block 515) outliers within a correspondence set of the feature points between the image of the template document (300) and an image of a target document (400) by generating a hypothesis and evaluating the hypothesis a number of iterations, in which the image of the target document (400) is captured by an imaging device (110) from a physical document.