We describe the Photobook system, which is a set of interactive tools for browsing and searching images and image sequences. These query tools differ from those used in standard image databases in that they make direct use of the image content rather than relying on text annotations. Direct search on image content is made possible by use of semantics-preserving image compression, which reduces images to a small set of perceptually-significant coefficients. We describe three types of Photobook descriptions in detail: one that allows search based on appearance, one that uses 2-D shape, and a third that allows search based on textural properties. These image content descriptions can be combined with each other and with textbased descriptions to provide a sophisticated browsing and search capability. In this paper we demonstrate Photobook on databases containing images of people, video keyframes, hand tools, fish, texture swatches, and 3-D medical data.

In the QBIC (Query By Image Content) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical ("Give me other images that contain a tumor with a texture like this one"), photo-journalism ("Give me images that have blue at the top and red at the bottom"), and many others in art, fashion, cataloging, retailing, and industry. We describe a set of novel features and similarity measures allowing query by color, texture, and shape of image object. We demonstrate the effectiveness of the QBIC system with normalized precision and recall experiments on test databases containing over 1000 images and 1000 objects populated from commercially available photo clip art images, and of images of airplane silhouettes. We also consider the efficient indexing of these features, specifically addre...

We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.

In this work, we report on a prototype of a clinical radiograph image database to be indexed by image content. The underlying content-based search engine is based on the modal shape description method for characterizing the shape of a 2-D image region [24]. Using a similarity metric defined in a modal vector space, our system successfully classified radiographic images according to the dental pathologies they depicted. This successful classification demonstrates that the proposed similarity metric effectively captures clinical similarity between images in the database. The prototype is implemented in a Web-based environment, allowing remote users in the field to search a central repository of images. Examples of classification performance and typical queries are provided.

. Object recognition is the classi#cation of objects into one of many a priori known object classes. In addition, it mayinvolve the estimation of the pose of the object and#or the track of the object in a sequence of images. Bayesian statistical pattern recognition, neural networks and rule based systems have been used to address the object recognition problem. In the case of statistical pattern recognition it is assumed that the a priori probability density functions are known or that they can be estimated from the given samples. For neural networks the samples may be used to train a network and the coe#cients for the network function may be estimated. Whereas, in the case of the rule based system, rules may be given by an expert or they may be estimated from the samples. However, Bayesian framework provides a methodology for the estimation of error bounds on the performance of the recognition system. The paper discusses the Bayesian paradigm and contrasts its ability to...

We describe the Photobook system, which is a set of interactive tools for browsing and searching images and image sequences. These query tools differ from those used in standard image databases in that they make direct use of the image content rather than relying on text annotations. Direct search on image content is made possible by use of semantics-preserving image compression, which reduces images to a small set of perceptually-significant coefficients. We describe three types of Photobook descriptions in detail: one that allows search based on appearance, one that uses 2-D shape, and a third that allows search based on textural properties. These image content descriptions can be combined with each other and with textbased descriptions to provide a sophisticated browsing and search capability. In this paper we demonstrate Photobook on databases containing images of people, video keyframes, hand tools, fish, texture swatches, and 3-D medical data. 1 Introduction: The Problem Digita...

Current methods for shape- based image retrieval are restricted to images containing 2-0 objects. We pro-pose a novel approach to querying images conta,ining 3-0 objects, based on a view-based encoding of a finite domain of 3-0 parts used to model the 3-0 objects ap-pearing in,images. To build a quevy, the user manually identifies the salient parts of the object in a query im-age. The extracted views of these parts are then vsed to hypothesize the 3-0 identities of the parts which, in turn, are used to hypothesize other possible views of the parts. The resulting set of part views, along with their spatial relations (constraints) in the query im-age, form a composite q%ery that is passed to the im-age database. Images containing objects,with the same parts (in any viezu),with similar spatial relations are returned to the user. The resulting viewpoint invariant indexing technique does not require training the system for all possible,views of each object. Rather, the sys-tem requires only kno,wledge of the possible views for a finite vocab,ulary of 3-0 parts from which the objects are constructed.

We describe our work on shape-based image database search using the technique of modal matching. Modal matching employs a deformable shape decomposition that allows users to select example objects and have the computer efficiently sort the set of objects based on the similarity of their shape. Shapes are compared in terms of the types of nonrigid deformations (differences) that relate them. The modal decomposition provides deformation "control knobs" for flexible matching and thus allows for selecting weighted subsets of shape parameters that are deemed significant for a particular category or context. We demonstrate the utility of this approach for shape comparison in 2-D image databases; however, the general formulation is applicable to signals of any dimensionality.

Abstract—A vision-based system for detecting and classifying moving obstacles in a cluttered environment using a single camera mounted on a small unmanned aerial vehicle is presented. Feature correspondences between successive image frames are found using the SIFT algorithm, and a model of the background motion is generated by Random Sample Consensus (RANSAC). The transformed frames are then differenced to produce a clean image of any objects moving relative to the background. These object silhouettes are then classified as airplane vs. non-airplane in order to reject false positives and to improve obstacle state-estimation information.

(UAVs) are currently banned from the national airspace due to safety concerns despite many potential uses. Key area for improvement: See and Avoid capability. www.xconomy.com