Retrieval by content of 3-D models is becoming more and more important due to the advancements in 3-D hardware and software technologies for acquisition, authoring and display of 3-D objects, their ever-increasing availability at affordable costs, and the establishment of open standards for 3-D data interchange. In this paper, we present a new method, referred to as Spin Image Signatures, that develops on the original spin images approach, with adaptations to support effective retrieval by content. According to the method proposed, a set of spin images is derived for each model, to obtain a view-independent description of its 3-D shape and a signature is evaluated for each spin image in the set. Clustering is hence performed on the set of Spin Image Signatures to obtain a compact representation. Experimental results are presented, showing the effectiveness of the Spin Image Signatures method for retrieval, also in comparison with other methods, and its sensitivity to model deformations.

Along with images and videos, 3D models have recently gained increasing attention for a number of reasons: advancements in 3D hardware and software technologies; their ever decreasing prices and increasing availability; affordable 3D authoring tools; the establishment of open standards for 3D data interchange. The ever increasing availability of 3D models demands tools to support their effective and efficient management. Among these tools, those enabling content-based retrieval play a key role. We present a novel approach to 3D content-based retrieval that is based on spin images. Spin images are used to derive a view-independent description of both database and query objects: a set of spin images is first created for each object; then, a descriptor is evaluated for each spin image in the set; clustering is performed on the set of image-based descriptors of each object to achieve a compact representation of the object, thus allowing for efficient indexing and matching. Experimental results are presented for a test database of about 300 models. These results indicate that spin images can be successfully exploited for content-based retrieval of 3D objects

Broadcasters are demonstrating interest in systems that ease the process of annotation the huge amount of live and archived video materials. Exploitation of such assets is considered a key method for the improvement of production quality, and sport videos are one of the most marketable assets. In particular, in Europe, soccer is one of the most relevant sport types. This paper deals with detection and recognition of soccer highlights, using an approach based on temporal logic models.

Along with images and videos, 3D models have recently gained increasing attention for a number of reasons: advancements in 3D hardware and software technologies, their ever decreasing prices and increasing availability, affordable 3D authoring tools, and the establishment of open standards for 3D data interchange. In this paper we address the problem of content-based retrieval of 3D models. The solution we present here relies on the description of each model by means a curvature map: after an initial pre-processing of the model, differential properties of points on the surface of the 3D object are evaluated; the model surface is then warped into an ellipsoid, and is mapped onto a 2D image retaining curvature information of the original model. Matching is performed by comparing the 2D map of the query against the 2D maps of the database models. This method has been implemented in a prototype system supporting retrieval by content of 3D objects through a Web interface.

In this paper we report on our experience in the detection and recognition of soccer highlights in videos using hidden Markov models. A first approach relies on camera motion only, whereas a second one also includes information regarding the location of players on the playing field. While the former approach requires less information, the latter has proven to be more precise. Our experimental evaluation yields interesting results.

Image databases are widely exploited in a number of different contexts, ranging from history of art, through medicine, to education. Existing querying paradigms are based either on the usage of textual strings, for high-level semantic queries or on 2D visual examples for the expression of perceptual queries. Semantic queries require manual annotation of the database images. Instead, perceptual queries only require that image analysis is performed on the database images in order to extract salient perceptual features that are matched with those of the example. However, usage of 2D examples is generally inadequate as effective authoring of query images, attaining a realistic reproduction of complex scenes, needs manual editing and sketching ability. Investigation of new querying paradigms is therefore an important-yet still marginally investigated-factor for the success of content-based image retrieval. In this paper, a novel querying paradigm is presented which is based on usage of 3D interfaces exploiting navigation and editing of 3D virtual environments. Query images are obtained by taking a snapshot of the framed environment and by using the snapshot as an example to retrieve similar database images. A comparative analysis is carried out between the usage of 3D and 2D interfaces and their related query paradigms. This analysis develops on a user test on retrieval efficiency and effectiveness, as well as on an evaluation of users' satisfaction.

The authors discuss the method to classify raw material sports videos for broadcasting. Because the raw material sports videos sometimes do not get edited, one cannot use the knowledge on edited videos. The authors use the color and edge features and evaluate whether one can classify the sports videos with those features. Also introduced is the "player" and "audience" class - apart from each sport class - to improve the classification results.

Systems for content based image retrieval typically support access to database images through the query-by-example paradigm. This includes query-by-image and query-by-sketch. Since query-by-sketch can be difficult in some cases-lack of sketching abilities, difficulty to detect distinguishing image features-generally querying is performed through the query-by-image paradigm. A limiting factor of this paradigm is that a single sample image rarely includes all and only the characterizing elements the user is looking for. In this paper a system is presented that supports query-by-image using multiple image examples. Examples can be positive and negative and can be edited in order to disregard irrelevant image features.