M. La Cascia

3D stereoscopic image pairs by depth-map generation

This work presents a new unsupervised technique aimed to generate stereoscopic views estimating depth information from a single input image. Using a single input image, vanishing lines/points are extracted using a few heuristics to generate an approximated depth map. The depth map is then used to generate stereo pairs. The overall method is well suited for real time application and works also on CFA (colour filtering array) data acquired by consumer imaging devices. Experimental results on a large dataset are reported.

JACOB: just a content-based query system for video databases

The increasing development of advanced multimedia applications requires new technologies for organizing and retrieving by content databases of still digital images or digital video sequences. The authors describe JACOB, a prototypal system allowing content-based browsing and querying in video databases. The JACOB system automatically splits a video into a sequence of shots, extracts a few representative frames (said r-frames) from each shot and computes r-frame descriptors based on features like color and texture. No user action is required during the database population step. Queries exploit this image content description and may be direct or by example.

Video indexing using optical flow field

The increasing development of advanced multimedia applications requires new technologies for organizing and retrieving by content databases of digital video. Several content based features (color, texture, motion, etc.) are needed to perform a reliable content based retrieval. We present a method for automatic motion based video indexing and retrieval. A prototypal system has been developed to prove the validity of our approach. Our system automatically splits a video into a sequence of shots, extracts a few representative frames (said r-frames) from each shot and computes some motion based features related to the optical flow field. Motion based queries are then performed either in a qualitative or quantitative way. The results obtained with our system proved that motion based query can play a central role in content based video retrieval.

Motion and color-based video indexing and retrieval

In this paper we present a method for automatic motion and color based video indexing and retrieval. Our system automatically splits a video into a sequence of shots and extracts a few representative frames (r-frames) from each shot. For each r-frame we compute the optical flow field; motion features are then derived from the flow field. Color features are related to the three-dimensional RGB color histogram. Queries (direct or by example) are based on these features. Obtained results proved that motion and color based querying can play a central role in content based video retrieval.

Content-based indexing of image and video databases by global and shape features

Indexing and retrieval methods based on the image content are required to effectively use information from the large repositories of digital images and videos currently available. Both global (colour, texture, motion, etc.) and local (object shape, etc.) features are needed to perform a reliable content based retrieval. We present a method for automatic extraction of global image features, like colour and motion parameters, and their use for data restriction in video database querying. Further retrieval is therefore accomplished, in a restricted set of images, by shape feature (skeleton, local symmetry moments, correlation, etc.) local search. The proposed indexing methodology has been developed and tested inside JACOB, a prototypal system for content-based video database querying.

Restoration of out-of-focus images based on circle of confusion estimate

In this paper a new method for a fast out-of-focus blur estimation and restoration is proposed. It is suitable for CFA (Color Filter Array) images acquired by typical CCD/CMOS sensor. The method is based on the analysis of a single image and consists of two steps: 1) out-of-focus blur estimation via Bayer pattern analysis; 2) image restoration. Blur estimation is based on a block-wise edge detection technique. This edge detection is carried out on the green pixels of the CFA sensor image also called Bayer pattern. Once the blur level has been estimated the image is restored through the application of a new inverse filtering technique. This algorithm gives sharp images reducing ringing and crisping artifact, involving wider region of frequency. Experimental results show the effectiveness of the method, both in subjective and numerical way, by comparison with other techniques found in literature.

Real-Time Object Detection in Embedded Video Surveillance Systems

In this paper we report a new method to detect both moving objects and new stationary objects in video sequences. On the basis of temporal consideration we classify pixels into three classes: background, midground and foreground to distinguish between long-term, medium-term and short term changes. The algorithm has been implemented on a hardware platform with limited resources and it could be used in a wider system like a wireless sensor networks. Particular care has been put in realizing the algorithm so that the limited available resources are used in an efficient way. Experiments have been conducted on publicly available datasets and performance measures are reported.

Mean shift clustering for personal photo album organization

In this paper we propose a probabilistic approach for the automatic organization of pictures in personal photo album. Images are analyzed in term of faces and low-level visual features of the background. The description of the background is based on RGB color histogram and on Gabor filter energy accounting for texture information. The face descriptor is obtained by projection of detected and rectified faces on a common low dimensional eigenspace. Vectors representing faces and background are clustered in an unsupervised fashion exploiting a mean shift clustering technique. We observed that, given the peculiarity of the domain of personal photo libraries where most of the pictures contain faces of a relatively small number of different individuals, clusters tend to be not only visually but also semantically significant. Experimental results are reported.

Multi-modal non-rigid registration of medical images based on mutual information maximization

In this paper, a new multi-modal non-rigid registration technique for medical images is presented. Firstly, the registration problem is outlined and some of the most common approaches reported, then, the proposed algorithm is presented. The proposed technique is based on mutual information maximization and computes a deformation field through a suitable globally smoothed affine piecewise transformation. The algorithm has been conceived with particular attention to computational load and accuracy of results. Experimental results involving intra-patient, inter-patients and atlas images on brain CT and MR (T1, T2 and PD modalities) are reported.

A Data Association Algorithm for People Re-identification in Photo Sequences

In this paper, a new system is presented to support the user in the face annotation task. Every time a photo sequence becomes available, the system analyses it to detect and cluster faces in set corresponding to the same person. We propose to model the problem of people re-identification in photos as a data association problem. In this way, the system takes advantage from the assumption that each person can appear at most once in each photo. We propose a fully automated method for grouping facial images, the method does not require any initialization neither a priori knowledge of the number of persons that are in the photo sequence. We compare the results obtained with our method and with standard clustering methods on three personal collections and on a publicly available dataset.