Edoardo Ardizzone

Automatic Video Database Indexing and Retrieval

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. To this aim image and image sequence contents must be described and adequately coded. In this paper we describe a system allowing content-based annotation and querying in video databases. No user action is required during the database population step. The 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 color, texture and motion. Queries based on one or more features are possible. Very interesting results obtained during the severe tests the system was subjected to are reported and discussed.

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.

Copy–Move Forgery Detection by Matching Triangles of Keypoints

Copy-move forgery is one of the most common types of tampering for digital images. Detection methods generally use block-matching approaches, which first divide the image into overlapping blocks and then extract and compare features to find similar ones, or point-based approaches, in which relevant keypoints are extracted and matched to each other to find similar areas. In this paper, we present a very novel hybrid approach, which compares triangles rather than blocks, or single points. Interest points are extracted from the image, and objects are modeled as a set of connected triangles built onto these points. Triangles are matched according to their shapes (inner angles), their content (color information), and the local feature vectors extracted onto the vertices of the triangles. Our methods are designed to be robust to geometric transformations. Results are compared with a state-of-the-art block matching method and a point-based method. Furthermore, our data set is available for use by academic researchers.

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.

Detecting multiple copies in tampered images

Copy-move forgeries are parts of the image that are duplicated elsewhere into the same image, often after being modified by geometrical transformations. In this paper we present a method to detect these image alterations, using a SIFT-based approach. First we describe a state of the art SIFT-point matching method, which inspired our algorithm, then we compare it with our SIFT-based approach, which consists of three parts: keypoint clustering, cluster matching, and texture analysis. The goal is to find copies of the same object, i.e. clusters of points, rather than points that match. Cluster matching proves to give better results than single point matching, since it returns a complete and coherent comparison between copied objects. At last, textures of matching areas are analyzed and compared to validate results and to eliminate false positives.

Copy-move forgery detection via texture description

Copy-move forgery is one of the most common type of tampering in digital images. Copy-moves are parts of the image that are copied and pasted onto another part of the same image. Detection methods in general use block-matching methods, which first divide the image into overlapping blocks and then extract features from each block, assuming similar blocks will yield similar features. In this paper we present a block-based approach which exploits texture as feature to be extracted from blocks. Our goal is to study if texture is well suited for the specific application, and to compare performance of several texture descriptors. Tests have been made on both uncompressed and JPEG compressed images.

Automatic Volumetric Liver Segmentation Using Texture Based Region Growing

In this paper an automatic texture based volumetric region growing method for liver segmentation is proposed. 3D seeded region growing is based on texture features with the automatic selection of the seed voxel inside the liver organ and the automatic threshold value computation for the region growing stop condition. Co-occurrence 3D texture features are extracted from CT abdominal volumes and the seeded region growing algorithm is based on statistics in the features space. Each CT volume is composed by 230 slices, having 512 x 512 pixels as spatial resolution, and 12-bit gray level resolution. In this initial feasible study, 5 healthy volunteer acquisitions has been used. Tests have been performed on both basal phase and arterial phase images. Segmentation result shows the effectiveness of the proposed method: liver organ is correctly recognized and segmented, leaving out liver vessels form the segmented area and overcoming the “organ-splitting” problem. The goodness of the proposed method has been confirmed by manual liver segmentation results, having analogous and super-imposable behavior.

Experiences with cicerobot, a museum guide cognitive robot

The paper describes CiceRobot, a robot based on a cognitive architecture for robot vision and action. The aim of the architecture is to integrate visual perception and actions with knowledge representation, in order to let the robot to generate a deep inner understanding of its environment. The principled integration of perception, action and of symbolic knowledge is based on the introduction of an intermediate representation based on Gardenfors conceptual spaces. The architecture has been tested on a RWI B21 autonomous robot on tasks related with guided tours in the Archaeological Museum of Agrigento. Experimental results are presented.

Geometric and conceptual knowledge representation within a generative model of visual perception

A representation scheme of knowledge at both the geometric and conceptual levels is offered which extends a generative theory of visual perception. According to this theory, the perception process proceeds through different scene representations at various levels of abstraction. The geometric domain is modeled following the CSG (constructive solid geometry) approach, taking advantage of the geometric modelling scheme proposed by A. Pentland, based on superquadrics as representation primitives. Recursive Boolean combinations and deformations are considered in order to enlarge the scope of the representation scheme and to allow for the construction of real-world scenes. In the conceptual domain, objects and relationships are represented using KL-ONE, a frame-based knowledge representation formalism which allows hierarchical and structural descriptions. Questions arising from the integration of logical and analogical knowledge representation are also faced; in the end likeness and approximation relationships between objects and prototypical conceptual models for classification purposes are investigated within the framework of fuzzy set theory.