Andrea F. Abate

A haptic-based approach to virtual training for aerospace industry

In the last years, the industrial world has been increasingly adopting computer-aided solutions for design for maintainability and maintenance training tasks with the goal to reduce development costs and to shorten time, and to improve product and service quality. Computer-based training systems created to simulate machine assembly maintenance are normally operated by means of ordinary human-computer interfaces (keyboard, mouse, etc.), but this usually results in systems that are far from the real procedures, and therefore not effective in terms of training. In this study, we show that a better solution may come from the combination of virtual reality techniques and haptic interaction. To this regard, we present the results of a research aimed at testing and evaluating the effectiveness of the haptic feedback for first-person maintenance tasks targeted to the aerospace industry. The proposed system implements an interaction environment in which each of the main maintenance activities can be simulated by the trainee exploiting a hand-based commercial haptic device, operated by means of specific haptic-rendering techniques to provide realistic feedbacks during manipulation. A usability study is included to help assessing the potential of this approach.

Workflow performance evaluation through WPQL

The problem of performance evaluation of business processes supported by Workflow Management Systems is a recent research issue. In this paper, we propose an approach to the performance evaluation of automated business processes based on the measurement language WPQL (Workflow Performance Query Language). The paper first describes the WPQL architecture together with a selection mechanism by means of which the workflow entities to measure are isolated. Then, the main constructs of WPQL for measure definition and measure application are presented and exemplified. Finally, we show a working session of the support tool and discuss some guideline for further research.

IME: an image management environment with content-based access

Abstract The article describes an experimental visual environment to handle digital images by contents. A suitable spatial index is used to organize the images in a spatial access structure for efficient storage and retrieval. An image is indexed according to both the spatial arrangement of its objects and the morphological and geometrical measures of these objects. Therefore, in the database population phase a user identifies the objects that characterize the visual content of each image by a user-friendly interface. In order to let the system retrieve images based on the presence of given patterns, it is necessary to define similarity matching criteria between a query and an image. To efficiently perform such a match, each image is stored together with a collection of metadata that are a very compact representation of the visual contents of the image. These metadata form the index of the image. The system implements a Spatial Access Method based on k-d-trees to achieve a significant speedup over sequential search. We prove the effectiveness and the efficiency of the system by performing standard tests on a database containing a large number of medical images, namely lung CT scans.

Fast 3D face recognition based on normal map

This paper presents a 3D face recognition method aimed to biometric applications. The proposed method compares any two faces represented as 3D polygonal surfaces through their corresponding normal map, a bidimensional array which stores local curvature (mesh normals) as the pixels RGB components of a color image. The recognition approach, based on the computation of a difference map resulting from the comparison of normal maps, is simple yet fast and accurate. A weighting mask, automatically generated for each subject using a set of expression variations, improves the robustness to a broad range of facial expressions. First results show the effectiveness of the method on a database of 3D faces featuring different genders, ages and expressions.

A visual query-by-example image database for chest CT images: potential role as a decision and educational support tool for radiologists.

Primary reading or further evaluation of diagnostic imaging examination often needs a comparison between the actual findings and the relevant prior images of the same patient or similar radiological data found in other patients. This support is of clinical importance and may have significant effects on physicians’ examination reading efficiency, service-quality, and work satisfaction. We developed a visual query-by-example image database for storing and retrieving chest CT images by means of a visual browser Image Management Environment (IME) and tested its retrieval efficiency. The visual browser IME included four fundamental features (segmentation, indexing, quick load and recall, user-friendly interface) in an integrated graphical environment for a user-friendly image database management. The system was tested on a database of 2000 chest CT images, randomly chosen from the digital archives of our institutions. A sample of eight heterogeneous images were used as queries and, for each of them a team of three expert radiologists selected the most similar images from the database (a set of 15 images containing similar abnormalities in the same position of the query). The sensitivity and the positive predictive factor, both averaged over the 8 test queries and 15 answers, were respectively 0.975 and 0.91 The IME system is currently under evaluation at our institutions as an experimental application. We consider it a useful work-in-progress tool for clinical practice facilitating searches for a variety of radiological tasks.

BIRD: Watershed Based IRis Detection for mobile devices ✩

Communications with a central iris database system using common wireless technologies, such as tablets and smartphones, and iris acquisition out of the field are important functionalities and capabilities of a mobile iris identification device. However, when images are acquired by means of mobile devices under uncontrolled acquisition conditions, noisy images are produced and the effectiveness of the iris recognition system is significantly conditioned. This paper proposes a technique based on watershed transform for iris detection in noisy images captured by mobile devices. The method exploits the information related to limbus to segment the periocular region and merges its score with the iris’ one to achieve greater accuracy in the recognition phase.

FACES: 3D FAcial reConstruction from anciEnt Skulls using content based image retrieval

Abstract Powerful techniques for modelling and rendering tridimensional organic shapes, like human body, are today available for applications in many fields such as special effects, ergonomic simulation or medical visualization, just to name a few. These techniques, combined with Content Based Image Retrieval (CBIR), are proving to be very useful also to archaeologists and anthropologists committed to reconstruct the aspect of the inhabitants of historically relevant sites like Pompei. This paper presents an integrated system to provide 3D FAcial reConstruction from anciEnt Skulls (FACES). FACES, starting from radiological analysis of an ancient skull and a database of modern individuals of the same area/gender/age, produces a tridimensional facial model compatible to the anthropological and craniometrical features of the original skull. Finally, we compare FACES peculiarities to the most used facial reconstruction methodologies available today.

Face, Ear and Fingerprint: Designing Multibiometric Architectures

The number of biometrics as well as the number of publications about biometrics have noticeably grown in the last ten years. However, it was not possible yet to identify a bodily or behavioral feature able by itself to satisfy the acceptability and reliability constraints imposed by real applications. In this work we analyze the combination of the three different biometries face, ear and fingerprint using both a new multimodal schema, namely the N-Cross Testing Protocol, and a fast hierarchical architecture. Experimental results in the final part of our work provide a positive feedback.

Face and ear: a bimodal identification system

In this paper, several configurations for a hybrid face/ear recognition system are investigated. The system is based on IFS (Iterated Function Systems) theory that are applied on both face and ear resulting in a bimodal architecture. One advantage is that the information used for the indexing and recognition task of face/ear can be made local, and this makes the method more robust to possible occlusions. The amount of information provided by each component of the face and the ear image has been assessed, first independently and then jointly. At last, results underline that the system significantly outperforms the existing approaches in the state of the art.

Face authentication using speed fractal technique

Abstract In this paper, a new fractal based recognition method, Face Authentication using Speed Fractal Technique (FAST), is presented. The main contribution is the good compromise between memory requirements, execution time and recognition ratio. FAST is based on Iterated Function Systems (IFS) theory, largely studied in still image compression and indexing, but not yet widely used for face recognition. Indeed, Fractals are well known to be invariant to a large set of global transformations. FAST is robust with respect to meaningful variations in facial expression and to the small changes of illumination and pose. Another advantage of the FAST strategy consists in the speed up that it introduces. The typical slowness of fractal image compression is avoided by exploiting only the indexing phase, which requires time O ( D log ( D )), where D is the size of the domain pool. Lastly, the FAST algorithm compares well to a large set of other recognition methods, as underlined in the experimental results.