Publications

The new technologies characterizing the Internet of Things (IoT) allow realizing real smart environments able to provide advanced services to the users. Recently, these smart environments are also being exploited to renovate the users' interest on the cultural heritage, by guaranteeing real interactive cultural experiences. In this paper, we design and validate an indoor location-aware architecture able to enhance the user experience in a museum. In particular, the proposed system relies on a wearable device that combines image recognition and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. The localization information is obtained by a Bluetooth low energy (BLE) infrastructure installed in the museum. Moreover, the system interacts with the Cloud to store multimedia contents produced by the user and to share environment-generated events on his/her social networks. Finally, several location-aware services, running in the system, control the environment status also according to users' movements. These services interact with physical devices through a multiprotocol middleware. The system has been designed to be easily extensible to other IoT technologies and its effectiveness has been evaluated in the MUST museum, Lecce, Italy.

The advent of modern approaches to education, like Massive Open Online Courses (MOOC), made video the basic media for educating and transmitting knowledge. However, IT tools are still not adequate to allow video content re-use, tagging, annotation and personalization. In this paper we analyze the problem of identifying coherent sequences, called scenes, in order to provide the users with a more manageable editing unit. A simple spectral clustering technique is proposed and compared with state-of-the-art results. We also discuss correct ways to evaluate the performance of automatic scene detection algorithms.

Background and objectives. The automated analysis of Indirect Immunofluorescence images for Anti-Nuclear Autoantibody (ANA) testing is a fairly recent field that is receiving ever-growing interest from the research community. ANA testing leverages on the categorization of intensity level and fluorescent pattern of IIF images of HEp-2 cells to perform a differential diagnosis of important autoimmune diseases. Nevertheless, it suffers from tremendous lack of repeatability due to subjectivity in the visual interpretation of the images. The automatization of the analysis is seen as the only valid solution to this problem. Several works in literature address individual steps of the work-flow, nonetheless integrating such steps and assessing their effectiveness as a whole is still an open challenge. Methods. We present a modular tool, ANAlyte, able to characterize a IIF image in terms of fluorescent intensity level and fluorescent pattern without any user-interactions. For this purpose, ANAlyte integrates the following: (i) Intensity Classifier module, that categorizes the intensity level of the input slide based on multi-scale contrast assessment (ii) Cell Segmenter module, that splits the input slide into individual HEp-2 cells; (iii) Pattern Classifier module, that determines the fluorescent pattern of the slide based on the pattern of the individual cells. Results. To demonstrate the accuracy and robustness of our tool, we experimentally validated ANAlyte on two different public benchmarks of IIF HEp-2 images with rigorous leave-one-out cross-validation strategy. We obtained overall accuracy of fluorescent intensity and pattern classification respectively around 85% and above 90%. We assessed all results by comparisons with some of the most representative state of the art works. Conclusions. Unlike most of the other works in the recent literature, ANAlyte aims at the automatization of all the major steps of ANA image analysis. Results on public benchmarks demonstrate that the tool can characterize HEp-2 slides in terms of intensity and fluorescent pattern with accuracy better or comparable with the state of the art techniques, even when such techniques are run on manually segmented cells. Hence, ANAlyte can be proposed as a valid solution to the problem of ANA testing automatization.

Diabetic polyneuropathy is a major complication of diabetes mellitus, causing severe alterations of the neural circuits between spinal nerves and spinal cord. The analysis of 3D confocal images of dorsal root ganglia in diabetic mice, where different fluorescent markers are used to identify different types of nociceptors, can help understanding the unknown mechanisms of this pathology. Nevertheless, due to the inherent challenges of 3D confocal imaging, a thorough and comprehensive visual investigation is very difficult. In this work we introduce a tool, 3DRG, that provides a fully-automated segmentation and 3D rendering of positively labeled nociceptors in a dorsal root ganglion, as well a quantitative characterisation of its immunopositivity to each fluorescent marker. Our preliminary experiments on 3D confocal images of entire dorsal root ganglia from healthy and diabetic mice provided very interesting insights about the effects of the pathology on two different types of nociceptors.

The pervasive availability of the Internet, coupled with the development of increasingly powerful technologies, has led digital images to be the primary source of visual information in nowadays society. However, their reliability as a true representation of reality cannot be taken for granted, due to the affordable powerful graphics editing softwares that can easily alter the original content, leaving no visual trace of any modification on the image making them potentially dangerous. This motivates developing technological solutions able to detect media manipulations without a prior knowledge or extra information regarding the given image. At the same time, the huge amount of available data has also led to tremendous advances of data-hungry learning models, which have already demonstrated in last few years to be successful in image classification. In this work we propose a deep learning approach for tampered image classification. To our best knowledge, this the first attempt to use the deep learning paradigm in an image forensic scenario. In particular, we propose a new blind deep learning approach based on Convolutional Neural Networks (CNN) able to learn invisible discriminative artifacts from manipulated images that can be exploited to automatically discriminate between forged and authentic images. The proposed approach not only detects forged images but it can be extended to localize the tampered regions within the image. This method outperforms the state-of-the-art in terms of accuracy on CASIA TIDE v2.0 dataset. The capability of automatically crafting discriminant features can lead to surprising results. For instance, detecting image compression filters used to create the dataset. This argument is also discussed within this paper.

This paper discusses the role of computer vision to bridge the experiential gap between the cultural and emotional experience of the visitors in museums or cultural heritage sites. We don't argue against the use of multiple sensors to provide a more complete cultural experience but claim the primary role of computer vision for such a task. Although many research challenges are still far to be solved effectively, especially for detection, re-identification, tracking and recognition, we believe that technology can be deployed already in real contexts and support concrete applications with interesting results that will open the door to valuable future applications.

With the increasing popularity of wearable cameras, such as GoPro or Narrative Clip, research on continuous activity monitoring from egocentric cameras has received a lot of attention. Research in hardware and software is devoted to find new efficient, stable and long-time running solutions; however, devices are too power-hungry for truly always-on operation, and are aggressively duty-cycled to achieve acceptable lifetimes. In this paper we present a wearable system for context change detection based on an egocentric camera with ultra-low power consumption that can collect data 24/7. Although the resolution of the captured images is low, experimental results in real scenarios demonstrate how our approach, based on Siamese Neural Networks, can achieve visual context awareness. In particular, we compare our solution with hand-crafted features and with state of art technique and propose a novel and challenging dataset composed of roughly 30000 low-resolution images.

Optical coherence tomography (OCT) represents a non-invasive imaging technology, which may be applied to the diagnosis of non-melanoma skin cancer and which has recently been shown to improve the diagnostic accuracy of basal cell carcinoma. Technical developments of OCT continue to expand the applicability of OCT for different neoplastic and inflammatory skin diseases. Of these, dynamic OCT (D-OCT) based on speckle variance OCT is of special interest as it allows the in vivo evaluation of blood vessels and their distribution within specific lesions, providing additional functional information and consequently greater density of data. In an effort to assess the potential of D-OCT for future scientific and clinical studies, we have therefore reviewed the literature and preliminary unpublished data on the visualization of the microvasculature using D-OCT. Information on D-OCT in skin cancers including melanoma, as well as in a variety of other skin diseases, is presented in an atlas. Possible diagnostic features are suggested, although these require additional validation.

Augmented user experiences in the cultural heritage domain are in increasing demand by the new digital native tourists of 21st century. In this paper, we propose a novel solution that aims at assisting the visitor during an outdoor tour of a cultural site using the unique first person perspective of wearable cameras. In particular, the approach exploits computer vision techniques to retrieve the details by proposing a robust descriptor based on the covariance of local features. Using a lightweight wearable board the solution can localize the user with respect to the 3D point cloud of the historical landmark and provide him with information about the details he is currently looking at. Experimental results validate the method both in terms of accuracy and computational effort. Furthermore, user evaluation based on real-world experiments shows that the proposal is deemed effective in enriching a cultural experience.