Learning sparse latent representation and distance metric for image retrieval

The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG.

On film character retrieval in feature-length films

The problem of automatic face recognition (AFR) concerns matching a detected (roughly localized) face against a database of known faces with associated identities. This task, although very intuitive to humans and despite the vast amounts of research behind it, still poses a significant challenge to computer-based methods. For reviews of the literature and commercial state-of-the-art see [21, 372] and [252, 253]. Much AFR research has concentrated on the user authentication paradigm (e.g. [10, 30, 183]). In contrast, we consider the content-based multimedia retrieval setup: our aim is to retrieve, and rank by confidence, film shots based on the presence of specific actors. A query to the system consists of the user choosing the person of interest in one or more keyframes.

Combining collaborative and content-based filtering to recommend research papers

The number of research papers available today is growing at a staggering rate, generating a huge amount of information that people cannot keep up with. According to a tendency indicated by the United States’ National Science Foundation, more than 10 million new papers will be published in the next 20 years. Because most of these papers will be available on the Web, this research focus on exploring issues on recommending research papers to users, in order to directly lead users to papers of their interest. Recommender systems are used to recommend items to users among a huge stream of available items, according to users’ interests. This research focuses on the two most prevalent techniques to date, namely Content-Based Filtering and Collaborative Filtering. The first explores the text of the paper itself, recommending items similar in content to the ones the user has rated in the past. The second explores the citation web existing among papers. As these two techniques have complementary advantages, we explored hybrid approaches to recommending research papers. We created standalone and hybrid versions of algorithms and evaluated them through both offline experiments on a database of 102,295 papers, and an online experiment with 110 users. Our results show that the two techniques can be successfully combined to recommend papers. The coverage is also increased at the level of 100% in the hybrid algorithms. In addition, we found that different algorithms are more suitable for recommending different kinds of papers. Finally, we verified that users’ research experience influences the way users perceive recommendations. In parallel, we found that there are no significant differences in recommending papers for users from different countries. However, our results showed that users’ interacting with a research paper Recommender Systems are much happier when the interface is presented in the user’s native language, regardless the language that the papers are written. Therefore, an interface should be tailored to the user’s mother language.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

A clinically applicable laser-based image-guided system for laparoscopic liver procedures

PURPOSE Laser range scanners (LRS) allow performing a surface scan without physical contact with the organ, yielding higher registration accuracy for image-guided surgery (IGS) systems. However, the use of LRS-based registration in laparoscopic liver surgery is still limited because current solutions are composed of expensive and bulky equipment which can hardly be integrated in a surgical scenario. METHODS In this work, we present a novel LRS-based IGS system for laparoscopic liver procedures. A triangulation process is formulated to compute the 3D coordinates of laser points by using the existing IGS system tracking devices. This allows the use of a compact and cost-effective LRS and therefore facilitates the integration into the laparoscopic setup. The 3D laser points are then reconstructed into a surface to register to the preoperative liver model using a multi-level registration process. RESULTS Experimental results show that the proposed system provides submillimeter scanning precision and accuracy comparable to those reported in the literature. Further quantitative analysis shows that the proposed system is able to achieve a patient-to-image registration accuracy, described as target registration error, of [Formula: see text]. CONCLUSIONS We believe that the presented approach will lead to a faster integration of LRS-based registration techniques in the surgical environment. Further studies will focus on optimizing scanning time and on the respiratory motion compensation.

Feasibility of stereotactic MRI-based image guidance for the treatment of vascular malformations: a phantom study.

PURPOSE Treatment of vascular malformations requires the placement of a needle within vessels which may be as small as 1 mm, with the current state of the art relying exclusively on two-dimensional fluoroscopy images for guidance. We hypothesize that the combination of stereotactic image guidance with existing targeting methods will result in faster and more reproducible needle placements, as well as reduced radiationexposure, when compared to standard methods based on fluoroscopy alone. METHODS The proposed navigation approach was evaluated in a phantom experiment designed to allow direct comparison with the conventional method. An anatomical phantom of the left forearm was constructed, including an independent control mechanism to indicate the attainment of the target position. Three interventionalists (one inexperienced, two of them frequently practice the conventional fluoroscopic technique) performed 45 targeting attempts utilizing the combined and 45 targeting attempts utilizing the standard approaches. RESULTS In all 45 attempts, the users were able to reach the target when utilizing the combined approach. In two cases, targeting was stopped after 15 min without reaching the target when utilizing only the C-arm. The inexperienced user was faster when utilizing the combined approach and applied significantly less radiation than when utilizing the conventional approach. Conversely, both experienced users were faster when using the conventional approach, in one case significantly so, with no significant difference in radiation dose when compared to the combined approach. CONCLUSIONS This work presents an initial evaluation of a combined navigation fluoroscopy targeting technique in a phantom study. The results suggest that, especially for inexperienced interventionalists, navigation may help to reduce the time and the radiation dose. Future work will focus on the improvement and clinical evaluation of the proposed method.

Icebergs in the Amundsen Sea (Antarctica) determined from ENVISAT ASAR WSM data using object-based image analysis approach, link to ESRI GDB files

ENVISAT ASAR WSM images with pixel size 150 × 150 m, acquired in different meteorological, oceanographic and sea ice conditions were used to determined icebergs in the Amundsen Sea (Antarctica). An object-based method for automatic iceberg detection from SAR data has been developed and applied. The object identification is based on spectral and spatial parameters on 5 scale levels, and was verified with manual classification in four polygon areas, chosen to represent varying environmental conditions. The algorithm works comparatively well in freezing temperatures and strong wind conditions, prevailing in the Amundsen Sea during the year. The detection rate was 96% which corresponds to 94% of the area (counting icebergs larger than 0.03 km**2), for all seasons. The presented algorithm tends to generate errors in the form of false alarms, mainly caused by the presence of ice floes, rather than misses. This affects the reliability since false alarms were manually corrected post analysis.

Benthic cover map of Heron Reef derived from a high-spatial-resolution multi-spectral satellite image using object based image analysis

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.

Patient-based Literature Retrieval and Integration: A Use Case for Diabetes and arterial Hypertension

Specialized search engines such as PubMed, MedScape or Cochrane have increased dramatically the visibility of biomedical scientific results. These web-based tools allow physicians to access scientific papers instantly. However, this decisive improvement had not a proportional impact in clinical practice due to the lack of advanced search methods. Even queries highly specified for a concrete pathology frequently retrieve too many information, with publications related to patients treated by the physician beyond the scope of the results examined. In this work we present a new method to improve scientific article search using patient information. Two pathologies have been used within the project to retrieve relevant literature to patient data and to be integrated with other sources. Promising results suggest the suitability of the approach, highlighting publications dealing with patient features and facilitating literature search to physicians.

Object-based image analysis for mapping Natura 2000 habitats.

Clasificación de una imagen de alta resolución "Quickbird" con la técnica de análisis de imágenes en base a objetos.

Objects-based Image Analysis for Mapping Natura 2000 Habitats to Improve Forest Management.

Clasificación de una imagen de alta resolución "Quickbird" con la técnica de análisis de imágenes en base a objetos