Current Projects   

Current image acquisition and storage technologies allow the use of large image databases to support entertainment, research, and development of new technologies. However, image annotation has become a crucial problem as the image databases grow large. Our ultimate goals in this project are to minimize user involvement for image annotation and to organize the image database to support different types of queries. We have investigated active learning techniques which can select the most representative images for user annotation in a few iterations of relevance feedback, resulting in a more effective and efficient classifier for automatic annotation of the remaining database images. We have also investigated image segmentation methods, image descriptors, optimization techniques for feature selection and/or combination of image descriptors, and unsupervised, supervised, and semi-supervised techniques for image classification. Our methods have been validated on some real applications, such as the identification of coffee crop regions in satellite images, characterization of graphite particles in metallographic images of industrial materials, and the diagnosis of parasites in optical microscopy images.

The project currently counts with the collaboration of Leo Pini Magalhães (FEEC-UNICAMP), André Tavares da Silva (UDESC),  Priscila Saito  (PhD student, IC-UNICAMP), Pedro Rezende  (IC-UNICAMP),  João Paulo Papa (UNESP-Bauru), Jefersson  Santos (PhD student, IC-UNICAMP), Ricardo Torres (IC-UNICAMP), Sylvie Philipp-Foliguet (ETIS, CNRS, ENSEA, University of Cergy-Pontoise, France) and Philippe-Henri Gosselin (ETIS, CNRS, ENSEA, University of Cergy-Pontoise, France), Victor Hugo Costa de Albuquerque (UNIFOR), João Manuel R. S. Tavares (Universidade do Porto, Portugal), and the financial support from FAPESP and CNPq. 

Image segmentation is a tool to extract relevant information for image analysis. In most applications, it requires the approximate location  of an interest object and  its  precise delineation in the image.  Both tasks can be greatly facilitated when possible appearance variations of the object/"object assembly" (e.g., a person in a digital video, a set of organs in a region of the human body) are enconded in a fuzzy object model. Such models can be created by supervised learning from multiple segmentations of a given object/"object assembly" and/or by affine transformations on a segmentation result. A fuzzy object model can also encode the position relation among objects in the case of multiple object segmentation. For image segmentation, the matching between the fuzzy object model and a new image provides at the same time object location and delineation (segmentation), because the criterion function used for matching is computed on the delineation result for any given model location inside the image. The figures below illustrate 2D and 3D fuzzy models used to segment different parts of a person in video frames (left),  of a brain in MR-images (center), and of a thorax in CT-images (right). 

We are also interested in interactive image segmentation techniques which are needed in many applications, to provide training images for model construction, and to correct the segmentation results.  In all cases, we are interested in minimizing user involvement  and, at the same time,  maintaining  user control over the segmentation process.  Current applications of interest involve MR-images of the brain, CT-images of the thorax, and digital video with people (articulated objects).

The project currently counts with the collaboration of Jayaram Udupa (Dept. of Radiology, University of Pennsylvania, USA), Krzysztof   Chris Ciesielski (Dept. of Mathematics, West Virginia University, USA), Paulo Miranda (IME-USP-SP), Thiago Spina (PhD Student, IC-UNICAMP), Guillermo Sapiro (Dept. of Electrical and Computer Engineering, Duke University, USA) and Filip Malmberg (Uppsala University, Sweeden), and the financial support from FAPESP and CNPq.

Face recognition has been addressed from many different perspectives. However, the majority of the techniques consists, at some point, on representing the faces in a given way, common to all individuals, in order to compare them. By doing so, a consensus about the most discriminant way to represent all the faces must be obtained, where common face aspects that better distinguish different persons are emphasized, with the purpose of achieving a good representation. By ignoring face aspects that are not good at distinguishing among all the considered persons, we may also be ignoring aspects that are actually good at discerning a particular person from the others, i.e., we may be disregarding important person-specific face aspects only because their discriminability does not generalize to all persons. Therefore, in this project, we are interested in representing person's face in a feature space conceived to underline its discriminating aspects in order to improve face recognition. The project investigates image registration and segmentation techniques, face characterization,  machine learning  and pattern recognition  methods.

The project counts with the collaboration of Giovani Chiachia  (PhD  student, IC-UNICAMP), Anderson Rocha (IC-UNICAMP), Nicolas Pinto (Rowland Institute, Harvard University, USA), David Cox (Center for Brain Science, Harvard University, USA), William Schwartz (IC-UNICAMP), and the financial support from FAPESP and CNPq.

The project counts with the collaboration of Jancarlo Ferreira Gomes (IB and IC, UNICAMP), Celso Suzuki (Immunocamp), Priscila Saito (PhD student, IC-UNICAMP), Pedro Rezende (IC-UNICAMP), Sumie Shimizo (FCF-USP-SP),  Juliana Carvalho (MSc Student, IB-UNICAMP),  Kátia Bresciani (UNESP-Araçatuba-SP), Willian Coelho (UNESP-Araçatuba-SP), and the financial support from Immunocamp, FAEPEX, FAPESP, CAPES, and CNPq.

MR images of the human brain provide visual information about tissues and structures, whose anatomy and function may be affected by neuronal disturbs and cerebral diseases. The visualization and quantification of such information are important to understand the natural course of the disease, plan a treatment, and study the effects of the treatment. This project investigates methods for filtering, segmentation, registration, visualization and morphometric analysis of cerebral structures in patients with epilepsy and other degenerative diseases. We are particularly interested in the associations between asymmetry analysis (shape and texture) and the cerebral diseases (focal cortical dysplasia, tumors). The figure below shows a screen of the software under development for brain asymmetry analysis. Fuzzy models together with delineation and clustering methods based on the IFT algorithm are used to separate left hemisphere, right hemispheres, cerebellum,CSF ,  gray matter, and white matter from MR-images.

The project currently counts with the collaboration of Fernando Cendes (FCM-UNICAMP), Clarissa Yasuda (FCM-UNICAMP), Iscia Cendes (FCM-UNICAMP), Guilherme Ruppert (CenPra), Fabio Cappabianco (UNIFESP), and Paulo Miranda (IME-USP-SP), and the financial support from FAEPEX, CAPES, CNPq, and FAPESP (CInAPCe).

Cone beam computed tomography (CBCT) is an X-ray imaging modality capable of acquiring three-dimensional information of the human anatomy with a substantially lower radiation dose to the patient as compared to conventional medical computed tomography (CT) systems. The use of CBCT as a diagnostic tool for endodontics has increased the need for better image quality and its ability to display very low contrast tissue regions, usually associated to fractures within the tooth structure. Vertical root fracture (VRF), for instance, is a severe type of tooth fracture that affects the root causing pain due to infection and inflammation, leading to the tooth extraction. VRFs’ diagnosis remains a challenge, but supporting evidences indicate that CBCT has superior ability to detect VRFs as compared to periapical radiograph. In this project, we are interested in tooth segmentation and visualization techniques from CBCT images for tooth fracture inspection. Image segmentation and visualization must be interactive, but we aim at minimizing user involvement by using effective algorithms for tooth delineation, reslicing, and 3D rendition combined with an intuitive user interface. The IFT algorithm has been used for segmentation of the teeth from CBCT images (left) and internal structures, such as fracture and canal (right).

The project currently counts with the collaboration of Andre Souza (Carestream, USA) and Lawrence Ray (Carestream, USA).

The morfology of plant organs, such as roots, leaves, and seeds, is closely related to relevant properties of the plant.  For example, there are evidences that the architecture of a plant root system can indicate  its tolerance  to acid soils and its capacity to absorb nutrients, such as water and phosphorus. These properties are related to certain genes and the identification of those genes can improve the genetics of the plants.  Genome-wide association studies (GWAS) look for the genes associated with a given trait of the plant. In this project, we are interested in  phenotypic traits, which can be computed by 2D and 3D image analysis techniques. We are also interested in designing imaging systems suitable for phenotyping. We have investigated 3D image analysis of plant root systems (rice and sorghum) and 2D image analysis of rice panicles. The methods require image reconstruction, filtering, segmentation, and representation (e.g., skeletonization); phenotypic trait extraction;  machine learning and pattern recognition techniques; and statistical data analysis.  The figure below shows different root architectures of rice along days 3, 6, and 9 of growing.

The project counts with the collaboration of Leon Vicent Kochian (USDA, Cornell University, USA), Susan McCouch (Plant Breeding and Genetics, Cornell University, USA), Randy Clark (PhD Student, Cornell University, USA), Samuel Crowell (PhD Student, Cornell University, USA), Jon Shaff (USDA, Cornell University, USA), and Alexandru Telea (University of Groningen, The Netherlands).