Digital video image and is an active area of research that attracts the attention of a number of institutional research groups and R & D departments of companies and organizations throughout the world.
The advance of technology, communications and content distribution tools has led to an unprecedented expansion of the use of the image in our daily activities. And this explosion in the use we make of the image (both fixed and moving) on the web is the main vehicle for expansion.
Social networking, blogging platforms like Google Images and YouTube made available to users with tools that help us and also encourage their use. No wonder that some of the image is an important research activity focuses on finding solutions to new problems a few years ago we had not even raised.
It is in this context GIM research develops its main activity. In this section you will find relevant information about this activity, but before we get into it, we will introduce a little more detail, the context of our research.
The multimedia information query by example (Multimedia Information Retrieval, MIR) is a search system to retrieve similar information (videos, images, sounds, etc.) based on its content. In the case of images (Content-Based Image Retrieval, CBIR), the features are treated according to their context in relation with colors, shapes, textures or any other information that may result from the image itself.
Most of the techniques used in CBIR from the viewpoint of image processing are based on the field of Pattern Recognition and Image Analysis, where the main objective is the classification of objects in a number of categories or classes. If we work with videos (Content-Based Video Retrieval, CBVR), the techniques can be very similar, incorporating in addition features such as movement, relations between frames, quick changes between scenes, etc.. (digital video processing).
This does not conflict at all with the classic treatment of this information in databases by metadata (tags) or additional textual information can be entered automatically (eg, date of collection, device characteristics, making parameters , etc.) or manually (author, descriptive labels in the document, etc.).
Information should be indexed to reach an efficent (fast) and effective retrieval. Therefore metadata are meaningless for large image collections and the automatic indexing and retrieval are considered based on what is in the multimedia information according to its content or features (primitive or processed properties).
In these types of searches the subjectivity of human perception greatly affects the outcome of the queries. Thus, the automatic feature extraction through computational methods must take into account this human factor. It is sometimes quite difficult to obtain similar results computationally and the user really wants. This discrepancy is what is called semantic gap. The computational characteristics is not that high semantic content that have the human perceptual characteristics. Hence this is the reason for MIR systems.
Today the members of the group GIM provide teaching in many degrees of UEx. The following links will find detailed information on specific subjects of middle and top grade imparted by the members of the research group.
tau-Lop is a new parallel performance model aimed to help in the design and optimization of parallel algorithms inside multicore clusters. It represents a parallel algorithm and predicts accurately its costs through the concept of concurrent transfers.
By now, tau-Lop has been applied to underlying algorithms in MPICH and Open MPI mainstream implementations of some MPI collectives in shared memory. Current work is in the application of the model to collective operations when deployed in networks of multicore nodes.
The initial module aimed to detect the Biceps femoris muscle by using Active Contours.
The second module consisted in the selection procedure for the Region of Interest (ROI) on each image; this selection drew up the maximum rectangular area on the muscle.
The third and last module included the analysis of the ROIs by applying three common methods in computational texture analysis, which require the use of rectangular images. All three methods used matrices based on second order statistics. The first one, GLCM (Grey Level Coocurrence Matrix), was constructed with information of the complete ROI. The second one, the so-called NGLDM (Neighbouring Grey Level Dependence Matrix), gathered information from square neighbourhoods inside the ROI. The third one, the GLRLM (Grey Level Run Length Matrix), only accounted for information about lineal segments of the ROI.