Leticia Flores-Pulido

Facial feature model for emotion recognition using fuzzy reasoning

In this paper we present a fuzzy reasoning system that can measure and recognize the intensity of basic or non-prototypical facial expressions. The system inputs are the encoded facial deformations described either in terms of Ekmans Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG-4 standard. The proposed fuzzy system uses a knowledge base implemented on knowledge acquisition and ontology editor Protege. It allows the modeling of facial features obtained from geometric parameters coded by AUs - FAPs and also the definition of rules required for classification of measured expressions. This paper also presents the designed framework for fuzzyfication of input variables for fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn-Kanades and Pantics MMI face databases. The proposed system has been tested in order to evaluate its capability for detection, classifying, and interpretation of facial expressions.

Wavelets vs Shape-Based Approaches for Image Indexing and Retrieval

This paper presents a comparative analysis of some novel approaches proposed by authors for content based image retrieval (CBIR). One of them uses Two-Segments Turning Functions (2STF) and provides searching and retrieval of the multimedia documents within digital collections. Another technique retrieves images computing similarity between wavelet coefficients of querying and preprocessed images. For this purpose the Symlet transform has been used in designed system called Image Retrieval by Neural Network and Wavelet Coefficients RedNeW. However both of approaches operate with low-level characteristics processing color regions, shapes, texture, and they do not provide the analysis of image semantics. In order to improve these systems a novel approach is proposed that combines non-sensitive to spatial variations shape analysis of objects in image with their indexing by textual descriptions as part of semantic Web techniques. In the proposed approach the user’ s textual queries are converted to image features, which are used for images searching, indexing, interpretation, and retrieval. A decision about similarity between retrieved images and user’ s query is taken computing the shape convergence and matching to ontological annotations of objects in image providing in this way definition of the machine-understandable semantics. In order to evaluate the proposed approach the Image Retrieval by Ontological Description of Shapes IRONS system has been designed and tested using some standard domains of images.

HABE: Huffman Algorithm and Bit Extraction Applied to Image Equalization

Computer vision imply several morphological and mathematical process. Image compression requires methods which involves algorithms and image processing techniques. AHBE system (Algorithm of Huffman combined with Bit Extraction method) provides a novel method to image compression when high quantity of light is present. Preprocessing images is useful in robotics applications to avoid noised data providing relevant information and normalized vector of data. Hoffman algorithm is a greedy technique that is combined with Bit Extraction technique that improves bright and contrast for images obtaining an image equalization in a novel way compressing binary pixels code.

Computing Polynomial Segmentation through Radial Surface Representation

The Visual Information Retrieval (VIR) area requires robust implementations achieved trough mathematical representations for images or data sets. The implementation of a mathematical modeling goes from the corpus image selection, an appropriate descriptor method, a segmentation approach and the similarity metric implementation whose are treated as VIR elements. The goal of this research is to found an appropriate modeling to explain how its items can be represented to achieve a better performance in VIR implementations. A direct method is tested with a subspace arrangement approach. The General Principal Component Analysis (GPCA) is modified inside its segmentation process. Initially, a corpus data sample is tested, the descriptor of RGB colors is implemented to obtain a three dimensional description of image data. Then a selection of radial basis function is achieved to improve the similarity metric implemented. It is concluded that a better performance can be achieved applying powerful extraction methods in visual image retrieval (VIR) designs based in a mathematical formulation. The results lead to design VIR systems with high level of performance based in radial basis functions and polynomial segmentations for handling data sets.

Similarity metric behavior for image retrieval modeling in the context of spline radial basis function

In this paper, the analysis of similarity metrics used for performance evaluation of image retrieval frameworks is provided. Image retrieval based on similarity metrics obtains remarkable results in comparison with robust discrimination methods. Thus, the similarity metrics are used in matching process between visual query from user and descriptors of images in preprocessed collection. In contrast, the discrimination methods usually compare feature vectors computing distances between visual query and images in collections. In this research, a behavior of spline radial basis function used as metric for image similarity measurement is proposed and evaluated, comparing it with discrimination methods, particularly with general principal component analysis algorithm (GPCA). Spline radial basis function has been tested in image retrieval using a standard image collections, such as COIL-100. The obtained results using spline radial basis function report 88% of correct image retrieval avoiding a classification phase required in other well-known methods. The discussion of tests with designed Image Data Segmentation with Spline (IDSS) framework illustrates optimization and improvement of image retrieval process.

Radial Basis Function for Visual Image Retrieval

Visual image retrieval systems imply novel approaches to improve their performance. Search methods require mathematical formalism for feature extraction of multi medial data. Euclidean distance is not enough approach for extracting similarity between images. Radial basis function provides necessary optimization for similarity measures when query image is used as reference. This paper explains a novel method exploiting radial basis function applied for visual image retrieval area. The goal of the approach is the searching process optimization for image retrieval which is obtained results reveal interesting conclusions about advantages of the proposed method even conventional similarity measurement approaches used in content based retrieval area.

JADE: A Graphical Tool for Fast Development of Imaging Applications

This paper presents a novel graphic tool to develop imaging applications. Users interact with this tool by means of constructing a DIP graph, which is a series of nodes and edges indicating the processing flow of the images to be analyzed. Solutions created using our tool can run inside the developing environment and also we can get the equivalent Java source code; so that, we can reused the code in other platforms. Another advantage of our software tool is the fact that users can easily propose and construct new algorithms following the Java beans rules. Our proposal can be seen as a DIP compiler because our tool produces fullfunctional Java programs that can solve an specific problem. The program specification is not a text based one, but a graphic specification and that is one of the main contributions of this work.