Urban Traffic Monitoring from Aerial LIDAR Data with a Two-Level Marked Point Process Model

In this paper we present a new model for joint extrac- tion of vehicles and coherent vehicle groups in airborne LIDAR point clouds collected from crowded urban areas. Firstly, the 3D point set is segmented into terrain, vehicle, roof, vegetation and clutter classes. Then the points with the corresponding class labels and intensity values are projected to the ground plane, where the optimal vehicle and traffic segment configuration is described by a Two-Level Marked Point Process (L2MPP) model of 2D rectangles. Finally, a stochastic algorithm is utilized to find the optimal configuration

HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC

Autoimmune diseases occur when an inappropriate immune response takes place and produces autoantibodies to fight against human antigens. In order to detect autoimmune disease, a test called indirect immunofluorescence (IIF) will be carried out to identify antinuclear autoantibodies (ANA) in the HEp-2 cell. The outcome of the test includes observing fluorescence intensity of the sample and classifying the staining pattern of the cell. Current method of analysing the results is limited to subjective factors such as experience and skill of the medical experts. The results obtained from the visual analysis are debatable as it is inconsistent. Thus, there is a need for an automated recognition system to reduce the variability and increase the reliability of the test results. Automated system also saves time and cost as the system is able to process large amount of image data at one time. This project proposes a pattern recognition algorithm consisting of statistical methods to extract seven textural features from the HEp-2 cell images followed by classification of staining patterns by using fuzzy logic. This method is applied to the data set of the ICPR 2012 contest in which each cell has been manually segmented and annotated by specialists. The textural features extracted are based on the first-order statistics and second-order statistics computed from grey level co-occurrence matrices (GLCM). The first-order statistics features are mean, standard deviation and entropy while the features extracted by GLCM are contrast, correlation, energy and homogeneity. The extracted features will then be used as an input parameter to classify the staining pattern of the HEp-2 cell images by using Fuzzy Logic. The staining patterns are divided into five categories; homogeneous, nucleolar, centromere, fine speckled and coarse speckled. A working classification algorithm is developed by using MATLAB and the Fuzzy Logic Toolbox to differentiate and classify the staining pattern of HEp-2 cell images. The algorithm gives a mean accuracy of 84% out of 125 test images

Subspace segmentation with a minimal square frobenius norm representation

We introduce a novel subspace segmentation method called Minimal Squared Frobenius Norm Representation (MSFNR). MSFNR performs data clustering by solving a convex optimization problem. We theoretically prove that in the noiseless case, MSFNR is equivalent to the classical Factorization approach and always classifies data correctly. In the noisy case, we show that on both synthetic and real-word datasets, MSFNR is much faster than most state-of-the-art methods while achieving comparable segmentation accuracy.published_or_final_versio

Classification of biological cells using bio-inspired descriptors

International audienceThis paper proposes a novel automated approach for the categorization of cells in fluorescence microscopy images. Our supervised classification method aims at recognizing patterns of unlabeled cells based on an annotated dataset. First, the cell images need to be indexed by encoding them in a feature space. For this purpose, we propose tailored bio-inspired features relying on the distribution of contrast information. Then, a supervised learning algorithm is proposed for classifying the cells. We carried out experiments on cellular images related to the diagnosis of autoimmune diseases, testing our classification method on the HEp-2 Cells dataset of Foggia et al (CBMS 2010). Results show classification precision larger than 96% on average, thus confirming promising application of our approach to the challenging application of cellular image classification for computer-aided diagnosis

MITOTIC HEP-2 CELL RECOGNIITON USING SUPPORT VECTOR MACHINE UNDER CLASS SKEW

A person with an autoimmune diseases will became hypersensitive to the surrounding that other normal person would usually not consider at all such as an allergy. This reaction happened when our immune system recognise our normal tissue as a dangerous foreign elements and proceed to attack them. The presence of antinuclear autoantibodies (ANA) in a patient serum can be detected by using the Indirect Immunofluorescence (IIF) image. By adding the mitotic cells into the well, the level of accuracy of the results achieved can be increased. The mitotic cells itself plays a crucial role in diagnosing an autoimmune diseases. This paper will focuses on the extracting the features of a mitotic HEp-2 cell in order to determine the presence of an ANA by noting the cells fluorescent-stained pattern, their intensity and also the presence of the mitotic cell itself. A skewed distribution of both mitotic and non-mitotic cells in the samples will also be considered to ensure the practicality of the project. To assist in the objectives, all the techniques used are explain in more detailed in this paper along with the result obtained by simulation from MATLAB for every steps from pre-processing to user interface menu. The procedures for the recognition of mitotic cells are image acquisition, pre-processing, segmentation, feature extraction and classification. The results obtained were tested using HEp-2 cell image datasets from MIVIA and from collaboration with Hospital Universiti Sains Malaysia (HUSM). The feature extractor used is the gray level co-occurrence matrix (GLCM) and classified using support vector machine (SVM) which will be presented in the RESULTS section

Inverse biometrics: A case study in hand geometry authentication

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. M. Gómez-Barrero, J. Galbally, J. Fiérrez and J. Ortega-Garcia, "Inverse biometrics: A case study in hand geometry authentication" in 21st International Conference on Pattern Recognition (ICPR), Tsukuba (Japan), 2012, 1281 - 1284Recently, a considerable amount of research has been focused on inverse biometrics, that is, regenerating the original biometric sample from its template. In this work, the first reconstruction approach to recover hand geometry samples from their feature vectors is proposed. Experiments are carried out on the publicly available GPDS Hand DB, where the method has shown a remarkable performance, after reconstructing a very high percentage of the hands included in the dataset. Furthermore, the proposed technique is general, being able to successfully reproduce the original hand shape sample regardless of the information and format of the template used.This work has been partially supported by projects Contexts (S2009/TIC-1485) from CAM, TEC2009- 11186 and TEC2009-14123 from Spanish MINECO, TABULA RASA (FP7-ICT-257289) and BEAT (FP7-SEC-284989) from EU, and Cátedra UAM-Telefónica