New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component

License Plate recognition plays an important role on the traffic monitoring and parking management systems. In this paper, a fast and real time method has been proposed which has an appropriate application to find tilt and poor quality plates. In the proposed method, at the beginning, the image is converted into binary mode using adaptive threshold. Then, by using some edge detection and morphology operations, plate number location has been specified. Finally, if the plat has tilt, its tilt is removed away. This method has been tested on another paper data set that has different images of the background, considering distance, and angel of view so that the correct extraction rate of plate reached at 98.66%.Comment: 3rd IEEE International Conference on Computer and Knowledge Engineering (ICCKE 2013), October 31 & November 1, 2013, Ferdowsi Universit Mashha

A PCNN Framework for Blood Cell Image Segmentation

This research presents novel methods for segmenting digital blood cell images under a Pulse Coupled Neural Network (PCNN) framework. A blood cell image contains different types of blood cells found in the peripheral blood stream such as red blood cells (RBCs), white blood cells (WBCs), and platelets. WBCs can be classified into five normal types – neutrophil, monocyte, lymphocyte, eosinophil, and basophil – as well as abnormal types such as lymphoblasts and others. The focus of this research is on identifying and counting RBCs, normal types of WBCs, and lymphoblasts. The total number of RBCs and WBCs, along with classification of WBCs, has important medical significance which includes providing a physician with valuable information for diagnosis of diseases such as leukemia. The approach comprises two phases – segmentation and cell separation – followed by classification of WBC types including detection of lymphoblasts. The first phase presents two methods based on PCNN and region growing to segment followed by a separate method that combines Circular Hough Transform (CHT) with a separation algorithm to find and separate each RBC and WBC object into separate images. The first method uses a standard PCNN to segment. The second method uses a region growing PCNN with a maximum region size to segment. The second phase presents a WBC classification method based on PCNN. It uses a PCNN to capture the texture features of an image as a sequence of entropy values known as a texture vector. First, the parameters of the texture vector PCNN are defined. This is then used to produce texture vectors for the training images. Each cell type is represented by several texture vectors across its instances. Then, given a test image to be classified, the texture vector PCNN is used to capture its texture vector, which is compared to the texture vectors for classification. This two-phase approach yields metrics based on the RBC and WBC counts, WBC classification, and identification of lymphoblasts. Both the standard and region growing PCNNs were successful in segmenting RBC and WBC objects, with better accuracy when using the standard PCNN. The separate method introduced with this research provided accurate WBC counts but less accurate RBC counts. The WBC subimages created with the separate method facilitated cell counting and WBC classification. Using a standard PCNN as a WBC classifier, introduced with this research, proved to be a successful classifier and lymphoblast detector. While RBC accuracy was low, WBC accuracy for total counts, WBC classification, and lymphoblast detection were overall above 96%

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Physiologically-Based Vision Modeling Applications and Gradient Descent-Based Parameter Adaptation of Pulse Coupled Neural Networks

In this research, pulse coupled neural networks (PCNNs) are analyzed and evaluated for use in primate vision modeling. An adaptive PCNN is developed that automatically sets near-optimal parameter values to achieve a desired output. For vision modeling, a physiologically motivated vision model is developed from current theoretical and experimental biological data. The biological vision processing principles used in this model, such as spatial frequency filtering, competitive feature selection, multiple processing paths, and state dependent modulation are analyzed and implemented to create a PCNN based feature extraction network. This network extracts luminance, orientation, pitch, wavelength, and motion, and can be cascaded to extract texture, acceleration and other higher order visual features. Theorized and experimentally confirmed cortical information linking schemes, such as state dependent modulation and temporal synchronization are used to develop a PCNN-based visual information fusion network. The network is used to fuse the results of several object detection systems for the purpose of enhanced object detection accuracy. On actual mammograms and FLIR images, the network achieves an accuracy superior to any of the individual object detection systems it fused. Last, this research develops the first fully adaptive PCNN. Given only an input and a desired output, the adaptive PCNN will find all parameter values necessary to approximate that desired output

Sparse Representation-Based Framework for Preprocessing Brain MRI

This thesis addresses the use of sparse representations, specifically Dictionary Learning and Sparse Coding, for pre-processing brain MRI, so that the processed image retains the fine details of the original image, to improve the segmentation of brain structures, to assess whether there is any relationship between alterations in brain structures and the behavior of young offenders. Denoising an MRI while keeping fine details is a difficult task; however, the proposed method, based on sparse representations, NLM, and SVD can filter noise while prevents blurring, artifacts, and residual noise. Segmenting an MRI is a non-trivial task; because normally the limits between regions in these images may be neither clear nor well defined, due to the problems which affect MRI. However, this method, from both the label matrix of the segmented MRI and the original image, yields a new improved label matrix in which improves the limits among regions.DoctoradoDoctor en Ingeniería de Sistemas y Computació

A Public Fabric Database for Defect Detection Methods and Results

[EN] The use of image processing for the detection and classification of defects has been a reality for some time in science and industry. New methods are continually being presented to improve every aspect of this process. However, these new approaches are applied to a small, private collection of images, which makes a real comparative study of these methods very difficult. The objective of this paper was to compile a public annotated benchmark, that is, an extensive set of images with and without defects, and make these public, to enable the direct comparison of detection and classification methods. Moreover, different methods are reviewed and one of these is applied to the set of images; the results of which are also presented in this paper.The authors thank for the financial support provided by IVACE (Institut Valencia de Competitivitat Empresarial, Spain) and FEDER (Fondo Europeo de Desarrollo Regional, Europe), throughout the projects: AUTOVIMOTION and INTELITEX.Silvestre-Blanes, J.; Albero Albero, T.; Miralles, I.; Pérez-Llorens, R.; Moreno, J. (2019). A Public Fabric Database for Defect Detection Methods and Results. AUTEX Research Journal. 19(4):363-374. https://doi.org/10.2478/aut-2019-0035S36337419

Red blood cell segmentation and classification method using MATLAB

Red blood cells (RBCs) are the most important kind of blood cell. Its diagnosis is very important process for early detection of related disease such as malaria and anemia before suitable follow up treatment can be proceed. Some of the human disease can be showed by counting the number of red blood cells. Red blood cell count gives the vital information that help diagnosis many of the patient’s sickness. Conventional method under blood smears RBC diagnosis is applying light microscope conducted by pathologist. This method is time-consuming and laborious. In this project an automated RBC counting is proposed to speed up the time consumption and to reduce the potential of the wrongly identified RBC. Initially the RBC goes for image pre-processing which involved global thresholding. Then it continues with RBCs counting by using two different algorithms which are the watershed segmentation based on distance transform, and the second one is the artificial neural network (ANN) classification with fitting application depend on regression method. Before applying ANN classification there are step needed to get feature extraction data that are the data extraction using moment invariant. There are still weaknesses and constraints due to the image itself such as color similarity, weak edge boundary, overlapping condition, and image quality. Thus, more study must be done to handle those matters to produce strong analysis approach for medical diagnosis purpose. This project build a better solution and help to improve the current methods so that it can be more capable, robust, and effective whenever any sample of blood cell is analyzed. At the end of this project it conducted comparison between 20 images of blood samples taken from the medical electronic laboratory in Universiti Tun Hussein Onn Malaysia (UTHM). The proposed method has been tested on blood cell images and the effectiveness and reliability of each of the counting method has been demonstrated