Empirical Study of Car License Plates Recognition

The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison

Fibronectin rescues estrogen receptor α from lysosomal degradation in breast cancer cells

Estrogen receptor α (ERα) is expressed in tissues as diverse as brains and mammary glands. In breast cancer, ERα is a key regulator of tumor progression. Therefore, understanding what activates ERα is critical for cancer treatment in particular and cell biology in general. Using biochemical approaches and superresolution microscopy, we show that estrogen drives membrane ERα into endosomes in breast cancer cells and that its fate is determined by the presence of fibronectin (FN) in the extracellular matrix; it is trafficked to lysosomes in the absence of FN and avoids the lysosomal compartment in its presence. In this context, FN prolongs ERα half-life and strengthens its transcriptional activity. We show that ERα is associated with β1-integrin at the membrane, and this integrin follows the same endocytosis and subcellular trafficking pathway triggered by estrogen. Moreover, ERα+ vesicles are present within human breast tissues, and colocalization with β1-integrin is detected primarily in tumors. Our work unravels a key, clinically relevant mechanism of microenvironmental regulation of ERα signaling.Fil: Sampayo, Rocío Guadalupe. Universidad Nacional de San Martin. Instituto de Nanosistemas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Oncología "Ángel H. Roffo"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Toscani, Andrés Martin. Universidad Nacional de Luján; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Rubashkin, Matthew G.. University of California; Estados UnidosFil: Thi, Kate. Lawrence Berkeley National Laboratory; Estados UnidosFil: Masullo, Luciano Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Violi, Ianina Lucila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; ArgentinaFil: Lakins, Jonathon N.. University of California; Estados UnidosFil: Caceres, Alfredo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Hines, William C.. Lawrence Berkeley National Laboratory; Estados UnidosFil: Coluccio Leskow, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina. Universidad Nacional de Luján; ArgentinaFil: Stefani, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Chialvo, Dante Renato. Universidad de Buenos Aires; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología. Centro Internacional de Estudios Avanzados; ArgentinaFil: Bissell, Mina J.. Lawrence Berkeley National Laboratory; Estados UnidosFil: Weaver, Valerie M.. University of California; Estados UnidosFil: Simian, Marina. Universidad Nacional de San Martin. Instituto de Nanosistemas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Oncología "Ángel H. Roffo"; Argentin

An End-to-End License Plate Localization and Recognition System

An end-to-end license plate recognition (LPR) system is proposed. It is composed of pre-processing, detection, segmentation and character recognition to find and recognize plates from camera based still images. The system utilizes connected component (CC) properties to quickly extract the license plate region. A novel two-stage CC filtering is utilized to address both shape and spatial relationship information to produce high precision and recall values for detection. Floating peak and valleys (FPV) of projection profiles are used to cut the license plates into individual characters. A turning function based method is proposed to recognize each character quickly and accurately. It is further accelerated using curvature histogram based support vector machine (SVM). The INFTY dataset is used to train the recognition system. And MediaLab license plate dataset is used for testing. The proposed system achieved 89.45% F-measure for detection and 87.33% accuracy for overall recognition rate which is comparable to current state-of-the-art systems

Training-Free License Plate Detection Using Vehicle Symmetry and Simple Features

In this paper, we propose a training free license plate detection method. We use a challenging benchmark dataset for license plate detection. Unlike many existing approaches, the proposed approach is a training free method, which does not require supervised training procedure and yet can achieve a reasonably good performance. Our motivation comes from the fact that, although license plates are largely variant in color, size, aspect ratio, illumination condition and so on, the rear view of vehicles is mostly symmetric with regard to the vehicles central axis. In addition, license plates for most vehicles are usually located on or close to the vertical axis of the vehicle body along which the vehicle is nearly symmetric. Taking advantage of such prior knowledge, the license plate detection problem is made simpler compared to the conventional scanning window approach which not only requires a large number of scanning window locations, but also requires different parameter settings such as scanning window sizes, aspect ratios and so on