Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution

Although most current license plate (LP) recognition applications have been significantly advanced, they are still limited to ideal environments where training data are carefully annotated with constrained scenes. In this paper, we propose a novel license plate recognition method to handle unconstrained real world traffic scenes. To overcome these difficulties, we use adversarial super-resolution (SR), and one-stage character segmentation and recognition. Combined with a deep convolutional network based on VGG-net, our method provides simple but reasonable training procedure. Moreover, we introduce GIST-LP, a challenging LP dataset where image samples are effectively collected from unconstrained surveillance scenes. Experimental results on AOLP and GIST-LP dataset illustrate that our method, without any scene-specific adaptation, outperforms current LP recognition approaches in accuracy and provides visual enhancement in our SR results that are easier to understand than original data.Comment: Accepted at VISAPP, 201

Unconstrained Road Marking Recognition with Generative Adversarial Networks

Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning. Although considerable advances have been made, they are often over-dependent on unrepresentative datasets and constrained conditions. In this paper, to overcome these drawbacks, we propose an alternative method that achieves higher accuracy and generates high-quality samples as data augmentation. With the following two major contributions: 1) The proposed deblurring network can successfully recover a clean road marking from a blurred one by adopting generative adversarial networks (GAN). 2) The proposed data augmentation method, based on mutual information, can preserve and learn semantic context from the given dataset. We construct and train a class-conditional GAN to increase the size of training set, which makes it suitable to recognize target. The experimental results have shown that our proposed framework generates deblurred clean samples from blurry ones, and outperforms other methods even with unconstrained road marking datasets.Comment: Accepted at IEEE Intelligent Vehicles Symposium (IV), 201

Adaptive GDDA-BLAST: Fast and Efficient Algorithm for Protein Sequence Embedding

A major computational challenge in the genomic era is annotating structure/function to the vast quantities of sequence information that is now available. This problem is illustrated by the fact that most proteins lack comprehensive annotations, even when experimental evidence exists. We previously theorized that embedded-alignment profiles (simply “alignment profiles” hereafter) provide a quantitative method that is capable of relating the structural and functional properties of proteins, as well as their evolutionary relationships. A key feature of alignment profiles lies in the interoperability of data format (e.g., alignment information, physio-chemical information, genomic information, etc.). Indeed, we have demonstrated that the Position Specific Scoring Matrices (PSSMs) are an informative M-dimension that is scored by quantitatively measuring the embedded or unmodified sequence alignments. Moreover, the information obtained from these alignments is informative, and remains so even in the “twilight zone” of sequence similarity (<25% identity) [1]–[5]. Although our previous embedding strategy was powerful, it suffered from contaminating alignments (embedded AND unmodified) and high computational costs. Herein, we describe the logic and algorithmic process for a heuristic embedding strategy named “Adaptive GDDA-BLAST.” Adaptive GDDA-BLAST is, on average, up to 19 times faster than, but has similar sensitivity to our previous method. Further, data are provided to demonstrate the benefits of embedded-alignment measurements in terms of detecting structural homology in highly divergent protein sequences and isolating secondary structural elements of transmembrane and ankyrin-repeat domains. Together, these advances allow further exploration of the embedded alignment data space within sufficiently large data sets to eventually induce relevant statistical inferences. We show that sequence embedding could serve as one of the vehicles for measurement of low-identity alignments and for incorporation thereof into high-performance PSSM-based alignment profiles

Chronic Epstein-Barr virus infection causing both benign and malignant lymphoproliferative disorders

The Epstein-Barr virus (EBV) is oncogenic and can transform B cells from a benign to a malignant phenotype. EBV infection is also associated with lymphoid interstitial pneumonia (LIP). Here, we report the case of a 14-year-old boy who was diagnosed with a latent EBV infection and underlying LIP, without any associated immunodeficiency. He had been EBV-seropositive for 8 years. The first clinical presentations were chronic respiratory symptoms and recurrent pneumonia. The symptoms worsened in the following 2 years. The results of in situ hybridization were positive for EBV, which led to a diagnosis of LIP. The diagnosis was confirmed by the results of a thoracoscopic lung biopsy. The EBV titer of the bronchoalveolar lavage specimens obtained after acyclovir treatment was found to be fluctuating. The patient had latent EBV infection for 8 years, until presented at the hospital with intermittent abdominal pain and distension. Physical examination and pelvic computed tomography revealed a large mesenteric mass. A biopsy of the excised mass led to a diagnosis of Burkitt's lymphoma (BL). The patient received combination chemotherapy for 4 months, consisting of vincristine, methotrexate, cyclophosphamide, doxorubicin, and prednisolone. He is now tumor-free, with the LIP under control, and is being followed-up at the outpatient clinic. This is the first report of a Korean case of chronic latent EBV infection that developed into LIP and BL in a nonimmunocompromised child

Polarized Signaling Endosomes Coordinate BDNF-Induced Chemotaxis of Cerebellar Precursors

During development, neural precursors migrate in response to positional cues such as growth factor gradients. However, the mechanisms that enable precursors to sense and respond to such gradients are poorly understood. Here we show that cerebellar granule cell precursors (GCPs) migrate along a gradient of brain-derived neurotrophic factor (BDNF), and we demonstrate that vesicle trafficking is critical for this chemotactic process. Activation of TrkB, the BDNF receptor, stimulates GCPs to secrete BDNF, thereby amplifying the ambient gradient. The BDNF gradient stimulates endocytosis of TrkB and associated signaling molecules, causing asymmetric accumulation of signaling endosomes at the subcellular location where BDNF concentration is maximal. Thus, regulated BDNF exocytosis and TrkB endocytosis enable precursors to polarize and migrate in a directed fashion along a shallow BDNF gradient