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Fine-grained vehicle classiflcation is a challenging task due to the subtle differences between vehicle classes. Several successful approaches to fine-grained image classification rely on part-based models, where the image is classified according to discriminative object parts. Such approaches require however that parts in the training images be manually annotated, a laborintensive process. We propose...
We present an Automatic License Plate Recognition system designed around Convolutional Neural Networks (CNNs) and trained over synthetic plate images. We first design CNNs suitable for plate and character detection, sharing a common architecture and training procedure. Then, we generate synthetic images that account for the varying illumination and pose conditions encountered with real plate images...
Convolution is most computationally intensive task of Convolutional Neural Network(CNN). It demands both computational power and memory storage of processing unit. There are different approaches to compute the solution of convolution. In this paper, matrix multiplication based convolution(ConvMM) approach is implemented and accelerated using concurrent resources of Graphics Processing Unit(GPU). CUDA...
Data representation plays an important role in a classifier's accuracy. A given dataset may lead to better results by simply applying a change of basis while keeping the original number of parameters. In this paper, Gabor Filter based image representation has been exploited for object classification. First, Gabor filter based convolution is computed for features extraction, then down-sampling is performed...
The choice for image descriptor in a visual navigation system is not straightforward. Descriptors must be distinctive enough to allow for correct localization while still offering low matching complexity and short descriptor size for real-time applications. MPEG Compact Descriptor for Visual Search is a low complexity image descriptor that offers several levels of compromises between descriptor distinctiveness...
In a compact descriptor for visual search only a limited number of local features may be included. The estimated probability for correct match between keypoints provides a good criterion for selection of a subset.
The matching of keypoints present in two images is an uncertain process in which many matches may be incorrect. The statistical properties of the log distance ratio for pairs of incorrect matches are distinctly different from the properties of that for correct matches. Based on a statistical model, we propose a goodness-of-fit test in order to establish whether two images contain views of the same...
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