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Existing trackers often decompose the task of visual tracking into multiple independent components, such as target appearance sampling, classifier learning, and target state inferring. In this paper, we present a transform-aware attentive tracking framework, which uses a deep attentive network to directly predict the target states via spatial transform parameters. During off-line training, the proposed network learns generic motion patterns of target objects from auxiliary large-scale videos. These leaned motion patterns are then applied to track target objects on test sequences. Built on the Spatial Transform Network (STN), the proposed attentive network is fully differentiable and can be trained in an end-to-end manner. Notably, we only fine-tune the pre-trained network in the initial frame. The proposed tracker requires neither online model update nor appearance sampling during the tracking process. Extensive experiments on OTB-2013, OTB-2015, VOT-2014 and UAV-123 datasets demonstrate the competitive performance of our method against state-of-the-art attentive tracking methods. [ABSTRACT FROM AUTHOR]

Abstract A single image super-resolution (SR) algorithm that combines deep convolutional neural networks (CNNs) with multi-scale similarity is presented in this work. The aim of this method is to address the incapability of the existing CNN methods in digging the potential information in the image itself. In order to dig these information, the image patches that look similar within the same scale and across the different scales are firstly searched inside the input image. Subsequently, a spatial transform networks (STNs) are embedded into the CNNs to make the similar patches well aligned. The STNs allow the CNNs to have the ability of spatial manipulation of data. Finally, when SR is performing through the proposed pyramid-shaped CNNs, the high-resolution (HR) image will be predicted gradually according to the complementary information provided by these aligned patches. The experimental results confirm the effectiveness of the proposed method and demonstrate it can be compared with state-of-the-art approaches for single image SR. Highlights • Super resolution with convolutional neural networks and multi-scale similarity. • Embedded spatial transformer into convolutional neural network (CNN) to align data. • Enhanced image resolution gradually using a pyramid-shaped CNN. [ABSTRACT FROM AUTHOR]

Abstract The recognition of license plates is very important for intelligent transportation systems. Generally, the performance of an intelligent recognition algorithm is greatly affected by different shooting angles, illumination conditions and backgrounds of the license plate images. This paper presents a sequence recognition approach for intelligent recognition of Chinese license plates. Firstly, a spatial transformer network (STN) is employed to adjust the inclined and deformed license plates such that all the plates have a uniform orientation and thus are easier to be recognized. Then, an improved convolutional neural network (CNN) is designed to extract sequence features of the rectified license plates. The features of different convolutional layers are integrated as input to a bi-directional recurrent neural network (BRNN), where the character segmentation is not needed. Finally, the recognition is accomplished by the BRNN and connectionist temporal classification (CTC). Due to the lack of adequate Chinese license plates, an effective training method is presented in which the network is pre-trained by sufficiently enough synthetic license plates and is fine-tuned by our collected real Chinese license plates. The experimental results show that our model achieves better recognition accuracy and lower average edit distance than some existing methods. [ABSTRACT FROM AUTHOR]

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): A novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image; second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation; and third, a final segmentation is performed on the transformed image. In this work, Ω-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA; four-chamber, 4C; two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained using three-fold cross-validation on a cohort of patients with hypertrophic cardiomyopathy (HCM, N = 42 ) and healthy control subjects ( N = 21 ). Network performance, as measured by weighted foreground intersection-over-union (IoU), was substantially improved for the best-performing Ω-Net compared with U-Net segmentation without localization or orientation (0.858 vs 0.834). In addition, to be comparable with other works, Ω-Net was retrained from scratch using five-fold cross-validation on the publicly available 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset. The Ω-Net outperformed the state-of-the-art method in segmentation of the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude that this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally. [ABSTRACT FROM AUTHOR]