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Traditional methods for motion estimation estimate the motion field F between a pair of images as the one that minimizes a predesigned cost function. In this paper, we propose a direct method and train a Convolutional Neural Network (CNN) that when, at test time, is given a pair of images as input it produces a dense motion field F at its output layer. In the absence of large datasets with ground...
We present a novel approach to segment text lines from handwritten document images. In contrast to existing approaches which mainly use hand-designed features or heuristic rules to estimate the location of text lines, we train a fully convolutional network (FCN) to predict text line structure in document images. By using the FCN, a line map which is a rough estimation of text line is obtained. From...
The major challenge of machine learning based image steganalysis lies in obtaining powerful feature representations. Recently, Qian et al. have shown that Convolutional Neural Network (CNN) is effective for learning features automatically for steganalysis. In this paper, we follow up this new paradigm in steganalysis, and propose a framework based on transfer learning to help the training of CNN for...
Convolutional neural networks show their advantage in human attribute analysis (e.g. age, gender and ethnicity). However, they experience issues (e.g. robustness and responsiveness) when deployed in an intelligent video system. We propose one compact CNN model and apply it in our video system motivated by the full consideration of performance and usability. With the proposed web image mining and labelling...
In this paper, we propose a method to estimate head pose with convolutional neural network, which is trained on synthetic head images. We formulate head pose estimation as a regression problem. A convolutional neural network is trained to learn head features and solve the regression problem. To provide annotated head poses in the training process, we generate a realistic head pose dataset by rendering...
In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks (CNN) in a situation where the blur kernels are partially constrained. We focus on blurred images from a real-life traffic surveillance system, on which we, for the first time, demonstrate that neural networks trained on artificial data provide superior reconstruction...
In this paper, we aim at detecting vehicles from the point clouds scanned from the urban area. Our detection method consists of a segmentation stage and a classification stage. Prior knowledge for vehicles and urban environment is utilized to help the detection process. Specifically, we incorporate curb detection and removal in the segmentation stage. Moreover, our approach is able to estimate the...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
This paper presents a novel method of fixation identification for mobile eye trackers. The most significant benefit of our method over the state-of-the-art is that it achieves high accuracy for low-sample-rate devices worn during locomotion. This in turn delivers higher quality datasets for further use in human behaviour research, robotics and the development of guidance aids for the visually impaired...
Accurate region proposals are of importance to facilitate object localization in the existing convolutional neural network (CNN)-based object detection methods. This paper presents a novel iterative localization refinement (ILR) method which, undertaken at a mid-layer of a CNN architecture, iteratively refines region proposals in order to match as much ground-truth as possible. The search for the...
In this paper, we propose a new facial landmarks detection method based on deep learning with facial contour and facial components constraints. The proposed deep convolutional neural networks (DCNNs) for facial landmark detection consists of two deep networks: one DCNN is to detect landmarks constrained on the facial contour and the other is to detect landmarks constrained on facial components. A...
With the widespread of user-generated Internet videos, emotion recognition in those videos attracts increasing research efforts. However, most existing works are based on framelevel visual features and/or audio features, which might fail to model the temporal information, e.g. characteristics accumulated along time. In order to capture video temporal information, in this paper, we propose to analyse...
This paper presents a no reference image (NR) quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN takes unpreprocessed image patches as an input and estimates the quality without employing any domain knowledge. By that, features and natural scene statistics are learnt purely data driven and combined with pooling and regression in one framework. We evaluate the...
This paper presents a novel method of salience and priority estimation for the human visual system during locomotion. This visual information contains dynamic content derived from a moving viewpoint. The priority map, ranking key areas on the image, is created from probabilities of gaze fixations, merged from bottom-up features and top-down control on the locomotion. Two deep convolutional neural...
This paper proposes a hybrid wavelet convolution network (HWCN) which is composed of a scattering convolution component and a convolution neural component. The hierarchical end-to-end network implements sparse-coding and high-dimensional reconstruction for inverse problem through cascade convolutions. With the pre-defined scattering convolutions from nonlinear operators, the network can be tailored...
How to learn view-invariant facial representations is an important task for view-invariant face recognition. The recent work [1] discovered that the brain of the macaque monkey has a face-processing network, where some neurons are view-specific. Motivated by this discovery, this paper proposes a deep convolutional learning model for face recognition, which explicitly enforces this view-specific mechanism...
Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named Local Binary Pattern Network (LBPNet), is proposed to efficiently extract and compare high-level over-complete features in multilayer hierarchy. The LBPNet retains the same topology of Convolutional Neural Network (CNN) — one of the most...
Cultivar identification is an important aspect in agriculture and also a typical task of fine-grained visual categorization (FGVC). In comparison with other common topics in FGVC, studies on this problem are somewhat lagged and limited. In this paper, targeting four Chinese maize cultivars of Jundan No.20, Wuyue No.3, Nongda No.108, and Zhengdan No.958, we first consider the problem of identifying...
Convolutional Neural Network (CNN) based image representations have achieved high performance in image retrieval tasks. However, traditional CNN based global representations either provide high-dimensional features, which incurs large memory consumption and computing cost, or inadequately capture discriminative information in images, which degenerates the functionality of CNN features. To address...
Computing matching cost by Convolutional neural networks(CNNs) work well in fetching accurate dense disparity maps. But these methods still have problems: (1) they always employ equal weights for left and right images in convolutional layers, losing relational information of patches; (2) they don't solve the balance between patches' size and processing efficiency, the larger size the more information...
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