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In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets. The properties of their features remain, however, largely unstudied under the transfer perspective. In this work, we present an extensive analysis of the resiliency of feature vectors extracted from deep models, with special focus on the trade-off between performance and compression...
Stochastic models of images are very useful for applications such as segmentation, deblurring, and reconstruction. Sometimes it is important to be able to simulate, or draw samples from, a stochastic image model. For example, simulation can be used as an optimization tool for segmenting, deblurring, or reconstructing an image. Also, simulation of images that characterize a system can be helpful in...
We propose a fast and efficient two-stage hypothesis filtering technique that can improve performance of clustering based robust multi-model fitting algorithms. Sampling based hypothesis generation is nondeterministic and permits little control over generating poor model hypotheses, often leading to a significant proportion of bad hypotheses. Our novel filtering approach leverages the asymmetry in...
The main purpose of transfer learning is to resolve the problem of different data distribution, generally, when the training samples of source domain are different from the training samples of the target domain. Prediction of salient areas in natural video suffers from the lack of large video benchmarks with human gaze fixations. Different databases only provide dozens up to one or two hundred of...
Usually, most of hashing methods for information retrieval have a two-step procedure, embedding the data into a low-dimensional intermediate space and then quantizing them into binary codes. In the hyperplane-based hashing methods, the distance between the data in the intermediate space can replace the Hamming distance to improve the retrieval accuracy. In this paper, a novel asymmetric distance for...
In this paper we propose an online multi-task learning algorithm for video concept detection. In particular, we extend the Efficient Lifelong Learning Algorithm (ELLA) in the following ways: a) we solve the objective function of ELLA using quadratic programming instead of solving the Lasso problem, b) we add a new label-based constraint that considers concept correlations, c) we use linear SVMs as...
An efficient stereo matching algorithm, which applies adaptive smoothness constraints using texture and edge information, is proposed in this work. First, we determine non-textured regions, on which an input image yields flat pixel values. In the non-textured regions, we penalize depth discontinuity and complement the primary CNN-based matching cost with a color-based cost. Second, by combining two...
In distributed sensing systems that use compressed videos for video analysis tasks, the lossy compression of videos can damage the accuracy of object detection, which is an essential step for various vision applications. This paper aims at constructing a new quality model to predict the performance of object detection. To achieve this goal, a distorted video database is constructed by applying object...
This paper reconciles Kingsbury's dual-tree complex wavelets with Nason and Eckley's locally stationary model. We here establish that the dual-tree wavelets admit an invertible de-biasing matrix and that this matrix can be used to invert the covariance relation. We also show that the added directional selectivity of the proposed model adds utility to the standard two-dimensional local stationary model...
In this paper, we present a probabilistic approach to segmentation of malignant breast masses which have irregular shape, spiculated margins and which may be embedded in high density glandular tissue. First, we perform contrast enhancement of the image using a simple logarithmic transformation. Then, we derive a segmentation technique based on a specific class of Markov random fields (MRFs) known...
We address the problem of full body human pose estimation in video. Most previous work consider body part, pose or trajectory of body part as basic unit to compose the pose sequence. In contrast, we consider tracklet of body part as the basic unit. Based on this medium granularity representation we develop a spatio-temporal graphical model to select an optimal tracklet for each part in each video...
A novel approach to spatio-temporal saliency detection in video is proposed. Saliency computation is considered as an optimization problem that maximizes the energy of a fully-connected graphical model based on spatio-temporal feature distinctiveness. Each pixel in a video is modeled by a node, and the spatio-temporal feature distinctiveness between pixels by edges connecting the nodes in the graph...
The emergence of UHD video format induces larger screens and involves a wider stimulated visual angle. Therefore, its effect on visual attention can be questioned since it can impact quality assessment, metrics but also the whole chain of video processing and creation. Moreover, changes in visual attention from different viewing conditions challenge visual attention models. In this paper, we present...
In this study, we make use of brain activation data to investigate the perceptual plausibility of a visual and an auditory model for visual and auditory saliency in video processing. These models have already been successfully employed in a number of applications. In addition, we experiment with parameters, modifications and suitable fusion schemes. As part of this work, fMRI data from complex video...
In recent years, correlation filter based trackers outperform better than other trackers. Nevertheless, they only employ one feature and a single kernel, so they are usually not robust in complex scenes. In this paper, we derive a multi-feature and multi-kernel correlation filter based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features and kernels...
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.
Catenary system maintenance is an important task to the operation of a high-seed railway system. Currently, the inspection of damaged parts in the catenary system is performed manually, which is often slow and unreliable. This paper proposes a method to detect and locate the rod-insulators in the image taken from the high-speed railway catenary system. Sub-images containing bar-shaped devices such...
This paper proposes a novel and efficient shape-based approach for hand dorsal vein recognition. A coarse-to-fine segmentation method is first introduced to precisely detect the boundaries of the vein areas. A generalized graph model, namely Width Skeleton Model (WSM), is built then, which takes both the topology of the vein network and the width of the vessel into account, thereby achieving more...
Hazy images hinder image understanding in many applications such as autonomous vehicle. In this paper, we propose an efficient method to improve image quality of hazy images. Our method estimates the transmission function based on a linear model that allows efficient computation and employs quadtree to search for a region that best represents the scatter of airlight. Experiments were conducted using...
We present an application of the Layer-wise Relevance Propagation (LRP) algorithm to state of the art deep convolutional neural networks and Fisher Vector classifiers to compare the image perception and prediction strategies of both classifiers with the use of visualized heatmaps. Layer-wise Relevance Propagation (LRP) is a method to compute scores for individual components of an input image, denoting...
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