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In this work, two enhancement methods are proposed to speed up junction detection performed by the JUDOCA detector. The first enhancement method minimizes the number of junction candidates on which the circular kernel is applied. This is achieved by introducing a suppression technique that takes both the thin and thick edge images into consideration. The second method works on relaxing the step of...
This paper proposes a new approach for image classification by combining pyramid match kernel(PMK) with spatial pyramid. Unlike the conventional spatial pyramid matching (SPM) approach which only uses a single-resolution feature vector to represent an image, we use a multi-resolution feature vector to represent an image for SPM. We then calculate the match scores at each resolution of SPM representation...
Exemplar-based methods have shown their potential in synthesizing novel but visually plausible contents for image super-resolution (SR), by using the implicit knowledge conveyed by the exemplar database. In practice, however, it is common that unwanted artifacts and low quality results are produced due to the using of inappropriate exemplars. How are the “right” exemplars defined and identified? This...
Single image super-resolution (SR) generates a high-resolution (HR) image by estimating the mapping function between image patches of different resolutions. By leveraging the notion of regression, the mapping function estimation task is often transformed into predicting mapping function's derivatives. Although higher-orders of derivative lead to a more accurate mapping function, current algorithms...
Classification of high-resolution remote-sensing images is a challenging research area. In this paper we proposed a novel decision fusion framework to combine bag of features (BOF) based classifiers. The proposed framework, can also be used in multi category image classification applications. A single voting algorithm is used for decision fusion and an ambiguity detection module is used to determine...
Deep learning has shown great successes in solving various problems of computer vision. To the best of our knowledge, however, little existing work applies deep learning to saliency modeling. In this paper, a new saliency model based on convolutional neural network is proposed. The proposed model is able to produce a saliency map directly from an image's pixels. In the model, multi-level output values...
Parametric linear autoregressive (AR) model has been widely used in image processing but is known to induce unstable results. The recently emerged nonparametric kernel regression is an effective structural method for forestalling outliers but often brings over-smoothed output. This paper introduces a hybrid algorithm for image interpolation through combining the strength of parametric and nonparametric...
Mechanical properties such as elasticity and viscosity are highly related to tissue pathology state. Images that provide the geometry information of an object as well as its shear elasticity and viscosity are important in clinical applications. In the supersonc shear imaging (SSI) technique, image reconstruction in an inhomogeneous medium could be performed by varying the reconstruciton kernel size,...
In this paper, a patch based method for multi-temporal analysis of high resolution image is proposed. Conventionally, multi-temporal analysis performed at pixel level suffer from several restrictions, e.g., registration, bi-temporal analysis. To overcome these restrictions, two methods for multi-temporal analysis are proposed at patch level. One is for change detection in time series data by classifying...
Bag-of-Words is widely used to describe images for image classification. However, this approach is limited because the spatial relation over visual words is not well exploited and also it is difficult to generate a single comprehensive vocabulary. In this paper, we propose novel effective schemes to handle these two issues. First, we propose a structure propagation technique to build more reasonable...
In this paper, we propose logic processing to carry out in the directional edge space in order to establish multiple-resolution image perception in our VLSI perception system. In the con-ventional system, the resolution of input images is lowered by shrinking their sizes before edge ex-traction. However, in the human visual system, scaling of images for perception is carried out after the oriented...
This paper proposes an efficient approach to extract salient objects in an image. A scale-invariant saliency map is first constructed based on a multi-resolution feature contrast calculation, meanwhile the image is segmented into homogenous regions using nonparametric kernel density estimation (NKDE). Then the region saliency ratio of each region combination to its complement is calculated in turn...
In this paper a novel implementation of the saliency map model on a multi-GPU platform using CUDA technology is presented. The saliency map model is a well-known computational model for bottom-up attention selection and serves as a basis of many attention control strategies of cognitive vision systems. A real-time implementation is the prerequisite of an application of bottom-up attention on mobile...
In this paper, we propose a systematic framework for action recognition in unconstrained amateur videos. Inspired by the success of local features used in object and pose recognition, we extract local static features from the sampled frames to capture local pose shape and appearance. In addition, we extract spatiotemporal features (ST features), which have been successfully used in action recognition,...
We present a patch-based tracking algorithm in which both appearance and spatial information are taken into account for target localization. We decompose a target into several patches based on appearance similarity and spatial distribution. Each patch has its distinctive appearance and spatial distribution. Appearance information is described by kernels which are non-parametric; while spatial information...
This paper presents a spatiotemporal pyramid representation for recognizing facial expressions and hand gestures. This approach works by partitioning video sequence into increasingly fine subdivisions in the space and time domains and modeling the distribution of the local motion features inside each subdivision such that the set of motion features are mapped into spatial and temporal multi-resolution...
In this paper we present two efficient GPU-based visual hull computation algorithms. We compare them in terms of performance using image sets of varying size and different voxel resolutions. In addition, we present a real-time 3D reconstruction system which uses the proposed GPU-based reconstruction method to achieve real-time performance (30 fps) using 16 cameras and 4 PCs.
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