The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a novel scheme to automatically and directly detect smoking events in video. In this scheme, a color-based ratio histogram analysis is introduced to extract the visual clues from appearance interactions between lighted cigarette and its human holder. The techniques of color re-projection and Gaussian Mixture Models (GMMs) enable the tasks of cigarette segmentation and tracking...
Diabetic retinopathy (DR) is a common complication of diabetes that damages the retina and leads to sight loss if treated late. In its earliest stage, DR can be diagnosed by micro aneurysm (MA). Although some algorithms have been developed, the accurate detection of MA in color retinal images is still a challenging problem. In this paper we propose a new method to detect MA based on Sparse Representation...
Over the last several years, a new probabilistic representation for 3-d volumetric modeling has been developed. The main purpose of the model is to detect deviations from the normal appearance and geometry of the scene, i.e. change detection. In this paper, the model is utilized to characterize changes in the scene as vehicles. In the training stage, a compositional part hierarchy is learned to represent...
In this paper, the results of a semi-supervised approach based on the Expectation-Maximisation algorithm for model-based clustering are presented. We show in this work that, if the appropriate generative model is chosen, the classification accuracy on clustering for image segmentation can be significantly improved by the combination of a reduced set of labelled data and a large set of unlabelled data...
Core point prediction is of critical importance to latent fingerprints individuality assessment. While tremendous effort have been made in core point detection, locating core points in latent fingerprints continues to be a difficult problem because latent prints usually contain only partial images with core points left outside the print. A novel method is proposed that predicts the locations and orientations...
This paper presents a new algorithm for hematoxylin and eosin (H&E) stained histology image segmentation. With both local and global clustering, Gaussian mixture models (GMMs) are applied sequentially to extract tissue constituents such as nuclei, stroma, and connecting contents from background. Specifically, local GMM is firstly applied to detect nuclei by scanning the input image, which is followed...
This paper proposes an adaptive image retrieval method via spatial-frequency mixed features (SFMF). The SFMF can describe spatial and frequency information of image simultaneously. More specifically, spatial feature is local binary pattern (LBP) histogram extracted from image. Frequency features are described as the generalized Gaussian density (GGD) of Contourlet transform detail coefficients and...
This paper examines the implementation considerations of Compressive Sampling (CS) in Field Programmable Gate Array (FPGA) and proposes computation-free linear projection implementation for CS encoding in imaging applications. A simplified sensing matrix is implemented to eliminate the multiplication and summation processes in the sensing stage. This sensing paradigm does not require all pixels in...
Statistical background subtraction has proved to be a robust and effective approach for segmenting and extracting objects without any prior information of the foreground objects. This paper presents two contributions on this topic. The first contribution of this paper proposes a novel approach which introduces the motion mask into the Gaussian Mixture Models to reduce the errors of classical GMMs,...
In this paper, a quasi-automatic video matting approach which can preserve the temporal consistency of the alpha mattes is presented. “Quasi-automatic” means that it only needs a few user interactions on the first frame. A new algorithm which incorporates the Bayesian Estimation, Weighted Kernel Density Estimation (WKDE) and graph cut is presented to automatically and accurately segment each frame...
We propose a novel method to address object localization in a weakly supervised framework. Unlike prior work using exhaustive search methods such as sliding windows, we advocate the use of visual attention maps which are constructed by class-specific visual words. Based on dense SIFT descriptors, these visual words are selected by support vector machines and feature ranking techniques. Therefore,...
Intelligent video analysis, which analyzes behaviors of moving objects in the scene, determines their trajectories, morphological changes and detects abnormal behaviors by setting certain rules, is a combination of techniques such as image processing, computer vision, artificial intelligence, and so on. The very algorithm system mainly includes four parts. They are foreground extraction, object recognition,...
The foreground detection is a key processing in crowd motion analysis containing abnormal behavior detections and crowd density estimations. This paper proposes a new foreground detection approach called optical flow and background model (OFBM) based on Lucas-Kanade optical flow and Gaussian background model methods. This approach overcomes the shortages of optical flow and background subtract. Experimental...
Spectral clustering algorithms are newly developing technique in recent years. In this paper, we derive a new pairwise affinity function for spectral clustering based on a measure of texture features represented by Gaussian Markov Random Field (GMRF) model. This model is used to capture the statistical properties of the neighborhood at a pixel, and then pairwise affinities represented by it can cluster...
This paper describes an approach for lane segmentation in traffic monitoring systems based on probability map extracted form vehicles movement information. In traffic monitoring system, the region that a vehicle passes through might belong to a certain lane. The more vehicles pass through a certain region, the more likely it belongs to a certain lane, and then we can get probability map of the lane...
Change detection represents an important tool in environmental monitoring and disaster management. Here, a novel unsupervised change-detection method is proposed for very high-resolution SAR images, by integrating wavelet multiscale feature extraction, Markov random fields for contextual modeling, and generalized Gaussian models. Experiments with COSMO-SkyMed data remark the effectiveness of the method...
Image classification is an important task for many aspects of global change studies and environmental applications. This paper emphasizes on the analysis and usage of different advanced image classification techniques like Cloud Basis Functions (CBFs) Neural Networks, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for object based classification to get better accuracy. For comparison,...
In this paper we discuss Gauss-Markov Random Field (GMRF) based on multiple sub-aperture decomposition method for the analysis of targets in complex-valued high-resolution SAR data. Gauss-Markov Random Field (GMRF) model with a quadratic energy function as a parametric analysis parameterizes the spectogram of the signal, whereas sub-aperture decomposition method exploits the holographic property of...
We propose a multiclass hierarchical abductive learning classifier and apply it to improve the recognition rate of handwritten numerals while reduce the dimensionality of the feature space. For handwritten recognition, there are ten classes. Using 9 binary GMDH-based neural network models structured in a hierarchy has led to improving balance factor of the dataset for each classifier and improving...
An algorithm for automated extraction of interest points (IP) in hyperspectral images is presented. IP are features of the image that capture information from its neighbors and are distinctive and stable under translation and rotation. IP operators for gray level images were proposed more than a decade ago and have since been studied extensively. IP are helpful in data reduction to reduce the computational...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.