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 addresses the problem of remote sensing image multi-scale classification by: (i) showing that using multiple scales does improve classification results, but not all scales have the same importance; (ii) showing that image descriptors do not offer the same contribution at all scales, as commonly thought, and some of them are very correlated; (iii) introducing a simple approach to automatically...
Scene detection is the fundamental step for efficient accessing and browsing videos. In this paper, we propose to segment movie into scenes which utilizes fused visual and audio features. The movie is first segmented into shots by an accelerating algorithm, and the key frames are extracted later. While feature movies are often filmed in open and dynamic environments using moving cameras and have continuously...
Sparsity-based super-resolution has attracted lots of attention. Due to the high dimensionality of image data, sparsity-based methods are often in a patch-wise manner and simply impose the smoothness constraints on the overlapped regions between reconstructed patches. However, the imposed smoothness constraint is commonly weak to regularize super-resolution problem when the observed low-resolution...
KNN-based image annotation method is proved to be very successful. However, it suffers from two issues: (1) high computational cost; (2) the difficulty of finding semantically similar images. In this paper, we propose a graph-based dimensionality reduction method to solve the two problems by adapting the locality sensitive discriminant analysis method [1] to multilabel setting. We first determine...
Recent years have witnessed a growing interest in the sparse representation problem. Prior work demonstrated that adaptive dictionary learning techniques can greatly improve the performance of sparse representation approaches. Existing techniques mainly focus on the reconstructive accuracies and the discriminative power of the learned dictionary, whereas the mutual incoherence between any two basis...
Symbol retrieval for technical documents is still a hot challenge in the document analysis community. In this paper we propose another way to spot symbols. A pixel-based template operator which is an adaptation of the hit-or-miss transform is defined. This operator is robust to translation, rotation and reflection. Experimental results on a real application show the efficiency of our approach.
Fingerprint representation is important in fingerprint recognition systems and has great impact on its performance. In this paper, we first introduce complex continuous density functions named Complex Gaussian Mixture Model (CGMM) to represent the fingerprint minutiae. In this model, a Gaussian mixture model is constructed according to the positions of fingerprint minutiae, and the direction of minutiae...
Stereo matching is a challenging problem, especially in the presence of noise or of weakly textured objects. Using temporal information in a binocular video sequence to increase the discriminability for matching has been introduced in the recent past, but all the proposed methods assume either constant disparity over time, or small object motions, which is not always true. We introduce a novel stereo...
Many human actions are correlated, because of compound and/or sequential actions, and similarity. Indeed, human actions are highly correlated in human annotations of 48 actions in the 4,774 videos from visint.org. We exploit such correlations to improve the detection of these 48 human actions, ranging from simple actions such as walk to complex actions such as exchange. We apply a basic pipeline of...
We present a vision based method to estimate the respiration rate of subjects from their chest movements. In contrast to alternative approaches, our method is fully automated, non-invasive, robust to occlusions, and only depends on off-the-shelf hardware. We project a fixed infrared (IR) dot pattern. The dots are detected using a camera with a matching IR filter. We estimate the dots' barycenters...
Environment illumination is a key to achieving a realistic visualization of material appearance. One way to achieve such an illumination is an approximation by rendering of the material surface lit by a finite set of point light sources. In this paper we employed visual psychophysics to identify a minimal number of point light sources approximating realistic illumination. Furthermore, we analyzed...
In this paper, we propose a new method for remote sensing image pan-sharpening which is based on weighted red-black (WRB) wavelet and adaptive principal component analysis (PCA), where the adaptive PCA is used to reduce spectral distortions and the utilization of WRB wavelet is used to extract the spatial details in PAN images. To reduce the artifacts and spectral distortions in the pan-sharpened...
We developed a motion blur restoration technique for surface orientation images using a correlation image sensor. This system consists of two components; one is ring-shaped modulation illumination for encoding surface orientation into the amplitude and phase of the reflected light intensity, and the other is the three-phase correlation image sensor (3PCIS) for demodulating the amplitude and phase...
Semi-supervised learning is important when labeled data are scarce. In this paper, we develop a novel semi-supervised spectral feature selection technique using label regression and by using l\-norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique for semi-supervised feature selection. In the first step, we use label propagation and label...
Feature selection is an important issue in pattern recognition. In face recognition, one of the state-of-the-art methods is that some feature selection methods (e.g., AdaBoost) are first utilized to select the most discriminative features and then the subspace learning methods (e.g., LDA) are further applied to learn the discriminant subspace for classification. However, in these methods, the objective...
In this paper we propose a generic 6d object localization approach based on surface normal images and CAD model data. Normal images or “normal maps” can be obtained using only one single camera shot of a simple camera-projector system. The advantages of this sensor setup are very short acquisition times and the exclusive use of consumer hardware, namely a projector and a grey value camera, making...
Traumatic brain injury (TBI) endangers many patients and lays great burden on the neural intensive-care units in the whole world. To improve the outcome of TBI patients, it is desirable to forecast the intracranial Pressure (ICP) so to enable timely or early interventions to control the ICP level. Past research mainly focused on ICP pulse morphology analysis and ICP waveform forecast, but results...
Early detection of Alzheimer's disease is expected to aid in the development and monitoring of more effective treatments. Classification methods have been proposed to distinguish Alzheimer's patients from normal controls using Magnetic Resonance Images. However, their performance drops when classifying patients at a prodromal stage, such as in Mild Cognitive Impairment. Most often, the features used...
Images of document pages have different characteristics than images of natural scenes, and so the sharpness measures developed for natural scene images do not necessarily extend to document images primarily composed of text. We present an efficient and simple method for effectively estimating the sharp-ness/blurriness of document images that also performs well on natural scenes. Our method can be...
This paper presents a renewed image annotation baseline method under the nearest neighbor tag transfer framework. Two key problems are considered in this paper: (1) which images are determined as the neighbors; (2) how their keywords are transferred. Firstly, a soft neighbor selection scheme is designed by image embedding technique, with which we can provide more power to the crucial neighbors in...
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.