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Recognizing human-object interactions in videos is a very challenging problem in computer vision research. There are two major difficulties lying in this task: (1) The detection of human body parts and objects is usually affected by the quality of the videos, for instance, low resolutions of the videos, camera motions, and blurring frames caused by fast motions, as well as the self-occlusions during...
Surface vegetation reflects various characteristics of the soil on which it grows. Vegetation type and growth rate differences were observed between recently cracked surfaces and stable soil on earthen levees along the lower Mississippi River. We attempted to directly characterize the levee surface beneath the vegetation cover using X-band synthetic aperture radar from TerraSAR-X. Due to its short...
Content Based Image Retrieval (CBIR) is a technical area focused on answering “Who, What, Where and When,” questions associated with the imagery. A multi-scale feature extraction scheme based on wavelet and Contourlet transforms is proposed to reliably extract objects in images. First, we explore Contourlet transformation in association with Pulse Coupled Neural Network (PCNN) while the second technique...
Analysis of human interaction in a social gathering is of high interest in security and surveillance applications. It is also of psychological interest to study the interaction to get a better understanding of the participant behavior. This paper is an attempt to explore and analyze interactions among the individuals from a single calibrated camera. We are particularly interested in trajectory prediction...
This paper explores anomaly detection algorithms to detect vulnerabilities on Mississippi river levees using remotely sensed Synthetic Aperture Radar (SAR) data. Earthen levees protect large areas of populated and cultivated land in the United States. One sign of potential levee failure is the occurrence of landslides due to slope instabilities. Such slides could lead to further erosion and through...
In this paper, we present a robust technique for predicting anomalies in the near future of an observed signal. First, wavelet de-noising is applied to the signal. Next, peak-finding algorithms search for smaller anomalies that appear frequently throughout the signal. Then the data from the peak-finding algorithm is fed into a feed-forward neural which predicts the likelihood of an anomalous event...
This research presents a method for the automatic detection of a dismounted human at long range from a single, highly compressed image. The histogram of oriented gradients (HOG) method provides the feature vector, a support vector machine performs the classification, and the JPEG2000 standard compresses the image. This work presents an understanding of how HOG for human detection holds up as range...
Nonlinear dimensionality reduction techniques are a thriving area of research in many fields, including pattern recognition, statistical learning, medical imaging, and statistics. This is largely driven by our need to collect, represent, manipulate, and understand high-dimensional data in practically all areas of science. Here we define “high-dimensional” to be where dimension d > 10, and in many...
In this paper, we present an enhanced 3D reconstruction algorithm designed to support an autonomously navigated unmanned ground vehicle. An unmanned system can use the technique to construct a point cloud model of its unknown surroundings. The algorithm presented focuses on the 3D reconstruction of a scene using image sequences captured by only a single moving camera. The original reconstruction process,...
People gather for parades, sports, musical events, and mass gatherings for pilgrimage at religious places like Mecca, Jerusalem, Vatican, etc. Most often, these mass gatherings lead to crowd disasters. In this research, a new automated algorithm for the Detection of Abnormal behavior in Dynamic Crowded Gatherings (DADCG) is proposed that has reduced processing speed, sensitivity to noise, and improved...
In this paper, we introduce a technique for predicting anomalies in a signal by observing relationships between multiple meaningful transformations of the signal called perspectives. In particular, we use the Fourier transform to provide a holistic view of the frequencies present in a signal, along with a wavelet denoised signal that is filtered to locate anomalous peaks. Then we input these perspectives...
A lot of difficulties for disabled persons can be attenuated using imagery. This is not only true for the visually impaired, but also for other kinds of handicap. We will review the major contributions of the image processing in the different contexts, showing in the same time the benefit of this technology for everybody. The gap between valid and impaired persons is not so large. This will lead us...
Most aircraft, whether manned or unmanned, depend on the Global Positioning System (GPS) for navigation. However, GPS signals are susceptible to jamming and spoofing. A researcher recently demonstrated the use of spoofing to take control of a ship in the Mediterranean Sea. By feeding counterfeit signals to the ship, he was able to drive it far off course. All the time, the navigation system reported...
As the collection of multiple modalities over a single region of interest becomes more common, users are provided with the capability to better overcome limitations of one data type by using the strengths of another. Often, when working only with hyperspectral imagery, scene classification is limited both by the generally lower spatial resolution of the hyperspectral imagery as well as the inability...
This research work presents a novel approach for generation of future image frames using optical flow method that estimates velocity of all the pixels in both axes of images in an image sequence. Direction and magnitude of velocity vector field helps in finding the changes in term of pixel intensity from one image to another. The pixel intensity change from one image to another is modeled Takagi-Sugeno...
A brain machine interface (BMI) facilitates the control of machines through the analysis and classification of signals directly from the human brain. Using an electroencephalograph (EEG) to detect neurological activity permits the collection of data representing brain signals without the need for invasive technology or procedures. A 14-electrode EPOC headset produced by the Emotiv Company is used...
Background subtraction is a common technique used for motion tracking. It involves segmenting the foreground from the background in a given set of video frames. Background subtraction as proposed by Stauffer-Grimson [1] models each pixel using a mixture of Gaussians. The parameters of the Gaussian model are adaptive, and can adjust to gradual changes in image intensity over time. But in cases when...
Semagrams are a subset of steganography. When a message is transmitted in a non-textual format, (i.e., in the visual content of an image), it is referred to as a semagram. While semagrams are relatively easy to create (as shown in published papers covering hiding techniques), detecting a hidden message in or embedded as an image-based semagram is a greater magnitude of difficultly than typical digital...
A closed-set identification is implemented using Elastic bunch graph matching (EBGM) algorithm. It uses cosine similarity as its matching criterion instead of a classifier for recognition. The proposed method makes use of facial features like fuducial points to differentiate between faces. It is insensitive to variation in facial expressions, illumination and poses on frontal and ¾ frontal images...
We have developed an algorithm, called ShareBoost, for combining mulitple classifiers from multiple information sources. The algorithm offer a number of advantages, such as increased confidence in decision-making, resulting from combined complementary data, good performance against noise, and the ability to exploit interplay between sensor subspaces.We have also developed a randomized version of ShareBoost,...
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