A neighborhood-dependent component feature learning method for regression analysis in single-image superresolution is presented. Given a low-resolution input, the method uses a directional Fourier phase feature component to adaptively learn the regression kernel based on local covariance to estimate the high-resolution image. The unique feature of the proposed method is that it uses image features to learn about the local covariance from geometric similarity between the low-resolution image and its high-resolution counterpart. For each patch in the neighborhood, we estimate four directional variances to adapt the interpolated pixels. This gives us edge information and Fourier phase gives features, which are combined to interpolate using kernel regression. In order to compare quantitatively with other state-of-the-art techniques, root-mean-square error and measure mean-square similarity are computed for the example images, and experimental results show that the proposed algorithm outperforms similar techniques available in the literature, especially at higher resolution scales.
Detection of small vessels is a challenging task for navy, coast guard and port authority for security purposes. Vessel
identification is more complex as compared to other object detection because of its variability in shapes, features and
orientations. Current methods for vessel detection are primarily based on segmentation techniques which are not as
efficient and also require different algorithms for visible and infrared images. In this paper, a new vessel detection
technique is proposed employing anomaly detection. The input intensity image is first converted to feature space using
difference of Gaussian filters. Then a detector filter in the form of Mahalanobis distance is applied to the feature points
to detect anomalies whose characteristics are different from their surroundings. Anomalies are detected as bright spots in
both visible and infrared image. The larger the gray value of the pixels the more anomalous they are to be. The detector
output is then post-processed and a binary image is constructed where the boat edges with strong variance relative to the
background are identified along with few outliers from the background. The resultant image is then clustered to identify
the location of the vessel. The main contribution in this paper is developing an algorithm which can reliably detect small
vessels in visible and infrared images. The proposed method is investigated using real-life vessel images and found to
perform excellent in both visible and infrared images with the same system parameters.
Edge detection is the primary step in image segmentation and target detection applications.
The edge operators proposed so far in the literature, namely, Canny, Sobel, Prewitt, provide a
number of unwanted edges which complicate the foreground object detection process. In this
paper, a novel technique is proposed for edge detection and foreground segmentation
employing two mean filters of different window sizes. A ratio of the filtered images is taken
and normalized. Then a threshold is applied on the histogram of the resultant image to derive
the final output which can detect the edges and hence separate the foreground from the
background. Performance of the proposed method has been investigated through computer
simulation and compared with other existing edge detection techniques using complex reallife
image sequences, which verifies that the technique provides better detection results for
any input scene.
Hyperspectral data (HS) is increasingly used in target detection applications since it provides
both spatial and spectral information about the scene. One of the main challenges in HS data is to
handle a large volume of data. On the other hand, mutispectral data provides the information
with reduced number of bands. As a result, target detection in multispectral image is more challenging due to lack of information about the objects. In this paper, we presented a new approach to detect land mines in multispectral images. We showed that application of matched filter (MF) to multispectral data is not suitable to detect the targets but after selecting some features based on principal component analysis (PCA) enables it to detect all the targets. We also described a segmentation technique-sliding concentric window (SCW) to extract the land mines from the clutter.
A new algorithm for automatic identification of vehicle license plate is proposed in this paper.
The proposed algorithm uses image segmentation and morphological operation to accurately
identify the location of license plate with various background illuminations. The license plate is
identified in two steps. At first the original image is segmented using edge detection and
morphological operations. Then, the power spectrum (PS) is analyzed in horizontal and vertical
directions to identify the license plate. The magnitude of the power frequency spectrum shows
special characteristics corresponding to the license plate segment. The proposed algorithm is tested
with different gray level car images from different angles of view and the results are all consistent.
The proposed algorithm is fast and can effectively identify license plates under various illumination
conditions with high accuracy.
Human motion tracking is an active area of research in computer vision and machine intelligence. It has many
applications in video surveillance and human-computer interface. Most of the existing algorithms track
multiple humans in a given image. This paper proposes a detection approach which can track a specific person
from a crowded environment. Mean shift clustering algorithm is employed in the difference image to get the
candidate cluster which is found to converge within few iterations. The number of clusters and the cluster
centers are automatically derived by mode seeking with the mean shift procedure. Discrete cosine transform is
applied to each cluster and to the known target to extract features of the clusters and the target. To get the
target cluster from a given image, Mahalanobis distance is measured between each transformed candidate
cluster and the target. The cluster with the minimum distance is taken as the desired target. Tracking is carried
out by updating the cluster parameters over time using the mean shift procedure.
Hyperspectral sensor imagery (HSI) is a relatively new area of research, however, it is extensively being used in
geology, agriculture, defense, intelligence and law enforcement applications. Much of the current research focuses on the
object detection with low false alarm rate. Over the past several years, many object detection algorithms have been
developed which include linear detector, quadratic detector, adaptive matched filter etc. In those methods the available
data cube was directly used to determine the background mean and the covariance matrix, assuming that the number of
object pixels is low compared to that of the data pixels.
In this paper, we have used the orthogonal subspace projection (OSP) technique to find the background matrix from the
given image data. Our algorithm consists of three parts. In the first part, we have calculated the background matrix using
the OSP technique. In the second part, we have determined the maximum likelihood estimates of the parameters. Finally,
we have considered the likelihood ratio, commonly known as the Neyman Pearson quadratic detector, to recognize the
objects. The proposed technique has been investigated via computer simulation where excellent performance has been
observed.
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