This paper concerns the problem of fast vehicle license plate location. A new method has been
proposed for locating the vehicle license plate in the color image with a high speed. The color image is
transformed into HSV color space and each single channel image is operated by different operations
such as image equalization, image binary and so on. Then the results of each channel image is
integrated, and a mathematical morphology method together with image smoothing, image filtering and
contour extraction are used to get the candidate vehicle license plate. Finally, a minimum rectangle
enclosing the extracted contour is obtained, and affine transformation is done to the rectangle. The
experimental results of more than 140 images collected demonstrated that the accuracy of detection is
95.21%, and the average time cost for each image is about 50 milliseconds.
This paper proposed a method that can measure confidence for cascade classifier. It
has great value in fusion of multi-classifier, sorting object detection results and so on. Differs from
the traditional qualitative description results though using cascade classifier, this method could
give a quantitative description for the results. Confidence evaluation function is constructed to
calculate the confidence for the targets and the classifiers. Several experiments have been done
and the results show that false alarm rate could be greatly reduced with little time cost.
Affine invariant image comparison is always consequential in computer vision. In this paper, affine-SURF (ASURF) is
introduced. Through a series of affine transformations and feature extraction, the matching algorithm becomes more
robust with the view and scale change. A kd-tree structure is build to store the feature sets and BBF search algorithm is
used in feature matching, then duplicates are removed by the conditional of Euclidean distance ratio. Experiments show
it has a good result, comparisons with SIFT and SURF is made to prove its performance.
The use of remote sensing images collected by space platforms is becoming more and more widespread. The increasing value of space data and its use in critical scenarios call for adoption of proper security measures to protect these data against unauthorized access and fraudulent use. In this paper, based on the characteristics of remote sensing image data and application requirements on secure distribution, a secure distribution method is proposed, including users and regions classification, hierarchical control and keys generation, and multi-level encryption based on regions. The combination of the three parts can make that the same remote sensing images after multi-level encryption processing are distributed to different permission users through multicast, but different permission users can obtain different degree information after decryption through their own decryption keys. It well meets user access control and security needs in the process of high resolution remote sensing image distribution. The experimental results prove the effectiveness of the proposed method which is suitable for practical use in the secure transmission of remote sensing images including confidential information over internet.
Image mosaic can be widely used in remote measuring, scout in battlefield and Panasonic image demonstration. In this
project, we find a general method for video (or sequence images) mosaic by techniques, such as extracting invariant
features, gpu processing, multi-color feature selection, ransac algorithm for homograph matching. In order to match the
image sequence automatically without influence of rotation, scale and contrast transform, local invariant feature
descriptor have been extracted by graph card unit. The gpu mosaic algorithm performs very well that can be compare to
slow CPU version of mosaic program with little cost time.
In this paper a face-recognition algorithm with a confidence evaluation function for batch of SIFT feature is presented.
Confidence evaluation function is rarely used in traditional face recognition, which is an important index in future
recognition. In our face-recognition algorithm, two main steps are provided, that is primary election and strict
identification. Adaboost algorithm can detect rough features to collect candidate face regions, it works as primary
election algorithm. SIFT can describe the detail features in the face regions, the confidence evaluation function for batch
of SIFT feature is highly distinctive, and it work as strict identification algorithm. This confidence evaluation function is
a reliable measurement for matching multi-candidate regions containing invariant features. And, it can also be used in
image retrieval, object recognition.
Image matching is a critical issue in many image processing applications. It is very likely that the real-time sensed image and the reference image have significant differences in terms of brightness, contrast, and angle due to the changing parameters of imaging devices, the illumination conditions, and the view angles. This has greatly affected the precision and the efficiency of the target recognition tasks in remote sensing. We construct a novel moment invariant as a confidence measure for the image matching task. Using the distance between template and local region in a real-time image in the feature space of moment invariant, a target detection algorithm is implemented that does not rely on either the imaging angles or the illumination conditions. Based on the phases of complex moments, the direction of each matching region in relation to the template can also be obtained. The experiments show that the algorithm can be used in target identification with changing conditions of the brightness, contrast, and the rotation angles in relation to the template.
We propose a unified approach that incorporates the mean shift-based image segmentation algorithm and the SST (shortest spanning tree)-minmax-based graph grouping method to achieve effective IR object segmentation performance amenable for real-time application. It preprocesses an image by using the mean shift algorithm to form segmented regions that can not only remove the noise, but also preserve the desirable discontinuity characteristics of the ship object. The segmented regions can then effectively represent the original image by using the graph structures, and we apply the SST-minmax method to perform merging procedure to form the final segmented regions. Due to the good discontinuity-preserving filtering characteristic, we can effectively remove the clutter disturbance of the sea background without loss of the IR ship object information, and significantly reduce the number of basic image entities. Therefore, the region merging based on SST-minmax can produce excellent segmentation performance at low computational cost due to smaller clutter disturbance and less region nodes. The superiority of the proposed method is examined and demonstrated through a large number of experiments using a real IR ship image sequence.