In this paper, an edge extraction model based on artificial bee colony algorithm has been proposed to overcome the problems of concrete defect detection in complex underwater environment specialized to dam cracks detection. To enhance weak-object edge gray contrast under different brightness, the adaptive enhancement method is presented in which a concept of two-dimensional lateral inhibitory network is introduced and a border highlighting rule is designed. Furthermore, to increase the edge extraction effective, the improved artificial bee colony algorithm is used in which an optimization strategy is based on edge direction information. Some experiments are carried out on underwater dam crack images in different environments and the experimental results show the efficiency and effectiveness of the algorithm.
Due to the light scattering and absorption, underwater images were blurred such as fog, uneven illumination, overexposure or lack of light. We proposed an image enhancement algorithm based on granular computing to enhance underwater optical image in this paper. First, the illumination information of underwater image was extracted. Then, we dividing the illumination information into granularity of different sizes from wide to thin by calculated the effectiveness indicators. Finally, by calculating their value and compensation for each granularity, we obtained the adaptive enhancement image. The simulation and experiment results verify the effectiveness of the algorithm.
This paper researches on the key and difficult issues in stereo measurement deeply, including camera calibration, feature extraction, stereo matching and depth computation, and then put forwards a novel matching method combined the seed region growing and SIFT feature matching. It first uses SIFT characteristics as matching criteria for feature points matching, and then takes the feature points as seed points for region growing to get better depth information. Experiments are conducted to validate the efficiency of the proposed method using standard matching graphs, and then the proposed method is applied to dimensional measurement of mechanical parts. The results show that the measurement error is less than 0.5mm for medium sized mechanical parts, which can meet the demands of precision measurement.
The multi-sensor image fusion technology can obtain a more comprehensive and more accurate and reliable image, in
order to understand the scene or recognize the target more easily. However, most existing algorithms are mainly based on
optical remote sensing images, which is highly susceptible by media interference, supplemented by SAR images. The
image fusion between SAR images and PAN images also cannot save the textural feature and the color information
effectively at the same time. In view of these problems, this paper presents a multi-sensor image fusion algorithm based
on region-based selection and IHS transform. The SAR image and PAN image are firstly IHS transformed to achieve the
intensity (I), hue (H) and saturation (S) weights. The I weights of SAR image and PAN image are separately decomposed
using SIDWT algorithm to extract wavelet coefficients. Then, the I weight of SAR image is divided into regular area and
irregular area based on a new adaptive segmentation method. A new fusion rules is presented according to local feature,
and then used to fuse corresponding wavelet coefficients of the I weight of SAR image and PAN image. Inverse SIDWT
is carried out on the fused wavelet coefficients to get the I weight (I’) of fused image. Finally, the fused image is obtained
by inverse IHS transform of I’ weight with the H, S weight of PAN image. Experimental results of real images validated
the effectiveness of the proposed algorithm by objective evaluation such as standard deviation, entropy, average gradient,
Proc. SPIE. 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
KEYWORDS: Target detection, Information fusion, Eye, Visual process modeling, Detection and tracking algorithms, Remote sensing, Biomimetics, Data processing, Mahalanobis distance, Information operations
Aimed to the limitation of present anomaly detection algorism under clutter background for multi-spectral remote sensing data, especially for the situations of dense spread target and exist different attributive of background objects, a bio-inspired anomaly detection algorithm was proposed. Simulate the information processing and fusion mechanism of fly multi-apertures vision system, multi-level background model was proposed to analysis and describe feature of clutter background. Then the threshold value can be chose adaptively according to the level of background model. The proposed algorithm didn’t need the prior knowledge about anomaly, and avoids the choosing of the background widow size. A fusion mechanism was proposed to fuse the different detection results with different level background model. Simulation experiment validated the effectiveness of proposed method.