Aimed at the two problems of underwater imaging, fog effect and color cast, an Improved Segmentation Dark Channel Prior(ISDCP) defogging method is proposed to solve the fog effects caused by physical properties of wat...Aimed at the two problems of underwater imaging, fog effect and color cast, an Improved Segmentation Dark Channel Prior(ISDCP) defogging method is proposed to solve the fog effects caused by physical properties of water. Due to mass refraction of light in the process of underwater imaging, fog effects would lead to image blurring. And color cast is closely related to different degree of attenuation while light with different wavelengths is traveling in water. The proposed method here integrates the ISDCP and quantitative histogram stretching techniques into the image enhancement procedure. Firstly, the threshold value is set during the refinement process of the transmission maps to identify the original mismatching, and to conduct the differentiated defogging process further. Secondly, a method of judging the propagating distance of light is adopted to get the attenuation degree of energy during the propagation underwater. Finally, the image histogram is stretched quantitatively in Red-Green-Blue channel respectively according to the degree of attenuation in each color channel. The proposed method ISDCP can reduce the computational complexity and improve the efficiency in terms of defogging effect to meet the real-time requirements. Qualitative and quantitative comparison for several different underwater scenes reveals that the proposed method can significantly improve the visibility compared with previous methods.展开更多
The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circ...The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.展开更多
A novel unsupervised ship detection and extraction method is proposed. A combination model based on visual saliency is constructed for searching the ship target regions and suppressing the false alarms. The salient ta...A novel unsupervised ship detection and extraction method is proposed. A combination model based on visual saliency is constructed for searching the ship target regions and suppressing the false alarms. The salient target regions are extracted and marked through segmentation. Radon transform is applied to confirm the suspected ship targets with symmetry profiles. Then, a new descriptor, improved histogram of oriented gradient(HOG), is introduced to discriminate the real ships. The experimental results on real optical remote sensing images demonstrate that plenty of ships can be extracted and located successfully, and the number of ships can be accurately acquired. Furthermore, the proposed method is superior to the contrastive methods in terms of both accuracy rate and false alarm rate.展开更多
In this paper, firstly, target candidate regions are extracted by combining maximum symmetric surround saliency detection algorithm with a cellular automata dynamic evolution model. Secondly, an eigenvector independen...In this paper, firstly, target candidate regions are extracted by combining maximum symmetric surround saliency detection algorithm with a cellular automata dynamic evolution model. Secondly, an eigenvector independent of the ship target size is constructed by combining the shape feature with ship histogram of oriented gradient(S-HOG) feature, and the target can be recognized by Ada Boost classifier. As demonstrated in our experiments, the proposed method with the detection accuracy of over 96% outperforms the state-of-the-art method. efficiency switch and modulation.展开更多
基金supported by the National Natural Science Foundation of China (No. 61401413)
文摘Aimed at the two problems of underwater imaging, fog effect and color cast, an Improved Segmentation Dark Channel Prior(ISDCP) defogging method is proposed to solve the fog effects caused by physical properties of water. Due to mass refraction of light in the process of underwater imaging, fog effects would lead to image blurring. And color cast is closely related to different degree of attenuation while light with different wavelengths is traveling in water. The proposed method here integrates the ISDCP and quantitative histogram stretching techniques into the image enhancement procedure. Firstly, the threshold value is set during the refinement process of the transmission maps to identify the original mismatching, and to conduct the differentiated defogging process further. Secondly, a method of judging the propagating distance of light is adopted to get the attenuation degree of energy during the propagation underwater. Finally, the image histogram is stretched quantitatively in Red-Green-Blue channel respectively according to the degree of attenuation in each color channel. The proposed method ISDCP can reduce the computational complexity and improve the efficiency in terms of defogging effect to meet the real-time requirements. Qualitative and quantitative comparison for several different underwater scenes reveals that the proposed method can significantly improve the visibility compared with previous methods.
基金Sponsored by The National Natural Science Foundation of China(60872065)Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation(20105152026)State Key Laboratory Open Fund of Novel Software Technology,Nanjing University(KFKT2010B17)
文摘The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.
基金supported by the National Natural Science Foundation of China(No.60902067)the Key Programs for Science and Technology Development of Jilin Province of China(No.11ZDGG001)
文摘A novel unsupervised ship detection and extraction method is proposed. A combination model based on visual saliency is constructed for searching the ship target regions and suppressing the false alarms. The salient target regions are extracted and marked through segmentation. Radon transform is applied to confirm the suspected ship targets with symmetry profiles. Then, a new descriptor, improved histogram of oriented gradient(HOG), is introduced to discriminate the real ships. The experimental results on real optical remote sensing images demonstrate that plenty of ships can be extracted and located successfully, and the number of ships can be accurately acquired. Furthermore, the proposed method is superior to the contrastive methods in terms of both accuracy rate and false alarm rate.
基金supported by the National Natural Science Foundation of China(No.61401425)
文摘In this paper, firstly, target candidate regions are extracted by combining maximum symmetric surround saliency detection algorithm with a cellular automata dynamic evolution model. Secondly, an eigenvector independent of the ship target size is constructed by combining the shape feature with ship histogram of oriented gradient(S-HOG) feature, and the target can be recognized by Ada Boost classifier. As demonstrated in our experiments, the proposed method with the detection accuracy of over 96% outperforms the state-of-the-art method. efficiency switch and modulation.