Segmentation of infrared ship is an important step for maritime surveillance. Due to the low contrast of the image and fuzziness of gray level distribution, the high-precision maximum fuzzy correlation method can take place of existing maximum fuzzy entropy method to segment infrared ship, since it can measure the appropriateness of the fuzzy segmentation better. Nevertheless, the maximum fuzzy correlation segmentation is computationally expensive. Straightforward implementation of maximum fuzzy correlation segmentation on infrared ship images may obtain incomplete segmentation results, since spatial coherence is not enforced. For addressing that, the image enhancement technique based on fuzzy sure entropy is used before the segmentation to alleviate the low-contrast problem. Then the fuzzy correlation model with three-parameter membership function is used to segment infrared ship images. For reducing the computational cost, an iterative calculation strategy is proposed for eliminating the repeated computation. Finally, the probabilities of fuzzy events obtained from maximum fuzzy correlation are used to set the data terms of graph cut for getting the spatial coherent segmentation results. Quantitative evaluations over 80 low-contrast infrared ship images demonstrate that the proposed method could effectively segment infrared ship targets and outperform several existing segmentation methods in terms of precision and efficiency.
With the development of computer vision technology, crack detection based on digital image segmentation method arouses global attentions among researchers and transportation ministries. Since the crack always exhibits the random shape and complex texture, it is still a challenge to accomplish reliable crack detection results. Therefore, a novel crack image segmentation method based on fractal DBC (differential box counting) is introduced in this paper. The proposed method can estimate every pixel fractal feature based on neighborhood information which can consider the contribution from all possible direction in the related block. The block moves just one pixel every time so that it could cover all the pixels in the crack image. Unlike the classic DBC method which only describes fractal feature for the related region, this novel method can effectively achieve crack image segmentation according to the fractal feature of every pixel. The experiment proves the proposed method can achieve satisfactory results in crack detection.
Using aerial color images or remote sensing color images to obtain the earth surface information is an important way for
gathering geographic information. In order to improve visibility in the poor quality weather for aerial color images, a
changing scale Retinex algorithm based on fractional differential and depth map is proposed. After a new fractional
differential operation, it requires the image dark channel prior treatment to obtain the estimated depth map. Then
according to the depth map, Retinex scales are calculated in each part of the image. Finally the single scale Retinex
transform is performed to obtain the enhanced image. Experimental results show that the studied algorithm can
effectively improve the visibility of an aerial color image without halo phenomena and color variation which happens if
using a similar ordinary algorithm. Compared with the He’s algorithm and others, the new algorithm has the faster speed
and better image enhancement effect for the images that have greatly different scene depths.
An automatic road extraction method for vague aerial images is proposed in this paper. First, a high-resolution but low-contrast image is enhanced by using a Retinex-based algorithm. Then, the enhanced image is segmented with an improved Canny edge detection operator that can automatically threshold the image into a binary edge image. Subsequently, the linear and curved road segments are regulated by the Hough line transform and extracted based on several thresholds of road size and shapes, in which a number of morphological operators are used such as thinning (skeleton), junction detection, and endpoint detection. In experiments, a number of vague aerial images with bad uniformity are selected for testing. Similarity and discontinuation-based algorithms, such as Otsu thresholding, merge and split, edge detection-based algorithms, and the graph-based algorithm are compared with the new method. The experiment and comparison results show that the studied method can enhance vague, low-contrast, and unevenly illuminated color aerial road images; it can detect most road edges with fewer disturb elements and trace roads with good quality. The method in this study is promising.
Height measurement for a moving human body is a hard task for human body estimation. We propose a novel algorithm for real-time human height measurement without knowing any camera parameter, just having a vertical reference height. As contrasted with the previous research methods in the context of camera calibration, the studied algorithm reduces the complexity of user operations and the economy cost. First, three or more pairs of top-points and bottom-points are extracted by detecting the moving human body to solve the vertical vanishing point and the horizontal vanishing line. Then, the height of the moving human body on ground plane or stepped plane is obtained using the solved vanishing point, the vanishing line, and a given reference height. Considering the importance of the vanishing point and the vanishing line and the sensitivity of both to noise, an optimal approach is adopted. Finally, we show the optimal number and position of the human body in a camera field. Both computer simulation and real testing data validate the robustness and the effectiveness of the proposed algorithm.
This paper presents a novel colony analysis system including an adjustable image acquisition subsystem and a wavelet-watershed-based image segmentation algorithm. An illumination box was constructed-both front lightning and back lightning illuminations can be chosen by users based on the properties of Petri dishes. In the illumination box, the lightning is uniform, which makes image processing easy. A digital camera at the top of the box is connected to a PC computer; all the camera functions are controlled by the developed computer software in this study. As usual, in the image processing part, the hardest task is image segmentation which is carried out by the four different algorithms: 1. recursive image segmentation on gray similarity; 2. canny edge detection-based segmentation; 3. the combination of 1 and 2, and 4. colony delineation on wavelet and watershed. The first three algorithms can obtain good results for ordinary colony images, and for the images including a lot of small (tiny) colonies and dark colonies and overlapping (or touching) colonies, the algorithm 4 can obtain better results than the others. The algorithms are tested by using a large number of different colony images, and the testing results are satisfactory.