1. A new image segmentation method based on grey relational analysis and fuzzy entropy is presented.

2. A algorithm of image segmentation is proposed based on relative entropy selection thresholding and edge detection.

3. In it, image segmentation plays very important role in the quantity and quality analysis for the medical ultrasound image, and it influences the continuous processing and analysis directly.

4. Image segmentation of micro printing images is the foundation of the line printing quality detection.

4. is a sentence dictionary, on which you can find nice sentences for a large number of words.

5. This paper proposed a genetic clustering image segmentation algorithm on entropy.

6. FCM algorithm used in image segmentation is a course of unsupervised fuzzy clustering followed by demarcating.

7. Image segmentation and object classification are two important topics of digital image processing.

8. Thus, image segmentation algorithm and image visual feature extraction method are briefly introduced at the beginning of this paper.

9. A practical approach of object description after image segmentation to obtain objects' features is introduced in this paper.

10. Image segmentation algorithm procedure, using Prewitt operator implementation Laplacian operator are examples of implementation.

11. The paper presents an algorithm of automatic SAR image segmentation based on minimum error ratio.

12. Source 2 : What threshold image segmentation and contour extraction.

13. Region growing image segmentation method, the effect of a good factory, worth learning!

14. The optimum threshold determined by the theory of image segmentation is to be the crossover point of the fuzzy enhancement, in order to enhance the contrast of image.

15. This paper presents a new method for image segmentation via adaptive thresholding.

16. A range image segmentation algorithm based on tree structure ellipse cluster split is proposed.

17. An image segmentation approach based on watershed translation and graph theory is proposed.

18. The experimental results show that the image segmentation method based on the biology vision model is effective and efficient.

19. Image segmentation experiments based on the color property of objects shows that the model is effective.

20. Toboggan algorithm is an important tool to image segmentation, and the result of image segmentation depends on how to compute the gradient image to a great extent if applying toboggan to it.

21. An improved fuzzy C-means multi-resolution image segmentation algorithm is proposed. The fuzzy membership function of pixel is modified by the adjacent field information in the algorithm.

22. The definition of Centre of Mass and NMI feature is proposed and the principle and methods of Threshold calculation, Image segmentation, Target recognition and tracking are presented.

23. To solve segmentation problem of dynamic images, this paper presents an algorithm of 2-D minimum cross entropy based on genetic algorithm for dynamic image segmentation.

24. The experiments show the approach is a practical and successful method in KIMONO image segmentation.

25. The preprocessing in an automatic fingerprint identification system usually includes five steps:normalization, directional graph computation, image segmentation, filtering and binarization.

26. Image processing is the core of the system. It consists of image pre-processing, treatment of rifling, image segmentation, feature pick-up, defects recognition and texture analysis of rusts.

27. Parametric active contour model incorporating regions information was studied for image segmentation.

28. Before adopting a fuzzy entropy similarity metric, edge detection, image segmentation and segmentation description are accomplished.

29. One algorithm, isoperimetric algorithm based on graph theory is applied and researched on image segmentation.

30. In order to improve the quality of Laplace operator image segmentation at a high real time capability this paper presented a synchronization dimensional structure filter for Laplace operator.

31. Aimed at SAR image interpretation, SVM shows good performance in image filtering, image segmentation, target discrimination and classification, as well as polarimetric SAR data classification.

32. This image representation method has many advantages. It can reduce redundancy, and can avoid the difficulty of image segmentation and object-oriented descriptions semantically .

33. A new approach of object description after image segmentation to obtain objects' feature is introduced.

34. Simulation results preferably indicate that, under the estimated a prior probability, an approach to image segmentation is superiority.

34. Wish you can benefit from our online sentence dictionary and make progress every day!

35. But most of the past research based on monochrome image segmentation.

36. To improve the real-time performance for visual navigation of the mobile robot, a parallel color image segmentation algorithm using peer group filter(PGF) and fuzzy membership is studied.

37. Secondly, a cluster validity function, named modified partition fuzzy degree, is introduced for realization of automatically determining the optimal category number of image segmentation.

38. This paper aims to do some applicational simulation and algorithm improvement research on medical image segmentation algorithms.

39. Based on the laws of Gestalt psychology, a geometric active contour model for image segmentation is proposed in this paper.

40. A new method for natural color image segmentation is proposed. The phase congruency is applied to detect edge and obtain the major geometric structures in an image.

41. Image segmentation which subordinates digital image processing takes significant impact on analyzing knitted fabric, especially on pattern segmentation and recognition of weft jacquard knitted fabric.

42. Image segmentation is the basic and challengeable problem in image processing field.

43. An image segmentation method based on the velocity feature vector of moving target was proposed.

44. Finally, the particle size is measured based on the image segmentation and circularity theory.

45. Region-based Image Retrieval(RBIR) is a sub-branch of Contentbased Image Retrieval(CBIR). It employs image segmentation to extract local visual feature and retrieves images by similarity matching.

46. To improve the technology of the textile CAD, we proposed a new way of textile image segmentation by the minimum error Bayes decision theory based on the semi-supervised clustering.

47. This thesis studies the digital image processing, focusing on image segmentation techniques and image representation and description.

48. The algorithm could improve the effect of image segmentation through region merging and smooth after original segmentation.

49. This paper discusses the data parallel algorithm and its implementation on region growing image segmentation, the correctness is proved, and the performance is also analyzed.

50. The primary contents of this thesis are: 1 Image segmentation pretreatment.

51. Image segmentation is the base of image analysis image recognition and the image understanding.

52. Isoperimetric algorithm does well in image segmentation, and it is a fast graph partitioning method.

53. As the effect of tomography used in industry and medicine fields becoming more markedly, image segmentation becomes a challengeable research subject in these fields.

54. This paper proposes a novel algorithm about image segmentation based on the vision lateral inhibition model and statistics of consecutive fields.

55. Multiple-objects contour extraction is an important area in image segmentation.

56. A novel algorithm based on watershed for color image segmentation was presented.

57. The geodesic active contour model has been wide used in image segmentation for its computational stability.

58. In the candidate building area searching step, an image segmentation method based on grey level co-occurrence matrix (GLCM) is adopted to get candidate building areas in an urban image.

59. "Three-D reconstruction, massive-scale image segmentation ..." he says. "People can do these things in almost real time now."

60. An image segmentation method based on edge detection and region growing is proposed in view of the features of IR image.

61. A method for infrared image segmentation based on fuzzy kernel clustering using spatial relation was proposed.

62. A color image segmentation algorithm and an adaptive region growing algorithm are adopted and proved to be effective in defect region extraction.

63. Polygonal approximation is first applied to the discrete points collected from image segmentation and contour tracing, and the points that are almost collinear with others are deleted as a result.

64. A range image segmentation algorithm based on Gaussian mixture model of surface normal is proposed.

65. The model is applied in face recognition and image segmentation approach using gradient operator is proposed to increase the recognition accuracy.

66. Finally, the output of this algorithm is the optimal threshold. Using this threshold to partition off the pixels, image segmentation is implemented.

67. The image processing of the strip surface defects realized the task of defect detecting and image segmentation.

68. Compared the existing image segmentation algorithms,[Sentence dictionary] thethe new algorithm is rapid and veracious.

69. Image segmentation is the process of divided into which contains real-world objects or regions have strong correlation integral part of the image.

70. Firstly, isoperimetric algorithm is applied in still image segmentation, and the result is compared with two methods based on edge detection and region detection.

71. This system mainly includes four aspects: image segmentation, feature extraction, image matching, and relevance feedback.

72. Second, the method of constructing fuzzy membership function is proposed in image segmentation based on fuzzy mutual information.

73. The advantages of this model lie in: Firstly, the model is entirely based on perceptual objects, its results can be easily applied to object detection, image segmentation, and scene analysis.

74. Fuzzy c-means clustering(FCM) algorithm works as an unsupervised classification method has been applied in object identification and image segmentation.