Control of variable structure elements in adaptive mathematical morphology for boundary enhancement of ultrasound images

Tsubai, Masayoshi; Ito, Masayasu

doi:10.1002/ecjc.20117

Cited by 16 publications

(2 citation statements)

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“…Besides simple morphology [9], there are other methods of morphological segmentation using adaptive methods [254,255,256]. The simple morphology methods rely on using a fixed kernel across the entire image at each pixel and assume the threshold is already applied to the image; while the adaptive methods combine the morphology operations with variable kernels and variable thresholds based on the local pixel intensity statistics.…”

Section: Morphology Optimizations and Refinementsmentioning

confidence: 99%

Computer Vision Metrics

Krig¹

2014

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Distance From Sensor in meters Depth granularity or resolution in mm Depth granularity or resolution in mm Raw Z depth map Raw YYZ vertex map & Surface normal map 6DOF pose via ICP Volume surface integration 3D surface rendering Volume YYZ vertex map & Surface normal map a. Method of volume integration, 6DOF camera pose, and surface rendering used in KinectFusion [474][475]. Name Middlebury Computer Vision Datasets Description Scholarly and comprehensive datasets, and algorithm comparisons over most of the datasets Categories Stereo vision (excellent), multi-view stereo (excellent), MRF, Optical Flow (excellent), Color processing Contributions Algorithm benchmarks over the datasets can be submitted Key papers Several; see website Owner Middlebury College Link http://vision.middlebury.edu/ Name ADL Activity Recognition Dataset Description Annotated scenes for activity recognition of common living scenes Categories Daily life Tools and apps Activity recognition code available (see link below) Key papers [73] Link http://deepthought.ics.uci.edu/ADLdataset/adl.html Name MIT Indoor Scenes 67, Scene Classification Description Annotated dataset specifically containing diverse indoor scenes Categories 15,620 images organized into 67 indoor categories, some annotations in Labelme format Key papers [74] Link http://web.mit.edu/torralba/www/indoor.html Name RGB-D Object Recognition Dataset, U of W Description Dataset contains RGB and corresponding depth images Categories 300 common household objects, 51 categories using Wordnet similar to Imagenet style (Imagenet dataset reviewed above), each object recorded in RGB and Kinect depth at various rotational angles and viewpoints Key papers [75] Link http://www.cs.washington.edu/rgbd-dataset/ APPENDIX B ■ SurvEy of GrouND TruTh DATASETS 405 Name NYU Depth Datasets Description Annotated dataset of indoor scenes using RGB-D datasets + accelerometer data Categories Over 500,000 frames, many different indoor scenes and scene types, thousands of classes, accelerometer data, inpainted and raw depth information Tools and apps Matlab toolbox + g++ code Key papers [76] Link http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html Name Intel Labs Seattle -Egocentric Recognition of Handled Objects Description Annotated dataset for egocentric handled objects using a wearable camera Categories Over 42 everyday objects under varied lighting, occlusion, perspectives; over 6GB total video sequence data Key papers [77] [78] Link http://seattle.intel-research.net/~xren/egovision09/ Name Georgia Tech GTEA Egocentric Activities -Gaze(+) Description Annotated dataset for egocentric handled objects using a wearable camera Categories Many everyday objects under varied lighting, occlusion, perspectives Tools and apps Code library of vision functions and mathematical functions Key papers [79] Link http://www.cc.gatech.edu/~afathi3/GTEA_Gaze_Website/ Name CUReT: Columbia-Utrecht Reflectance and Texture Database Description Extensive texture sample and illumination datasets directions Categories Over 60 di...

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Section: Morphology Optimizations and Refinementsmentioning

confidence: 99%

Computer Vision Metrics

Krig¹

2014

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“…Desirable shape of a domain G x of the DSE should satisfy following conditions. Width of the G x in the direction ascending over the edge slope must be more than a width of the speckle cell to sharpen the blurred edge (15) . According to our experimental results (14) (16) , the number of pixels within G x should be more than 15 times of the number of pixels of the speckle cell in order not to emphasize the speckle.…”

Section: Morphological Image Sharpeningmentioning

confidence: 99%

Fast Algorithms of Morphological Operations by Double Structuring Elements and Their Application to Ultrasound Image

Tsubai

Nishimura

2006

IEEJ Trans.EIS

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Image Pre-Processing

Krig

2014

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This chapter describes the methods used to prepare images for further analysis, including interest point and feature extraction. Some of these methods are also useful for global and local feature description, particularly the metrics derived from transforms and basis spaces. The focus is on image pre-processing for computer vision, so we do not cover the entire range of image processing topics applied to areas such as computational photography and photo enhancements, so we refer the interested reader to various other standard resources in Digital Image Processing and Signal Processing as we go along [4,9,325,326], and we also point out interesting research papers that will enhance understanding of the topics. Note ■ Readers with a strong background in image processing may benefit from a light reading of this chapter. Perspectives on Image Processing Image processing is a vast field that cannot be covered in a single chapter. So why do we discuss image pre-processing in a book about computer vision? The reason is to advance the science of local and global feature description, as image pre-processing is typically ignored in discussions of feature description. Some general image processing topics are covered here in light of feature description, intended to illustrate rather than to proscribe, as applications and image data will guide the image pre-processing stage. Some will argue that image pre-processing is not a good idea, since it distorts or changes the true nature of the raw data. However, intelligent use of image pre-processing can provide benefits and solve problems that ultimately lead to better local and global feature detection. We survey common methods for image enhancements and corrections that will affect feature analysis downstream in the vision pipeline in both favorable and unfavorable ways, depending on how the methods are employed.

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Control of variable structure elements in adaptive mathematical morphology for boundary enhancement of ultrasound images

Cited by 16 publications

References 17 publications

Computer Vision Metrics

Computer Vision Metrics

Fast Algorithms of Morphological Operations by Double Structuring Elements and Their Application to Ultrasound Image

Image Pre-Processing

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