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News and Resources for Members of the IEEE Signal Processing Society
For our March 2018 issue, we cover recent patents granted in the area of image segmentation
In patent no 9,886,647 systems, devices, and methods are presented for segmenting an image of a video stream with a client device by accessing a set of images within a video stream, identifying an object of interest within one or more images of the set of images, and detecting a region of interest within the one or more images. The systems, devices, and method identify a first set of median pixels in a first portion of the object of interest and a second set of median pixels in a second portion of the object of interest. The systems, devices, and methods determine a polyline approximating the first and second sets of median pixels and generate a model for the polyline.
Patent no. 9,846,816 presents an image segmentation threshold value determining method, comprising: defining a plurality of image regions of a first sensing image; determining a first, a second part image segmentation threshold values according to a first, second image regions of the image regions; performing a first, a second image segmentation operation to the first sensing image according to the first, the second part image segmentation threshold values to acquire a first, a second segmented images; and selecting one of the first part image segmentation threshold value and the second part image segmentation threshold value as the first image segmentation threshold value according to the first segmented image and the second segmented image.
In patent no. 9,842,260 a clustering processing unit sets representative data to be used in first clustering, for each of temporally continuous images, and repeats clustering of pixels based on the representative data. A representative data updating unit updates the representative data whenever clustering is repeated. As the representative data to be used in first clustering of each image, an initial value or representative data used in last clustering of an immediately preceding image is set based on predetermined input information. An example of the predetermined input information is operation information for an image capturing unit for capturing an image. If a dead cluster predictively forms due to an image change, the initial value is set as the representative data. This makes it possible to restrict accumulation of dead clusters, and suppress the decrease in segmentation accuracy.
Patent no. 9,836,849 introduces a method that comprises obtaining an image of a network section through which flow occurs; where the flow is selected from a group consisting of fluid, electrons, protons, neutrons and holes; subjecting the image to a low pass filter to increase contrast in portions of the network sections; computing a local mean of visible light intensity at each pixel that is present in the image; calculating a visible light intensity difference between each pixel and the local mean of visible light intensity and producing a differentiated image using this calculation; creating a base image of the differentiated image; where the base image comprises a hand segmented gold standard dataset; removing objects below a minimum threshold size from the base image; and retaining the remaining objects if they approximate the line or spine.
In patent no. 9,830,708 systems, devices, media, and methods are presented for segmenting an image of a video stream with a client device, binarizing an area of interest within one or more image, identifying an initial pupil location and an initial iris radius, and determining a final pupil location and a final iris radius. Some embodiments enable the client device to perform one or more operations within a user interface based on the image segmentation.
In patent no. 9,818,186 disclosed is a method for forming a three-dimensional (3D) model of skin and mandible by automatic medical image segmentation which is performed in an automatic image segmentation and model formation server. The method includes (a) receiving 3D medical image data that is a set of two-dimensional (2D) images for horizontal planes of a face, (b) obtaining a contrast histogram based on distribution of contrasts of the 3D medical image data, and segmenting the 3D medical image data for the face into multiple regions separated into at least one partial region based on the contrast histogram, (c) extracting only the face by removing portions other than the face from the multiple regions for the face, and extracting a skin region of the face, (d) extracting the mandible from each of the 2D images for the horizontal planes of the face through a 2D detailed segmentation technique using an active contour method based on a level set function, and (e) reconstructing the extracted skin region and mandible as the 3D model.
An image segment method is provided in disclosure no. 9,807,316. The method is suitable for an electronic apparatus including a first camera and a motion sensor. The method includes steps of: providing at least one pre-defined model mask; fetching pose data from the motion sensor, the pose data being related to an orientation or a position of the first camera; adjusting one of the at least one pre-defined model mask into an adaptive model mask according to the pose data; and, extracting an object from an image captured by the first camera according to the adaptive model mask.
In patent no. 9,795,351 a three-dimensional cardiac image segmentation method and apparatus are provided according to examples of the present disclosure. The method includes: searching from layers of cardiac computerized tomography CT images to obtain a layer of cardiac CT image located between a heart bottom portion and a heart upper portion as a first key layer; searching on the first key layer to obtain a boundary of the heart as a contour of the heart bottom portion, and generating a curved surface model based on the contour of the heart bottom portion and a lowest point of the heart bottom portion as a heart bottom model; searching layers of cardiac CT images above the first key layer to obtain boundaries of the heart as a contour of the heart upper portion, and generating a curved surface model based on the contour of the heart upper portion as a heart upper model.
If you have an interesting patent to share when we next feature patents related to image segmentation, or if you are especially interested in a signal processing research field that you would want to be highlighted in this section, please send email to Csaba Benedek (benedek.csaba AT sztaki DOT mta DOT hu).
Title: Image segmentation threshold value deciding method, gesture determining method, image sensing system and gesture determining system
Inventors: Chang; Bo-Yi (Hsin-Chu, TW)
Issued: December 19, 2017
Assignee: PixArt Imaging Inc. (Hsin-Chu, TW)
Title: Image processing apparatus and image processing method of performing image segmentation
Inventors: Hashiguchi; Noriyasu (Kawasaki, JP)
Issued: December 12, 2017
Assignee: Canon Kabushiki Kaisha (Tokyo, JP)
Title: Method for the autonomous image segmentation of flow systems
Inventors: Dickrell, III; Daniel John (Gainesville, FL), Panikulam; Jacob J. (Fort Lauderdale, FL)
Issued: December 5, 2017
Assignee: University of Florida Research Foundation, Inc. (Gainesville, FL)
Title: Method for forming 3D maxillofacial model by automatically segmenting medical image, automatic image segmentation and model formation server performing the same, and storage medium storing the same
Inventors: Kim; Sun Hee (Seoul, KR), Kim; Young Jun (Seoul, KR), Kim; Lae Hyun (Seoul, KR), Lee; Deuk Hee (Seoul, KR), Park; Se Hyung (Seoul, KR)
Issued: November 14, 2017
Assignee: Korea Institute of Science and Technology (Seoul, KR)
Title: Method for image segmentation
Inventors: Jen; Cheng-Lung (Taoyuan, TW), Tai; Pol-Lin (Taoyuan, TW), Wu; Jing-Lung (Taoyuan, TW), Chueh; Hsin-Ti (Taoyuan, TW)
Issued: October 31, 2017
Assignee: HTC Corporation (Taoyuan, TW)
Title: Three-dimensional cardiac image segmentation method and apparatus
Inventors: Li; Bingsheng (Shenyang, CN), He; Wei (Shenyang, CN)
Issued: October 24, 2017
Assignee: Shenyang Neusoft Medical Systems Co., Ltd.
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