Effect of Acquisition and Analysis Conditions on Accuracy of DTI-Based Muscle Architecture Estimates: Predictions Using Numerical Simulations
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Diffusion-tensor imaging (DTI) based fiber tractography is a useful tool to study the architecture of human skeletal muscle. However, effects of image acquisition and analysis conditions on the outcome of architectural estimates are challenging to examine in vivo . In this work, we describe a numerical simulation framework where the ground truth of muscle architecture is known and the outcome can be tested under different conditions. Results show that the estimate of fiber curvature is most affected by image noise. While second-order polynomial fitting of fiber tracts is more robust to image noise, third-order fitting performs better on highly curved fibers.We propose a new automatic image segmentation method. Color edges in an image are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained color edges have provided the major geometric structures in an image, the centroids between these adjacent edge regions are taken as the initial seeds for seeded region growing (SRG). These seeds are then replaced by the centroids of the generated homogeneous image regions by incorporating the required additional pixels step by step. Moreover, the results of color-edge extraction and SRG are integrated to provide homogeneous image regions with accurate and closed boundaries. We also discuss the application of our image segmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds.
Centroid
Region growing
Image gradient
Range segmentation
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Object segmentation can be an important step between object detection and tracking. Especially in image processing applications, where object detection is difficult due to high object distance, camera motion, or noise, the detection result might not be precise enough to robustly initialize tracks and perform multi-target tracking. In this paper we present the detection and segmentation of moving objects in image sequences coming from a small Unmanned Aerial Vehicle (UAV). Based on the detection and tracking of local image features, camera motion is compensated and independent motion created by moving vehicles and people on the ground is found. By clustering the independent motion vectors initial object hypotheses are generated which may be affected by over- and under-segmentation. For improvement, several object segmentation approaches are introduced and tested. Best results are achieved with a spatiotemporal fusion of some approaches. Both spatial and temporal information is provided by the local image features. The object segmentation approaches and the fusion methods are evaluated for their completeness and precision.
Segmentation-based object categorization
Match moving
Tracking (education)
Motion Detection
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When a digital image acquisition system captures a scene, image degradation due to motion blur is unavoidable. Motion blur is caused by an imperfect imaging geometry, and it makes the obtained image lose important information. In this work we propose a new degradation model for boundary region between multiple moving objects. Based on the proposed model, we also propose a segmentation-based spatially adaptive image restoration algorithm. Accordingly, we present a proper segmentation algorithm and a motion estimation method for moving object.
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Segmentation-based object categorization
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Moving objects often contain almost important information for surveillance videos, traffic monitoring, human motion capture etc. Background subtraction methods are widely exploited for moving object detection in videos in many applications. Moving object segmentation is the application in video processing. Segmentation helps in detecting various features of moving objects for further video/image processing. In this paper object detection and segmentation is proposed and they are compared using background subtraction algorithm (object detection) and segmentation algorithm (edge detection and thresholding). The experiment results show that the proposed method gives better results.
Object-class detection
Segmentation-based object categorization
Motion Detection
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Estimating white matter fiber pathways from a diffusion tensor MRI dataset has many important applications in medical research. However, the standard approach of performing tracking on single-tensor estimates per voxel is confounded by regions of multiple pathways in different directions. Building on previous work for estimating multiple tensors from MR value partitioning, we present here a two-tensor fiber tractography method that estimates two tensors from the acquired MR values, interpolated at each step of the path, and follows the tensor most aligned with the current direction. The method is verified on a synthetic dataset and applied to two locations of fiber crossing in an in vivo diffusion MRI
Fiber tract
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A novel colour image segmentation method designed for region based image registration and video object tracking is proposed. For these applications consistency of segmentation across video frames is very important. To reduce computational complexity, the image is separately segmented in the hue and value feature spaces before combining the results together. Experimental results comparing the proposed and other competing methods are presented and show that the proposed method achieves promising results for natural colour image segmentation in image registration application.
Segmentation-based object categorization
Range segmentation
Feature (linguistics)
Hue
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Computer Vision describes some of its typical assignments as motion analysis, scene reconstruction, image restoration and image segmentation. Enormous tasks could be demonstrated while segmenting an image. The goal might be stated as, "Extraction of object from background" or "Analysing sharp edges in the image". The goal could be any, but the initial information needed to visualize the difference between different segments of image, should be accurately picked and evaluated. This paper talks about such an initial information i.e. depth value of image pixels and gives an insight of its importance in the field of Image segmentation. Moreover, the paper also describes some of the typical techniques to incur depth information in the process of segmentation, solely or combined with other pixel values like color.
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In this paper, we present a novel method called coloring technique for image segmentation. Coloring technique uses the edges detected by the Sobel operator and in many cases successfully identifies the object boundary. This method can be used to eliminate unimportant edges in the background created by clutter and noise while preserving the edges inside the object boundary. The results show that the new method yields superior segmentations compared to conventional segmentation methods.
Machine Vision
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This paper on object detection by image processing to ensure the people suffering from various vision disorders to detect and to perceive the real image before them. The image perceived by the patients are rendered and developed so that the closest similar object is detected. The input to the program is a image which is fed to the set of processes which in turn gives a image that can be compared to the set of data to give the accuracy in (percentage) to which extend the image is similar to an object or animal.
Image subtraction
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The problems of object detection on an image and labeling of pixels, corresponding to each detected object (segmentation) are solved. In the work we have examined such issues as filtering and preparation depth data, extraction of semantically rich features vectors from the RGB-D images and classification methods, allowing implementing the objects detection and segmentation.
RGB color model
Segmentation-based object categorization
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