This paper introduces some new imagesegmentation methods in the framework of shadowed c-means clustering. By implanting the local and non-local spatial information in the membership value estimation procedure, we pro...
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ISBN:
(纸本)9781479906505
This paper introduces some new imagesegmentation methods in the framework of shadowed c-means clustering. By implanting the local and non-local spatial information in the membership value estimation procedure, we propose the Local Spatial Shadowed C-Means (LSSCM) algorithm, Non-local Spatial Shadowed C-Means (NLSSCM) algorithm and their combination - L+NLSSCM. Compared to traditional fuzzy c-means and shadowed c-means based approaches, the proposed image segmentation algorithms can obtain better segmentation results on test images. It is observed the proposed algorithms can effectively tackle the overlapping among segments and the noise problem in images.
An interactive image segmentation algorithm, which accepts user-annotations about a target object and the background, is proposed in this work. We convert user-annotations into interaction maps by measuring distances ...
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ISBN:
(纸本)9781728132945
An interactive image segmentation algorithm, which accepts user-annotations about a target object and the background, is proposed in this work. We convert user-annotations into interaction maps by measuring distances of each pixel to the annotated locations. Then, we perform the forward pass in a convolutional neural network, which outputs an initial segmentation map. However, the user-annotated locations can be mislabeled in the initial result. Therefore, we develop the backpropagating refinement scheme (BRS), which corrects the mislabeled pixels. Experimental results demonstrate that the proposed algorithm outperforms the conventional algorithms on four challenging datasets. Furthermore, we demonstrate the generality and applicability of BRS in other computer vision tasks, by transforming existing convolutional neural networks into user-interactive ones.
In this study, the authors present a new image segmentation algorithm based on two-dimensional digital fractional integration (2D-DFI) that was inspired from the properties of the fractional integration function. Alth...
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In this study, the authors present a new image segmentation algorithm based on two-dimensional digital fractional integration (2D-DFI) that was inspired from the properties of the fractional integration function. Although obtaining a good segmentation result corresponds to finding the optimal 2D-DFI order, the authors propose a new alternative based on Legendre moments. This framework, called two dimensional digital fractional integration and Legendre moments' (2D-DFILM), allows one to include contextual information such as the global object shape and exploits the properties of the 2D fractional integration. The efficiency of 2D-DFILM is shown by the comparison to other six competing methods recently published and it was tested on real-world problem.
Traditionally, image segmentation algorithms are viewed as black-box measurement sources. In this work, the system-level formulation of closed-loop tracking is reformulated under the assumption of access to physical s...
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ISBN:
(纸本)9781467331074
Traditionally, image segmentation algorithms are viewed as black-box measurement sources. In this work, the system-level formulation of closed-loop tracking is reformulated under the assumption of access to physical sensor data in addition to the video stream. The scope is restricted to algorithms that iteratively minimize a functional to localize the target in each image frame. A nominal iterative algorithm is an open-loop system;IMU data is then used to generate a control to modify the algorithm's behavior and thus create a closed-loop system. Compensating using IMU data in a feedback setting creates the possibility of induced track loss, whereby the compensation effectively undoes the action of the motion control. This issue is overcome by solving an optimization program that exploits known control intent to separate ego-motion disturbances from the control. As a result, the reformulation proposed in this paper enables improved tracking of a fast-moving target even in the presence of strong disturbances/jitter in the camera platform.
A novel RGB-D image segmentation algorithm is proposed in this work. This is the first attempt to achieve imagesegmentation based on the theory of multiple random walkers (MRW). We construct a multi-layer graph, whos...
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ISBN:
(纸本)9781467399623
A novel RGB-D image segmentation algorithm is proposed in this work. This is the first attempt to achieve imagesegmentation based on the theory of multiple random walkers (MRW). We construct a multi-layer graph, whose nodes are superpixels divided with various parameters. Also, we set an edge weight to be proportional to the similarity of color and depth features between two adjacent nodes. Then, we segment an input RGB-D image by employing MRW simulation. Specifically, we decide the initial probability distribution of agents so that they are far from each other. We then execute the MRW process with the repulsive restarting rule, which makes the agents repel one another and occupy their own exclusive regions. Experimental results show that the proposed MRW image segmentation algorithm provides competitive segmentation performances, as compared with the conventional state-of-the-art algorithms.
Video object segmentation often fails when the background and foreground contain a similar distribution of colours. Proposed is a novel image segmentation algorithm to detect salient motion under a complex environment...
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Video object segmentation often fails when the background and foreground contain a similar distribution of colours. Proposed is a novel image segmentation algorithm to detect salient motion under a complex environment by combining temporal difference and background generation. Experimental results show that the proposed algorithm provides a twice higher matching ratio than the conventional Gaussian mixture-based approaches under various conditions.
Purpose The purpose of the present study was to establish a semi-automated threshold-based image segmentation algorithm to detect and objectively quantify corneal cystine crystal deposition in ocular cystinosis with a...
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Purpose The purpose of the present study was to establish a semi-automated threshold-based image segmentation algorithm to detect and objectively quantify corneal cystine crystal deposition in ocular cystinosis with anterior segment optical coherence tomography (AS-OCT). Methods This prospective, observational, comparative study included 88 eyes of 45 patients from the German Cystinosis Registry Study as well as 68 eyes of 35 healthy control subjects. All eyes were imaged with AS-OCT (Cirrus HD-OCT 5000, Carl Zeiss Meditec AG, Jena, Germany). As an initial step, B-scan images were subjectively analysed for typical changes in morphology in comparison to healthy controls. Based on the experience gained, an objective semi-automated B-scan image segmentation algorithm was developed using a grey scale value-based threshold method to automatically quantify corneal crystals. Results On AS-OCT B-scans, corneal crystals appeared as hyperreflective deposits within the corneal stroma. The crystals were distributed either in all stromal layers (43 eyes, 49%) or confined to the anterior (23 eyes, 26%) or posterior stroma (22 eyes, 25%), respectively. The novel automatic B-scan image segmentation algorithm was most efficient in delineating corneal crystals at higher grey scale thresholds (e.g. 226 of a maximum of 255). Significant differences in suprathreshold grey scale pixels were observable between cystinosis patients and healthy controls (p < 0.001). In addition, the algorithm was able to detect an age-dependent depth distribution profile of crystal deposition. Conclusion Objective quantification of corneal cystine crystal deposition is feasible with AS-OCT and can serve as a novel biomarker for ocular disease control and topical treatment monitoring.
The camera is one of the important sensors to realise the intelligent driving environment. It can realise lane detection and tracking, obstacle detection, traffic sign detection, identification and discrimination and ...
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The camera is one of the important sensors to realise the intelligent driving environment. It can realise lane detection and tracking, obstacle detection, traffic sign detection, identification and discrimination and visual simultaneous localisation and mapping. The visual sensor model, quantity and installation location are different on different intelligent driving hardware experimental platform as well as the visual sensor information processing module, thus a number of intelligent driving system software modules and interfaces are different. In this study, the software architecture of the autonomous vehicle based on the driving brain is used to adapt to different types of visual sensors. The target segment is extracted by the image segmentation algorithm, and then the segmentation of the region of interest is carried out. According to the input feature calculation results, the obstacle search is done in the second segmentation region, the output of the accessible road area. As driving information is complete, the authors will increase or reduce one or more visual sensors, change the visual sensor model or installation location, which will no longer directly affect the intelligent driving decision, they make the multi-vision sensors adapted to the requirements of different intelligent driving hardware test platforms.
Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-ju...
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ISBN:
(纸本)9781479928392
Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take advantage of the structure present in local image patches to learn both an accurate and computationally efficient edge detector. We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests. Our novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated. The result is an approach that obtains realtime performance that is orders of magnitude faster than many competing state-of-the-art approaches, while also achieving state-of-the-art edge detection results on the BSDS500 segmentation dataset and NYU Depth dataset. Finally, we show the potential of our approach as a general purpose edge detector by showing our learned edge models generalize well across datasets.
A new version of Pixel Level Snake (PLS), which is an image segmentation algorithm, is proposed. The proposed algorithm is based on processes of Cellular Automaton (CA). In CA, the forces of resistance to stretch and ...
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ISBN:
(纸本)9781479948857
A new version of Pixel Level Snake (PLS), which is an image segmentation algorithm, is proposed. The proposed algorithm is based on processes of Cellular Automaton (CA). In CA, the forces of resistance to stretch and bending are defined by the CA rule, and comparing these forces and the gradients of image pixels determines deforming of the active contour. The CA based PLS consists of simple transition rules, and therefore easy implementation and high speed processing are expected on our dedicated hardware engine. In our experiments, we confirmed the feasibility of the imagesegmentation with the CA based PLS.
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