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Annual Conference on Neural Information Processing Systems

作者： Kamiya Motwani Nagesh Adluru Chris Hinrichs Andrew Alexander Vikas Singh Computer Sciences University of Wisconsin Biostatistics & Medical Informatics University of Wisconsin Biostatistics & Medical Informatics University of Wisconsin Medical Physics University of Wisconsin

ISBN: (纸本)9781617823800

We study the problem of segmenting specific white matter structures of interest from Diffusion Tensor (DT-MR) images of the human brain. This is an important requirement in many Neuroimaging studies: for instance, to evaluate whether a brain structure exhibits group level differences as a function of disease in a set of images. Typically, interactive expert guided segmentation has been the method of choice for such applications, but this is tedious for large datasets common today. To address this problem, we endow an image segmentation algorithm with "advice" encoding some global characteristics of the region(s) we want to extract. This is accomplished by constructing (using expert-segmented images) an epitome of a specific region - as a histogram over a bag of 'words' (e.g., suitable feature descriptors). Now, given such a representation, the problem reduces to segmenting a new brain image with additional constraints that enforce consistency between the segmented foreground and the pre-specified histogram over features. We present combinatorial approximation algorithms to incorporate such domain specific constraints for Markov Random Field (MRF) segmentation. Making use of recent results on image co-segmentation, we derive effective solution strategies for our problem. We provide an analysis of solution quality, and present promising experimental evidence showing that many structures of interest in Neuroscience can be extracted reliably from 3-D brain image volumes using our algorithm.

关键词： Cerebrum image segmentation algorithm histograms Tensor group level redundant constraint interest Neurosciences Approximation algorithms Neurophysiology image segmentation image

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Higher-Order Correlation Clustering for image segmentation 11

Higher-Order Correlation Clustering for Image Segmentation

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Annual Conference on Neural Information Processing Systems

作者： Sungwoong Kim Sebastian Nowozin Pushmeet Kohli Chang D. Yoo Department of EE KAIST Daejeon South Korea Microsoft Research Cambridge Cambridge UK

ISBN: (纸本)9781618395993

For many of the state-of-the-art computer vision algorithms, image segmentation is an important preprocessing step. As such, several image segmentation algorithms have been proposed, however, with certain reservation due to high computational load and many hand-tuning parameters. Correlation clustering, a graph-partitioning algorithm often used in natural language processing and document clustering, has the potential to perform better than previously proposed image segmentation algorithms. We improve the basic correlation clustering formulation by taking into account higher-order cluster relationships. This improves clustering in the presence of local boundary ambiguities. We first apply the pairwise correlation clustering to image segmentation over a pairwise superpixel graph and then develop higher-order correlation clustering over a hypergraph that considers higher-order relations among superpixels. Fast inference is possible by linear programming relaxation, and also effective parameter learning framework by structured support vector machine is possible. Experimental results on various datasets show that the proposed higher-order correlation clustering outperforms other state-of-the-art image segmentation algorithms.

关键词： image segmentation algorithm image segmentation higher-order Cluster Analysis hypergraph Natural Language Processing Support Vector Network

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Spatial image segmentation for Breast Cancer Detection in Terahertz Imaging

Spatial Image Segmentation for Breast Cancer Detection in Te...

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IEEE International Symposium on Antennas and Propagation

作者： Tanny Chavez Nagma Vohra Jingxian Wu Magda El-Shenawee Keith Bailey University of Arkansas Fayetteville USA Veterinary Diagnostic Laboratory University of Illinois at Urbana-Champaign Urbana USA

ISBN: (数字)9781728166704

ISBN: (纸本)9781728166711

This paper proposes a new spatial image segmentation algorithm for breast cancer detection in terahertz (THz) images of freshly excised human tumors. Region classifications of fresh tissue with 3 or more regions, such as cancer, fat, and collagen, remain a challenge for cancer detection. We propose to tackle this problem by exploiting the spatial correlation among neighboring pixels in THz images, that is, pixels that are close to each other are more likely to belong to the same region. The spatial correlation among pixels is modeled by using Markov random fields (MRF). A Gaussian mixture model (GMM) with expectation maximization (EM) is then used to represent the statistical distributions of the THz images in both the frequency and spatial domain. Experiment results demonstrated that the proposed spatial image segmentation algorithm outperforms existing algorithms that do not consider spatial information.

关键词： cancer test image segmentation image segmentation algorithm terahertz pixel Terahertz Imaging statistical distribution Breast Cancer Collagen

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image thresholding framework based on two-dimensional digital fractional integration and Legendre moments'

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IET image PROCESSING 2012年第6期6卷 717-727页

作者： Nakib, A. Schulze, Y. Petit, E. Univ Paris EST Creteil LISSI EA 3956 F-94010 Creteil France

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.

关键词： integration fractional integration function two dimensional digital fractional integration-Legendre moments image segmentation 2D fractional integration image segmentation algorithm Computer vision and image processing techniques 2D-DFILM global object shape optimal 2D-DFI order Optical, image and video signal processing Mathematical analysis image thresholding framework

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Establishing an objective biomarker for corneal cystinosis using a threshold-based Spectral domain optical coherence tomography imaging algorithm

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ACTA OPHTHALMOLOGICA 2021年第2期99卷 E189-E195页

作者： Keidel, Leonie Elhardt, Carolin Hohenfellner, Katharina Priglinger, Siegfried Schworm, Benedikt Wertheimer, Christian Priglinger, Claudia Luft, Nikolaus Ludwig Maximilians Univ Munchen Dept Ophthalmol Mathildenstr 8 D-80336 Munich Germany RoMed Klinikum Rosenhe Dept Pediat Nephrol Rosenheim Germany

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.

关键词： biomarker cornea cystine crystals cystinosis greyscale image segmentation algorithm optical coherence tomography

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Technology and application of intelligent driving based on visual perception

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2017年第3期2卷 126-132页

作者： Zhang, Xinyu Gao, Hongbo Xie, Guotao Gao, Buyun Li, Deyi Tsinghua Univ Informat Technol Ctr Beijing 100083 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100083 Peoples R China Beijing Inst Technol Sch Software Beijing 100081 Peoples R China Inst Elect Engn China Beijing 100039 Peoples R China

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.

关键词： robot vision sensors intelligent driving system software modules visual perception road vehicles intelligent transportation systems image sensors camera visual simultaneous localisation and mapping obstacle detection software architecture feature extraction object detection driving brain visual sensor model intelligent driving hardware test platforms image segmentation algorithm intelligent driving hardware experimental platform multivision sensors intelligent driving decision target segment installation location traffic engineering computing image segmentation SLAM (robots) intelligent driving environment visual sensor information processing module segmentation region traffic sign detection visual sensors

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Vision-based multiple vehicle detection and tracking at nighttime

Vision-based multiple vehicle detection and tracking at nigh...

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International Symposium on Photoelectronic Detection and Imaging 2011 - Advances in Imaging Detectors and Applications

作者： Xu Wencong Liu Hai Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China

ISBN: (纸本)9780819488350

In this paper, we develop a robust vision-based approach for real-time traffic data collection at nighttime. The proposed algorithm detects and tracks vehicles through detection and location of vehicle headlights. First, we extract headlights candidates by an adaptive image segmentation algorithm. Then we group headlights candidates that belong to the same vehicle by spatial clustering and generate vehicle hypotheses by rule-based reasoning. The potential vehicles are then tracked over frames by region search and pattern analysis methods. The spatial and temporal continuity extracted from tracking process is used to confirm vehicle's presence. To handle problem of occlusions, we apply Kalman Filter to motion estimation. We test the algorithm on the video clips of nighttime traffic under different conditions. The experimental results show that real-time vehicle counting and tacking for multi-lanes are achieved and the total detection rate is above 96%.

关键词： Nighttime vehicle Vehicle detection Headlights video clips tracking image segmentation algorithm Pattern analysis motion estimation detection rate (DR) tack

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Comparative Study On segmentation Methods Of Fundus images 12

Comparative Study On Segmentation Methods Of Fundus Images

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Cao, Juan Liu, JinJia Chongqing Jiaotong Univ Sch Informat Chongqing 400074 Peoples R China

ISBN: (纸本)9798350321050

As a traditional image segmentation method, image segmentation methods based on threshold, edge detection, and region have some differences in the effect of fundus image segmentation. In this paper, the watershed algorithm, Otsu, and Canny operator edge detection algorithms are selected for comparative study. By comparing the segmentation performance of two CHASEDB1 datasets and DRIVE datasets of fundus images, the experimental results showed that the Otsu had the highest segmentation accuracy and best segmentation effect on fundus images.

关键词： image segmentation algorithm Comparative Analysis

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NeTra: A toolbox for navigating large image databases

NeTra: A toolbox for navigating large image databases

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International Conference on image Processing

作者： Ma, WY Manjunath, BS Univ of California Santa Barbara United States

ISBN: (纸本)0818681837

We present here art implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object or region based search. image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Another important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as ''retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper one-third of the image'' where the individual objects could be regions belonging to different images.

关键词： visual databases POSITION INFORMATION color representation image segmentation algorithm Color image retrieval Structural Elements (Construction) geolocation measurement tool cases texture Colour texture

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Improving RANSAC-Based segmentation Through CNN Encapsulation 30

Improving RANSAC-Based Segmentation Through CNN Encapsulatio...

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30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Morley, Dustin Foroosh, Hassan Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9781538604571

In this work, we present a method for improving a random sample consensus (RANSAC) based image segmentation algorithm by encapsulating it within a convolutional neural network (CNN). The improvements are gained by gradient descent training on the set of pre-RANSAC filtering and thresholding operations using a novel RANSAC-based loss function, which is geared toward optimizing the strength of the correct model relative to the most convincing false model. Thus, it can be said that our loss function trains the network on metrics that directly dictate the success or failure of the final segmentation rather than metrics that are merely correlated to the success or failure. We demonstrate successful application of this method to a RANSAC method for identifying the pupil boundary in images from the CASIA-IrisV3 iris recognition data set, and we expect that this method could be successfully applied to any RANSAC-based segmentation algorithm.

关键词： Metronidazole image segmentation algorithm segmentation segmentation encapsulating LOSS FUNCTION encapsulation iris recognition

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