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Rapid shallow coastal coral reef mapping using the teardrop system

Rapid shallow coastal coral reef mapping using the teardrop ...

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OCEANS 2012 MTS/IEEE Hampton Roads Conference: Harnessing the Power of the Ocean

作者： Corpuz, Francis James Naval Jr., Prospero Capili, Eusebio Jauod, Jaylord Judilla, Roel John Soriano, Maricor Vision and Image Processing Group National Institute of Physics University of the Philippines Diliman Quezon City Philippines Computer Vision and Machine Intelligence Group Department of Computer Science University of the Philippines Diliman Quezon City Philippines Mapua Robotics Center Institutional Laboratory Mgmt Office Mapua Institute of Technology Intramuros Manila Philippines

ISBN: (纸本)9781467308298

We present a rapid shallow coastal reef mapping system using a previously developed inexpensive diver less video capture system coupled with a fast-image mosaicking technique, using an affordable GPS logging device. The system replaces the traditional manta tow system of assessing and monitoring large areas of coastal coral reefs and at the same time provides historical visual record for future references. The system utilizes Teardrop, a hollow, teardrop-shaped that houses a commercial underwater point-and-shoot camera. The camera is set to video mode and captures video of the reef while the Teardrop is being towed by a wooden fishing boat. The resulting video is parsed into frames and mosaicked by using fast image labeling. The approximate location of the mosaicked images is recorded by using an inexpensive GPS logger. The coordinates are embedded on the mosaicked images and overlaid on mapping software such as Google Earth for visual record and spatial information integration. The same points are used to compute for the total distance covered by the capturing process. The whole process from coral reef video capture to mosaicking the images and finally embedding the mosaics in mapping software can be done in as short as 1 day, providing coastal managers and local government units with a tool to rapidly visualize and monitor shallow coral reefs. © 2012 IEEE.

关键词： Mapping

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Analysis of texture images using robust fractal description

Analysis of texture images using robust fractal description

引用

1995 IEEE Southwest Symposium on image Analysis and Interpretation, IAI 1994

作者： Avadhanam, N. Mitra, S. Center for Image Processing and Integrated Computing Dept. of Electr. and Comput. Eng. California Univ. DavisCA95616 United States Computer Vision and Image Analysis Lab Dept. of Electrical Engineering Texas Tech University LubbockTX79401 United States

ISBN: (纸本)0818662506

A maximum likelihood estimation (MLE) method is used to estimate the fractal dimension of a number of natural texture images with and without the presence of noise. An additional texture measure which can be linked to the lacunarity measure is used to characterize natural textures since fractal dimension alone cannot totally characterize texture images. Segmentation of natural textures is successfully achieved by a k-means clustering algorithm using fractal dimension and the additional measure as representative features. © 1994 IEEE.

关键词： image texture

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Is there progress in activity progress prediction?

Is there progress in activity progress prediction?

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Frans de Boer Jan C. van Gemert Jouke Dijkstra Silvia L. Pintea Computer Vision Lab Delft University of Technology Division of Image Processing (LKEB) Leiden University Medical Center

Activity progress prediction aims to estimate what percentage of an activity has been completed. Currently this is done with machine learning approaches, trained and evaluated on complicated and realistic video datasets. The videos in these datasets vary drastically in length and appearance. And some of the activities have unanticipated developments, making activity progression difficult to estimate. In this work, we examine the results obtained by existing progress prediction methods on these datasets. We find that current progress prediction methods seem not to extract useful visual information for the progress prediction task. Therefore, these methods fail to exceed simple frame-counting baselines. We design a precisely controlled dataset for activity progress prediction and on this synthetic dataset we show that the considered methods can make use of the visual information, when this directly relates to the progress prediction. We conclude that the progress prediction task is ill-posed on the currently used real-world datasets. Moreover, to fairly measure activity progression we advise to consider a, simple but effective, frame-counting baseline.

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Math Information Retrieval with Contrastive Learning of Formula Embeddings 24th

Math Information Retrieval with Contrastive Learning of Fo...

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24th International Conference on Web Information Systems Engineering, WISE 2023

作者： Wang, Jingyi Tian, Xuedong School of Cyber Security and Computer Hebei University Baoding071002 China Institute of Intelligent Image and Document Information Processing Hebei University Baoding071002 China Hebei Machine Vision Engineering Research Center Hebei University Baoding071002 China

ISBN: (纸本)9789819972531

The core and hard part of Mathematical Information Retrieval (MathIR) is formula retrieval. The datasets used for formula retrieval are usually scientific documents containing formulas. However, there is a lack of labeled datasets specifically for formula similarity. Contrastive learning can learn general features of datasets from unlabeled data and autonomously discover latent structures in the data. Furthermore, dense retrieval methods based on bi-encoders have gained increasing attention. Therefore, we propose CLFE, a simple framework for contrastive learning of formula embeddings. It can learn the latent structure and content information of formulas from unlabeled formulas and generate formula embeddings for formula retrieval. We design two frameworks, Contrastive Presentation MathML-Content MathML Learning and Contrastive LaTeX-MathML Learning, which initialize the encoders using transformer-based models and can produce superior LaTeX, Presentation MathML, and Content MathML embeddings for each formula. The combination of these three embeddings results in the Add embedding and Concat embedding. Finally, the formula retrieval task is achieved by finding the k nearest formulas in the vector space through nearest neighbor search. Experimental results show that when applying our proposed method on the NTCIR-12 dataset to retrieve 20 non-wildcard formulas and scoring the top-10 results, the highest achieved scores for mean P@10, mean nDCG@10, and MAP@10 are 0.6250, 0.9792, and 0.9381. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information retrieval

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Motion constraint patterns

Motion constraint patterns

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1993 IEEE Workshop on Qualitative vision, WQV 1993

作者： Fermüller, Cornelia Computer Vision Laboratory Center for Automation Research University of Maryland College ParkMD20742-3275 United States Department for Pattern Recognition and Image Processing Institute for Automation Technical University Vienna Treitlstraße 3 ViennaA-1040 Austria

ISBN: (纸本)0818636920

The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements. © 1993 IEEE.

关键词： Flow measurement

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A Novel Level Set Method for Segmentation of Left and Right Ventricles from Cardiac MR images

A Novel Level Set Method for Segmentation of Left and Right ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Yu Liu Chunming Li Shuxu Guo Yihua Song Yue Zhao Center for Image Processing and Computer Vision Jilin University Center for Biomedical Image Computing and Analytics University of Pennsylvania Center for Key Laboratory of Medical Image Computing Ministry of Education Northeastern University

ISBN: (纸本)9781424479276

In this paper, we propose a novel level set method for segmentation of cardiac left and right ventricles based on the distance regularized level set evolution (DRLSE) framework [7] and the distance regularized two-layer level set (DR2LS) model [17]. First, DRLSE is applied to obtain a preliminary segmentation of left and right ventricles, which is then used to initialize the endocardial contour, which is represented by the zero level contour of the level set function in our method. Then, the epicardial contour is represented by a different level contour of the same level set function. These two level sets are optimized by an energy minimization process to best fit the true endocardium and epicardium. In order to ensure smoothly varying distance between the two level contours, we introduce a distance regularization constraint in the energy function. With the region-scalable fitting (RSF) energy [8] as the data term, our method is able to deal with intensity inhomogeneities in the images, which is a main source of difficulty in image segmentation. Our method has been tested on cardiac MR images with promising results.

关键词： Level set image segmentation Nonhomogeneous media Biomedical imaging Educational institutions Myocardium

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A non-linear diffeomorphic framework for prostate multimodal registration

A non-linear diffeomorphic framework for prostate multimodal...

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2011 International Conference on Digital image Computing: Techniques and Applications, DICTA 2011

作者： Mitra, Jhimli Kato, Zoltan Martí, Robert Oliver, Arnau Lladó, Xavier Ghose, Soumya Vilanova, Joan C. Meriaudeau, Fabrice Le2i-UMR CNRS 5158 Université de Bourgogne Le Creusot France Computer Vision and Robotics Group Universitat de Girona Girona Spain Department of Image Processing and Computer Graphics University of Szeged Szeged Hungary Girona Magnetic Resonance Center Girona Spain

ISBN: (纸本)9780769545882

This paper presents a novel method for non-rigid registration of prostate multimodal images based on a nonlinear framework. The parametric estimation of the non-linear diffeomorphism between the 2D fixed and moving images has its basis in solving a set of non-linear equations of thin-plate splines. The regularized bending energy of the thin-plate splines along with the localization error of established correspondences is jointly minimized with the fixed and transformed image difference, where, the transformed image is represented by the set of non-linear equations defined over the moving image. The traditional thin-plate splines with established correspondences may provide good registration of the anatomical targets inside the prostate but may fail to provide improved contour registration. On the contrary, the proposed framework maintains the accuracy of registration in terms of overlap due to the non-linear thinplate spline functions while also producing smooth deformations of the anatomical structures inside the prostate as a result of established corrspondences. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate midgland ultrasound and magnetic resonance images in terms of Dice similarity coefficient with an average of 0.982 ± 0.004, average 95% Hausdorff distance of 1.54 ± 0.46 mm and mean target registration and target localization errors of 1.90±1.27 mm and 0.15 ± 0.12 mm respectively. © 2011 IEEE.

关键词： Ultrasonics

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From early biological models to CNNs: do they look where humans look?

From early biological models to CNNs: do they look where hum...

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International Conference on Pattern Recognition

作者： Marinella Iole Cadoni Andrea Lagorio Enrico Grosso Tan Jia Huei Chan Chee Seng Computer Vision Laboratory University of Sassari Sassari Italy Faculty of Computer Science and Information Technology Center of Image and Signal Processing University of Malaya

Early hierarchical computational visual models as well as recent deep neural networks have been inspired by the functioning of the primate visual cortex system. Although much effort has been made to dissect neural networks to visualize the features they learn at the individual units, the scope of the visualizations has been limited to a categorization of the features in terms of their semantic level. Considering the ability humans have to select high semantic level regions of a scene, the question whether neural networks can match this ability, and if similarity with humans attention is correlated with neural networks performance naturally arise. To address this question we propose a pipeline to select and compare sets of feature points that maximally activate individual networks units to human fixations. We extract features from a variety of neural networks, from early hierarchical models such as HMAX up to recent deep convolutional neural netwoks such as Densnet, to compare them to human fixations. Experiments over the ETD database show that human fixations correlate with CNNs features from deep layers significantly better than with random sets of points, while they do not with features extracted from the first layers of CNNs, nor with the HMAX features, which seem to have low semantic level compared with the features that respond to the automatically learned filters of CNNs. It also turns out that there is a correlation between CNN's human similarity and classification performance.

关键词： Visualization Correlation Databases Neural networks Semantics Pipelines Visual systems

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SHREC'13 Track: Retrieval of objects captured with low-cost depth-sensing cameras 6

SHREC'13 Track: Retrieval of objects captured with low-cost ...

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6th Eurographics Workshop on 3D Object Retrieval, 3DOR 2013

作者： Machado, J. Ferreira, A. Pascoal, P.B. Abdelrahman, M. Aono, M. El-Melegy, M. Farag, A. Johan, H. Li, B. Lu, Y. Tatsuma, A. INESC-ID IST Technical University of Lisbon Portugal Microsoft Language Development Center Portugal Toyohashi University of Technology Japan Department of Computer Science Texas State University San Marcos United States Fraunhofer IDM at NTU Singapore Singapore Computer Vision and Image Processing Laboratory University of Louisville Louisville United States

ISBN: (纸本)9783905674446

The SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras is a first attempt at evaluating the effectiveness of 3D shape retrieval algorithms in low fidelity model databases, such as the ones captured with commodity depth cameras. Both target and query set are composed by objects captured with a Kinect camera and the objective is to retrieve the models in the target set who were considered relevant by a human-generated ground truth. Given how widespread such devices are, and how easy it is becoming for an everyday user to capture models in his household, the necessity of algorithms for these new types of 3D models is also increasing. Three groups have participated in the contest, providing rank lists for the set of queries, which is composed of 12 models from the target set. © The Eurographics Association 2013.

关键词： Cameras

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Experiments on Sensitivity of Template Matching for Lung Nodule Detection in Low Dose CT Scans

Experiments on Sensitivity of Template Matching for Lung Nod...

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IEEE International Symposium on Signal processing and Information Technology (ISSPIT)

作者： Shireen Y Elhabian Hossam Abd el Munim Salwa Elshazly AlyA. Farag Mohamed Aboelghar Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Urology and Nephrology Center Mansoura University Mansoura Egypt

Template matching is a common approach for detection of lung nodules from CT scans. Templates may take different shapes, size and intensity distribution. The process of nodule detection is essentially two steps: isolation of candidate nodules, and elimination of false positive nodules. The processes of outlining the detected nodules and their classification (i.e., assigning pathology for each nodule) complete the CAD system for early detection of lung nodules. This paper is concerned with the template design and evaluating the effectiveness of the first step in the nodule detection process. The paper will neither address the problem of reducing false positives nor would it deal with nodule segmentation and classification. Only parametric templates are considered. Modeling the gray scale distribution for the templates is based on the prior knowledge of typical nodules extracted by radiologists. The effectiveness of the template matching is investigated by cross validation with respect to the ground truth and is described by hit rate curves indicating the probability of detection as function of shape, size and orientation, if applicable, of the templates. We used synthetic and sample real CT scan images in our experiments. It is found that template matching is more sensitive to additive noise than image blurring when tests conducted on synthetic data. On the sample CT scans small size circular and hollow-circular templates provided comparable results to human experts.

关键词： Lungs Computed tomography Shape image segmentation Design automation Pathology image analysis Signal processing Information technology computer vision

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