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检索条件"机构=Video/Image Modeling and Synthesis Laboratory Computer Information & Sciences"

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A Benchmark Grocery Dataset of Realworld Point Clouds From Single View

A Benchmark Grocery Dataset of Realworld Point Clouds From S...

引用

International Conference on 3D Imaging, modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Shivanand Venkanna Sheshappanavar Tejas Anvekar Shivanand Kundargi Yufan Wang Chandra Kambhamettu Dept. of Computer and Information Sciences Video/Image Modeling and Synthesis (VIMS) Lab University of Delaware Newark DE USA

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

Fine-grained grocery object recognition is an important computer vision problem with broad applications in automatic checkout, in-store robotic navigation, and assistive technologies for the visually impaired. Existing datasets on groceries are mainly 2D images. Models trained on these datasets are limited to learning features from the regular 2D grids. While portable 3D sensors such as Kinect were commonly available for mobile phones, sensors such as LiDAR and TrueDepth, have recently been integrated into mobile phones. Despite the availability of mobile 3D sensors, there are currently no dedicated real-world large-scale benchmark 3D datasets for grocery. In addition, existing 3D datasets lack fine-grained grocery categories and have limited training samples. Furthermore, collecting data by going around the object versus the traditional photo capture makes data collection cumbersome. Thus, we introduce a large-scale grocery dataset called 3DGrocery100. It constitutes 100 classes, with a total of 87,898 3D point clouds created from 10,755 RGB-D single-view images. We benchmark our dataset on six recent stateof-the-art 3D point cloud classification models. Additionally, we also benchmark the dataset on few-shot and continual learning point cloud classification tasks. Project Page: https://***/3DGrocery100/.

关键词： Point cloud compression Training Solid modeling Three-dimensional displays Benchmark testing Robot sensing systems Mobile handsets

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3DSAINT Representation for 3D Point Clouds

3DSAINT Representation for 3D Point Clouds

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab. Computer and Information Sciences University of Delaware Newark Delaware

This paper introduces a Sphere-based representation to model a 3D scene and show its performance on various tasks, including Structure from Motion (SfM) and 3D scene classification. A significant target application of this work is Mixed Reality, where 3D data can be efficiently represented, and synthetic and real data can be mixed for an immersive experience. Over the past few decades, 3D big data has garnered increased attention in computer vision. Acquiring, representing, reconstructing, querying, classifying, and visualizing 3D models for Mixed Reality has become crucial for many applications, such as medicine, architecture, entertainment, and bioinformatics. With the ever-increasing amount of data that the 3D scanners produce, storing, processing, and transmitting the data becomes challenging. Techniques that exploit the shape information need to be developed to model, classify and visualize the data. Our work offers a novel multi-scale surface representation based on spheres, with the ultimate goal of helping scientists to see and work with 3D data in Mixed Reality more effectively and efficiently.

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REFRACTIVE STEREO RAY TRACING FOR RECONSTRUCTING UNDERWATER STRUCTURES

REFRACTIVE STEREO RAY TRACING FOR RECONSTRUCTING UNDERWATER ...

引用

IEEE International Conference on image Processing

作者： Scott Sorensen Abhishek Kolagunda Philip Saponaro Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab Computer and Information Sciences University of Delaware

ISBN: (纸本)9781479983407

Underwater objects behind a refractive surface pose problems for traditional 3D reconstruction techniques. Scenes where underwater objects are visible from the surface are commonplace, however the refraction of light causes 3D points in these scenes to project non-linearly. Refractive Stereo Ray Tracing allows for accurate reconstruction by modeling the refraction of light. Our approach uses techniques from ray tracing to compute the 3D position of points behind a refractive surface. This technique aims to reconstruct underwater structures in situations where access to the water is dangerous or cost prohibitive. Experimental results in real and synthetic scenes show this technique effectively handles refraction.

关键词： Underwater Stereo Vision Refraction Underwater refractions Stereoviews REFRACTION Light refraction Ray tracing Underwater structures Solar Terrestrial Relations Observatory

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Dog breed classification via landmarks

Dog breed classification via landmarks

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作者： Wang, Xiaolong Ly, Vincent Sorensen, Scott Kambhamettu, Chandra Video/Image Modeling and Synthesis Laboratory Department of Computer and Information Sciences University of Delaware NewarkDE United States

ISBN: (纸本)9781479957514

Object recognition is an important problem with a wide range of applications. It is also a challenging problem, especially for animal categorization as the differences among breeds can be subtle. In this paper, based on statistical techniques for landmark-based shape representation, we propose to model the shape of dog breed as points on the Grassmann manifold. We consider the dog breed categorization as the classification problem on this manifold. The proposed scheme is tested on a dataset including 8,351 images of 133 different breeds. Experimental results demonstrate the advocated scheme outperforms state of the art approaches by nearly 20%. © 2014 IEEE.

关键词： Learning systems

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Towards Auto-calibration of Smart Phones Using Orientation Sensors

Towards Auto-calibration of Smart Phones Using Orientation S...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Philip Saponaro Chandra Kambhamettu Video Image Modeling and Synthesis (VIMS) Laboratory Department of Computer and Information Science University of Delaware Newark DE USA

In this paper, we address the problem of auto calibration of cameras which can rotate freely and change focal length, and we present an algorithm for finding the intrinsic parameters using only two images. We utilize orientation sensors found on many modern smart phones to help decompose the infinite homography into two equivalent upper triangular matrices based only on the intrinsic parameters. We account for small translations between views by calculating the homography based on correspondences on objects that are far away from the camera. We show results based on both real and synthetic data, and quantify the tolerance of our system to small translations and errors in the orientation sensors. Our results are comparable to other recent auto-calibration work while requiring only two images and being tolerant to some translation.

关键词： Cameras Calibration Smart phones Sensors Equations Mathematical model Accuracy

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Can faces verify blood-relations?

Can faces verify blood-relations?

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IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： Gowri Somanath Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Laboratory Department of Computer and Information Sciences University of Delaware Newark DE USA

Humans can verify unknown parent-offspring and sibling pairs over unrelated subject pairs. A computational scheme to accomplish the task robustly, in the presence of challenges due to gender and age gap between related-pairs, finds many applications such as matching orphaned/lost children, identifying relatives from a photo collection. We propose one of the first computational schemes to verify sibling pairs along with parent-child relation. Towards the same, we present a novel ensemble metric learning scheme that combines the advantages of task-specific learning, adaptive prototype and feature selection and `late fusion'. We demonstrate the robustness of the scheme on a very large scale, real-world dataset. Specifically, we show that the gender difference among related-pairs leads to lower performance of traditional verification and metric learning algorithms. Through various experiments, we quantitatively study the robustness of the proposed scheme in the general and specific case of different gender related-pairs, achieving up to 80%, 75% accuracy for the parent-child and siblings relations respectively.

关键词： Kernel Measurement Training Testing Robustness Support vector machines Covariance matrix

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VADANA: A dense dataset for facial image analysis

VADANA: A dense dataset for facial image analysis

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Gowri Somanath MV Rohith Chandra Kambhamettu Video/Image Modeling and Synthesis Lab University of Delaware USA Video/Image Modeling and Synthesis Lab Department of Computer and Information Sciences University of Delaware USA

Analysis of face images has been the topic of in-depth research with wide spread applications. Face recognition, verification, age progression studies are some of the topics under study. In order to facilitate comparison and benchmarking of different approaches, various datasets have been released. For the specific topics of face verification with age progression, aging pattern extraction and age estimation, only two public datasets are currently available. The FGNET and MORPH datasets contain a large number of subjects, but only a few images are available for each subject. We present a new dataset, VADANA, which complements them by providing a large number of high quality digital images for each subject within and across ages (depth vs. breadth). It provides the largest number of intrapersonal pairs, essential for better training and testing. The images also offer a natural range of pose, expression and illumination variation. A parallel version with aligned faces is also created. Additionally, we provide relationships between subjects. We demonstrate the difference and difficulty of VADANA by testing with state-of-the-art algorithms. Our findings from experiments show how VADANA can aid further research on different types of verification algorithms. The variety of characteristics our data offers facilitate testing and benchmarking of other facial analysis algorithms.

关键词： Face Training Lighting Databases Algorithm design and analysis Testing Aging

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Towards estimation of dense disparities from stereo images containing large textureless regions

Towards estimation of dense disparities from stereo images c...

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International Conference on Pattern Recognition

作者： M.V. Rohith Gowri Somanath Chandra Kambhamettu Cathleen Geiger Dept. of Computer and Information Sciences Video/Image Modeling and Synthesis(VIMS) Lab Department. of Geography University of Delaware USA

Stereo algorithms for structure reconstruction demand accurate disparities with low mismatch errors and false positives. Mismatch errors in large textureless regions force most accurate algorithms to be sparse, with disparities known only in textured regions. We propose a novel method which uses characteristics of the multi-valued disparity to segregate image regions into unambiguous, occluded but textured and regions with low color variation. The disparity in the unambiguous region is calculated using stable matching with local disparity filtering. The disparity is interpolated into other regions by diffusion using unstructured triangulation and method of finite elements for rapid convergence. The boundary conditions for each of the region are appropriately modified so that accurate discontinuities in the disparity are preserved. A comparison of our method with some existing methods through experiments reveal that this algorithm indeed performs significantly better in producing dense accurate disparities.

关键词： image reconstruction Stereo image processing Interpolation image matching computer errors Matched filters Filtering Geography Finite element methods Convergence

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Integrating spatial and discriminant strength for feature selection and linear dimensionality reduction

Integrating spatial and discriminant strength for feature se...

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2006 Conference on computer Vision and Pattern Recognition Workshops

作者： Qi, Li Kambhamettu, Chandra Jieping, Ye Video/Image Modeling and Synthesis Lab. Computer Information and Sciences University of Delaware Computer Science and Engineering Arizona State University

ISBN: (纸本)0769526462

Interest strength assignment to image points is important for selecting good features. Strength assignments using spatial information aim to detect interest points repeatable across different image/illumination transformations, and have been widely adopted in many interest point detectors. Recently, strength assignment schemes using discriminant information received attention, and studies showed the superiority of discriminant strength. In this paper, we introduce a strength assignment scheme integrating spatial and discriminant information, with the motivation that strong spatial information can be helpful in improving the robustness of the discriminant strength estimation, e.g., in under-sampled training scenario. Our integrated strength uses a new discriminant strength assignment, so-called locality oriented Fisher criterion score. The integrated strength leads to new methods for feature selection and weighted linear dimensionality reduction. Experimental results in two case studies (embryo developmental stage classification and face recognition) show the favorable performance of the proposed methods. © 2006 IEEE.

关键词： Feature extraction

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Integrating Spatial and Discriminant Strength for Feature Selection and Linear Dimensionality Reduction

Integrating Spatial and Discriminant Strength for Feature Se...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Qi Li C. Kambhamettu Jieping Ye Video/Image Modeling and Synthesis Laboratory Computer Information & Sciences University of Delaware USA Computer Science & Engineering Arizona State University USA

Interest strength assignment to image points is important for selecting good features. Strength assignments using spatial information aim to detect interest points repeatable across different image/illumination transformations, and have been widely adopted in many interest point detectors. Recently, strength assignment schemes using discriminant information received attention, and studies showed the superiority of discriminant strength. In this paper, we introduce a strength assignment scheme integrating spatial and discriminant information, with the motivation that strong spatial information can be helpful in improving the robustness of the discriminant strength estimation, e.g., in undersampled training scenario. Our integrated strength uses a new discriminant strength assignment, so-called locality oriented Fisher criterion score. The integrated strength leads to new methods for feature selection and weighted linear dimensionality reduction. Experimental results in two case studies (embryo developmental stage classification and face recognition) show the favorable performance of the proposed methods.

关键词： Linear discriminant analysis Detectors Robustness Lighting Autocorrelation computer science Embryo Face recognition Object detection Object recognition

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