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ACM International conference Proceeding Series

ACM International Conference Proceeding Series

引用

10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

ISBN: (纸本)9781450347532

the proceedings contain 95 papers. the topics discussed include: reinforced random forest;prototypes based discriminative appearance model for object tracking;adaptive artistic stylization of images;improving person re-identification systems: a novel score fusion framework for rank-n recognition;an image analysis approach for transcription of music played on keyboard-like instruments;deep automatic license plate recognition system;how much can a Gaussian smoother denoise?;supervised deep segmentation network for brain extraction;robust registration of mouse brain slices with severe histological artifacts;and hierarchical structured learning for indoor autonomous navigation of quadcopter.

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STEP RING BASED 3D PAth PLANNING VIA GPU SIMULATION FOR SUBTRACTIVE 3D PRINTING 11

STEP RING BASED 3D PATH PLANNING VIA GPU SIMULATION FOR SUBT...

引用

11th ASME International Manufacturing Science and Engineering conference (MSEC)

作者： Wu, Zhengkai Tucker, thomas M. Nath, Chandra Kurfess, thomas R. Vuduc, Richard W. Georgia Tech Sch Elect & Comp Engn Atlanta GA 30332 USA Tucker Innovat Charlotte NC USA Georgia Tech George W Woodruff Sch Mech Engn Atlanta GA USA Georgia Tech Sch Computat Sci & Engn Atlanta GA USA Hitachi Amer Ltd Div Res & Dev Farmington Hills MI USA

ISBN: (纸本)9780791849903

In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring based 3-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and CNC machining via GPU simulated software. Path planning is applied through material surface removal visualization in high resolution and 3D path simulation via ring selective path planning based on accessibility of path through pattern selection. First, the step ring selects critical features to reconstruct computer aided design (CAD) design model as STL (stereolithography) voxel, and then local optimization is attained within interested ring area for time and energy saving of GPU volume generation as compared to global all automatic path planning with longer latency. the reconstructed CAD model comes from an original sample (GATech buzz) with 2D image information. CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback. To avoid collision with the produced path from retraction path, we pick adaptive ring path generation and prediction in each planning iteration, which may also minimise material removal. Moreover, we did partition analysis and g-code optimization for large scale model and high density volume data. image classification and grid analysis based on adaptive 3D tree depth are proposed for multi-level set partition of the model to define no cutting zones. After that, accessibility map is computed based on accessibility space for rotational angular space of path orientation to compare step ring based pass planning verses global all path planning. Feature analysis via central processing unit (CPU) or GPU processor for GPU map computation contributes to high performance computing and clo

关键词： Stereolithography Feedback Resolution (Optics) Additive manufacturing Machining Design Cutting Collisions (Physics) Simulation Density Optimization computer-aided design Path planning Cloud computing computer numerical control machine tools Surfaces (Materials) Visualization graphics processing units Computation Modeling computer software Manufacturing

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Text and non-text separation in scanned color-official documents 10

Text and non-text separation in scanned color-official docum...

引用

10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Nandedkar, Amit Vijay Mukherjee, Jayanta Sural, Shamik Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

Official documents consist of text and non-textual elements such as logo, stamp, and signature. Separation of these elements from a scanned document plays a significant role in document image retrieval, recognition, and verification. this paper presents a novel scheme to separate text and non-text elements of official documents using part-based features. In this work, we exploit the fact that intensity distributions of text and non-text elements in HSV color space are of distinctive nature. A new approach to compute part-based features using S and V channels is proposed. the classification of text and non-text components is performed based on majority voting scheme and K-approximate nearest neighbors. the knowledge base acquired during training is indexed using kD-tree indexing scheme. Subsequently, the method is extended for detection of logo, stamp, and signature. Experimental results show the effectiveness of the proposed approach. © Springer International Publishing AG 2017.

关键词： Nearest neighbor search

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11th International conference on Green, Pervasive and Cloud Computing, GPC 2016

11th International Conference on Green, Pervasive and Cloud ...

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11th International conference on Green, Pervasive and Cloud Computing, GPC 2016

ISBN: (纸本)9783319390765

the proceedings contain 20 papers. the special focus in this conference is on Green Computing, Cloud Computing and Virtualisation. the topics include: A polynomial time algorithm for a generalized longest common subsequence problem;improved survey propagation on graphics processing units;the optimization of election algorithm in complex network circumstance;an efficient dynamic provable data possession scheme in cloud storage;a cloud green energy management system in data center;a scalable cloud-based android app repackaging detection framework;virtual machine migration supporting cascading overload management in cloud;heterogeneous computation migration on LLVM;a flow scheduling algorithm based on VM migration in data center networks;user-centric and real-time activity recognition using smart glasses;minimizing confident information coverage breach in rechargeable wireless sensor networks with uneven recharging rates;an improved dynamic ID based remote user authentication scheme for multi-server environment;mining frequent attack sequence in web logs;practical server-aided k-out-of-n oblivious transfer protocol;improved power analysis attack based on the preprocessed power traces;preserving user location privacy for location-based service and towards secure private image matching.

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A hybrid deep architecture for face recognition in real-life scenario 10

A hybrid deep architecture for face recognition in real-life...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Sanya, A. Bhattacharya, U. Parui, S.K. Department of CSE Indian Institute of Technology Kanpur India CVPR Unit Indian Statistical Institute Kolkata India

this article describes our recent study of a real-life face recognition problem using a hybrid architecture consisting of a very deep convolution neural network (CNN) and a support vector machine (SVM). the novel aspects of this study include (i) implementation of a really deep CNN architecture consisting of 11 layers to study the effect of increasing depth on recognition performance by a subsequent SVM, and (ii) verification of the recognition performance of this hybrid classifier trained by samples of a certain standard size on test face images of smaller sizes reminiscent to various real-life scenarios. Results of the present study show that the features computed at various shallow levels of a deep architecture have identical or at least comparable performances and are more robust than the deepest feature computed at the inner most sub-sampling layer. We have also studied a simple strategy of recognizing face images of very small sizes using this hybrid architecture trained by standard size face images and the recognition performance is reported. We obtained simulation results using the cropped images of the standard extended Yale Face Database which show an interesting characteristic of the proposed architecture with respect to face images captured in a very low intensity lighting condition. © Springer International Publishing AG 2017.

关键词： Face recognition

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SPODS: A dataset of color-official documents and detection of logo, stamp, and signature 10

SPODS: A dataset of color-official documents and detection o...

引用

10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Nandedkar, Amit Vijay Mukherjee, Jayanta Sural, Shamik Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

Office automation is an active area of research. It involves archival and retrieval of official documents. For developing a system for this purpose, it is necessary to have an extensive benchmark dataset consisting various types of official documents. However, it is hard to make available real world official documents as they are mostly confidential. In the absence of such benchmark datasets, it is difficult to evaluate newly developed algorithms. Hence, efforts have been made to build dataset consisting of different categories of documents that resemble real world official documents. In this work, we present a dataset called as scanned pseudo-official data-set (SPODS) which is created by us and made available online. Official documents are usually distinguished by presence of logo, stamp, signature, date, etc. the paper also presents a new approach for the detection of logo, stamp, and signature using spectral filtering and part based features. A comparative study on performances of the proposed method and existing algorithms on the SPODS dataset demonstrates the effectiveness of the proposed technique. © Springer International Publishing AG 2017.

关键词： Office automation

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vision based Counting of Texture-less Objects using Shape and Color Features

Vision based Counting of Texture-less Objects using Shape an...

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IEEE International conference on Industrial and Information Systems

作者： Nishchal K. Verma Teena Sharma Shreedharkumar D. Rajurkar Rakesh Ranjan Al Salour Indian Institute of Technology Kanpur India - 208016 Indian School of Mines (ISM) Dhanbad The BOEING Company St. Louis MO USA

Automatic object recognition for texture-less objects using computer vision is a difficult task in comparison of textured one since class discriminative information is rarely available. Herein, an algorithm to count such objects using shape and color attributes for recognition with scale, rotation and illumination invariance is proposed. Initially, the algorithm extracts shape and color features of the prototype image to find its instance in the real-time pre-processed scene image captured by the vision interface. the pre-processing is achieved by morphological boundary extraction and segmentation techniques. Color and shape features are extracted based on mean hue value and Hu-moments respectively from the obtained segments. SVM, kNN, neural network and tree-bagging are then applied for classification. Tree-bagging is found to eclipse over the other classifiers in terms of accuracy. Finally, the classified objects are counted and localized in the image by drawing bounding boxes around them. A desktop application of the proposed algorithm is also developed. To assess the performance of the proposed algorithm, experimentation has been carried out for various objects having different shapes and colors. the algorithm proved out to be robust and effective for recognition and counting of the texture-less objects.

关键词： Feature extraction Prototypes image segmentation Shape image color analysis Support vector machines image edge detection Edge detection image colour analysis Support Vector Network image segmentation Feature extraction color feature PROTOTYPE Sight Shape desktop application images Color algorithms

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Segmenting Printed Text and Handwritten Annotation by Spectral Partitioning 5

Segmenting Printed Text and Handwritten Annotation by Spectr...

引用

5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Pandey, Shilpa Harit, Gaurav Indian Inst Technol Comp Sci & Engn Jodhpur 342011 Rajasthan India

ISBN: (纸本)9781467385640

this paper addresses the problem of segmenting handwritten annotations on scientific research papers. the motivation of this work is to geometrically segment the complex cases of handwritten annotations, including marks, cuts and special symbols along, with the regular text. Our work particularly focuses on documents that have multi-oriented handwritten [1] annotations rather than annotations in controlled scenario [2]. Spectral Partitioning is adopted as the segmentation scheme to separate the printed text and annotations. A new feature Envelope Straightness is developed and included in our feature set. this leads to an improvement of accuracy over the state-of-the-art features. the experiments are performed on two datasets: 40 documents authored by two writers from IAM dataset, comprising only printed and handwritten text and a self created dataset of 40 scientific papers from various proceedings annotated by a reader, comprising varied types of annotations. In the framework of spectral partitioning, our feature set has achieved a recall of 98.39% for printed text and precision of 85.40% for handwritten annotations on our dataset. For IAM dataset our feature set has achieved a recall of 81.89% for printed text and a precision of 69.67% for handwritten annotations. the results achieved on both dataset are better compared with results obtained using [3] [1].

关键词： computer vision

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Stamp and Logo Detection from Document images by Finding Outliers 5

Stamp and Logo Detection from Document Images by Finding Out...

引用

5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Dey, Soumyadeep Mukherjee, Jayanta Sural, Shamik Indian Inst Technol Kharagpur Sch Informat Technol Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9781467385640

Stamps and logos are generally used for authenticating the source of a document. For automatic document processing, identification and segmentation of stamps and logos are essential. In the past, methods to detect stamps and logos were limited to specific shapes, colors, or training data. However, stamps and logos can be of any shape or color. In this paper, we have proposed a novel stamp and logo detection technique. Our approach is based on the fact that stamps and logos, in general, are not the primary contents of a document. this fact motivates us to propose an outlier detection technique for the same purpose in a feature space. Based on some geometric features, the detected outliers are classified as stamps and logos. Our method shows good performance in case of separating them from text. Moreover, this technique is capable of detecting logos as well as chromatic and achromatic stamps.

关键词： Statistics

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A Spectral Filtering Based Deep Learning for Detection of Logo and Stamp 5

A Spectral Filtering Based Deep Learning for Detection of Lo...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Nandedkar, Amit V. Mukherjee, Jayanta Sural, Shamik IIT Kharagpur Sch Informat Technol Kharagpur W Bengal India IIT Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9781467385640

this paper presents a novel spectral filtering based deep learning algorithm (SFDL) for detecting logos and stamps in a scanned document image. In a document image, textual contents are main source of high spatial frequency components. Accordingly, the high frequency filtering is used to suppress the text symbols. In the next step, segmentation process is used for localizing the candidate regions of interests such as logos and stamps. Preprocessing of these candidate regions is essential before classification. the proposed preprocessing includes steps such as region fusion, resizing and key point based pooling. Finally, the preprocessed candidate regions are classified using deep convolutional neural network. the main advantage of the SFDL is its capability to detect logos without prior information or assumption about their locations in a document. the performance of the proposed SFDL algorithm is evaluated using publicly accessible document image database StaVer. It is observed that SFDL performs satisfactorily for detecting logo and stamp. the precision and recall measures of the proposed SFDL are compared with existing techniques. Experimental results show that recall and precision of logo detection are 86.8%, 97.2%, respectively. Similarly, recall and precision for stamp detection are 85.3% and 94.8%.

关键词： Learning algorithms

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