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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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Diagnosis of Pomegranate Plant Diseases using Neural Network 5

Diagnosis of Pomegranate Plant Diseases using Neural Network

引用

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

作者： Dhakate, Mrunmayee Ingole, A. B. SPPU Sinhgad Acad Engn Dept E&TC Pune Maharashtra India

ISBN: (纸本)9781467385640

Pomegranate is a fruit which grows with a very high yield in many states of India and one of the most profits gaining fruit in the market. But due to various conditions, the plants are infected by various diseases which destroy the entire crop leaving very less product yield. So, the work proposes an image processing and neural network methods to deal with the main issues of phytopathology i.e. disease detection and classification. The Pomegranate fruit as well as the leaves are affected by various diseases caused by fungus, bacteria and the climatic conditions. These diseases are like Bacterial Blight, Fruit Spot, Fruit rot and Leaf spot. The system uses some images for training, some for testing purpose and so on. The color images are pre-processed and undergo k-means clustering segmentation. The texture features are extracted using GLCM method, and given to the artificial neural network. The overall accuracy of this method is 90%. The results are proved to be accurate and satisfactory in contrast to manual grading and hopefully take a strong rise in establishing itself in the market as one of the most efficient process.

关键词： disease detection and classification pomegranate plant diseases k-means clustering segmentation GLCM method artificial neural network

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HSGA: A Novel Acceleration Descriptor for Human Action Recognition 5

HSGA: A Novel Acceleration Descriptor for Human Action Recog...

引用

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

作者： Edison, Anitha Jiji, C., V Coll Engn Dept Elect & Commun Engn Trivandrum Kerala India

ISBN: (纸本)9781467385640

Tracking dense features has become one of the most popular methods for human action recognition. Proper descriptors should be used to capture the motion information contained in these trajectories and motion boundary histogram (MBH), which encodes velocity information, gives best performance among state of art action recognition descriptors. In this paper, we propose to use a new descriptor, histogram of spatial gradient of acceleration (HSGA) in combination with MBH to describe actions. Our new descriptor combination is based on studies which reveal that acceleration is as important as velocity in motion description. HSGA is computed by taking histogram of orientation of spatial gradient of optical acceleration in a 3D space-time block divided into cells around dense trajectories. Optical acceleration is obtained by taking time derivative of optical flow. This combination of descriptors gave good performance on a variety of data sets. Combining these motion descriptors with a scene descriptor like HOG further improved the recognition accuracy for realistic action datasets.

关键词： Acceleration

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Singer Based Classification of Song Dataset Using Vocal Signature Inherent in Signal 5

Singer Based Classification of Song Dataset Using Vocal Sign...

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

作者： Sarkar, Rajib Saha, Sanjoy Kumar Jadavpur Univ Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9781467385640

Singer based classification of song data is important in the applications like, organized archival and indexing of music data, music retrieval. In a song, singing voice is mixed with accompanying instrument signal. To extract the vocal characteristics of the singer, the effect of non-voiced part is to be minimized. In this work a simple methodology is proposed to remove the non-voiced segments and to reduce the impression of instruments from the voice-dominating signal. To extract the vocal signature, proposed features extract the variation pattern of zero crossing rate and short term energy. In broad sense, the features try to capture the range of pitch and energy over which a singer mostly operates. This is motivated by the way a human being tries to identify a singer. Finally, singer based classification is done using multi-layer perceptron network. Experiment is carried out with artist 20 dataset and 63% classification accuracy is achieved. Comparison with reported works on the same dataset shows that the performance of the proposed simple methodology is better than the majority and very close to others.

关键词： singer identification time domain filtering frequency domain filtering STE and ZCR based feature MFCC

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Weighted Fast Dynamic Time Warping Based Multi-View Human Activity Recognition Using a RGB-D Sensor 5

Weighted Fast Dynamic Time Warping Based Multi-View Human Ac...

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

作者： Agarwal, Ishan Kushwaha, Alok Kumar Singh Srivastava, Rajeev Jaypee Inst Informat Technol Dept Comp Sc & Engn Noida UP India Indian Inst Technol BHU Dept Comp Sc & Engn Varanasi 221005 Uttar Pradesh India

ISBN: (纸本)9781467385640

In this paper, a real time multi-view human activity recognition model using a RGB-D (Red Green BlueDepth) sensor is proposed. The method receives as input RGBD data streams in real time from a Kinect for Windows V2 sensor. Initially, a skeleton-tracking algorithm is applied which gives 3D joint information of 25 unique joints. The presented approach uses a weighted version of the Fast Dynamic Time Warping that weighs the importance of each skeleton joint towards the Dynamic Time Warping (DTW) similarity cost. To recognize multi-view human activities, the weighted Dynamic TimeWarping warps a time sequence of joint positions to reference time sequences and produces a similarity value. Experimental results demonstrate that the proposed method is robust, flexible and efficient with respect to multiple views activity recognition, scale and phase variations activities at different realistic scenes.

关键词： RGB-D Sensor Skeletal Tracking Human Activity Recognition Microsoft Kinect V2

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Study of Variation of Pelvis Positioning for Patients Suffering from Rectal Cancer using Daily Kilo-Voltage Cone Beam CT images 5

Study of Variation of Pelvis Positioning for Patients Suffer...

引用

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

作者： Mandal, Sayan Veduruparthi, Bijju Kranthi Mukherjee, Jayanta Das, Partha Pratim Mallick, Indranil Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India Tata Med Ctr Dept Radiat Oncol Kolkata India

ISBN: (纸本)9781467385640

Radiation therapy is a standard treatment for a large variety of cancers. Radiation treatment planning involves delineating tumor, areas-at-risk, as well as normal anatomical structures. Automated techniques for detection and segmentation of tumor is a challenging task because of the absence of any standard model to identify anatomical structures. As radiation therapy is delivered in multiple daily doses (called fractions) treatment fields need to be positioned so that the daily uncertainties in the target position are covered adequately. This is done by adding a margin called the Planning Target Volume (PTV) margin around the clinical target volume. The PTV margin is determined from an understanding of the daily uncertainties. In this paper, we intend to aid doctors in determining the uncertainties of daily pelvis positioning for radiotherapy by estimating the degree of change in the pelvic bone position. We propose a technique for automatically detecting regions of pelvic bone anatomy from CBCT images. We track the change in the bone positions in pelvic regions on a daily basis. A small change suggests that the uncertainty involved in the position of tumorous cell is less, indicating that the planning target margin may be reduced substantially.

关键词： Radiotherapy

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Synthetic Handwritten Odia Numeral Database: From Shallow Hundreds to Comprehensive Thousands 5

Synthetic Handwritten Odia Numeral Database: From Shallow Hu...

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

作者： Dash, Kalyan S. Puhan, N. B. Panda, Ganapati Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar Orissa India

ISBN: (纸本)9781467385640

Comprehensive database that contains all possible variations of handwriting is crucial for training and recognition. The primary challenge for an optical character recognizer (OCR) is that a number of interclass characters bear structural resemblance whereas images within a class render much dissimilarity. Acquisition of such a large database that ensures robust training of the recognizer is a painstaking task. Therefore, recent research interests have been to create, from a few samples of handwriting, a comprehensive synthetic database which not only ensures naturalness, but provides much needed pattern variability. In this paper, we propose a new approach of synthetic handwritten numeral generation for Odia language using interclass deformation. We experimentally evaluate the generated databases using the state-of-the-art recognition systems. The recognition results are compared on two benchmark databases (ISI Kolkata and IIT Bhubaneswar Odia numeral) as well as two newly created synthetic databases. The Odia numeral database sizes are increased by 20-fold each using our proposed approach. The introduction of nonlinear pattern variance because of interclass deformation is proved to pose better challenge to conventional recognizers. We also experimented on a mixture of original and synthetic database for training the OCR to achieve robustness and higher accuracy.

关键词： Synthetic database interclass deformation OCR handwritten numeral synthesis evaluation

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Validation Of Stimulus For EEG Signal Based Cognitive Load Analysis 5

Validation Of Stimulus For EEG Signal Based Cognitive Load A...

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

作者： Sinha, Aniruddha Chatterjee, Debatri Saha, Sanjoy Kumar Basu, Anupam Tata Consultancy Serv Ltd Innovat Lab Kolkata India Jadavpur Univ Comp Sci & Engn Kolkata India Indian Inst Technol Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9781467385640

Cognitive load is defined as the mental workload imparted on brain while doing a task. The amount of cognitive load experienced depends on individual's ability of perception, assimilation and response to a task. Real-time measurement of the level of cognitive load using low cost Electroencephalogram (EEG) signal enables understanding of personal cognitive skills. In this paper, we propose a methodology of selecting a reference task whose bio-markers closely match with a given task while probing different cognitive abilities. The benefit of this approach is to have a limited set of training models for the reference tasks related to various cognitive categories and use the same for a variety of unknown tasks. Experiment is performed for two levels of cognitive load with three different tasks namely Stroop color task, logical reasoning task and usage of on-screen keyboards. The training models of the reference tasks, selected by cluster analysis of low and high cognitive levels are used to evaluate an unknown task. Experimental results indicate that the Stroop is a better reference for On-Screen keyboard test compared to the Logical reasoning test. Support vector machine (SVM) and principal component analysis (PCA) followed by SVM (PCASVM) are used as the classifiers for the testing.

关键词： cognitive load EEG clustering

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Offline Handwritten Signature Verification using Zernike Moments 5

Offline Handwritten Signature Verification using Zernike Mom...

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

作者： Kaur, Harman Preet Sharma, Anmol DAV Inst Engn & Technol Dept Comp Sci Jalandhar Punjab India DAV Inst Engn & Technol Dept Informat Technol Jalandhar Punjab India

ISBN: (纸本)9781467385640

In this paper, a novel approach for the verification of offline handwritten signatures is proposed. Despite tremendous growth of digital technologies in the last 4 decades, the most used authentication method today remains to be handwritten signature. It is the most natural method of authenticating a person's identity as compared to other biometric and cryptographic forms of authentication. We propose a method for verifying the signatory's identity by using Zernike Moments as global shape descriptors. Zernike Moments are image moments that are rotation invariant. The moments are also orthogonal on a unit circle which ensures minimum redundancy between the features representing the object shape. The features extracted in our approach have a relatively low dimensionality as compared to other studies, while retaining high representation power of the moments. Moreover, the module developed using our approach was able to demonstrate high performance coupled with low computation times in testing phase, making it suitable for real time applications. Experiments show high overall performance of our approach with an equal error rate EER of 13.42% and area under the curve A(z) equal to 0.84 using 1564 images from the NFI SigComp2009 dataset.

关键词： Authentication

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PD-Shift: Patch Detector Shift based Tracker 5

PD-Shift: Patch Detector Shift based Tracker

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

作者： Francis, Mathew Rajesh, Ratnakaram Guha, Prithwijit IIT Guwahati Dept EEE Gauhati India

ISBN: (纸本)9781467385640

We identify two important directions of algorithm development in the tracking literature - first, the older and more established mean-shift based approaches;and second, more recent formulations of viewing tracking as detection by parts (aka tracking-learning-detecting). Introductory versions of both these approaches had their own weaknesses due to the main challenges of tracking like illumination variations, abrupt motions, deformations/articulations and occlusions. Both these approaches have developed independently with a number of follow-ups in the literature and even the matured versions were never integrated. We propose to integrate these two different viewpoints of single object tracking where the contribution lies in the construction of the target model as a collection of object part/patch detectors integrated with a kernel. Similarly, the kernel weighted patch detector responses form the candidate model. The Bhattacharyya coefficient based similarity measure between the target and candidate model is maximized in a gradient ascent framework to deduce a mean-shift like centroid update rule to iteratively track the target in the subsequent frames. We call this unified one the Patch Detector Shift based Tracker (PD-Shift). Experimental results of PD-Shift based tracking performance analysis is presented for the BoBoT dataset.

关键词： Iterative methods

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