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Analysis of Framelets for the Microcalcification

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1st International conference on computer vision and image processing (CVIP)

作者： Thivya, K. S. Sakthivel, P. Easwari Engn Coll Dept Elect & Commun Engn Madras 600089 Tamil Nadu India Anna Univ Dept Elect & Commun Engn Madras Tamil Nadu India

ISBN: (纸本)9789811021046;9789811021039

Mammography is used commonly to detect the cancer in breast at the early stage. The early stage of breast cancer detection helps in avoiding the removal of breast in women and even the death caused due to breast cancer. There are many computer aided softwares that are designed to detect breast cancer but still only biopsy method is effective in predicting the exact scenario. This biopsy technique is a painful one. To avoid this, a novel classification approach for classifying microcalcification clusters based on framelet transform is proposed. The real -time mammography images were collected from Sri Ramachandra Medical Centre, Chennai, India in order to evaluate the performance of the proposed system. The GLCM features (contrast, energy and homogeneity) are extracted from the framelet decomposed mammograms with different resolution levels and support vector machine classifier is used to classify the unknown mammograms into normal or abnormal initially and then further classifies it as benign or malignant if detected as abnormal. The result shows that framelet transform-based classification provides considerable classification accuracy.

关键词： Framelet transform Support vector machine GLCM features (contrast, energy and homogeneity) Microcalcifications and Mammography

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proceedings of International conference on computer vision and image processing 1

引用

丛书名： Advances in Intelligent Systems and Computing

2017年

作者： Balasubramanian Raman Partha Pratim Roy Debashis Sen Sanjeev Kumar

ISBN: (数字)9789811021046

ISBN: (纸本)9789811021039

The technical contributions in the areas of segmentation and retrieval are classified as related to mid-level operations. They discuss some state-of-the-art techniques – non-rigid image registration, iterative image partitioning, egocentric object detection and video shot boundary detection.;The technical contributions in the areas of classification and retrieval are categorized as related to high-level operations. They discuss some state-of-the-art approaches – extreme learning machines, and target, gesture and action recognition. A non-regularized state preserving extreme learning machine is presented for natural scene classification. An algorithm for human action recognition through dynamic frame warping based on depth cues is given. Target recognition in night vision through convolutional neural network is also presented. Use of convolutional neural network in detecting static hand gesture is also discussed.

关键词： Signal, Speech and image processing computer vision Biometrics

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Using Musical Beats to Segment Videos of Bharatanatyam Adavus

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1st International conference on computer vision and image processing (CVIP)

作者： Mallick, Tanwi Anuj, Akash Das, Partha Pratim Majumdar, Arun Kumar Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9789811021046;9789811021039

We present an algorithm for audio-guided segmentation of the Kinect videos of Adavus in Bharatanatyam dance. Adavus are basic choreographic units of a dance sequence in Bharatanatyam. An Adavu is accompanied by percussion instruments (Tatta Palahai (wooden stick)-Tatta Kozhi (wooden block), Mridangam, Nagaswaram, Flute, Violin, or Veena) and vocal music. It is a combination of events that are either postures or small movements synchronized with rhythmic pattern of beats or Taals. We segment the videos of Adavus according to the percussion beats to determine the events for recognition of Adavus later. We use Blind Source Separation to isolate the instrumental sound from the vocal. Beats are tracked by onset detection to determine the instants in the video where the dancer assumes key postures. We also build a visualizer for test. From over 13000 input frames of 15 Adavus, 74 of the 131 key frames actually present get detected. Every detected key frame is correct. Hence, the system has 100% precision, but only about 56% recall.

关键词： Music-driven dance video segmentation Multimodal indian classical dance data captured by kinect Onset detection on indian music Music-to-dance video synchronization

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Thermal Face Recognition Using Face Localized Scale-Invariant Feature Transform

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1st International conference on computer vision and image processing (CVIP)

作者： Uke, Shruti R. Nandedkar, Abhijeet V. SGGSIE &T Dept Elect & Telecommun Engn Nanded India

ISBN: (纸本)9789811021046;9789811021039

Biometric face reorganization is an established means for the prevention of frauds in financial transactions and security issues. In particular, face verification has been extensively used to endorse financial transactions. Thermal face recognition is an upcoming approach in this field. This work proposes a robust thermal face recognition system based on face localized scale-invariant feature transform (FLSIFT). FLSIFT tackles the problem of thermal face recognition with complex backgrounds. Experimental results of proposed FLSIFT thermal face recognition system are compared with the existing Blood Vessel Pattern method. To test the performance of proposed and existing method, a new thermal face database consisting of indian people and variations in the background is developed. The thermal facial images of 113 subjects are captured for this purpose. The test results show that the recognition accuracy of Blood Vessel Pattern technique and FLSIFT on face images with simple background is 79.28 % and 100 %, respectively. Moreover, the test performance on the complex background for the two methods is found to be 5.55 % and 98.14 %, respectively. It may be noted that FLSIFT is capable to handle background changes more efficiently and the performance is found to be robust.

关键词： Thermal face Scale-invariant feature transform Blood vessel pattern ITFDB dataset

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PCB Defect Classification Using Logical Combination of Segmented Copper and Non-copper Part

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1st International conference on computer vision and image processing (CVIP)

作者： Kumar, Shashi Iwahori, Yuji Bhuyan, M. K. Indian Inst Technol Guwahati Gauhati 781039 India Chubu Univ Kasugai Aichi 4878501 Japan

ISBN: (纸本)9789811021046;9789811021039

In this paper, a new model for defect classification of PCB is proposed which is inspired from bottom-up processing model of perception. The proposed model follows a non-referential based approach because aligning test and reference image may be difficult. In order to minimize learning complexity at each level, defect image is segmented into copper and non-copper parts. Copper and non-copper parts are analyzed separately. Final defect class is predicted by combining copper and non-copper defect classes. Edges provide unique information about distortion in copper disc. In this model, circularity measures are computed from edges of copper disc of a Copper part. For non-copper part, color information is unique for every defect type. A 3D color histogram can capture the global color distribution. The proposed model tries to compute the histogram using nonuniform bins. Variations in intensity ranges along each dimension of bins reduce irrelevant computations effectively. The bins dimensions are decided based on the amount of correlation among defect types. Discoloration type defect is analyzed independently from copper part, because it is a color defect. Final defect class is predicted by logical combination of defect classes of Copper and Non-copper part. The effectiveness of this model is evaluated on real data from PCB manufacturing industry and accuracy is compared with previously proposed non-referential approaches.

关键词： PCB AVI Copper part Non-copper part SVM

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Design and Implementation of a Real-Time Autofocus Algorithm for Thermal imagers

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1st International conference on computer vision and image processing (CVIP)

作者： Srivastava, Anurag Kumar Kandpal, Neeta Instruments Res & Dev Estab Dehra Dun India

ISBN: (纸本)9789811021046;9789811021039

Good image quality is the most important requirement of a thermal imager or any other imaging system in almost all applications. Degree of focus in an image plays a very important role in determining the image quality, thus focusing mechanism is a very important requirement in thermal imagers. A real-time and reliable passive autofocus algorithm has been developed and implemented in FPGA-based hardware. This autofocus module has been integrated with the video processing pipeline of thermal imagers. Prior to the hardware implementation, different algorithms for image sharpness evaluation have been implemented in MATLAB and simulations have been done with test video sequences acquired by a thermal imager with motorized focus control to analyze the algorithms efficiency. Cumulative gradient algorithm has been developed for image sharpness evaluation. The algorithm has been tested on images taken from a thermal imager under varying contrast and background conditions, and it shows high precision and good discriminating power. The images have been prefiltered by a median rank-order filter using a 3 x 3 matrix to make it more robust in handling noisy images. Complete autofocus algorithm design comprising of a frame acquisition module for acquiring user selectable central region in the incoming thermal imager video, Cumulative Gradient-based image sharpness evaluation module, fixed step size search-based focal plane search module and a motor pulse generation module for generating motor drives have been implemented on Xilinx FPGA device XC4VLX100 using Xilinx ISE EDA tool.

关键词： Autofocus Gradient IR FPGA VHDL MATLAB NUC DRC

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Video Synopsis for IR imagery Considering Video as a 3D Data Cuboid

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1st International conference on computer vision and image processing (CVIP)

作者： Kumar, Nikhil Kumar, Ashish Kandpal, Neeta Instruments Res & Dev Estab Dehra Dun Uttar Pradesh India

ISBN: (纸本)9789811021046;9789811021039

Video synopsis is a way to transform a recorded video into a temporal compact representation. Surveillance videos generally contain huge amount of recorded data as there are a lot of inherent spatio-temporal redundancies in the form of segments having no activities;browsing and retrieval of such huge data has always remained an inconvenient job. We present an approach to video synopsis for IR imagery in which considered video is mapped into a temporal compact and chronologically analogous way by removing these inherent spatio-temporal redundancies significantly. A group of frames of video sequence is taken to form a 3D data cuboid with X, Y and T axes, this cuboid is re-represented as stack of contiguous X - T slices. With the help of Canny's edge detection and Hough transform-based line detection, contents of these slices are analysed and segments having spatio-temporal redundancy are eliminated. Hence, recorded video is dynamically summarized on the basis of its content.

关键词： Video synopsis Video summarization IR MWIR Canny's edge detection Hough transform-based line detection Spatio-temporal redundancy

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A Nonlinear Modified CONVEF-AD Based Approach for Low-Dose Sinogram Restoration

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1st International conference on computer vision and image processing (CVIP)

作者： Tiwari, Shailendra Srivastava, Rajeev Arya, K. V. Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India ABV Indian Inst Informat Technol & Management Multimedia & Informat Secur Lab Gwalior 474015 India

ISBN: (纸本)9789811021046;9789811021039

Preprocessing the noisy sinogram before reconstruction is an effective and efficient way to solve the low-dose X-ray computed tomography (CT) problem. The objective of this paper is to develop a low-dose CT image reconstruction method based on statistical sonogram smoothing approach. The proposed method is casted into a variational framework and the solution of the method is based on minimization of energy functional. The solution of the method consists of two terms, viz., data fidelity term and a regularization term. The data fidelity term is obtained by minimizing the negative log likelihood of the signal-dependent Gaussian probability distribution which depicts the noise distribution in low-dose X-ray CT. The second term, i.e., regularization term is a nonlinear CONvolutional Virtual Electric Field Anisotropic Diffusion (CONVEF-AD) based filter which is an extension of Perona-Malik (P-M) anisotropic diffusion filter. The main task of regularization function is to address the issue of ill-posedness of the solution of the first term. The proposed method is capable of dealing with both signal-dependent and signal-independent Gaussian noise, i.e., mixed noise. For experimentation purpose, two different sinograms generated from test phantom images are used. The performance of the proposed method is compared with that of existing methods. The obtained results show that the proposed method outperforms many recent approaches and is capable of removing the mixed noise in low-dose X-ray CT.

关键词： X-ray computed tomography Statistical sinogram smoothing image reconstruction algorithm Noise reduction Anisotropic diffusion

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Differentiating photographic and PRCG images using tampering localization features

Differentiating photographic and PRCG images using tampering...

引用

International conference on computer vision and image processing, CVIP 2016

作者： Ayyalasomayajula, Roshan Sai Pankajakshan, Vinod Electronics and Communication Engineering Department Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9789811021060

A large number of sophisticated, yet easily accessible computer graphics softwares (STUDIO MAX, 3D MAYA, etc.) have been developed in the recent past. The images generated with these softwares appear to be realistic and cannot be distinguished from natural images visually. As a result, distinguishing between photographic images (PIM) and Photo-realistic computer generated (PRCG) images of real world objects has become an active area of research. In this paper, we propose that "a computer generated image" would have the features corresponding to a "completely tampered image", whereas a camera generated picture would not. So, the differentiation is done on the basis of tampering localization features viz., block measure factors based on JPEG compression and re-sampling. It has been observed experimentally, that these measure factors vary for a PIM from a PRCG image. The experimental results show that the proposed simple and robust classifier is able to differentiate between PIM and PRCG images with an accuracy of 96 %. © Springer Science+Business Media Singapore 2017.

关键词： Three dimensional computer graphics

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Parameter Free Clustering Approach for Event Summarization in Videos

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1st International conference on computer vision and image processing (CVIP)

作者： Mishra, Deepak Kumar Singh, Navjot Natl Inst Technol Dept Comp Sci & Engn Srinagar Uttarakhand India

ISBN: (纸本)9789811021046;9789811021039

Digital videos, nowadays, are becoming more common in various fields like education, entertainment, etc. due to increased computational power and electronic storage capacity. With an increasing size of video collection, a technology is needed to effectively and efficiently browse through the video without losing contents of the video. The user may not always have sufficient time to watch the entire video or the entire content of the video may not be of interest of user. In such cases, user may just want to go through the summary of the video instead of watching the entire video. In this paper we propose an approach for event summarization in videos based on clustering method. Our proposed method provides a set of key frames as a summary for a video. The key frames which are closer to the cluster heads of the optimal clustering are combined to form the summarized video. The evaluation of the proposed model is done on a publicly available dataset and compared with ten state-of-the-art models in terms of precision, recall and F-measure. The experimental results demonstrate that the proposed model outperforms the rest in terms of F-measure.

关键词： Event summarization Key frames Clustering SD index Video skimming

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