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检索条件"任意字段=Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1469 条记录，以下是331-340 订阅

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Fused Spectral Features in Kernel Weighted Collaborative Representation for Gender Classification Using Ocular images 3rd

Fused Spectral Features in Kernel Weighted Collaborative Rep...

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3rd International conference on computer vision and image processing (CVIP)

作者： Raja, Kiran B. Raghavendra, R. Busch, Christoph Univ South Eastern Norway USN Kongsberg Norway Norwegian Univ Sci & Technol NTNU Gjovik Norway

ISBN: (纸本)9789813290884;9789813290877

Ocular images have been used to supplement and complement the face-based biometrics. Ocular images are further investigated for identifying the gender of a person such that the soft label can be used to boost biometric performance of the system. Although there are number of works in visible spectrum and Near-Infrared spectrum for gender classification using ocular images, there are limited works in spectral imaging which explore ocular images for gender classification. Considering the advantages of spectral imaging, we explore the problem of gender identification using ocular images obtained using spectral imaging. To this end, we have employed a recent database of 104 unique ocular instances across 2 different sessions and 5 different attempts in each session with a spectral imaging camera capable of capturing 8 different images corresponding to different bands. Further, we present a new framework of using fused feature descriptors in kernalized space to fully leverage the number of spectral images for robust gender classification. With the set of experiments, we obtain an average classification accuracy of 81% with the proposed approach of using fused GIST features along with the weighted kernel representation of features in collaborative space.

关键词： Biometrics

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Person Identification Using Footprint Minutiae 3rd

Person Identification Using Footprint Minutiae

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3rd International conference on computer vision and image processing (CVIP)

作者： Kushwaha, Riti Nain, Neeta Malaviya Natl Inst Technol Dept Comp Sci & Engn Jaipur 302017 Rajasthan India

ISBN: (纸本)9789813292918;9789813292901

Increasing demand of biometric security system is leading us to evaluate more novel approach which could recognize and identify humans using their unique traits. Like fingerprint, footprint of human also share the unique traits which could be used as additional biometric trait in absence of major biometric trait (fingerprint, face). This could be installed at the places where it is mandatory to remove shoe for security or religious reasons, for example, airports and places of worship like mosques, pilgrimage, etc. We propose a minutiae-based footprint recognition system for person identification. Furthermore, credibility of fingerprint matching matrices is used for the evaluation of our footprint approach. Minutiae features of foot fingers are used to evaluate the performance of person identification using footprint. We have collected dataset of 120 persons including 78 males and 42 females at different resolution (500, 600, and 1200 dpi). Collected dataset involves two samples of each person for every foot finger. A series of dataset preprocessing operations are applied for getting good quality images which also ensures the correct region of interest detection for getting the large number of minutiae in a small region. Concept of crossing number is used for ridge termination and bifurcation extraction. The method achieves False Non Match Rate (FNMR) of 0.4% with the False Match Rate (FMR) of 0.2%.

关键词： Footprint Biometrics Local orientations Local frequency Level 2 features Minutiae

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LR-HyClassify: A Low Rank Based Framework for Classification of Degraded Hyperspectral images 7th

LR-HyClassify: A Low Rank Based Framework for Classification...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Sarkar, Sourish Sahay, Rajiv R. Department of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789811586965

We propose a novel framework for the classification of hyperspectral data corrupted by severe degradation. We propose an optimization framework for extracting discriminative features from noisy hyperspectral data which are then passed onto a simple classifier which exploits both the spatial and spectral correlations in the data. Instead of directly extracting the features from the noisy input data, we learn a basis matrix from the underlying clean data using a combination of non-negative matrix factorization and nuclear-norm minimization. The input degraded data are then projected onto the basis vectors to obtain features. We use structural incoherence to maximize the discriminative ability of the features, which, to the best of our knowledge, is being used for the first time in hyperspectral image processing literature. We show the deterioration in the performance of classification algorithms with the progressive addition of noise while demonstrating the robustness of our algorithm. We evaluate the performance of our algorithm on three well known datasets, namely, Botswana, Salinas and indian Pines, obtaining state-of-the-art results that validate the efficacy of our algorithm. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Non-negative matrix factorization

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Facial Expression Recognition Using Improved Adaptive Local Ternary Pattern 3rd

Facial Expression Recognition Using Improved Adaptive Local ...

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3rd International conference on computer vision and image processing (CVIP)

作者： Saurav, Sumeet Singh, Sanjay Saini, Ravi Yadav, Madhulika Acad Sci & Innovat Res AcSIR Chennai Tamil Nadu India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India Banasthali Vidyapith Dept Elect Vidyapith Rajasthan India

ISBN: (纸本)9789813292918;9789813292901

Recently, there has been a huge demand for assistive technology for industrial, commercial, automobile, and societal applications. In some of these applications, there is a requirement of an efficient and accurate system for automatic facial expression recognition (FER). Therefore, FER has gained enormous interest among computer vision researchers. Although there has been a plethora of work available in the literature, an automatic FER system has not yet reached the desired level of robustness and performance. In most of these works, there has been the dominance of appearance-based methods primarily consisting of local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP). Keeping in view the popularity of appearance-based methods, in this paper, we have proposed an appearance-based descriptor called Improved Adaptive Local Ternary Pattern (IALTP) for automatic FER. This new descriptor is an improved version of ALTP, which has been proved to be effective in face recognition. We have investigated ALTP in more details and have proposed some improvements like the use of uniform patterns and dimensionality reduction via principal component analysis (PCA). The reduced features are then classified using kernel extreme learning machine (K-ELM) classifier. In order to validate the performance of the proposed method, experiments have been conducted on three different FER datasets using well-known evaluation measures such as accuracy, precision, recall, and F1-Score. The proposed approach has also been compared with some of the state-of-the-art works in literature and found to be more accurate and efficient.

关键词： Facial expression recognition (FER) Adaptive local ternary pattern (ALTP) Principal component analysis (PCA) Kernel extreme learning machine (K-ELM)

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An Efficient Pipeline for Distant Person Detection and Identification in 4K Video using GPUs

An Efficient Pipeline for Distant Person Detection and Ident...

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International conference on COMmunication Systems and NETworkS (COMSNETS)

作者： Mattela, Govardhan Tripathi, Manmohan Pal, Chandrajit Dhiraj, Rampelli Sai Acharyya, Amit Indian Inst Technol IIT Hyderabad India Redpine Signals Inc San Jose CA USA

ISBN: (纸本)9781728131870

The gradual advent of machine learning has been assisting to shift the field of computer vision from statistical methods to deep neural networks. These networks should be able to process high resolution video streams coming from the HD camera sources in real time. However, due to the fixed network size and to maintain the processing speed, high resolution frames need to be resized and down-sampled before feeding into the networks resulting in loss of feature information, hampering recognition accuracy. This motivated us to propose a methodology which focuses on creating and processing the active region of interests in the foreground image through an active region generator (ARG) module, eliminating the need to traverse the entire frame and down-sample the resolution before feeding it to the neural network. This resulted in saving 25x more image feature information, whilst maintaining a given person detection accuracy of 92 % mAP for longer distance up to 30 similar to 35 metre executing in real time w.r.t it's classical counterpart based on singleshot detector model. Besides, our proposed pipeline architecture utilizing multi-core TESLA GPU increases the execution throughput by a factor of 3X verified in NVIDIA DGX system.

关键词： Active region generator (ARG) module CCTV ReID (reidentification) COTS (component of the shelf) GPU (graphics processing unit)

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Object Labeling in 3D from Multi-view Scenes Using Gaussian-Hermite Moment-Based Depth Map 3rd

Object Labeling in 3D from Multi-view Scenes Using Gaussian-...

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3rd International conference on computer vision and image processing (CVIP)

作者： Enan, Sadman Sakib Rahman, S. M. Mahbubur Haque, Samiul Howlader, Tamanna Hatzinakos, Dimitrios BUET Dept EEE Dhaka 1205 Bangladesh North Carolina State Univ Dept ECE Raleigh NC 27606 USA Univ Dhaka ISRT Dhaka 1000 Bangladesh Univ Toronto Dept ECE Toronto ON M5S 2E4 Canada

ISBN: (纸本)9789813292918;9789813292901

Depth as well as intensity of a pixel plays a significant role in labeling objects in 3D environments. This paper presents a novel approach of labeling objects from multi-view video sequences by incorporating rich depth information. The depth map of a scene is estimated from focus-cues using the Gaussian-Hermite moments (GHMs) of local neighboring pixels. It is expected that the depth map obtained from GHMs provides robust features as compared to that provided by other popular depth maps such as those obtained from Kinect and defocus cue. We use the rich depth and intensity values of a pixel to score every point of a video frame for generating labeled probability maps in a 3D environment. These maps are then used to create a 3D scene wherein available objects are labeled distinctively. Experimental results reveal that our proposed approach yields excellent performance of object labeling for different multi-view scenes taken from RGB-D object dataset, in particular showing significant improvements in precision-recall characteristics and F1-score.

关键词： Depth map Object labeling 3D scene

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Trajectory Classification Using Feature Selection by Genetic Algorithm 3rd

Trajectory Classification Using Feature Selection by Genetic...

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3rd International conference on computer vision and image processing (CVIP)

作者： Saini, Rajkumar Kumar, Pradeep Roy, Partha Pratim Pal, Umapada IIT Roorkee Roorkee Uttar Pradesh India ISI Kolkata Kolkata India

ISBN: (纸本)9789813292918;9789813292901

Trajectory classification helps in understanding the behavior of objects being monitored. The raw trajectories may not yield satisfactory classification results. Therefore, features are extracted from raw trajectories to improve classification results. All the extracted features may not be useful for classification. Hence, an automatic selection scheme is essential to find optimal features from the pool of handcrafted features. This paper uses a genetic framework to choose the optimal set of features for trajectory classification purpose. Seven features costing 18 dimensions have been extracted from raw trajectories. Next, Genetic Algorithm (GA) has been used to find the optimal set of features from them. The binary encoding scheme has been used in GA. The 7-bit long chromosomes have been coded in this work. Bits of chromosomes represent trajectory features to be used in classification. Finally, trajectories have been classified using optimal features. Trajectory classification has been done using Random Forest (RF) based classifier and compared with Support Vector Machine (SVM). The results are evaluated using three trajectory datasets, namely I5, LabOmni2, and T15. The classification rates of 99.87%, 93.32%, and 90.58% have been recorded for datasets I5, LabOmni2, and T15, respectively.

关键词： Trajectory Surveillance Classification Genetic algorithm Random forest Support vector machine

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L1-Regulated Feature Selection and Classification of Microarray Cancer Data Using Deep Learning 3rd

L1-Regulated Feature Selection and Classification of Microar...

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3rd International conference on computer vision and image processing (CVIP)

作者： Shekar, B. H. Dagnew, Guesh Mangalore Univ Dept Comp Sci Mangalore India

ISBN: (纸本)9789813292918;9789813292901

The microarray cancer data obtained through microarray technology poses a lot of challenges during classification since the sample size is very small and the dimensionality of the data is very high. It is noticed that usually, the number of classes in multiclass datasets are highly imbalanced. In order to reduce the dimensionality thereby enabling accurate classification, in this work, we propose an L1-regulated feature selection and deep learning is applied for classification. The L1-regulated feature selection is based on Linear Support Vector Machine (LSVM) which is characterized by adding a penalty term to the prediction error in order to reduce the weight of the irrelevant features and to make the relevant features having nonzero weights. For classification purpose, deep learning neural network is initialized with sigmoid activation function in the input and hidden layers and to accommodate multiclass classification, the softmax activation function is used in the output layer. In order to demonstrate the suitability of the proposed approach, experiments are conducted on the six numbers of standard multiclass cancer datasets and to argue the predictive capability of the proposed approach, experiments are conducted on imbalanced class datasets such as 5-class lung cancer dataset, and 4-class Leukemia cancer dataset. Comparative study is also provided with state-of-the-art approaches and the results are presented considering classification accuracy, precision, recall, f-measure, confusion matrix, average precision, and ROC metrics to exhibit the performance of the proposed approach.

关键词： L1-regularization Feature selection Deep learning Classification

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Single Storage Semi-Global Matching for Real Time Depth processing 7th

Single Storage Semi-Global Matching for Real Time Depth Proc...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Sawant, Prathmesh Temburu, Yashwant Datar, Mandar Ahmed, Imran Shriniwas, Vinayak Patkar, Sachin Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai India Defence Research and Development Organization New Delhi India

ISBN: (纸本)9789811586965

Depth-map is the key computation in computer vision and robotics. One of the most popular approach is via computation of disparity-map of images obtained from Stereo Camera. Semi Global Matching (SGM) method is a popular choice for good accuracy with reasonable computation time. To use such compute-intensive algorithms for real-time applications such as for autonomous aerial vehicles, blind Aid, etc. acceleration using GPU, FPGA is necessary. In this paper, we show the design and implementation of a stereo-vision system, which is based on FPGA-implementation of More Global Matching (MGM)[7]. MGM is a variant of SGM. We use 4 paths but store a single cumulative cost value for a corresponding pixel. Our stereo-vision prototype uses Zedboard containing an ARM-based Zynq-SoC[10], ZED-stereo-camera/ELP stereo-camera/Intel RealSense D435i, and VGA for visualization. The power consumption attributed to the custom FPGA-based acceleration of disparity map computation required for depth-map is just 0.72 watt. The update rate of the disparity map is realistic 10.5 fps. © 2020, Springer Nature Singapore Pte Ltd.

关键词： System-on-chip

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A Performance Evaluation of Loss Functions for Deep Face Recognition 7th

A Performance Evaluation of Loss Functions for Deep Face Rec...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Srivastava, Yash Murali, Vaishnav Dubey, Shiv Ram Computer Vision Group Indian Institute of Information Technology Chittor Sri CityA.P. India

ISBN: (纸本)9789811586965

Face recognition is one of the most widely publicized feature in the devices today and hence represents an important problem that should be studied with the utmost priority. As per the recent trends, the Convolutional Neural Network (CNN) based approaches are highly successful in many tasks of computer vision including face recognition. The loss function is used on the top of CNN to judge the goodness of any network. In this paper, we present a performance comparison of different loss functions such as Cross-Entropy, Angular Softmax, Additive-Margin Softmax, ArcFace and Marginal Loss for face recognition. The experiments are conducted with two CNN architectures namely, ResNet and MobileNet. Two widely used face datasets namely, CASIA-Webface and MS-Celeb-1M are used for the training and benchmark Labeled Faces in the Wild (LFW) face dataset is used for the testing. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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