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2nd International Conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2018

作者： Vinay, A. Cholin, Ajaykumar S. Bhat, Aditya D. Deshpande, Arnav Ajay Murthy, K. N. Balasubramanya Natarajan, S. Centre for Pattern Recognition and Machine Intelligence PES University 100 Feet Ring Road BSK Stage III BengaluruKarnataka560085 India

ISBN: (纸本)9789811391835

Over the years, quite a lot of research has been done on using different machine learning techniques for Face recognition (FR) for identifying the faces of different people. Current FR techniques are still not accurate enough for real world scenarios and pose a lot of problems against varying illumination levels, pose variations, noise and occlusion in the image. Thus, a single keypoint extraction technique may not be suitable for all cases. Hence, in this paper a novel technique is proposed for Keypoint Fusion (KF) obtained by fusing SIFT, SURF and ORB keypoints which is more accurate and suitable for real time application. The paper is also focused on proposing a novel technique of using a Self-Organizing Map (SOM) and Vector of Locally Aggregated Descriptors (VLAD) for image clustering. VLAD is used to extend the SOM’s ability to cluster keypoint descriptors. Image classification is carried out using a SGD (Stochastic Gradient Descent) based SVM (Support Vector machine) classifier. The performance of classification of the proposed framework on benchmark datasets (Grimace, Faces95 and Faces96) has been tabulated and compared with other standard techniques. It is seen that the proposed framework performs better than the BOW (Bag Of Words) model and the KF technique was accurate and quick enough to beat the traditional keypoint extraction techniques. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Self organizing maps

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Probabilistic Inference for Camera Calibration in Light Microscopy Under Circular Motion

Probabilistic Inference for Camera Calibration in Light Micr...

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IEEE International Symposium on Biomedical Imaging

作者： Yuanhao Guo Fons J. Verbeek Ge Yang Computational Biology and Machine Intelligence National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences LIACS Bioinformatics and Imaging Leiden University School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Robust and accurate camera calibration is essential for 3D reconstruction in light microscopy under circular motion. Conventional methods require either accurate key point matching or precise segmentation of the axial-view images. Both remain challenging because specimens often exhibit transparency/translucency in a light microscope. To address these issues, we propose a probabilistic inference based method for the camera calibration that does not require sophisticated image pre-processing. Based on 3D projective geometry, our method assigns a probability on each of a range of voxels that cover the whole object. The probability indicates the likelihood of a voxel belonging to the object to be reconstructed. Our method maximizes a joint probability that distinguishes the object from the background. Experimental results show that the proposed method can accurately recover camera configurations in both light microscopy and natural scene imaging. Furthermore, the method can be used to produce high-fidelity 3D reconstructions and accurate 3D measurements.

关键词： Cameras Three-dimensional displays Calibration Probabilistic logic Microscopy Solid modeling

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Advancing biological super-resolution microscopy through deep learning: a brief review

arXiv

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arXiv 2021年

作者： Yang, Tianjie Luo, Yaoru Ji, Wei Yang, Ge Institute of Biophysics Chinese Academy of Sciences Beijing100101 China College of Life Sciences University of Chinese Academy of Sciences Beijing100049 China Laboratory of Computational Biology and Machine Intelligence School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

Super-resolution microscopy overcomes the diffraction limit of conventional light microscopy in spatial resolution. By providing novel spatial or spatiotemporal information on biological processes at nanometer resolution with molecular specificity, it plays an increasingly important role in life sciences. However, its technical limitations require trade-offs to balance its spatial resolution, temporal resolution, and light exposure of samples. Recently, deep learning has achieved breakthrough performance in many image processing and computer vision tasks. It has also shown great promise in pushing the performance envelope of super-resolution microscopy. In this brief Review, we survey recent advances in using deep learning to enhance performance of super-resolution microscopy. We focus primarily on how deep learning advances reconstruction of super-resolution images. Related key technical challenges are discussed. Despite the challenges, deep learning is set to play an indispensable and transformative role in the development of super-resolution microscopy. We conclude with an outlook on how deep learning could shape the future of this new generation of light microscopy technology. © 2021, CC BY.

关键词： Image reconstruction

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Effective Utilization of Whitening for Person Identification

SSRN

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SSRN 2019年

作者： Vinay, A. Kailasam, Siddharth Bharadwaj, Aprameya Balasubramanya, K.N. Natarajan, Murthy S. Center for Pattern Recognition and Machine Intelligence PES University Bangalore India

The growth of importance of Person Identification(PI) is so drastic these days, since it helps support the growth of many other fields like surveillance, robotics etc. Computer Vision Enthusiasts are working on making this robust to various factors such as noise, scale, pose, intensity, illumination, translation, expression and many more. Whitening is a pre-processing technique which makes an image robust to noise, intensity and illumination. There are many feature detectors such as FLANN matching algorithms, which use relatively lesser resources, but are not robust to one or more of the above mentioned problems. However, in general whitening is usually used with machine learning algorithms, which are already robust to these problems. This paper hence compares results obtained using SVD whitening and ZCA whitening to without pre-processing on four distinct pipelines. The first two pipelines use SIFT extracted keypoints and SURF extracted keypoints matched by KD-Tree FLANN matcher and classified using confidence probability matching. These pipelines as we know are not robust to the problems solved by whitening. They are implemented on ORL, grimace and faces 95 database. The third and fourth pipeline includes machine learning algorithms to classify the features extracted by Spatial Pyramid Matching and Earth Mover’s Distance on Dense SIFT keypoints. They were implemented on ORL, faces 95 and FEI database. Results show that SVD whitening and ZCA whitening increases the accuracy more when FLANN matcher is used than when machine learning algorithms are used to classify the images. In both cases, we encounter a scenario where the accuracy is slightly lesser than the accuracy obtained without whitening. However, we find that the decrease in accuracy is more when machine learning algorithms are used than when FLANN matchers are used. © 2019, The Authors. All rights reserved.

关键词： Pipelines

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A Robust Vehicle Detection Approach based on Faster R-CNN Algorithm 4

A Robust Vehicle Detection Approach based on Faster R-CNN Al...

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4th International Conference on pattern recognition and Image Analysis, IPRIA 2019

作者： Tourani, Ali Soroori, Sajjad Shahbahrami, Asadollah Khazaee, Saeed Akoushideh, Alireza Department of Computer Engineering University of Guilan Rasht Iran Centre for Pattern Recognition and Machine Intelligence Concordia University Montreal Canada Shahid-Chamran College Technical and Vocational University Tehran Iran

ISBN: (纸本)9781728116211

Video-based Intelligent Transportation Systems (V-ITS) can play an important role in developing a wide range of applications in transportation field. These systems use the outputs of video cameras to extract desired information by the means of various Artificial intelligence techniques. Considering impressive advantages of applying Deep Neural Networks (DNNs) in different fields of object detection and classification, these methods have attracted a huge attention among researchers in recent years. In this regard, Convolutional Neural Networks (CNNs) as an important class of DNNs have been used for visual imagery goals in a wide variety of applications such as image recognition and video analysis, and even made their way through ITS applications. One of the most important steps of V-ITS applications is the process of vehicle detection in video frames and the high accuracy rate in this step can provide applicable data for other complementary modules such as vehicle tracking and classification. In this paper, a robust method to detect vehicles in video frames based on CNNs is proposed which provides an almost real-time performance and impressive accuracy. To overcome the challenges of building a precise vehicle detection model from still images, we have transformed the main architecture of a pre-trained ResNet-50 residual network to Faster Region-based Convolutional Neural Network (Faster R-CNN). Experimental results show that the system's sensitivity factor is 0.985 and it needs an average of 74 milliseconds to detect vehicles in real condition data. Consequently, our method can provide acceptable results in vehicle detection in terms of accuracy and execution time. © 2019 IEEE.

关键词： Deep neural networks

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Deep Learning on Binary patterns for Face recognition

Deep Learning on Binary Patterns for Face Recognition

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2018 International Conference on Computational intelligence and Data Science, ICCIDS 2018

作者： Vinay, A. Gupta, Abhijay Bharadwaj, Aprameya Srinivasan, Arvind Murthy, K.N. Balasubramanya Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

In this paper an efficient and robust method for real-time face recognition is proposed. As a part of pre-processing to remove noise and unwanted features, a filter is applied to the images of standard datasets. Subsequently binary patterns are extracted from these images which are further fed into multilayer perceptron to classify images. The proposed method is tested on four benchmark datasets namely FACES94, FACES95, FACES96 and Grimace which pose challenges in illumination, pose, expression, head scale and rotation. This method delivers accuracies in the neighbourhood of 91% when tested on these datasets. Our methodology was further extended to embedded systems like Raspberry Pi 3 to give it more real-time and practical scenario. This test gave promising results and proved the model to be efficient and useful in day to day scenarios for face recognition. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Neural networks

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Sparse Locally Adaptive Regression Kernel for Face Verification

Sparse Locally Adaptive Regression Kernel for Face Verificat...

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2018 International Conference on Computational intelligence and Data Science, ICCIDS 2018

作者： Vinay, A. Kamath, Vinayaka R. Varun, M. Murthy, K.N. Balasubramanya Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

The paper presents several thresholds obtained by heuristic approach for face verification using Locally Adaptive Regression Kernel (LARK) descriptors for euclidean, cosine and chebyshev distance metrics. The absence of a threshold for several distance metrics possess several setbacks such as increased computational complexity and escalated runtime. The proposed method has significantly higher influence when the verification process is computationally intensive. The proposed approach requires minimal training and face verification is accomplished based on the threshold value obtained during training. The whole process is modest and nearly all of the time spent is solely on quantifying any of the distance metrics between the LARKs obtained from the two faces for verification. LARK descriptors compute a measure of resemblance on the basis of "signal-induced distance" between a pixel and its nearby pixels. We assess the interspace between the LARKs from these faces and analogize the resemblance from the threshold resulting in a binary decision. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Pixels

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On Detectors and Descriptors based Techniques for Face recognition

On Detectors and Descriptors based Techniques for Face Recog...

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2018 International Conference on Computational intelligence and Data Science, ICCIDS 2018

作者： Vinay, A. Aklecha, Nishant Meghana Murthy, K.N. Balasubramanya Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

Out of all forms of biometrics, Face recognition (FR) emerges as the most incredible one. Apart from offering revolutionary applications for business and law-enforcement purposes, it has also opened numerous research avenues in various domains like security, surveillance and social network. One of the many factors critical to having an efficient face recognition system is having at hand, a suitable combination of feature descriptor and feature detector. A feature detector makes use of methods that make local decisions regarding the presence/absence of image features of a given type. A feature descriptor, on the other hand, simplifies the image by extracting useful information and disposing irrelevant information. Our research discusses the goodness of various feature descriptor-detector combination. We do this by simply carrying out the process of feature matching using various combinations of detectors and descriptors. This experiment includes incorporation of dimensionality reduction on the images using Hypercomplex Fourier Transform (HFT) and RANSAC for noise reduction. Out of the diverse options available, we chose to test LGHD, PCEHD and EHD for feature descriptors;for feature detectors, we chose the ones that make use of popular algorithms like Harris-Stephen Algorithm, Minimum Eigen Value and SURF. A series of strict and thorough experiments on popularly available datasets - Faces94 and Grimace led us to an astonishing observation - an accuracy of 90.67% for the former and 71.3% for the latter for Minimum Eigen Value paired with LGHD! This in comparison to those for all other combinations is a lot superior. We thereby conclusively state that feature detector using Minimum Eigen Value algorithm paired with feature descriptor - LGHD outplays other combinations making this combination the best choice for Face recognition. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Feature extraction

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Learning referee evaluation and assessing action quality from coarse to fine in diving sport

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Neurocomputing 2025年

作者： Hong-Ming Qiu Hong-Bo Zhang Qing Lei Jing-Hua Liu Ji-Xiang Du Department of Computer Science and Technology Huaqiao University Xiamen 361021 China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen 361021 China Key Laboratory of Computer Vision and Machine Learning (Huaqiao University) Fujian Province University Xiamen 361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen 361021 China

Intelligently assessing the quality of athletic performances in sports scenarios remains a fascinating challenge in computer vision. However, unraveling the subtle distinctions between two similar actions in videos and mapping those video representations to quality scores remain significant obstacles. To address these challenges, this work redefines the paradigm of quality score estimation from traditional relative quality score prediction to relative referee score prediction. To make this shift, a cross-feature fusion module rooted in Transformer-based video representation is introduced, to improve pairwise video feature learning in the realm of action quality assessment. Then, a novel contrastive action parsing decoder module generates mid-level representations to effectively connect visual features with detailed quality scores. Both modules utilize cross-attention mechanisms; the former refines the pairwise video features to represent the differences between video pairs, while the latter updates the input queries corresponding to each referee’s evaluation. Finally, to achieve precise quality score estimation, we introduce a meticulous coarse-to-fine decision process, integrating a score classifier and offset regressor. After validation on challenging diving datasets, including MTL-AQA, FineDiving, and TASD-2, the experimental results show that the proposed approach demonstrates effectiveness and feasibility when compared with state-of-the-art methods.

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Face recognition using interest points and ensemble of classifiers 4

Face recognition using interest points and ensemble of class...

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4th IEEE International Conference on Recent Advances in Information Technology, RAIT 2018

作者： Vinay, A. Sampat, Pratik Rajesh Belavadi, Sagar V. Pratik, R. Rao, B. S. Nikitha Ragesh, Rahul Murthy, K. N. Balasubramanya Natarajan, S. Centre for Pattern Recognition and Machine Intelligence PES University Bangalore Karnataka India

ISBN: (纸本)9781538630396

In human beings, it is the responsibility of the temporal lobe of the brain for recognition of faces. Certain features of the face trigger the neurons of the temporal lobe which are then stored. These eventually lead to the identification of the face. In machine learning system, a huge dataset of images is used as a reference by the face recognition algorithms. The most prominent features are extracted from what is referred to as landmarks on the face and are analyzed based on their relative position amongst other features. The main challenge in computer vision lies in the identification and extraction of these features which in turn assist in face recognition owing to the exponentially large degree of variability that can be seen in appearances. Speed and accuracy are some of the most prominent challenges for the same. We explore ELM critically and study its performance. Owing to its extremely fast learning, it finds potential in real-time applications. The goal of this paper is to evaluate the performance of the extreme learning machine algorithm for facial recognition when used as a standalone model as well as when it is used with an ensemble learner. We then conclude with a voting classifier which is responsible for the final predictions made. Various combinations of ensembles are explored to come up with the best performing model. © 2018 IEEE.

关键词： Face recognition

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