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arXiv 2022年

作者： Srivastava, Abhishek Tomar, Nikhil Kumar Bagci, Ulas Jha, Debesh Computer Vision and Pattern Recognition Unit Indian Statistical Institute India School of Informatics and Computer Science Indira Gandhi National Open University India Department of Radiology Feinberg School of Medicine Northwestern University United States

Video capsule endoscopy is a hot topic in computer vision and medicine. Deep learning can have a positive impact on the future of video capsule endoscopy technology. It can improve the anomaly detection rate, reduce physicians’ time for screening, and aid in real-world clinical analysis. computer-Aided diagnosis (CADx) classification system for video capsule endoscopy has shown a great promise for further improvement. For example, detection of cancerous polyp and bleeding can lead to swift medical response and improve the survival rate of the patients. To this end, an automated CADx system must have high throughput and decent accuracy. In this paper, we propose FocalConvNet, a focal modulation network integrated with lightweight convolutional layers for the classification of small bowel anatomical landmarks and luminal findings. FocalConvNet leverages focal modulation to attain global context and allows global-local spatial interactions throughout the forward pass. Moreover, the convolutional block with its intrinsic inductive/learning bias and capacity to extract hierarchical features allows our FocalConvNet to achieve favourable results with high throughput. We compare our FocalConvNet with other state-of-the-art (SOTA) on Kvasir-Capsule, a large-scale VCE dataset with 44,228 frames with 13 classes of different anomalies. Our proposed method achieves the weighted F1-score, recall and Matthews correlation coefficient (MCC) of 0.6734, 0.6373 and 0.2974, respectively outperforming other SOTA methodologies. Furthermore, we report the highest throughput of 148.02 images/second rate to establish the potential of FocalConvNet in a real-time clinical environment. The code of the proposed FocalConvNet is available at https://***/ NoviceMAn-prog/FocalConvNet. © 2022, CC BY.

关键词： Modulation

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Signature segmentation and recognition from scanned documents

Signature segmentation and recognition from scanned document...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Ranju Mandal Partha Pratim Roy Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Queensland Australia Automation Engineering Department Synchromedia Lab Montreal Canada Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Signature as a query is important for content-based document image retrieval from a scanned document repository. This paper presents a two-stage approach towards automatic signature segmentation and recognition from scanned document images. In the first stage, signature blocks are segmented from the document using word-wise component extraction and classification. Gradient based features are extracted from each component at the word level to perform the classification task. In the 2nd stage, SIFT (Scale-Invariant Feature Transform) descriptors and Spatial Pyramid Matching (SPM)-based approaches are used for signature recognition. Support Vector Machines (SVMs) are employed as the classifier for both levels in this experiment. The experiments are performed on the publicly available “Tobacco-800” and GPDS [1] datasets and the results obtained from the experiments are promising.

关键词： Image segmentation Industries Vector quantization Hidden Markov models

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More Realistic and Efficient Face-Based Mobile Authentication using CNNs

More Realistic and Efficient Face-Based Mobile Authenticatio...

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International Joint Conference on Neural Networks

作者： Abhijit Das Abira Sengupta Muhammad Saqib Umapada Pal Michael Blumenstein Center for Artificial Intelligence School of Software University of Technology Sydney Australia Department of Computer Science Kalyani Government Engineering College Kalyani India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this work, we propose a more realistic and efficient face-based mobile authentication technique using CNNs. This paper discusses and explores an inevitable problem of using face images for mobile authentication, taken from varying distances with a front/selfie camera of the mobile phone. Incidentally, once an individual comes towards a certain distance from the camera, the face images get large and appear over-sized. Simultaneously sharp features of some portions of the face, such as forehead, cheek, and chin are changed completely. As a result, the face features change and the impact increases exponentially once the individual crosses a certain distance and gradually approaches towards the front camera. This work proposes a solution (achieving better accuracy and facial features, whereby face images were cropped and aligned around its close bounding box) to mitigate the aforementioned identified gap. The work investigated different frontier face detection and recognition techniques to justify the proposed solution. Among all the employed methods evaluated, CNNs worked best. For a quantitative comparison of the proposed method, manually cropped face images/annotations of the face images along with their close boundary were prepared. In turn, we have developed a database considering the above-mentioned scenario for 40 individuals, which will be publicly available for academic research purposes. The experimental results achieved indicate a successful implementation of the proposed method and the performance of the proposed technique is also found to be superior in comparison to the existing state-of-the-art.

关键词： Face Cameras Feature extraction Authentication Face recognition Optical distortion Face detection

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Frequency-plane analysis of normal and pathological ECG signals for disease identification

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Journal of Medical Engineering and Technology 2005年第5期29卷 219-227页

作者： Mitra, S. Mitra, M. Chaudhuri, B.B. Department of Applied Physics Faculty of Technology University of Calcutta 92 APC Road Kolkata - 700009 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata - 700 108 India

In this paper a frequency plane analysis of both normal and diseased ECG signals is performed specifically for disease identification. Image processing techniques are used to develop an automated data acquisition package of 12 lead ECG signals from paper records. A regeneration domain is also developed to check the captured pattern with the original wave shape. A QRS complex detector with an accuracy level ∼98.4% in up to 30% signal to noise level is developed. Discrete Fourier transform (DFT) is performed to obtain the frequency spectrum of every ECG signal. Some interesting amplitude and phase response properties of chest lead V2, V3, V4, V6 and limb lead I, II, III, AVL, AVF are seen. Both amplitude and phase properties are different for normal and diseased subjects and can serve an important role in disease identification. A statistical analysis of amplitude property is carried out to show that this property is significantly different for normal and diseased subjects. © 2005 Taylor & Francis.

关键词： Electrocardiography

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A Novel Approach to Skew Detection and Character Segmentation for Handwritten Bangla Words

A Novel Approach to Skew Detection and Character Segmentatio...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： A. Roy T.K. Bhowmik S.K. Parui U. Roy Department of Computer and System Sciences Visva Bharati University Santiniketan India Embassy Golf Link IBM Global Services Private Limited Bangalore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Character segmentation is a necessary preprocessing step for character recognition in many handwritten word recognition systems. The most difficult case in character segmentation is the cursive script. Fully cursive nature of Bangla handwriting, the natural skewness in words poses some challenges for automatic character segmentation. In this article a novel approach to skew detection, correction as well as character segmentation has been presented for handwritten Bangla words as a test case. Segmenting points are extracted on the basis of some patterns observed in the handwritten words. With these segmenting points a graphical path (hereafter referred to as a candidate path) has been constructed. The handwritten words contain some consistent and also inconsistent skewness. Our algorithm can cope with both types of skewness at a time. Further the method is so direct that with the help of a candidate path one can handle both skew correction and segmentation successfully. the algorithm has been tested on a database prepared for laboratory use. The method yields fairly good results for this database.

关键词： Image segmentation Character recognition Optical character recognition software computer vision pattern recognition Testing Databases Algorithm design and analysis Writing Handwriting recognition

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Offline Hand-Written Musical Symbol recognition

Offline Hand-Written Musical Symbol Recognition

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International Workshop on Frontiers in Handwriting recognition

作者： Sukalpa Chanda Debleena Das Umapada Pal Fumitaka Kimura Department of Computer Science and Media Technology Gjøvik University College Gjøvik Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University TSU Japan

recognition of offline musical symbols can aid in automatic retrieval of a particular piece of musical notation from a digital repository. Though some work on on-line Musical symbol notations exists, little work has been done on off-line recognition of the symbols. This article proposes a system for offline isolated musical symbol recognition. Efficacy of a texture analysis based feature extraction method is compared with a structural shape descriptor based feature extraction method coupled with a Support Vector Machine (SVM) classifier. Later three different kinds of feature selection techniques were also analyzed to gauge the contribution of each feature in the overall classification process. We compared our results with an existing method and we noted the proposed system exhibited encouraging results and it is better than existing method. The proposed system even worked better when we used MQDF classifier in place of SVM. In a five-fold cross validation experimental framework, considering 3795 music symbols we achieved 97.50% and 98.05% accuracy from SVM and MQDF classifiers, respectively when chain-code histogram features are applied.

关键词： Feature extraction Accuracy Support vector machines Histograms Handwriting recognition Educational institutions Classification algorithms

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DCT-phase statistics for forged IMEI numbers and air ticket detection

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Expert Systems with Applications 2021年 164卷 114014-114014页

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Kanchan, Swati Basavaraja, V. Guru, D.S. Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Studies in Computer Science University of Mysore Karnataka India National Key Lab for Novel Software Technology Nanjing University China Sydney Australia

New tools have been developing with the intention of having more flexibility and greater user-friendliness for editing the images and documents in digital technologies, but, unfortunately, they are also being used for manipulating and tampering information. Examples of such crimes include creating forged International Mobile Equipment Identity (IMEI) numbers which are embedded on mobile packages and inside smart mobile cases for illicit activities. Another example of such crimes is altering the name or date on air tickets for breaching security at the airport. This paper presents a new expert system for detecting forged IMEI numbers as well as altered air ticket images. The proposed method derives the phase spectrum using the Discrete Cosine Transform (DCT) to highlight the suspicious regions;it is unlike the phase spectrum from a Fourier transform, which is ineffective due to power spectrum noise. From the phase spectrum, our method extracts phase statistics to study the effect of distortions introduced by forgery operations. This results in feature vectors, which are fed to a Support Vector Machine (SVM) classifier for detection of forged IMEI numbers and air ticket images. Experimental results on our dataset of forged IMEI numbers (which is created by us for this work), on altered air tickets, on benchmark datasets of video caption text (which is tampered text), and on altered receipts of the ICPR 2018 FDC dataset, show that the proposed method is robust across different datasets. Furthermore, comparative studies of the proposed method with the existing methods on the same datasets show that the proposed method outperforms the existing methods. The dataset created will be available freely on request to the authors. © 2020 Elsevier Ltd

关键词： Discrete cosine transforms

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Efficient Window Block Retrieval in Quadtree-Based Spatial Databases

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GeoInformatica 1997年第1期1.0卷 59-91页

作者： Aref, Walid G. Samet, Hanan Computer Science Department Center for Automation Research University of Maryland College Park MD 20742 United States University of Alexandria Egypt University of Maryland College Park United States Matsushita Info. Technol. Laboratory Princeton United States IBM Research Almaden CA United States University of Maryland Inst. for Advanced Computer Studies College Park MD United States ACM IEEE United States Department of Computer Science University of Maryland United States Computer Vision Laboratory Stanford University United States ACM IEEE Intl. Assoc. of Pattern Recognition

An algorithm is presented to answer window queries in a quadtree-based spatial database environment by retrieving all of the quadtree blocks in the underlying spatial database that cover the quadtree blocks that comprise the window. It works by decomposing the window operation into sub-operations over smaller window partitions. These partitions are the quadtree blocks corresponding to the window. Although a block b in the underlying spatial database may cover several of the smaller window partitions, b is only retrieved once rather than multiple times. This is achieved by using an auxiliary main memory data structure called the active border which requires O(n) additional storage for a window query of size n × n. As a result, the algorithm generates an optimal number of disk I/O requests to answer a window query (i.e., one request per covering quadtree block). A proof of correctness and an analysis of the algorithm's execution time and space requirements are given, as are some experimental results.

关键词： Active border Clipping Data structures Databases Design of algorithms Quadtree space decomposition Range query Spatial databases Window block retrieval

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Design of surrogate models in civil engineering by neural networks

Design of surrogate models in civil engineering by neural ne...

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South Eastern European Design Automation, computer Engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Vojtěch Drahý Radek Mařík Heikki Kälviäinen Department of Computer Science Czech Technical University in Prague Prague Czech Republic Department of Telecommunication Engineering Czech Technical University in Prague Prague Czech Republic Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland

ISBN: (数字)9798331504489

ISBN: (纸本)9798331504496

We present a task from the critical infrastructure field in materials engineering. We created a surrogate model for the bridge construction object to determine the material parameters’ values. The work aims to use neural networks to conduct an initial investigation of the task and to find out the aspects of machine learning application. To reduce the computational complexity of the models, we designed specific neural networks whose architecture corresponds to the structure and characteristics of the processed data. Furthermore, we outcome also interpretability and justification of the model’s decision-making. The main contribution of the work is the replacement of the unknown or too complex physical, mathematical description of material objects with a neural network model.

关键词： Training Social networking (online) Computational modeling Neural networks Focusing computer architecture Machine learning Feature extraction Mathematical models Computational complexity

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Combining feature aggregation and geometric similarity for re-identification of patterned animals

arXiv

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arXiv 2023年

作者： Immonen, Veikka Nepovinnykh, Ekaterina Eerola, Tuomas Stewart, Charles V. Kälviäinen, Heikki Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering School of Engineering Sciences Lappeenranta-Lahti University of Technology LUT LappeenrantaFI-53851 Finland Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States

Image-based re-identification of animal individuals allows gathering of information such as migration patterns of the animals over time. This, together with large image volumes collected using camera traps and crowdsourcing, opens novel possibilities to study animal populations. For many species, the re-identification can be done by analyzing the permanent fur, feather, or skin patterns that are unique to each individual. In this paper, we address the re-identification by combining two types of pattern similarity metrics: 1) pattern appearance similarity obtained by pattern feature aggregation and 2) geometric pattern similarity obtained by analyzing the geometric consistency of pattern similarities. The proposed combination allows to efficiently utilize both the local and global pattern features, providing a general re-identification approach that can be applied to a wide variety of different pattern types. In the experimental part of the work, we demonstrate that the method achieves promising re-identification accuracies for Saimaa ringed seals and whale sharks. © 2023, CC BY-NC-SA.

关键词： Animals

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