| 1. |
- Chanda, Sukalpa, et al.
(författare)
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Face Recognition - A One-Shot Learning Perspective
- 2019
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Ingår i: 15th IEEE Conference on Signal Image Technology and Internet based Systems. - 9781728156866 ; , s. 113-119
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Konferensbidrag (refereegranskat)abstract
- Ability to learn from a single instance is something unique to the human species and One-shot learning algorithms try to mimic this special capability. On the other hand, despite the fantastic performance of Deep Learning-based methods on various image classification problems, performance often depends having on a huge number of annotated training samples per class. This fact is certainly a hindrance in deploying deep neural network-based systems in many real-life applications like face recognition. Furthermore, an addition of a new class to the system will require the need to re-train the whole system from scratch. Nevertheless, the prowess of deep learned features could also not be ignored. This research aims to combine the best of deep learned features with a traditional One-Shot learning framework. Results obtained on 2 publicly available datasets are very encouraging achieving over 90% accuracy on 5-way One-Shot tasks, and 84% on 50-way One-Shot problems.
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| 2. |
- Chanda, Sukalpa, et al.
(författare)
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Finding Logo and Seal in Historical Document Images - : An Object Detection based Approach
- 2019
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Ingår i: The 5th Asian Conference on Pattern Recognition (ACPR 2019). ; , s. 821-834
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Konferensbidrag (refereegranskat)abstract
- Logo and Seal serves the purpose of authenticating and referring to the source of a document. This strategy was also prevalent in the medieval period. Different algorithm exists for detection of logo and seal in document images. A close look into the present state-of-the-art methods reveals that those methods were focused toward detection of logo and seal in contemporary document images. However, such methods are likely to underperform while dealing with historical documents. This is due to the fact that historical documents are attributed with additional challenges like extra noise, bleed-through effect, blurred foreground elements and low contrast. The proposed method frames the problem of the logo and seals detection in an object detection framework. Using a deep-learning technique it counters earlier mentioned problems and evades the need for any pre-processing stage like layout analysis and/or binarization in the system pipeline. The experiments were conducted on historical images from 12th to the 16th century and the results obtained were very encouraging for detecting logo in historical document images. To the best of our knowledge, this is the first attempt on logo detection in historical document images using an object-detection based approach.
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| 3. |
- Mokayed, Hamam, et al.
(författare)
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A New DCT-PCM Method for License Plate Number Detection in Drone Images
- 2021
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 148, s. 45-53
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Tidskriftsartikel (refereegranskat)abstract
- License plate number detection in drone images is a complex problem because the images are generally captured at oblique angles and pose several challenges like perspective distortion, non-uniform illumination effect, degradations, blur, occlusion, loss of visibility etc. Unlike, most existing methods that focus on images captured by orthogonal direction (head-on), the proposed work focuses on drone text images. Inspired by the Phase Congruency Model (PCM), which is invariant to non-uniform illuminations, contrast variations, geometric transformation and to some extent to distortion, we explore the combination of DCT and PCM (DCT-PCM) for detecting license plate number text in drone images. Motivated by the strong discriminative power of deep learning models, the proposed method exploits fully connected neural networks for eliminating false positives to achieve better detection results. Furthermore, the proposed work constructs working model that fits for real environment. To evaluate the proposed method, we use our own dataset captured by drones and benchmark license plate datasets, namely, Medialab for experimentation. We also demonstrate the effectiveness of the proposed method on benchmark natural scene text detection datasets, namely, SVT, MSRA-TD-500, ICDAR 2017 MLT and Total-Text.
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| 4. |
- Mokayed, Hamam, et al.
(författare)
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A New Defect Detection Method for Improving Text Detection and Recognition Performances in Natural Scene Images
- 2020
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Ingår i: 2020 Swedish Workshop on Data Science (SweDS). - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- This paper presents a new idea for improving text detection and recognition performances by detecting defects in the text detection results. Despite the rapid development of powerful deep learning based models for scene text detection and recognition in the wild, in complex situations (logos or decorated components connected with text), existing methods do not yield satisfactory results. In this paper, we propose to use post-processing method to improve the text detection and recognition performance. The proposed method extracts features, namely phase congruency, entropy and compactness for the text detection results. To strengthen discriminative power for feature extraction, we explore the combination of SVM classifier and Gaussian distribution of text components to determine proper weight, which represents true text component. The weights are multiplied with the features to detect defect components though clustering. The bounding boxes are redrawn, which results proper bounding box without defects components. Experimental results show that the proposed defect detection reports satisfactory results. To validate the effectiveness of defect detection, we conduct experiments on benchmark datasets of MSRA-TD-500 and SVT for detection and recognition before and after defect detection. The result shows that the performance of text detection and recognition improves significantly after defect detection.
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| 5. |
- Mokayed, Hamam, et al.
(författare)
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Anomaly Detection in Natural Scene Images based on enhanced Fine-Grained Saliency and Fuzzy Logic
- 2021
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 129102-129109
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a simple yet effective method for anomaly detection in natural scene images improving natural scene text detection and recognition. In the last decade, there has been significant progress towards text detection and recognition in natural scene images. However, in cases where there are logos, company symbols, or other decorative elements for text, existing methods do not perform well. This work considers such misclassified components, which are part of the text as anomalies, and presents a new idea for detecting such anomalies in the text for improving text detection and recognition in natural scene images. The proposed method considers the result of the existing text detection method as input for segmenting characters or components based on saliency map and rough set theory. For each segmented component, the proposed method extracts feature from the saliency map based on density, pixel distribution, and phase congruency to classify text and non-text components by exploring a fuzzy-based classifier. To verify the effectiveness of the method, we have performed experiments on several benchmark datasets of natural scene text detection, namely, MSRATD-500 and SVT. Experimental results show the efficacy of the proposed method over the existing ones for text detection and recognition in these datasets.
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| 6. |
- Petrosino, Francesco, et al.
(författare)
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a brief review : a brief review
- 2021
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Ingår i: Euro-Mediterranean Journal for Environmental Integration. - : Springer Nature. - 2365-6433 .- 2365-7448. ; 6:2
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Forskningsöversikt (refereegranskat)abstract
- Over the past two decades, several deadly viral epidemics have emerged, which have placed humanity in danger. Previous investigations have suggested that viral diseases can spread through contaminants or contaminated surfaces. The transmission of viruses via polluted surfaces relies upon their capacity to maintain their infectivity while they are in the environment. Here, a range of materials that are widely used to manufacture personal protective equipment (PPE) are summarized, as these offer effective disinfection solutions and are the environmental variables that influence virus survival. Infection modes and prevention as well as disinfection and PPE disposal strategies are discussed. A coronavirus-like enveloped virus can live in the environment after being discharged from a host organism until it infects another healthy individual. Transmission of enveloped viruses such as SARS-CoV-2 can occur even without direct contact, although detailed knowledge of airborne routes and other indirect transmission paths is still lacking. Ground transmission of viruses is also possible via wastewater discharges. While enveloped viruses can contaminate potable water and wastewater through human excretions such as feces and droplets, careless PPE disposal can also lead to their transmission into our environment. This paper also highlights the possibility that viruses can be transmitted into the environment from PPE kits used by healthcare and emergency service personnel. A simulation-based approach was developed to understand the transport mechanism for coronavirus and similar enveloped viruses in the environment through porous media, and preliminary results from this model are presented here. Those results indicate that viruses can move through porous soil and eventually contaminate groundwater. This paper therefore underlines the importance of proper PPE disposal by healthcare workers in the Mediterranean region and around the world.
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| 7. |
- Rakesh, Sumit, 1987-, et al.
(författare)
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Static Palm Sign Gesture Recognition with Leap Motion and Genetic Algorithm
- 2021
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Ingår i: 2021 Swedish Artificial Intelligence Society Workshop (SAIS). - : IEEE. ; , s. 54-58
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Konferensbidrag (refereegranskat)abstract
- Sign gesture recognition is the field that models sign gestures in order to facilitate communication with hearing and speech impaired people. Sign gestures are recorded with devices like a video camera or a depth camera. Palm gestures are also recorded with the Leap motion sensor. In this paper, we address palm sign gesture recognition using the Leap motion sensor. We extract geometric features from Leap motion recordings. Next, we encode the Genetic Algorithm (GA) for feature selection. Genetically selected features are fed to different classifiers for gesture recognition. Here we have used Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers to have their comparative results. The gesture recognition accuracy of 74.00% is recorded with RF classifier on the Leap motion sign gesture dataset.
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| 8. |
- Roy, Ayush, et al.
(författare)
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Fourier Feature-based CBAM and Vision Transformer for Text Detection in Drone Images
- 2023
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Ingår i: Document Analysis and Recognition – ICDAR 2023 Workshops, Part II. - : Springer. ; , s. 257-271
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Konferensbidrag (refereegranskat)abstract
- The use of drones for several real-world applications is increasing exponentially, especially for the purpose of monitoring, surveillance, security, etc. Most existing scene text detection methods were developed for normal scene images. This work aims to develop a model for detecting text in drone as well as scene images. To reduce the adverse effects of drone images, we explore the combination of Fourier transform and Convolutional Block Attention Module (CBAM) to enhance the degraded information in the images without affecting high-contrast images. This is because the above combination helps us to extract prominent features which represent text irrespective of degradations. Therefore, the refined features extracted from the Fourier Contouring Network (FCN) are supplied to Vision Transformer, which uses the ResNet50 as a backbone and encoder-decoder for text detection in both drone and scene images. Hence, the model is called Fourier Transform based Transformer. Experimental results on drone datasets and benchmark datasets, namely, Total-Text and ICDAR 2015 of natural scene text detection show the proposed model is effective and outperforms the state-of-the-art models.
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