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Author  |
Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Petia Radeva; Fernando Azpiroz; Juan Malagelada | ||||
| Title | Device, system and method for automatic detection of contractile activity in an image frame | Type | Patent | ||
| Year | 2011 | Publication | US 2011/0044515 A1 | Abbreviated Journal | |
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| Abstract | A device, system and method for automatic detection of contractile activity of a body lumen in an image frame is provided, wherein image frames during contractile activity are captured and/or image frames including contractile activity are automatically detected, such as through pattern recognition and/or feature extraction to trace image frames including contractions, e.g., with wrinkle patterns. A manual procedure of annotation of contractions, e.g. tonic contractions in capsule endoscopy, may consist of the visualization of the whole video by a specialist, and the labeling of the contraction frames. Embodiments of the present invention may be suitable for implementation in an in vivo imaging system. | ||||
| Address | Pearl Cohen Zedek Latzer, LLP, 1500 Broadway 12th Floor, New York (NY) 10036 (US) | ||||
| Corporate Author | US Patent Office | Thesis | |||
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| Notes | MV;OR;MILAB;SIAI | Approved | no | ||
| Call Number | IAM @ iam @ SVV2011 | Serial | 1701 | ||
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| Author |
Paramveer S. Dhillon; Francisco Javier Orozco; Jordi Gonzalez | ||||
| Title | Real-Time Monocular Face Tracking Using and Active Camera | Type | Report | ||
| Year | 2008 | Publication | CVC Technical Report # 119 | Abbreviated Journal | |
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| Address | Bellaterra (Spain) | ||||
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| Notes | Approved | no | |||
| Call Number | ISE @ ise @ DOG2008 | Serial | 1006 | ||
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| Author |
Parichehr Behjati Ardakani | ||||
| Title | Towards Efficient and Robust Convolutional Neural Networks for Single Image Super-Resolution | Type | Book Whole | ||
| Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Single image super-resolution (SISR) is an important task in image processing which aims to enhance the resolution of imaging systems. Recently, SISR has witnessed great strides with the rapid development of deep learning. Recent advances in SISR are mostly devoted to designing deeper and wider networks to enhance their representation learning capacity. However, as the depth of networks increases, deep learning-based methods are faced with the challenge of computational complexity in practice. Moreover, most existing methods rarely leverage the intermediate features and also do not discriminate the computation of features by their frequencial components, thereby achieving relatively low performance. Aside from the aforementioned problems, another desired ability is to upsample images to arbitrary scales using a single model. Most current SISR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. In this thesis, we address the aforementioned issues and propose solutions to them: i) We present a novel frequency-based enhancement block which treats different frequencies in a heterogeneous way and also models inter-channel dependencies, which consequently enrich the output feature. Thus it helps the network generate more discriminative representations by explicitly recovering finer details. ii) We introduce OverNet which contains two main parts: a lightweight feature extractor that follows a novel recursive framework of skip and dense connections to reduce low-level feature degradation, and an overscaling module that generates an accurate SR image by internally constructing an overscaled intermediate representation of the output features. Then, to solve the problem of reconstruction at arbitrary scale factors, we introduce a novel multi-scale loss, that allows the simultaneous training of all scale factors using a single model. iii) We propose a directional variance attention network which leverages a novel attention mechanism to enhance features in different channels and spatial regions. Moreover, we introduce a novel procedure for using attention mechanisms together with residual blocks to facilitate the preservation of finer details. Finally, we demonstrate that our approaches achieve considerably better performance than previous state-of-the-art methods, in terms of both quantitative and visual quality. | ||||
| Address | April, 2022 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca;Pau Rodriguez | ||
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| ISSN | ISBN | 978-84-124793-1-7 | Medium | ||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Beh2022 | Serial | 3713 | ||
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| Author |
Parichehr Behjati Ardakani; Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Jordi Gonzalez | ||||
| Title | Catastrophic interference in Disguised Face Recognition | Type | Conference Article | ||
| Year | 2019 | Publication | 9th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 11868 | Issue | Pages | 64-75 | |
| Keywords | Neural network forgetness; Face recognition; Disguised Faces | ||||
| Abstract | It is commonly known the natural tendency of artificial neural networks to completely and abruptly forget previously known information when learning new information. We explore this behaviour in the context of Face Verification on the recently proposed Disguised Faces in the Wild dataset (DFW). We empirically evaluate several commonly used DCNN architectures on Face Recognition and distill some insights about the effect of sequential learning on distinct identities from different datasets, showing that the catastrophic forgetness phenomenon is present even in feature embeddings fine-tuned on different tasks from the original domain. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ AVG2019 | Serial | 3416 | ||
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| Author |
Parichehr Behjati Ardakani; Pau Rodriguez; Armin Mehri; Isabelle Hupont; Carles Fernandez; Jordi Gonzalez | ||||
| Title | OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 2693-2702 | ||
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| Abstract | Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-based SR methods have been faced with the challenge of computational complexity in practice. More- over, most SR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. To address these limitations we introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We make the following contributions: first, we introduce a lightweight feature extractor that enforces efficient reuse of information through a novel recursive structure of skip and dense connections. Second, to maximize the performance of the feature extractor, we propose a model agnostic reconstruction module that generates accurate high-resolution images from overscaled feature maps obtained from any SR architecture. Third, we introduce a multi-scale loss function to achieve generalization across scales. Experiments show that our proposal outperforms previous state-of-the-art approaches in standard benchmarks, while maintaining relatively low computation and memory requirements. | ||||
| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | ISE; 600.119; 600.098 | Approved | no | ||
| Call Number | Admin @ si @ BRM2021 | Serial | 3512 | ||
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| Author |
Parichehr Behjati Ardakani; Pau Rodriguez; Carles Fernandez; Armin Mehri; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez | ||||
| Title | Frequency-based Enhancement Network for Efficient Super-Resolution | Type | Journal Article | ||
| Year | 2022 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume | 10 | Issue | Pages | 57383-57397 | |
| Keywords | Deep learning; Frequency-based methods; Lightweight architectures; Single image super-resolution | ||||
| Abstract | Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low- and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model — Frequency-based Enhancement Network (FENet) — based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-of-the-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet | ||||
| Address | 18 May 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | IEEE | Place of Publication | Editor | ||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ BRF2022a | Serial | 3747 | ||
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| Author |
Parichehr Behjati; Pau Rodriguez; Carles Fernandez; Isabelle Hupont; Armin Mehri; Jordi Gonzalez | ||||
| Title | Single image super-resolution based on directional variance attention network | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 133 | Issue | Pages | 108997 | |
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| Abstract | Recent advances in single image super-resolution (SISR) explore the power of deep convolutional neural networks (CNNs) to achieve better performance. However, most of the progress has been made by scaling CNN architectures, which usually raise computational demands and memory consumption. This makes modern architectures less applicable in practice. In addition, most CNN-based SR methods do not fully utilize the informative hierarchical features that are helpful for final image recovery. In order to address these issues, we propose a directional variance attention network (DiVANet), a computationally efficient yet accurate network for SISR. Specifically, we introduce a novel directional variance attention (DiVA) mechanism to capture long-range spatial dependencies and exploit inter-channel dependencies simultaneously for more discriminative representations. Furthermore, we propose a residual attention feature group (RAFG) for parallelizing attention and residual block computation. The output of each residual block is linearly fused at the RAFG output to provide access to the whole feature hierarchy. In parallel, DiVA extracts most relevant features from the network for improving the final output and preventing information loss along the successive operations inside the network. Experimental results demonstrate the superiority of DiVANet over the state of the art in several datasets, while maintaining relatively low computation and memory footprint. The code is available at https://github.com/pbehjatii/DiVANet. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ BPF2023 | Serial | 3861 | ||
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| Author |
Partha Pratim Roy | ||||
| Title | An Approach to Text/Graphics Separation in Color Maps | Type | Report | ||
| Year | 2007 | Publication | CVC Technical Report #104 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Roy2007 | Serial | 819 | ||
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| Author |
Partha Pratim Roy | ||||
| Title | Multi-Oriented and Multi-Scaled Text Character Analysis and Recognition in Graphical Documents and their Applications to Document Image Retrieval | Type | Book Whole | ||
| Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | With the advent research of Document Image Analysis and Recognition (DIAR), an
important line of research is explored on indexing and retrieval of graphics rich documents. It aims at finding relevant documents relying on segmentation and recognition of text and graphics components underlying in non-standard layout where commercial OCRs can not be applied due to complexity. This thesis is focused towards text information extraction approaches in graphical documents and retrieval of such documents using text information. Automatic text recognition in graphical documents (map, engineering drawing, etc.) involves many challenges because text characters are usually printed in multioriented and multi-scale way along with different graphical objects. Text characters are used to annotate the graphical curve lines and hence, many times they follow curvi-linear paths too. For OCR of such documents, individual text lines and their corresponding words/characters need to be extracted. For recognition of multi-font, multi-scale and multi-oriented characters, we have proposed a feature descriptor for character shape using angular information from contour pixels to take care of the invariance nature. To improve the efficiency of OCR, an approach towards the segmentation of multi-oriented touching strings into individual characters is also discussed. Convex hull based background information is used to segment a touching string into possible primitive segments and later these primitive segments are merged to get optimum segmentation using dynamic programming. To overcome the touching/overlapping problem of text with graphical lines, a character spotting approach using SIFT and skeleton information is included. Afterwards, we propose a novel method to extract individual curvi-linear text lines using the foreground and background information of the characters of the text and a water reservoir concept is used to utilize the background information. We have also formulated the methodologies for graphical document retrieval applications using query words and seals. The retrieval approaches are performed using recognition results of individual components in the document. Given a query text, the system extracts positional knowledge from the query word and uses the same to generate hypothetical locations in the document. Indexing of documents is also performed based on automatic detection of seals from documents containing cluttered background. A seal is characterized by scale and rotation invariant spatial feature descriptors computed from labelled text characters and a concept based on the Generalized Hough Transform is used to locate the seal in documents. |
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| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Josep Llados;Umapada Pal | |
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-937261-7-1 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ Roy2010 | Serial | 1455 | ||
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| Author |
Partha Pratim Roy; Eduard Vazquez; Josep Llados; Ramon Baldrich; Umapada Pal | ||||
| Title | A System to Retrieve Text/Symbols from Color Maps using Connected Component and Skeleton Analysis | Type | Conference Article | ||
| Year | 2007 | Publication | Seventh IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 79–78 | ||
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| Address | Curitiba (Brasil) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | J. Llados, W. Liu, J.M. Ogier | ||
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| Area | Expedition | Conference | GREC | ||
| Notes | CAT; DAG;CIC | Approved | no | ||
| Call Number | CAT @ cat @ RVL2007 | Serial | 836 | ||
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| Author |
Partha Pratim Roy; Eduard Vazquez; Josep Llados; Ramon Baldrich; Umapada Pal | ||||
| Title | A System to Segment Text and Symbols from Color Maps | Type | Book Chapter | ||
| Year | 2008 | Publication | Graphics Recognition. Recent Advances and New Opportunities | Abbreviated Journal | |
| Volume | 5046 | Issue | Pages | 245-256 | |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Notes | DAG;CIC | Approved | no | ||
| Call Number | CAT @ cat @ RVL2008 | Serial | 1005 | ||
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| Author |
Partha Pratim Roy; Josep Llados | ||||
| Title | Multi-Oriented Character Recognition from Graphical Documents | Type | Conference Article | ||
| Year | 2008 | Publication | 2nd International Conference on Cognition and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 30–35 | ||
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| Address | Mandya (India) | ||||
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| Area | Expedition | Conference | ICCR | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ RLP2008 | Serial | 965 | ||
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| Author |
Partha Pratim Roy; Josep Llados; Umapada Pal | ||||
| Title | Text/Graphics Separation in Color Maps | Type | Conference Article | ||
| Year | 2007 | Publication | International Conference on Computing: Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 545–551 | ||
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| Address | Kolkata (India) | ||||
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| Area | Expedition | Conference | ICCTA | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ RLP2007a | Serial | 806 | ||
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| Author |
Partha Pratim Roy; Josep Llados; Umapada Pal | ||||
| Title | A Complete System for Detection and Recognition of Text in Graphical Documents using Background Information | Type | Conference Article | ||
| Year | 2009 | Publication | 5th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
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| Address | Lisboa, Portugal | ||||
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| ISSN | ISBN | 978-989-8111-69-2 | Medium | ||
| Area | Expedition | Conference | VISAPP | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ RLP2009 | Serial | 1238 | ||
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| Author |
Partha Pratim Roy; Umapada Pal; Josep Llados | ||||
| Title | Multi-oriented English Text Line Extraction using Background and Foreground Information | Type | Conference Article | ||
| Year | 2008 | Publication | Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, | Abbreviated Journal | |
| Volume | Issue | Pages | 315–322 | ||
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| Address | Nara (Japo) | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ RPL2008b | Serial | 1047 | ||
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