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Author  |
Parichehr Behjati Ardakani; Pau Rodriguez; Armin Mehri; Isabelle Hupont; Carles Fernandez; Jordi Gonzalez |
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Title |
OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network |
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Conference Article |
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Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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2693-2702 |
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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. |
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Virtual; January 2021 |
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WACV |
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ISE; 600.119; 600.098 |
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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 |
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Title |
Frequency-based Enhancement Network for Efficient Super-Resolution |
Type |
Journal Article |
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Year |
2022 |
Publication |
IEEE Access |
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ACCESS |
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Volume |
10 |
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Pages |
57383-57397 |
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Keywords |
Deep learning; Frequency-based methods; Lightweight architectures; Single image super-resolution |
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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 |
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18 May 2022 |
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IEEE |
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ISE |
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Admin @ si @ BRF2022a |
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3747 |
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Author |
Parichehr Behjati; Pau Rodriguez; Carles Fernandez; Isabelle Hupont; Armin Mehri; Jordi Gonzalez |
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Title |
Single image super-resolution based on directional variance attention network |
Type |
Journal Article |
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Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
133 |
Issue |
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Pages |
108997 |
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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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ISE |
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Admin @ si @ BPF2023 |
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3861 |
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Author |
Partha Pratim Roy |
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Title |
An Approach to Text/Graphics Separation in Color Maps |
Type |
Report |
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Year |
2007 |
Publication |
CVC Technical Report #104 |
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CVC (UAB) |
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Call Number |
Admin @ si @ Roy2007 |
Serial |
819 |
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Author |
Partha Pratim Roy |
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Title |
Multi-Oriented and Multi-Scaled Text Character Analysis and Recognition in Graphical Documents and their Applications to Document Image Retrieval |
Type |
Book Whole |
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Year |
2010 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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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Ph.D. thesis |
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Ediciones Graficas Rey |
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Josep Llados;Umapada Pal |
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978-84-937261-7-1 |
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Approved |
no |
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Call Number |
Admin @ si @ Roy2010 |
Serial |
1455 |
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Author |
Partha Pratim Roy; Eduard Vazquez; Josep Llados; Ramon Baldrich; Umapada Pal |
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Title |
A System to Retrieve Text/Symbols from Color Maps using Connected Component and Skeleton Analysis |
Type |
Conference Article |
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Year |
2007 |
Publication |
Seventh IAPR International Workshop on Graphics Recognition |
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79–78 |
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Curitiba (Brasil) |
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J. Llados, W. Liu, J.M. Ogier |
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GREC |
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Notes |
CAT; DAG;CIC |
Approved |
no |
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Call Number |
CAT @ cat @ RVL2007 |
Serial |
836 |
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Author |
Partha Pratim Roy; Eduard Vazquez; Josep Llados; Ramon Baldrich; Umapada Pal |
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Title |
A System to Segment Text and Symbols from Color Maps |
Type |
Book Chapter |
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Year |
2008 |
Publication |
Graphics Recognition. Recent Advances and New Opportunities |
Abbreviated Journal |
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Volume |
5046 |
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245-256 |
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LNCS |
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DAG;CIC |
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CAT @ cat @ RVL2008 |
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1005 |
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Author |
Partha Pratim Roy; Josep Llados |
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Title |
Multi-Oriented Character Recognition from Graphical Documents |
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Conference Article |
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2008 |
Publication |
2nd International Conference on Cognition and Recognition |
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30–35 |
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Mandya (India) |
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ICCR |
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DAG |
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DAG @ dag @ RLP2008 |
Serial |
965 |
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Author |
Partha Pratim Roy; Josep Llados; Umapada Pal |
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Title |
Text/Graphics Separation in Color Maps |
Type |
Conference Article |
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2007 |
Publication |
International Conference on Computing: Theory and Applications |
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545–551 |
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Kolkata (India) |
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ICCTA |
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DAG |
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DAG @ dag @ RLP2007a |
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806 |
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Partha Pratim Roy; Josep Llados; Umapada Pal |
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Title |
A Complete System for Detection and Recognition of Text in Graphical Documents using Background Information |
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Conference Article |
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2009 |
Publication |
5th International Conference on Computer Vision Theory and Applications |
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Lisboa, Portugal |
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978-989-8111-69-2 |
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VISAPP |
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DAG |
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DAG @ dag @ RLP2009 |
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1238 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Multi-oriented English Text Line Extraction using Background and Foreground Information |
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Conference Article |
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2008 |
Publication |
Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, |
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315–322 |
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Nara (Japo) |
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DAS |
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DAG |
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DAG @ dag @ RPL2008b |
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1047 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Morphology Based Handwritten Line Segmentation using Foreground and Background Information |
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Conference Article |
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2008 |
Publication |
International Conference on Frontiers in Handwriting Recognition, |
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241–246 |
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Montreal (Canada) |
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ICFHR |
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DAG |
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DAG @ dag @ RPL2008a |
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1050 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Recognition of Multi-oriented Touching Characters in Graphical Documents |
Type |
Conference Article |
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2008 |
Publication |
Computer Vision, Graphics & Image Processing, 2008. Sixth Indian Conference on, |
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16 |
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297–304 |
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ICVGIP ’08 |
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DAG |
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no |
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DAG @ dag @ RPL2008c |
Serial |
1080 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Seal detection and recognition: An approach for document indexing |
Type |
Conference Article |
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Year |
2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
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101–105 |
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Reliable indexing of documents having seal instances can be achieved by recognizing seal information. This paper presents a novel approach for detecting and classifying such multi-oriented seals in these documents. First, Hough Transform based methods are applied to extract the seal regions in documents. Next, isolated text characters within these regions are detected. Rotation and size invariant features and a support vector machine based classifier have been used to recognize these detected text characters. Next, for each pair of character, we encode their relative spatial organization using their distance and angular position with respect to the centre of the seal, and enter this code into a hash table. Given an input seal, we recognize the individual text characters and compute the code for pair-wise character based on the relative spatial organization. The code obtained from the input seal helps to retrieve model hypothesis from the hash table. The seal model to which we get maximum hypothesis is selected for the recognition of the input seal. The methodology is tested to index seal in rotation and size invariant environment and we obtained encouraging results. |
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Barcelona, Spain |
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ISSN |
1520-5363 |
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978-1-4244-4500-4 |
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ICDAR |
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DAG |
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Call Number |
DAG @ dag @ RPL2009b |
Serial |
1239 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Seal Object Detection in Document Images using GHT of Local Component Shapes |
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Conference Article |
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Year |
2010 |
Publication |
10th ACM Symposium On Applied Computing |
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23–27 |
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Abstract |
Due to noise, overlapped text/signature and multi-oriented nature, seal (stamp) object detection involves a difficult challenge. This paper deals with automatic detection of seal from documents with cluttered background. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors (distance and angular position) computed from recognition result of individual connected components (characters). Recognition of multi-scale and multi-oriented component is done using Support Vector Machine classifier. Generalized Hough Transform (GHT) is used to detect the seal and a voting is casted for finding possible location of the seal object in a document based on these spatial feature descriptor of components pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal object in a document. Experimental results show that, the method is efficient to locate seal instance of arbitrary shape and orientation in documents. |
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Address |
Sierre, Switzerland |
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SAC |
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DAG |
Approved |
no |
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Call Number |
DAG @ dag @ RPL2010a |
Serial |
1291 |
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