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Author | Fadi Dornaika; Angel Sappa | ||||
Title | Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression | Type | Journal Article | ||
Year | 2009 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 30 | Issue | 5 | Pages | 535–543 |
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Abstract | This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes. | ||||
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Publisher | Elsevier Science Inc. | Place of Publication | Editor | ||
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ISSN | 0167-8655 | ISBN | Medium | ||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ DoS2009a | Serial | 1115 | ||
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Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
Title | Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes | Type | Journal Article | ||
Year | 2009 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 30 | Issue | 3 | Pages | 285–297 |
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Abstract | Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied. | ||||
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Notes | MILAB;HuPBA | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ EPR2009a | Serial | 1153 | ||
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Author | Fadi Dornaika; Angel Sappa | ||||
Title | Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo 3D Models | Type | Journal Article | ||
Year | 2007 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 28 | Issue | 15 | Pages | 2116-2126 |
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ DoS2007c | Serial | 877 | ||
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Author | Xavier Otazu; Oriol Pujol | ||||
Title | Wavelet based approach to cluster analysis. Application on low dimensional data sets | Type | Journal Article | ||
Year | 2006 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 27 | Issue | 14 | Pages | 1590–1605 |
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Notes | MILAB; CIC; HuPBA | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ OtP2006 | Serial | 658 | ||
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Author | Debora Gil; Petia Radeva | ||||
Title | Inhibition of false landmarks | Type | Journal Article | ||
Year | 2006 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 27 | Issue | 9 | Pages | 1022-1030 |
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Abstract | Corners and junctions are landmarks characterized by the lack of differentiability in the unit tangent to the image level curve. Detectors based on differential operators are not, by their own definition, the best posed as they require a higher degree of differentiability to yield a reliable response. We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our inhibition orientation energy (IOE) landmark locator. | ||||
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Publisher | Elsevier Science Inc. | Place of Publication | New York, NY, USA | Editor | |
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ISSN | 0167-8655 | ISBN | Medium | ||
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Notes | IAM;MILAB | Approved | no | ||
Call Number | IAM @ iam @ GiR2006 | Serial | 1529 | ||
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Author | Oriol Ramos Terrades; Ernest Valveny | ||||
Title | A new use of the ridgelets transform for describing linear singularities in images | Type | Journal Article | ||
Year | 2006 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 27 | Issue | 6 | Pages | 587–596 |
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Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ RaV2006a | Serial | 635 | ||
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Author | Jaume Amores; N. Sebe; Petia Radeva | ||||
Title | Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier | Type | Journal Article | ||
Year | 2006 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 27 | Issue | 3 | Pages | 201–209 |
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Notes | ADAS;MILAB | Approved | no | ||
Call Number | ADAS @ adas @ ASR2006 | Serial | 643 | ||
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Author | Cristina Cañero; Petia Radeva | ||||
Title | Vesselness enhancement diffusion | Type | Journal Article | ||
Year | 2003 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 24 | Issue | 16 | Pages | 3141–3151 |
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Abstract | IF: 0.809 | ||||
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Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ CaR2003 | Serial | 371 | ||
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Author | Ruben Tito; Dimosthenis Karatzas; Ernest Valveny | ||||
Title | Hierarchical multimodal transformers for Multi-Page DocVQA | Type | Journal Article | ||
Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 144 | Issue | Pages | 109834 | |
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Abstract | Document Visual Question Answering (DocVQA) refers to the task of answering questions from document images. Existing work on DocVQA only considers single-page documents. However, in real scenarios documents are mostly composed of multiple pages that should be processed altogether. In this work we extend DocVQA to the multi-page scenario. For that, we first create a new dataset, MP-DocVQA, where questions are posed over multi-page documents instead of single pages. Second, we propose a new hierarchical method, Hi-VT5, based on the T5 architecture, that overcomes the limitations of current methods to process long multi-page documents. The proposed method is based on a hierarchical transformer architecture where the encoder summarizes the most relevant information of every page and then, the decoder takes this summarized information to generate the final answer. Through extensive experimentation, we demonstrate that our method is able, in a single stage, to answer the questions and provide the page that contains the relevant information to find the answer, which can be used as a kind of explainability measure. | ||||
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ISSN | ISSN 0031-3203 | ISBN | Medium | ||
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Notes | DAG; 600.155; 600.121 | Approved | no | ||
Call Number | Admin @ si @ TKV2023 | Serial | 3825 | ||
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Author | Souhail Bakkali; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades | ||||
Title | VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification | Type | Journal Article | ||
Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 139 | Issue | Pages | 109419 | |
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Abstract | Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream approach. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a common representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the common feature representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generalization capacity of our model on both low-scale and large-scale datasets. | ||||
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ISSN | ISSN 0031-3203 | ISBN | Medium | ||
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Notes | DAG; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ BMC2023 | Serial | 3826 | ||
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Author | Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados | ||||
Title | Table detection in business document images by message passing networks | Type | Journal Article | ||
Year | 2022 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 127 | Issue | Pages | 108641 | |
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Abstract | Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches. | ||||
Address | July 2022 | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | DAG; 600.162; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RGR2022 | Serial | 3729 | ||
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Author | Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu | ||||
Title | Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video | Type | Journal Article | ||
Year | 2018 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 80 | Issue | Pages | 64-82 | |
Keywords | Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition | ||||
Abstract | Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. | ||||
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Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RSJ2018 | Serial | 3096 | ||
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Author | Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas | ||||
Title | Improving patch‐based scene text script identification with ensembles of conjoined networks | Type | Journal Article | ||
Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 67 | Issue | Pages | 85-96 | |
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Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ GNK2017 | Serial | 2887 | ||
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Author | Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez; Xavier Roca | ||||
Title | A coarse-to-fine approach for fast deformable object detection | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 48 | Issue | 5 | Pages | 1844-1853 |
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Abstract | We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of
part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of [9]. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy. |
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Notes | ISE; 600.078; 602.005; 605.001; 302.012 | Approved | no | ||
Call Number | Admin @ si @ PVG2015 | Serial | 2628 | ||
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Author | Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu | ||||
Title | Combining where and what in change detection for unsupervised foreground learning in surveillance | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 48 | Issue | 3 | Pages | 709-719 |
Keywords | Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance | ||||
Abstract | Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. | ||||
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Notes | ISE; 600.063; 600.078 | Approved | no | ||
Call Number | Admin @ si @ HPG2015 | Serial | 2589 | ||
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