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Author |
Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez |
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Title |
Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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Volume |
8746 |
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Pages |
109-121 |
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Keywords |
Graphics recognition; Floor plan analysis; Object segmentation |
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Abstract |
In this paper we present a wall segmentation approach in floor plans that is able to work independently to the graphical notation, does not need any pre-annotated data for learning, and is able to segment multiple-shaped walls such as beams and curved-walls. This method results from the combination of the wall segmentation approaches [3, 5] presented recently by the authors. Firstly, potential straight wall segments are extracted in an unsupervised way similar to [3], but restricting even more the wall candidates considered in the original approach. Then, based on [5], these segments are used to learn the texture pattern of walls and spot the lost instances. The presented combination of both methods has been tested on 4 available datasets with different notations and compared qualitatively and quantitatively to the state-of-the-art applied on these collections. Additionally, some qualitative results on floor plans directly downloaded from the Internet are reported in the paper. The overall performance of the method demonstrates either its adaptability to different wall notations and shapes, and to document qualities and resolutions. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; ADAS; 600.076; 600.077 |
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no |
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Admin @ si @ HVS2014 |
Serial |
2535 |
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Author |
Lorenzo Seidenari; Giuseppe Serra; Andrew Bagdanov; Alberto del Bimbo |
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Title |
Local pyramidal descriptors for image recognition |
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Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
36 |
Issue |
5 |
Pages |
1033 - 1040 |
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Keywords |
Object categorization; local features; kernel methods |
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In this paper we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution
pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one’s bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain
further improvement.We achieve state-of-the-art results on Caltech-101
(80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines. |
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0162-8828 |
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LAMP; 600.079 |
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no |
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Admin @ si @ SSB2014 |
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2524 |
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Author |
M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer |
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Title |
Adaptive color attributes for real-time visual tracking |
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Conference Article |
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Year |
2014 |
Publication |
27th IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
1090 - 1097 |
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Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally
efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.
This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional
variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms
state-of-the-art tracking methods while running at more than 100 frames per second. |
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Nottingham; UK; September 2014 |
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CVPR |
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CIC; LAMP; 600.074; 600.079 |
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no |
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Call Number |
Admin @ si @ DKF2014 |
Serial |
2509 |
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Author |
Maedeh Aghaei; Petia Radeva |
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Title |
Bag-of-Tracklets for Person Tracking in Life-Logging Data |
Type |
Conference Article |
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Year |
2014 |
Publication |
17th International Conference of the Catalan Association for Artificial Intelligence |
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Volume |
269 |
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Pages |
35-44 |
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By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the data. |
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978-1-61499-451-0 |
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CCIA |
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MILAB |
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no |
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Call Number |
Admin @ si @ AgR2015 |
Serial |
2607 |
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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Spotting Graphical Symbols in Camera-Acquired Documents in Real Time |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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Volume |
8746 |
Issue |
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Pages |
3-10 |
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Abstract |
In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time. |
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Springer Berlin Heidelberg |
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Editor |
Bart Lamiroy; Jean-Marc Ogier |
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0302-9743 |
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978-3-662-44853-3 |
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Notes |
DAG; 600.045; 600.055; 600.061; 600.077 |
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no |
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Call Number |
Admin @ si @ RKL2014 |
Serial |
2700 |
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Author |
Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |
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Title |
Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images |
Type |
Conference Article |
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Year |
2014 |
Publication |
11th IAPR International Workshop on Document Analysis and Systems |
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181 - 185 |
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Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to re-capture later a document with temporary availability. In this context, out-of-focus blur is major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given. |
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Tours; France; April 2014 |
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978-1-4799-3243-6 |
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DAS |
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Notes |
DAG; 601.223; 600.077 |
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no |
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Call Number |
Admin @ si @ RCO2014a |
Serial |
2545 |
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Author |
Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |
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Title |
Normalisation et validation d'images de documents capturées en mobilité |
Type |
Conference Article |
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Year |
2014 |
Publication |
Colloque International Francophone sur l'Écrit et le Document |
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Pages |
109-124 |
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Keywords |
mobile document image acquisition; perspective correction; illumination correction; quality assessment; focus measure; OCR accuracy prediction |
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Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessment
step relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods. |
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Nancy; France; March 2014 |
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CIFED |
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Notes |
DAG; 601.223; 600.077 |
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no |
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Call Number |
Admin @ si @ RCO2014b |
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2546 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Boosting the Handwritten Word Spotting Experience by Including the User in the Loop |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
47 |
Issue |
3 |
Pages |
1063–1072 |
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Keywords |
Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling |
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In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list. |
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0031-3203 |
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Notes |
DAG; 600.045; 600.061; 600.077 |
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Call Number |
Admin @ si @ RuL2013 |
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2343 |
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Author |
Marçal Rusiñol; Lluis Pere de las Heras; Oriol Ramos Terrades |
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Title |
Flowchart Recognition for Non-Textual Information Retrieval in Patent Search |
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Journal Article |
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Year |
2014 |
Publication |
Information Retrieval |
Abbreviated Journal |
IR |
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17 |
Issue |
5-6 |
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545-562 |
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Flowchart recognition; Patent documents; Text/graphics separation; Raster-to-vector conversion; Symbol recognition |
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Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset. |
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1386-4564 |
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DAG; 600.077 |
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Admin @ si @ RHR2013 |
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2342 |
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Author |
Marçal Rusiñol; V. Poulain d'Andecy; Dimosthenis Karatzas; Josep Llados |
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Title |
Classification of Administrative Document Images by Logo Identification |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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49-58 |
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Administrative Document Classification; Logo Recognition; Logo Spotting |
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This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier’s graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; 600.056; 600.045; 605.203; 600.077 |
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Admin @ si @ RPK2014 |
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2701 |
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Author |
Marçal Rusiñol; Volkmar Frinken; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados |
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Multimodal page classification in administrative document image streams |
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Journal Article |
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2014 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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17 |
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4 |
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331-341 |
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Digital mail room; Multimodal page classification; Visual and textual document description |
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In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; LAMP; 600.056; 600.061; 601.240; 601.223; 600.077; 600.079 |
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Admin @ si @ RFK2014 |
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2523 |
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Author |
Marc Bolaños; Maite Garolera; Petia Radeva |
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Title |
Video Segmentation of Life-Logging Videos |
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Conference Article |
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2014 |
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8th Conference on Articulated Motion and Deformable Objects |
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8563 |
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1-9 |
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AMDO |
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MILAB |
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Admin @ si @ BGR2014 |
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2558 |
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Author |
Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras |
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Title |
The Photometry of Intrinsic Images |
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Conference Article |
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2014 |
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27th IEEE Conference on Computer Vision and Pattern Recognition |
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1494-1501 |
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Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. |
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Columbus; Ohio; USA; June 2014 |
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CVPR |
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CIC; 600.052; 600.051; 600.074 |
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Admin @ si @ SPB2014 |
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2506 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca |
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Title |
Toward Real-Time Pedestrian Detection Based on a Deformable Template Model |
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Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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15 |
Issue |
1 |
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355-364 |
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Abstract |
Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed. |
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1524-9050 |
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Notes |
ISE; 601.213; 600.078 |
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PGH2014 |
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2350 |
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Author |
Michal Drozdzal |
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Sequential image analysis for computer-aided wireless endoscopy |
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Book Whole |
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2014 |
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PhD Thesis, Universitat de Barcelona-CVC |
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Wireless Capsule Endoscopy (WCE) is a technique for inner-visualization of the entire small intestine and, thus, offers an interesting perspective on intestinal motility. The two major drawbacks of this technique are: 1) huge amount of data acquired by WCE makes the motility analysis tedious and 2) since the capsule is the first tool that offers complete inner-visualization of the small intestine,the exact importance of the observed events is still an open issue. Therefore, in this thesis, a novel computer-aided system for intestinal motility analysis is presented. The goal of the system is to provide an easily-comprehensible visual description of motility-related intestinal events to a physician. In order to do so, several tools based either on computer vision concepts or on machine learning techniques are presented. A method for transforming 3D video signal to a holistic image of intestinal motility, called motility bar, is proposed. The method calculates the optimal mapping from video into image from the intestinal motility point of view.
To characterize intestinal motility, methods for automatic extraction of motility information from WCE are presented. Two of them are based on the motility bar and two of them are based on frame-per-frame analysis. In particular, four algorithms dealing with the problems of intestinal contraction detection, lumen size estimation, intestinal content characterization and wrinkle frame detection are proposed and validated. The results of the algorithms are converted into sequential features using an online statistical test. This test is designed to work with multivariate data streams. To this end, we propose a novel formulation of concentration inequality that is introduced into a robust adaptive windowing algorithm for multivariate data streams. The algorithm is used to obtain robust representation of segments with constant intestinal motility activity. The obtained sequential features are shown to be discriminative in the problem of abnormal motility characterization.
Finally, we tackle the problem of efficient labeling. To this end, we incorporate active learning concepts to the problems present in WCE data and propose two approaches. The first one is based the concepts of sequential learning and the second one adapts the partition-based active learning to an error-free labeling scheme. All these steps are sufficient to provide an extensive visual description of intestinal motility that can be used by an expert as decision support system. |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Petia Radeva |
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978-84-940902-3-3 |
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Notes |
MILAB |
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no |
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Call Number |
Admin @ si @ Dro2014 |
Serial |
2486 |
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