WordFences: Text localization and recognition

En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV)In recent years, text recognition has achieved remarkable success in recognizing scanned document text. However, word recognition in natural images is still an open problem, which generally requires time consuming post-processing steps. We present a novel architecture for individual word detection in scene images based on semantic segmentation. Our contributions are twofold: the concept of WordFence, which detects border areas surrounding each individual word and a unique pixelwise weighted softmax loss function which penalizes background and emphasizes small text regions. WordFence ensures that each word is detected individually, and the new loss function provides a strong training signal to both text and word border localization. The proposed technique avoids intensive post-processing by combining semantic word segmentation with a voting scheme for merging segmentations of multiple scales, producing an end-to-end word detection system. We achieve superior localization recall on common benchmark datasets - 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT. Furthermore, end-to-end word recognition achieves state-of-the-art 86% F-Score on ICDAR13

FPGA-based architectures for acoustic beamforming with microphone arrays : trends, challenges and research opportunities

Over the past decades, many systems composed of arrays of microphones have been developed to satisfy the quality demanded by acoustic applications. Such microphone arrays are sound acquisition systems composed of multiple microphones used to sample the sound field with spatial diversity. The relatively recent adoption of Field-Programmable Gate Arrays (FPGAs) to manage the audio data samples and to perform the signal processing operations such as filtering or beamforming has lead to customizable architectures able to satisfy the most demanding computational, power or performance acoustic applications. The presented work provides an overview of the current FPGA-based architectures and how FPGAs are exploited for different acoustic applications. Current trends on the use of this technology, pending challenges and open research opportunities on the use of FPGAs for acoustic applications using microphone arrays are presented and discussed

An Image Processing Approach Toward a Visual Intra-Cortical Stimulator

Abstract Visual impairment may be caused by various factors varying from trauma, birth-defects, and diseases. Until today there are no viable medical treatments for this condition; hence bio-medical approaches are being employed to overcome that. The Cortivision team has been working on an intra-cortical implant that can bypass the retina and optic nerve and directly stimulate the visual cortex. In this work we aimed to implement a modular, reusable, and parameterizable object recognition system that tends to ``simplify'' video data prior to stimulation; hence opening new horizons for partial vision restoration, navigational and even recognition abilities. We identified the Scale Invariant Feature Transform (SIFT) algorithm as being a robust candidate for our application's needs. A multithreaded software prototype of the SIFT and Lucas-Kanade tracker was implemented to ensure proper overall operation. The feature extractor, difference of Gaussians (DoG) part of the SIFT, being the most computationally expensive, was migrated to an FPGA implementation due to the real-time restrictions that is not achievable on a host machine. The VHDL implementation is highly parameterizable for different application needs and tradeoffs. We introduced a novel architecture employing the sub-kernel trick to reduce resource usage compared to preexisting architectures while still being comparably accurate to a software floating point implementation. In order to alleviate transmission bottlenecks, the system also includes a new parallel Huffman encoder design that is capable of performing lossless compression of both images and scale space image pyramids taking into account spatial and scale data correlations during the predictor phase. The encoder was able to achieve compression ratios of 27.3% on the Caltech-256 data-set. Furthermore, a new camera and fiducial markers setup based on image processing was proposed in order to target the phosphene map estimation problem which affects the quality of the final stimulation that is perceived by the patient.----------RÉSUMÉ Introduction et objectifs La déficience visuelle, qui est définie par la perte totale ou partielle de la vision, n'est actuellement pas médicalement traitable. Des approches biomédicales modernes sont utilisées pour stimuler électriquement la vision; ces approches peuvent être divisées en trois groupes principaux: le premier ciblant les implants rétiniens Humayun et al. (2003), Kim et al. (2004), Chow et al. (2004); Palanker et al. (2005), Toledo et al. (2005); Yanai et al. (2007), Winter et al. (2007); Zrenner et al. (2011), le deuxième ciblant les implants du nerf optique Veraart et al. (2003), Sakaguchi et al. (2009), et le troisième ciblant les implants intra-corticaux Doljanu et Sawan (2007); Coulombe et al. (2007); Srivastava et al. (2007). L’inconvénient principal des deux premiers groupes, c'est qu'ils ne sont pas suffisamment génériques pour surmonter la majorité des maladies de déficience visuelle, car ils dépendent du fait que le patient doit avoir un nerf optique intact et/ou une rétine partiellement opérationnelle ; ce qui n'est pas le cas pour le troisième groupe. L'équipe du Laboratoire Polystim Neurotechnologies travaille actuellement sur un implant intra-cortical qui stimule directement le cortex visuel primaire (région V1) ; le nom du projet global est Cortivision. Le système utilise une caméra, un module de traitement d'image, un transmetteur RF (radiofréquence) et un stimulateur implantable. Cette méthode est robuste et générique car elle contourne l'oeil et le nerf optique. Un des défis majeurs est le traitement d'image nécessaire pour «simplifier» les données antérieures à la stimulation, l'extraction de l’information utile en écartant les données superflues. Les pixels qui sont capturés par la caméra n'ont pas de correspondance un-à-un sur le cortex visuel comme dans une image rectangulaire, ils sont plutôt mis en correspondance avec une carte complexe de «phosphènes» Coulombe et al. (2007); Srivastava et al. (2007). Les phosphènes sont des points lumineux qui apparaissent dans le champ de vision du patient quand le cerveau est stimulé électriquement. Ces points changent en terme de taille, de luminosité et d’emplacement en fonction de la façon dont la stimulation électrique est effectuée (c'est à dire un changement dans la fréquence, la tension, la durée, etc. ...) et même par le placement physique des électrodes dans le cortex visuel. Les approches actuelles visent à stimuler des images de phosphènes monochromes à basse résolution. Sachant cela, nous nous attendons plutôt à une vision de faible qualité qui rend des activités comme naviguer, interpréter des objets, ou encore lire, difficile pour le patient. Ceci est principalement dû à la complexité de l’étalonnage de la carte phosphène et sa correspondance, et aussi à la non-trivialité de savoir comment simplifier les données à partir des images qui viennent de la camera de façon qu’on conserve seulement les données pertinentes. La Figure 1.1 est un exemple qui démontre la non-trivialité de transformer une image grise en stimulation phosphène

Incremental Training of a Detector Using Online Sparse Eigen-decomposition

The ability to efficiently and accurately detect objects plays a very crucial role for many computer vision tasks. Recently, offline object detectors have shown a tremendous success. However, one major drawback of offline techniques is that a complete set of training data has to be collected beforehand. In addition, once learned, an offline detector can not make use of newly arriving data. To alleviate these drawbacks, online learning has been adopted with the following objectives: (1) the technique should be computationally and storage efficient; (2) the updated classifier must maintain its high classification accuracy. In this paper, we propose an effective and efficient framework for learning an adaptive online greedy sparse linear discriminant analysis (GSLDA) model. Unlike many existing online boosting detectors, which usually apply exponential or logistic loss, our online algorithm makes use of LDA's learning criterion that not only aims to maximize the class-separation criterion but also incorporates the asymmetrical property of training data distributions. We provide a better alternative for online boosting algorithms in the context of training a visual object detector. We demonstrate the robustness and efficiency of our methods on handwriting digit and face data sets. Our results confirm that object detection tasks benefit significantly when trained in an online manner.Comment: 14 page