Abstract: In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research.

Abstract: Lifelogging devices based on photo/video are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. The egocentric video sequence acquired by the camera, uses both the appearance extracted by means of a deep convolutional neural network and an object refill methodology that allow to discover objects even in case of small amount of object appearance in the collection of images. We validate our method on a sequence of 1000 egocentric daily images and obtain results with an F-measure of 0.5, 0.17 better than the state of the art approach.

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.

Abstract: 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.

Abstract: Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images.

Abstract: non-indexed
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources.

Abstract: Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case.