| Home | << 1 2 3 4 5 6 7 8 9 10 >> [11–11] |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Onur Ferhat | ||||
| Title | Analysis of Head-Pose Invariant, Natural Light Gaze Estimation Methods | Type | Book Whole | ||
| Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Eye tracker devices have traditionally been only used inside laboratories, requiring trained professionals and elaborate setup mechanisms. However, in the recent years the scientific work on easier–to–use eye trackers which require no special hardware—other than the omnipresent front facing cameras in computers, tablets, and mobiles—is aiming at making this technology common–place. These types of trackers have several extra challenges that make the problem harder, such as low resolution images provided by a regular webcam, the changing ambient lighting conditions, personal appearance differences, changes in head pose, and so on. Recent research in the field has focused on all these challenges in order to provide better gaze estimation performances in a real world setup.
In this work, we aim at tackling the gaze tracking problem in a single camera setup. We first analyze all the previous work in the field, identifying the strengths and weaknesses of each tried idea. We start our work on the gaze tracker with an appearance–based gaze estimation method, which is the simplest idea that creates a direct mapping between a rectangular image patch extracted around the eye in a camera image, and the gaze point (or gaze direction). Here, we do an extensive analysis of the factors that affect the performance of this tracker in several experimental setups, in order to address these problems in future works. In the second part of our work, we propose a feature–based gaze estimation method, which encodes the eye region image into a compact representation. We argue that this type of representation is better suited to dealing with head pose and lighting condition changes, as it both reduces the dimensionality of the input (i.e. eye image) and breaks the direct connection between image pixel intensities and the gaze estimation. Lastly, we use a face alignment algorithm to have robust face pose estimation, using a 3D model customized to the subject using the tracker. We combine this with a convolutional neural network trained on a large dataset of images to build a face pose invariant gaze tracker. |
||||
| Address | September 2017 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Fernando Vilariño | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-945373-5-6 | Medium | ||
| Area | Expedition | Conference | |||
Notes  |
MV | Approved | no | ||
| Call Number | Admin @ si @ Fer2017 | Serial | 3018 | ||
| Permanent link to this record | |||||
| Author | Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva | ||||
| Title | VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering | Type | Conference Article | ||
| Year | 2017 | Publication | 8th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks | ||||
| Abstract | In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed. | ||||
| Address | Faro; Portugal; June 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IbPRIA | ||
| Notes |
MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BPC2017 | Serial | 2939 | ||
| Permanent link to this record | |||||
| Author | Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
| Title | Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study | Type | Conference Article | ||
| Year | 2017 | Publication | International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI).
Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback. The pipeline of the method requires the identi cation of the stable frames of the sequence and the reliable detection of stent struts. Using this data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores. |
||||
| Address | Quebec; Canada; September 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | MICCAIW | ||
| Notes |
MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BCR2017 | Serial | 2968 | ||
| Permanent link to this record | |||||
| Author | Aniol Lidon; Marc Bolaños; Mariella Dimiccoli; Petia Radeva; Maite Garolera; Xavier Giro | ||||
| Title | Semantic Summarization of Egocentric Photo-Stream Events | Type | Conference Article | ||
| Year | 2017 | Publication | 2nd Workshop on Lifelogging Tools and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| Address | San Francisco; USA; October 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4503-5503-2 | Medium | ||
| Area | Expedition | Conference | ACMW (LTA) | ||
| Notes |
MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ LBD2017 | Serial | 3024 | ||
| Permanent link to this record | |||||
| Author | Karim Lekadir; Alfiia Galimzianova; Angels Betriu; Maria del Mar Vila; Laura Igual; Daniel L. Rubin; Elvira Fernandez-Giraldez; Petia Radeva; Sandy Napel | ||||
| Title | A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Journal Biomedical and Health Informatics | Abbreviated Journal | J-BHI |
| Volume | 21 | Issue | 1 | Pages | 48-55 |
| Keywords | |||||
| Abstract | Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes |
MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ LGB2017 | Serial | 2931 | ||
| Permanent link to this record | |||||
| Author | Alejandro Cartas; Mariella Dimiccoli; Petia Radeva | ||||
| Title | Batch-based activity recognition from egocentric photo-streams | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International workshop on Egocentric Perception, Interaction and Computing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries. | ||||
| Address | Venice; Italy; October 2017; | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICCV - EPIC | ||
| Notes |
MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ CDR2017 | Serial | 3023 | ||
| Permanent link to this record | |||||
| Author | Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva | ||||
| Title | All the people around me: face clustering in egocentric photo streams | Type | Conference Article | ||
| Year | 2017 | Publication | 24th International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | face discovery; face clustering; deepmatching; bag-of-tracklets; egocentric photo-streams | ||||
| Abstract | arxiv1703.01790
Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data. The problem is challenging since images are acquired under real world conditions; hence the visible appearance of the people in the images undergoes intensive variations. Our proposed pipeline consists of first arranging the photo-stream into events, later, localizing the appearance of multiple people in them, and finally, grouping various appearances of the same person across different events. Experimental results performed on a dataset acquired by wearing a photo-camera during one month, demonstrate the effectiveness of the proposed approach for the considered purpose. |
||||
| Address | Beijing; China; September 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICIP | ||
| Notes |
MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EDR2017 | Serial | 3025 | ||
| Permanent link to this record | |||||
| Author | Mireia Forns-Nadal; Federico Sem; Anna Mane; Laura Igual; Dani Guinart; Oscar Vilarroya | ||||
| Title | Increased Nucleus Accumbens Volume in First-Episode Psychosis | Type | Journal Article | ||
| Year | 2017 | Publication | Psychiatry Research-Neuroimaging | Abbreviated Journal | PRN |
| Volume | 263 | Issue | Pages | 57-60 | |
| Keywords | |||||
| Abstract | Nucleus accumbens has been reported as a key structure in the neurobiology of schizophrenia. Studies analyzing structural abnormalities have shown conflicting results, possibly related to confounding factors. We investigated the nucleus accumbens volume using manual delimitation in first-episode psychosis (FEP) controlling for age, cannabis use and medication. Thirty-one FEP subjects who were naive or minimally exposed to antipsychotics and a control group were MRI scanned and clinically assessed from baseline to 6 months of follow-up. FEP showed increased relative and total accumbens volumes. Clinical correlations with negative symptoms, duration of untreated psychosis and cannabis use were not significant. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes |
MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FSM2017 | Serial | 3028 | ||
| Permanent link to this record | |||||
| Author | Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo | ||||
| Title | Subspace Procrustes Analysis | Type | Journal Article | ||
| Year | 2017 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 121 | Issue | 3 | Pages | 327–343 |
| Keywords | |||||
| Abstract | Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several
instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our approach. |
||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes |
MILAB; HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ PTI2017 | Serial | 2841 | ||
| Permanent link to this record | |||||
| Author | Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio | ||||
| Title | The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Semantic Segmentation | ||||
| Abstract | State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.
Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train. In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. |
||||
| Address | Honolulu; USA; July 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes |
MILAB; ADAS; 600.076; 600.085; 601.281 | Approved | no | ||
| Call Number | ADAS @ adas @ JDV2016 | Serial | 2866 | ||
| Permanent link to this record | |||||
| Author | Marc Bolaños; Mariella Dimiccoli; Petia Radeva | ||||
| Title | Towards Storytelling from Visual Lifelogging: An Overview | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Human-Machine Systems | Abbreviated Journal | THMS |
| Volume | 47 | Issue | 1 | Pages | 77 - 90 |
| Keywords | |||||
| Abstract | Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging. |
||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes |
MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @ BDR2017 | Serial | 2712 | ||
| Permanent link to this record | |||||
| Author | Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva | ||||
| Title | SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation | Type | Journal Article | ||
| Year | 2017 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 155 | Issue | Pages | 55-69 | |
| Keywords | |||||
| Abstract | While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes |
MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @ DBT2017 | Serial | 2714 | ||
| Permanent link to this record | |||||
| Author | Jean-Pascal Jacob; Mariella Dimiccoli; L. Moisan | ||||
| Title | Active skeleton for bacteria modelling | Type | Journal Article | ||
| Year | 2017 | Publication | Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization | Abbreviated Journal | CMBBE |
| Volume | 5 | Issue | 4 | Pages | 274-286 |
| Keywords | |||||
| Abstract | The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulation for bacteria modelling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness and orientation), an improved boundary accuracy in noisy images and a natural bacteria-centred coordinate system that permits the intrinsic location of molecular components inside the cell. Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimising an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modelling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at http://fluobactracker.inrialpes.fr. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Taylor & Francis Group | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes |
MILAB; | Approved | no | ||
| Call Number | Admin @ si @JDM2017 | Serial | 2784 | ||
| Permanent link to this record | |||||
| Author | Laura Igual; Santiago Segui | ||||
| Title | Introduction to Data Science – A Python Approach to Concepts, Techniques and Applications. Undergraduate Topics in Computer Science | Type | Book Whole | ||
| Year | 2017 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | 1-215 | ||
| Keywords | |||||
| Abstract | |||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | 978-3-319-50016-4 | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-319-50016-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ IgS2017 | Serial | 3027 | ||
| Permanent link to this record | |||||
| Author | Mikhail Mozerov; Joost Van de Weijer | ||||
| Title | Improved Recursive Geodesic Distance Computation for Edge Preserving Filter | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 26 | Issue | 8 | Pages | 3696 - 3706 |
| Keywords | Geodesic distance filter; color image filtering; image enhancement | ||||
| Abstract | All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application, we consider a geodesic distance-based filter for image denoising. Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P). |
||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes |
LAMP; ISE; 600.120; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ Moz2017 | Serial | 2921 | ||
| Permanent link to this record | |||||