| Home | [11–20] << 21 22 23 24 25 26 27 28 29 30 >> [31–40] |
|
| Records | |||||
|---|---|---|---|---|---|
Author  |
Ikechukwu Ofodile; Ahmed Helmi; Albert Clapes; Egils Avots; Kerttu Maria Peensoo; Sandhra Mirella Valdma; Andreas Valdmann; Heli Valtna Lukner; Sergey Omelkov; Sergio Escalera; Cagri Ozcinar; Gholamreza Anbarjafari | ||||
| Title | Action recognition using single-pixel time-of-flight detection | Type | Journal Article | ||
| Year | 2019 | Publication | Entropy | Abbreviated Journal | ENTROPY |
| Volume | 21 | Issue | 4 | Pages | 414 |
| Keywords | single pixel single photon image acquisition; time-of-flight; action recognition | ||||
| Abstract | Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene.
Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47% accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network. |
||||
| 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 | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ OHC2019 | Serial | 3319 | ||
| Permanent link to this record | |||||
| Author |
Ivan Huerta; Ariel Amato; Xavier Roca; Jordi Gonzalez | ||||
| Title | Exploiting Multiple Cues in Motion Segmentation Based on Background Subtraction | Type | Journal Article | ||
| Year | 2013 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 100 | Issue | Pages | 183–196 | |
| Keywords | Motion segmentation; Shadow suppression; Colour segmentation; Edge segmentation; Ghost detection; Background subtraction | ||||
| Abstract | This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motionsegmentation. In our first contribution, a case analysis of motionsegmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motionsegmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Elsevier | 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 | ISE | Approved | no | ||
| Call Number | Admin @ si @ HAR2013 | Serial | 1808 | ||
| Permanent link to this record | |||||
| 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. | ||||
| 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 | ISE; 600.063; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ HPG2015 | Serial | 2589 | ||
| Permanent link to this record | |||||
| Author |
Ivan Huerta; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez | ||||
| Title | Chromatic shadow detection and tracking for moving foreground segmentation | Type | Journal Article | ||
| Year | 2015 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 41 | Issue | Pages | 42-53 | |
| Keywords | Detecting moving objects; Chromatic shadow detection; Temporal local gradient; Spatial and Temporal brightness and angle distortions; Shadow tracking | ||||
| Abstract | Advanced segmentation techniques in the surveillance domain deal with shadows to avoid distortions when detecting moving objects. Most approaches for shadow detection are still typically restricted to penumbra shadows and cannot cope well with umbra shadows. Consequently, umbra shadow regions are usually detected as part of moving objects, thus a ecting the performance of the nal detection. In this paper we address the detection of both penumbra and umbra shadow regions. First, a novel bottom-up approach is presented based on gradient and colour models, which successfully discriminates between chromatic moving cast shadow regions and those regions detected as moving objects. In essence, those regions corresponding to potential shadows are detected based on edge partitioning and colour statistics. Subsequently (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for each potential shadow region for detecting the umbra shadow regions. Our second contribution re nes even further the segmentation results: a tracking-based top-down approach increases the performance of our bottom-up chromatic shadow detection algorithm by properly correcting non-detected shadows.
To do so, a combination of motion lters in a data association framework exploits the temporal consistency between objects and shadows to increase the shadow detection rate. Experimental results exceed current state-of-the- art in shadow accuracy for multiple well-known surveillance image databases which contain di erent shadowed materials and illumination conditions. |
||||
| 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 | ISE; 600.078; 600.063 | Approved | no | ||
| Call Number | Admin @ si @ HHM2015 | Serial | 2703 | ||
| Permanent link to this record | |||||
| Author |
Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez | ||||
| Title | Enhancing spatio-chromatic representation with more-than-three color coding for image description | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 34 | Issue | 5 | Pages | 827-837 |
| Keywords | |||||
| Abstract | Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding. | ||||
| 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 | CIC; 600.087 | Approved | no | ||
| Call Number | Admin @ si @ RVB2017 | Serial | 2892 | ||
| Permanent link to this record | |||||
| Author |
Ivet Rafegas; Maria Vanrell | ||||
| Title | Color encoding in biologically-inspired convolutional neural networks | Type | Journal Article | ||
| Year | 2018 | Publication | Vision Research | Abbreviated Journal | VR |
| Volume | 151 | Issue | Pages | 7-17 | |
| Keywords | Color coding; Computer vision; Deep learning; Convolutional neural networks | ||||
| Abstract | Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. | ||||
| 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 | CIC; 600.051; 600.087 | Approved | no | ||
| Call Number | Admin @ si @RaV2018 | Serial | 3114 | ||
| Permanent link to this record | |||||
| Author |
Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias | ||||
| Title | Understanding trained CNNs by indexing neuron selectivity | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 318-325 | |
| Keywords | |||||
| Abstract | The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful. | ||||
| 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 | CIC; 600.087; 600.140; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ RVL2019 | Serial | 3310 | ||
| Permanent link to this record | |||||
| Author |
J. Mauri; E Fernandez-Nofrerias; A. Tovar; E. Martinez; L. Cano; V. Valle; David Rotger; Cristina Cañero; Petia Radeva | ||||
| Title | Ecografia Intracoronaria: Un Nou Pas, la Fusio de Imatges amb la Angiografia, el Software. | Type | Journal Article | ||
| Year | 2001 | Publication | Revista de la Societat Catalana de Cardiologia, XIIIe Congres de la Societat Catalana de Cardiologia, 4(1):48. | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| 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 | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MFT2001 | Serial | 136 | ||
| Permanent link to this record | |||||
| Author |
J. Stöttinger; A. Hanbury; N. Sebe; Theo Gevers | ||||
| Title | Spars Color Interest Points for Image Retrieval and Object Categorization | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 21 | Issue | 5 | Pages | 2681-2692 |
| Keywords | |||||
| Abstract | Impact factor 2010: 2.92
IF 2011/2012?: 3.32 Interest point detection is an important research area in the field of image processing and computer vision. In particular, image retrieval and object categorization heavily rely on interest point detection from which local image descriptors are computed for image matching. In general, interest points are based on luminance, and color has been largely ignored. However, the use of color increases the distinctiveness of interest points. The use of color may therefore provide selective search reducing the total number of interest points used for image matching. This paper proposes color interest points for sparse image representation. To reduce the sensitivity to varying imaging conditions, light-invariant interest points are introduced. Color statistics based on occurrence probability lead to color boosted points, which are obtained through saliency-based feature selection. Furthermore, a principal component analysis-based scale selection method is proposed, which gives a robust scale estimation per interest point. From large-scale experiments, it is shown that the proposed color interest point detector has higher repeatability than a luminance-based one. Furthermore, in the context of image retrieval, a reduced and predictable number of color features show an increase in performance compared to state-of-the-art interest points. Finally, in the context of object recognition, for the Pascal VOC 2007 challenge, our method gives comparable performance to state-of-the-art methods using only a small fraction of the features, reducing the computing time considerably. |
||||
| 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 | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ SHS2012 | Serial | 1847 | ||
| Permanent link to this record | |||||
| Author |
J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin | ||||
| Title | Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm | Type | Journal Article | ||
| Year | 2013 | Publication | Expert Systems with Applications | Abbreviated Journal | EXWA |
| Volume | 40 | Issue | 17 | Pages | 6707-6712 |
| Keywords | Neural gas; Expert vision; Eye-tracking; Fixations | ||||
| Abstract | Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves. | ||||
| 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 | 0957-4174 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ CRM2013 | Serial | 2438 | ||
| Permanent link to this record | |||||
| Author |
Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders | ||||
| Title | Selective Search for Object Recognition | Type | Journal Article | ||
| Year | 2013 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 104 | Issue | 2 | Pages | 154-171 |
| Keywords | |||||
| Abstract | This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html). | ||||
| 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 | 0920-5691 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ USG2013 | Serial | 2362 | ||
| Permanent link to this record | |||||
| Author |
Jaume Amores | ||||
| Title | MILDE: multiple instance learning by discriminative embedding | Type | Journal Article | ||
| Year | 2015 | Publication | Knowledge and Information Systems | Abbreviated Journal | KAIS |
| Volume | 42 | Issue | 2 | Pages | 381-407 |
| Keywords | Multi-instance learning; Codebook; Bag of words | ||||
| Abstract | While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer London | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0219-1377 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 601.042; 600.057; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ Amo2015 | Serial | 2383 | ||
| Permanent link to this record | |||||
| Author |
Jaume Amores | ||||
| Title | Multiple Instance Classification: review, taxonomy and comparative study | Type | Journal Article | ||
| Year | 2013 | Publication | Artificial Intelligence | Abbreviated Journal | AI |
| Volume | 201 | Issue | Pages | 81-105 | |
| Keywords | Multi-instance learning; Codebook; Bag-of-Words | ||||
| Abstract | Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL methods. |
||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Elsevier Science Publishers Ltd. Essex, UK | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0004-3702 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 601.042; 600.057 | Approved | no | ||
| Call Number | Admin @ si @ Amo2013 | Serial | 2273 | ||
| Permanent link to this record | |||||
| 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 |
| Keywords | |||||
| Abstract | |||||
| 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 | ADAS;MILAB | Approved | no | ||
| Call Number | ADAS @ adas @ ASR2006 | Serial | 643 | ||
| Permanent link to this record | |||||
| Author |
Jaume Amores; Petia Radeva | ||||
| Title | Registration and Retrieval of Highly Elastic Bodies using Contextual Information | Type | Journal Article | ||
| Year | 2005 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 26 | Issue | 11 | Pages | 1720–1731 |
| Keywords | |||||
| Abstract | IF: 1.138 | ||||
| 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 | ADAS;MILAB | Approved | no | ||
| Call Number | ADAS @ adas @ AmR2005b | Serial | 592 | ||
| Permanent link to this record | |||||