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Author |
Jun Wan; Sergio Escalera; Francisco Perales; Josef Kittler |
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Title |
Articulated Motion and Deformable Objects |
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Journal Article |
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Year |
2018 |
Publication |
Pattern Recognition |
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PR |
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Volume |
79 |
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55-64 |
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This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field. |
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HUPBA; no proj |
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no |
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Admin @ si @ WEP2018 |
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3126 |
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Author |
Joan Serrat; Felipe Lumbreras; Idoia Ruiz |
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Title |
Learning to measure for preshipment garment sizing |
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Journal Article |
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Year |
2018 |
Publication |
Measurement |
Abbreviated Journal |
MEASURE |
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130 |
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327-339 |
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Apparel; Computer vision; Structured prediction; Regression |
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Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively. |
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ADAS; MSIAU; 600.122; 600.118 |
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no |
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Admin @ si @ SLR2018 |
Serial |
3128 |
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Author |
Marta Diez-Ferrer; Debora Gil; Cristian Tebe; Carles Sanchez |
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Title |
Positive Airway Pressure to Enhance Computed Tomography Imaging for Airway Segmentation for Virtual Bronchoscopic Navigation |
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Journal Article |
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Year |
2018 |
Publication |
Respiration |
Abbreviated Journal |
RES |
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Volume |
96 |
Issue |
6 |
Pages |
525-534 |
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Multidetector computed tomography; Bronchoscopy; Continuous positive airway pressure; Image enhancement; Virtual bronchoscopic navigation |
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Abstract
RATIONALE:
Virtual bronchoscopic navigation (VBN) guidance to peripheral pulmonary lesions is often limited by insufficient segmentation of the peripheral airways.
OBJECTIVES:
To test the effect of applying positive airway pressure (PAP) during CT acquisition to improve segmentation, particularly at end-expiration.
METHODS:
CT acquisitions in inspiration and expiration with 4 PAP protocols were recorded prospectively and compared to baseline inspiratory acquisitions in 20 patients. The 4 protocols explored differences between devices (flow vs. turbine), exposures (within seconds vs. 15-min) and pressure levels (10 vs. 14 cmH2O). Segmentation quality was evaluated with the number of airways and number of endpoints reached. A generalized mixed-effects model explored the estimated effect of each protocol.
MEASUREMENTS AND MAIN RESULTS:
Patient characteristics and lung function did not significantly differ between protocols. Compared to baseline inspiratory acquisitions, expiratory acquisitions after 15 min of 14 cmH2O PAP segmented 1.63-fold more airways (95% CI 1.07-2.48; p = 0.018) and reached 1.34-fold more endpoints (95% CI 1.08-1.66; p = 0.004). Inspiratory acquisitions performed immediately under 10 cmH2O PAP reached 1.20-fold (95% CI 1.09-1.33; p < 0.001) more endpoints; after 15 min the increase was 1.14-fold (95% CI 1.05-1.24; p < 0.001).
CONCLUSIONS:
CT acquisitions with PAP segment more airways and reach more endpoints than baseline inspiratory acquisitions. The improvement is particularly evident at end-expiration after 15 min of 14 cmH2O PAP. Further studies must confirm that the improvement increases diagnostic yield when using VBN to evaluate peripheral pulmonary lesions. |
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IAM; 600.145 |
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no |
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Admin @ si @ DGT2018 |
Serial |
3135 |
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Author |
Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera |
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Title |
Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification |
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Journal Article |
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Year |
2018 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
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79 |
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76-85 |
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Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose to exploit a post-ranking approach and combine different feature representations through ranking aggregation. Spatial information, which potentially benefits the person matching, is represented using a 2D body model, from which color and texture information are extracted and combined. We also consider background/foreground information, automatically extracted via Deep Decompositional Network, and the usage of Convolutional Neural Network (CNN) features. To describe the matching between images we use the polynomial feature map, also taking into account local and global information. The Discriminant Context Information Analysis based post-ranking approach is used to improve initial ranking lists. Finally, the Stuart ranking aggregation method is employed to combine complementary ranking lists obtained from different feature representations. Experimental results demonstrated that we improve the state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on top-1 rank recognition rate, respectively, as well as obtaining competitive results on CUHK01 dataset. |
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HuPBA; 602.143 |
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no |
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Admin @ si @ JBE2018 |
Serial |
3138 |
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Author |
Xavier Soria; Angel Sappa; Riad I. Hammoud |
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Title |
Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images |
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Journal Article |
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Year |
2018 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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18 |
Issue |
7 |
Pages |
2059 |
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RGB-NIR sensor; multispectral imaging; deep learning; CNNs |
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Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 |
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Admin @ si @ SSH2018 |
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3145 |
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Author |
Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras |
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Title |
Segmentation of aerial images for plausible detail synthesis |
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Journal Article |
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Year |
2018 |
Publication |
Computers & Graphics |
Abbreviated Journal |
CG |
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71 |
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23-34 |
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Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation |
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The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. |
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0097-8493 |
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MSIAU; 600.086; 600.118 |
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Admin @ si @ ACC2018 |
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3147 |
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Author |
Xim Cerda-Company; Xavier Otazu; Nilai Sallent; C. Alejandro Parraga |
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Title |
The effect of luminance differences on color assimilation |
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Journal Article |
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Year |
2018 |
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Journal of Vision |
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JV |
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18 |
Issue |
11 |
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10-10 |
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The color appearance of a surface depends on the color of its surroundings (inducers). When the perceived color shifts towards that of the surroundings, the effect is called “color assimilation” and when it shifts away from the surroundings it is called “color contrast.” There is also evidence that the phenomenon depends on the spatial configuration of the inducer, e.g., uniform surrounds tend to induce color contrast and striped surrounds tend to induce color assimilation. However, previous work found that striped surrounds under certain conditions do not induce color assimilation but induce color contrast (or do not induce anything at all), suggesting that luminance differences and high spatial frequencies could be key factors in color assimilation. Here we present a new psychophysical study of color assimilation where we assessed the contribution of luminance differences (between the target and its surround) present in striped stimuli. Our results show that luminance differences are key factors in color assimilation for stimuli varying along the s axis of MacLeod-Boynton color space, but not for stimuli varying along the l axis. This asymmetry suggests that koniocellular neural mechanisms responsible for color assimilation only contribute when there is a luminance difference, supporting the idea that mutual-inhibition has a major role in color induction. |
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NEUROBIT; 600.120; 600.128 |
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Admin @ si @ COS2018 |
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3148 |
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Domicele Jonauskaite; Nele Dael; C. Alejandro Parraga; Laetitia Chevre; Alejandro Garcia Sanchez; Christine Mohr |
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Stripping #The Dress: The importance of contextual information on inter-individual differences in colour perception |
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Journal Article |
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2018 |
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Psychological Research |
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PSYCHO R |
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1-15 |
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In 2015, a picture of a Dress (henceforth the Dress) triggered popular and scientific interest; some reported seeing the Dress in white and gold (W&G) and others in blue and black (B&B). We aimed to describe the phenomenon and investigate the role of contextualization. Few days after the Dress had appeared on the Internet, we projected it to 240 students on two large screens in the classroom. Participants reported seeing the Dress in B&B (48%), W&G (38%), or blue and brown (B&Br; 7%). Amongst numerous socio-demographic variables, we only observed that W&G viewers were most likely to have always seen the Dress as W&G. In the laboratory, we tested how much contextual information is necessary for the phenomenon to occur. Fifty-seven participants selected colours most precisely matching predominant colours of parts or the full Dress. We presented, in this order, small squares (a), vertical strips (b), and the full Dress (c). We found that (1) B&B, B&Br, and W&G viewers had selected colours differing in lightness and chroma levels for contextualized images only (b, c conditions) and hue for fully contextualized condition only (c) and (2) B&B viewers selected colours most closely matching displayed colours of the Dress. Thus, the Dress phenomenon emerges due to inter-individual differences in subjectively perceived lightness, chroma, and hue, at least when all aspects of the picture need to be integrated. Our results support the previous conclusions that contextual information is key to colour perception; it should be important to understand how this actually happens. |
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NEUROBIT; no proj |
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Admin @ si @ JDP2018 |
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3149 |
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Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam |
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Subgraph spotting in graph representations of comic book images |
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Journal Article |
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2018 |
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Pattern Recognition Letters |
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PRL |
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112 |
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118-124 |
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Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images |
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Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. |
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DAG; 600.097; 600.121 |
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Admin @ si @ LLD2018 |
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3150 |
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Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen |
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Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification |
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2018 |
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ISPRS Journal of Photogrammetry and Remote Sensing |
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ISPRS J |
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138 |
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74-85 |
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Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis |
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Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene |
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LAMP; 600.109; 600.106; 600.120 |
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Admin @ si @ RKW2018 |
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3158 |
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Adrien Gaidon; Antonio Lopez; Florent Perronnin |
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The Reasonable Effectiveness of Synthetic Visual Data |
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2018 |
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International Journal of Computer Vision |
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IJCV |
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126 |
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9 |
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899–901 |
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ADAS; 600.118 |
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Admin @ si @ GLP2018 |
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3180 |
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Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli |
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Title |
Batch-based activity recognition from egocentric photo-streams revisited |
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Journal Article |
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2018 |
Publication |
Pattern Analysis and Applications |
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PAA |
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21 |
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4 |
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953–965 |
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Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks |
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Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average. |
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MILAB; no proj |
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no |
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Admin @ si @ CMR2018 |
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3186 |
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Mariella Dimiccoli; Cathal Gurrin; David J. Crandall; Xavier Giro; Petia Radeva |
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Introduction to the special issue: Egocentric Vision and Lifelogging |
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Journal Article |
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2018 |
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Journal of Visual Communication and Image Representation |
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JVCIR |
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55 |
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352-353 |
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MILAB; no proj |
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Admin @ si @ DGC2018 |
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3187 |
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Sumit K. Banchhor; Narendra D. Londhe; Tadashi Araki; Luca Saba; Petia Radeva; Narendra N. Khanna; Jasjit S. Suri |
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Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. |
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2018 |
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Computers in Biology and Medicine |
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CBM |
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101 |
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184-198 |
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Heart disease; Stroke; Atherosclerosis; Intravascular; Coronary; Carotid; Calcium; Morphology; Risk stratification |
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Purpose of review
Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins.
Recent finding
Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes. |
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MILAB; no proj |
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Admin @ si @ BLA2018 |
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3188 |
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Cristhian A. Aguilera-Carrasco; C. Aguilera; Angel Sappa |
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Title |
Melamine Faced Panels Defect Classification beyond the Visible Spectrum |
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Journal Article |
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2018 |
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Sensors |
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SENS |
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18 |
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11 |
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1-10 |
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industrial application; infrared; machine learning |
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In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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MSIAU; 600.122 |
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no |
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Admin @ si @ AAS2018 |
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3191 |
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