| |
Records |
Links |
|
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; 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 |
Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell |
|
| |
Title |
Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Journal of the Optical Society of America A |
Abbreviated Journal |
JOSA A |
|
| |
Volume |
37 |
Issue |
1 |
Pages |
1-15 |
|
| |
Keywords |
|
|
| |
Abstract |
Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results. |
|
| |
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; 600.140; 600.12; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SBV2019 |
Serial |
3311 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg |
|
| |
Title |
Coloring Action Recognition in Still Images |
Type |
Journal Article |
| |
Year |
2013 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
|
| |
Volume |
105 |
Issue |
3 |
Pages |
205-221 |
|
| |
Keywords |
|
|
| |
Abstract |
In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer US |
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 |
CIC; ADAS; 600.057; 600.048 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ KRW2013 |
Serial |
2285 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger |
|
| |
Title |
Limitations of visual gamma corrections in LCD displays |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
Displays |
Abbreviated Journal |
Dis |
|
| |
Volume |
35 |
Issue |
5 |
Pages |
227–239 |
|
| |
Keywords |
Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration |
|
| |
Abstract |
A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements. |
|
| |
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; DAG; 600.052; 600.077; 600.074 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PRK2014 |
Serial |
2511 |
|
| Permanent link to this record |
