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	Author	Qingshan Chen; Zhenzhen Quan; Yujun Li; Chao Zhai; Mikhail Mozerov
	Title	An Unsupervised Domain Adaption Approach for Cross-Modality RGB-Infrared Person Re-Identification			Type	Journal Article
	Year	2023	Publication	IEEE Sensors Journal	Abbreviated Journal	IEEE-SENS
	Volume	23	Issue	24	Pages
	Keywords	Q. Chen, Z. Quan, Y. Li, C. Zhai and M. G. Mozerov
	Abstract	Dual-camera systems commonly employed in surveillance serve as the foundation for RGB-infrared (IR) cross-modality person re-identification (ReID). However, significant modality differences give rise to inferior performance compared to single-modality scenarios. Furthermore, most existing studies in this area rely on supervised training with meticulously labeled datasets. Labeling RGB-IR image pairs is more complex than labeling conventional image data, and deploying pretrained models on unlabeled datasets can lead to catastrophic performance degradation. In contrast to previous solutions that focus solely on cross-modality or domain adaptation issues, this article presents an end-to-end unsupervised domain adaptation (UDA) framework for the cross-modality person ReID, which can simultaneously address both of these challenges. This model employs source domain classes, target domain clusters, and unclustered instance samples for the training, maximizing the comprehensive use of the dataset. Moreover, it addresses the problem of mismatched clustering labels between the two modalities in the target domain by incorporating a label matching module that reassigns reliable clusters with labels, ensuring correspondence between different modality labels. We construct the loss function by incorporating distinctiveness loss and multiplicity loss, both of which are determined by the similarity of neighboring features in the predicted feature space and the difference between distant features. This approach enables efficient feature clustering and cluster class assignment to occur concurrently. Eight UDA cross-modality person ReID experiments are conducted on three real datasets and six synthetic datasets. The experimental results unequivocally demonstrate that the proposed model outperforms the existing state-of-the-art algorithms to a significant degree. Notably, in RegDB → RegDB_light, the Rank-1 accuracy exhibits a remarkable improvement of 8.24%.
	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			Approved	no
	Call Number	Admin @ si @ CQL2023			Serial	3884
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	Author	Mikhail Mozerov; Fei Yang; Joost Van de Weijer
	Title	Sparse Data Interpolation Using the Geodesic Distance Affinity Space			Type	Journal Article
	Year	2019	Publication	IEEE Signal Processing Letters	Abbreviated Journal	SPL
	Volume	26	Issue	6	Pages	943 - 947
	Keywords
	Abstract	In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique.
	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; 600.120			Approved	no
	Call Number	Admin @ si @ MYW2019			Serial	3261
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	Author	Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov
	Title	Variable Rate Deep Image Compression with Modulated Autoencoder			Type	Journal Article
	Year	2020	Publication	IEEE Signal Processing Letters	Abbreviated Journal	SPL
	Volume	27	Issue		Pages	331-335
	Keywords
	Abstract	Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.
	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; ADAS; 600.141; 600.120; 600.118			Approved	no
	Call Number	Admin @ si @ YHW2020			Serial	3346
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	Author	Shiqi Yang; Kai Wang; Luis Herranz; Joost Van de Weijer
	Title	On Implicit Attribute Localization for Generalized Zero-Shot Learning			Type	Journal Article
	Year	2021	Publication	IEEE Signal Processing Letters	Abbreviated Journal
	Volume	28	Issue		Pages	872 - 876
	Keywords
	Abstract	Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions. Since attributes are often related to specific parts of objects, many recent works focus on discovering discriminative regions. However, these methods usually require additional complex part detection modules or attention mechanisms. In this paper, 1) we show that common ZSL backbones (without explicit attention nor part detection) can implicitly localize attributes, yet this property is not exploited. 2) Exploiting it, we then propose SELAR, a simple method that further encourages attribute localization, surprisingly achieving very competitive generalized ZSL (GZSL) performance when compared with more complex state-of-the-art methods. Our findings provide useful insight for designing future GZSL methods, and SELAR provides an easy to implement yet strong baseline.
	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; 600.120			Approved	no
	Call Number	YWH2021			Serial	3563
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	Author	Joost Van de Weijer; Theo Gevers; A. Gijsenij
	Title	Edge-Based Color Constancy			Type	Journal
	Year	2007	Publication	IEEE Trans. on Image Processing, vol. 16(9):2207–2214	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	ISE			Approved	no
	Call Number	CAT @ cat @ WGG2007			Serial	949
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	Author	H.M.G. Stokman; Theo Gevers
	Title	Selection and Fusion of Color Models for Image Feature Detection			Type	Journal
	Year	2007	Publication	IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.29(3):371–381	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	ALTRES;ISE			Approved	no
	Call Number	Admin @ si @ StG2007			Serial	948
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	Author	Yu Jie; Jaume Amores; N. Sebe; Petia Radeva; Tian Qi
	Title	Distance Learning for Similarity Estimation			Type	Journal
	Year	2008	Publication	IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(3):451–462	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	ADAS;MILAB			Approved	no
	Call Number	ADAS @ adas @ JAS2008			Serial	961
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	Author	Sergio Escalera; David M.J. Tax; Oriol Pujol; Petia Radeva; Robert P.W. Duin
	Title	Subclass Problem-Dependent Design for Error-Correcting Output Codes			Type	Journal
	Year	2008	Publication	IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(6):1041–1054	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;HuPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ ETP2008			Serial	951
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	Author	A. Martinez; Jordi Vitria
	Title	Clustering in Image Space for Place Recognition and Visiual Annotations for Human-Robot Interaction.			Type	Journal
	Year	2001	Publication	IEEE Trans. on Systems, Man, and Cybernatics–Part B: Cybernetics, 31(5):669–682 (IF: 0.789)	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	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ MVi2001			Serial	141
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	Author	M.A. Garcia; Angel Sappa
	Title	Efficient Generation of Discontinuity-Preserving Adaptive Triangulations from Range Images			Type	Journal
	Year	2004	Publication	IEEE Trans. on Systems, Man, and Cybernetics (Part B), 34(5):2003–2014 (IF: 1.052)	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				Approved	no
	Call Number	ADAS @ adas @ GaS2004			Serial	457
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	Author	Joost Van de Weijer; Cordelia Schmid; Jakob Verbeek; Diane Larlus
	Title	Learning Color Names for Real-World Applications			Type	Journal Article
	Year	2009	Publication	IEEE Transaction in Image Processing	Abbreviated Journal	TIP
	Volume	18	Issue	7	Pages	1512–1524
	Keywords
	Abstract	Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labelled color chips. These color chips are labelled with color names within a well-defined experimental setup by human test subjects. However naming colors in real-world images differs significantly from this experimental setting. In this paper, we investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labelling real-world images with color names we use Google Image to collect a data set. Due to limitations of Google Image this data set contains a substantial quantity of wrongly labelled data. We propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labelled color chips for both image retrieval and image annotation.
	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				Approved	no
	Call Number	CAT @ cat @ WSV2009			Serial	1195
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	Author	Adriana Romero; Carlo Gatta; Gustavo Camps-Valls
	Title	Unsupervised Deep Feature Extraction for Remote Sensing Image Classification			Type	Journal Article
	Year	2016	Publication	IEEE Transaction on Geoscience and Remote Sensing	Abbreviated Journal	TGRS
	Volume	54	Issue	3	Pages	1349 - 1362
	Keywords
	Abstract	This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.
	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	0196-2892	ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; 600.079;MILAB			Approved	no
	Call Number	Admin @ si @ RGC2016			Serial	2723
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	Author	David Geronimo; Antonio Lopez; Angel Sappa; Thorsten Graf
	Title	Survey on Pedestrian Detection for Advanced Driver Assistance Systems			Type	Journal Article
	Year	2010	Publication	IEEE Transaction on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	32	Issue	7	Pages	1239–1258
	Keywords	ADAS, pedestrian detection, on-board vision, survey
	Abstract	Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.
	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	0162-8828	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ GLS2010			Serial	1340
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	Author	K.E.A. van de Sande; Theo Gevers; C.G.M. Snoek
	Title	Evaluating Color Descriptors for Object and Scene Recognition			Type	Journal Article
	Year	2010	Publication	IEEE Transaction on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	32	Issue	9	Pages	1582 - 1596
	Keywords
	Abstract	Impact factor: 5.308 Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from http://www.colordescriptors.com) in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.
	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	0162-8828	ISBN		Medium
	Area		Expedition		Conference
	Notes	ALTRES;ISE			Approved	no
	Call Number	Admin @ si @ SGS2010			Serial	1846
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	Author	R. Valenti; Theo Gevers
	Title	Accurate Eye Center Location through Invariant Isocentric Patterns			Type	Journal Article
	Year	2012	Publication	IEEE Transaction on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	34	Issue	9	Pages	1785-1798
	Keywords
	Abstract	Impact factor 2010: 5.308 Impact factor 2011/12?: 5.96 Locating the center of the eyes allows for valuable information to be captured and used in a wide range of applications. Accurate eye center location can be determined using commercial eye-gaze trackers, but additional constraints and expensive hardware make these existing solutions unattractive and impossible to use on standard (i.e., visible wavelength), low-resolution images of eyes. Systems based solely on appearance are proposed in the literature, but their accuracy does not allow us to accurately locate and distinguish eye centers movements in these low-resolution settings. Our aim is to bridge this gap by locating the center of the eye within the area of the pupil on low-resolution images taken from a webcam or a similar device. The proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve in-plane rotational invariance, and to keep low-computational costs. To further gain scale invariance, the approach is applied to a scale space pyramid. In this paper, we extensively test our approach for its robustness to changes in illumination, head pose, scale, occlusion, and eye rotation. We demonstrate that our system can achieve a significant improvement in accuracy over state-of-the-art techniques for eye center location in standard low-resolution imagery.
	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	0162-8828	ISBN		Medium
	Area		Expedition		Conference
	Notes	ALTRES;ISE			Approved	no
	Call Number	Admin @ si @ VaG 2012a			Serial	1849
Permanent link to this record