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	Author	Wenjuan Gong; W.Zhang; Jordi Gonzalez; Y.Ren; Z.Li
	Title	Enhanced Asymmetric Bilinear Model for Face Recognition			Type	Journal Article
	Year	2015	Publication	International Journal of Distributed Sensor Networks	Abbreviated Journal	IJDSN
	Volume		Issue		Pages	Article ID 218514
	Keywords
	Abstract	Bilinear models have been successfully applied to separate two factors, for example, pose variances and different identities in face recognition problems. Asymmetric model is a type of bilinear model which models a system in the most concise way. But seldom there are works exploring the applications of asymmetric bilinear model on face recognition problem with illumination changes. In this work, we propose enhanced asymmetric model for illumination-robust face recognition. Instead of initializing the factor probabilities randomly, we initialize them with nearest neighbor method and optimize them for the test data. Above that, we update the factor model to be identified. We validate the proposed method on a designed data sample and extended Yale B dataset. The experiment results show that the enhanced asymmetric models give promising results and good recognition accuracies.
	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 @ GZG2015			Serial	2592
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	Author	Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez; Xavier Roca
	Title	A coarse-to-fine approach for fast deformable object detection			Type	Journal Article
	Year	2015	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	48	Issue	5	Pages	1844-1853
	Keywords
	Abstract	We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of [9]. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy.
	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; 602.005; 605.001; 302.012			Approved	no
	Call Number	Admin @ si @ PVG2015			Serial	2628
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	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 aecting 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 renes 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 dierent 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
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	Author	Josep M. Gonfaus; Marco Pedersoli; Jordi Gonzalez; Andrea Vedaldi; Xavier Roca
	Title	Factorized appearances for object detection			Type	Journal Article
	Year	2015	Publication	Computer Vision and Image Understanding	Abbreviated Journal	CVIU
	Volume	138	Issue		Pages	92–101
	Keywords	Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts
	Abstract	Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances. A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure. Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories.
	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 @ GPG2015			Serial	2705
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	Author	Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund
	Title	Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields			Type	Journal Article
	Year	2016	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	80	Issue		Pages	208–215
	Keywords
	Abstract	This paper proposes a technique for RGB-D scene segmentation using Multi-class Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset. The approach shows that simple multi-modal features with the power of the MMSSL paradigm can achieve better performance than state of the art results on the same dataset.
	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; ISE;MILAB; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ TEG2016			Serial	2843
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