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	Author	Fei Yang
	Title	Towards Practical Neural Image Compression			Type	Book Whole
	Year	2021	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Images and videos are pervasive in our life and communication. With advances in smart and portable devices, high capacity communication networks and high definition cinema, image and video compression are more relevant than ever. Traditional block-based linear transform codecs such as JPEG, H.264/AVC or the recent H.266/VVC are carefully designed to meet not only the rate-distortion criteria, but also the practical requirements of applications. Recently, a new paradigm based on deep neural networks (i.e., neural image/video compression) has become increasingly popular due to its ability to learn powerful nonlinear transforms and other coding tools directly from data instead of being crafted by humans, as was usual in previous coding formats. While achieving excellent rate-distortion performance, these approaches are still limited mostly to research environments due to heavy models and other practical limitations, such as being limited to function on a particular rate and due to high memory and computational cost. In this thesis, we study these practical limitations, and designing more practical neural image compression approaches. After analyzing the differences between traditional and neural image compression, our first contribution is the modulated autoencoder (MAE), a framework that includes a mechanism to provide multiple rate-distortion options within a single model with comparable performance to independent models. In a second contribution, we propose the slimmable compressive autoencoder (SlimCAE), which in addition to variable rate, can optimize the complexity of the model and thus reduce significantly the memory and computational burden. Modern generative models can learn custom image transformation directly from suitable datasets following encoder-decoder architectures, task known as image-to-image (I2I) translation. Building on our previous work, we study the problem of distributed I2I translation, where the latent representation is transmitted through a binary channel and decoded in a remote receiving side. We also propose a variant that can perform both translation and the usual autoencoding functionality. Finally, we also consider neural video compression, where the autoencoder is typically augmented with temporal prediction via motion compensation. One of the main bottlenecks of that framework is the optical flow module that estimates the displacement to predict the next frame. Focusing on this module, we propose a method that improves the accuracy of the optical flow estimation and a simplified variant that reduces the computational cost. Key words: neural image compression, neural video compression, optical flow, practical neural image compression, compressive autoencoders, image-to-image translation, deep learning.
	Address	December 2021
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	IMPRIMA	Place of Publication		Editor	Luis Herranz;Mikhail Mozerov;Yongmei Cheng
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-122714-7-8	Medium
	Area		Expedition		Conference
	Notes	LAMP			Approved	no
	Call Number	Admin @ si @ Yan2021			Serial	3608
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	Author	Federico Bartoli; Giuseppe Lisanti; Svebor Karaman; Andrew Bagdanov; Alberto del Bimbo
	Title	Unsupervised scene adaptation for faster multi- scale pedestrian detection			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3534 - 3539
	Keywords
	Abstract
	Address	Stockholm; Sweden; August 2014
	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	ICPR
	Notes	LAMP; 600.079			Approved	no
	Call Number	Admin @ si @ BLK2014			Serial	2519
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	Author	Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari
	Title	Fusion of Classifier Predictions for Audio-Visual Emotion Recognition			Type	Conference Article
	Year	2016	Publication	23rd International Conference on Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this paper is presented a novel multimodal emotion recognition system which is based on the analysis of audio and visual cues. MFCC-based features are extracted from the audio channel and facial landmark geometric relations are computed from visual data. Both sets of features are learnt separately using state-of-the-art classifiers. In addition, we summarise each emotion video into a reduced set of key-frames, which are learnt in order to visually discriminate emotions by means of a Convolutional Neural Network. Finally, confidence outputs of all classifiers from all modalities are used to define a new feature space to be learnt for final emotion prediction, in a late fusion/stacking fashion. The conducted experiments on eNTERFACE’05 database show significant performance improvements of our proposed system in comparison to state-of-the-art approaches.
	Address	Cancun; Mexico; December 2016
	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	ICPRW
	Notes	HuPBA;MILAB;			Approved	no
	Call Number	Admin @ si @ NMN2016			Serial	2839
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	Author	Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari
	Title	Audio-Visual Emotion Recognition in Video Clips			Type	Journal Article
	Year	2019	Publication	IEEE Transactions on Affective Computing	Abbreviated Journal	TAC
	Volume	10	Issue	1	Pages	60-75
	Keywords
	Abstract	This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases.
	Address	1 Jan.-March 2019
	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; 602.143; 602.133			Approved	no
	Call Number	Admin @ si @ NMN2017			Serial	3011
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	Author	Fatemeh Noroozi; Ciprian Corneanu; Dorota Kamińska; Tomasz Sapiński; Sergio Escalera; Gholamreza Anbarjafari
	Title	Survey on Emotional Body Gesture Recognition			Type	Journal Article
	Year	2021	Publication	IEEE Transactions on Affective Computing	Abbreviated Journal	TAC
	Volume	12	Issue	2	Pages	505 - 523
	Keywords
	Abstract	Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as “body language” and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g. human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce, there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical 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	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ NCK2021			Serial	3657
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	Author	Farshad Nourbakhsh; Dimosthenis Karatzas; Ernest Valveny
	Title	A polar-based logo representation based on topological and colour features			Type	Conference Article
	Year	2010	Publication	9th IAPR International Workshop on Document Analysis Systems	Abbreviated Journal
	Volume		Issue		Pages	341–348
	Keywords
	Abstract	In this paper, we propose a novel rotation and scale invariant method for colour logo retrieval and classification, which involves performing a simple colour segmentation and subsequently describing each of the resultant colour components based on a set of topological and colour features. A polar representation is used to represent the logo and the subsequent logo matching is based on Cyclic Dynamic Time Warping (CDTW). We also show how combining information about the global distribution of the logo components and their local neighbourhood using the Delaunay triangulation allows to improve the results. All experiments are performed on a dataset of 2500 instances of 100 colour logo images in different rotations and scales.
	Address	Boston; USA;
	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	978-1-60558-773-8	Medium
	Area		Expedition		Conference	DAS
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ NKV2010			Serial	1436
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	Author	Farshad Nourbakhsh
	Title	Colour logo recognition			Type	Report
	Year	2009	Publication	CVC Technical Report	Abbreviated Journal
	Volume	145	Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author	Computer Vision Center			Thesis	Master's thesis
	Publisher		Place of Publication	Bellaterra, Barcelona	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	DAG			Approved	no
	Call Number	Admin @ si @ Nou2009			Serial	2399
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	Author	Farhan Riaz; Fernando Vilariño; Mario Dinis-Ribeiro; Miguel Coimbraln
	Title	Identifying Potentially Cancerous Tissues in Chromoendoscopy Images			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	709-716
	Keywords	Endoscopy, Computer Assisted Diagnosis, Gradient.
	Abstract	The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG filters generally give best classification accuracy among the other feature extraction methods considered.
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher	Springer	Place of Publication	Berlin	Editor	J. Vitria, J.M. Sanches, and M. Hernandez
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-21256-7	Medium
	Area	800	Expedition		Conference	IbPRIA
	Notes	MV;SIAI			Approved	no
	Call Number	Admin @ si @ RVD2011; IAM @ iam @ RVD2011			Serial	1726
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	Author	Fares Alnajar; Theo Gevers; Roberto Valenti; Sennay Ghebreab
	Title	Calibration-free Gaze Estimation using Human Gaze Patterns			Type	Conference Article
	Year	2013	Publication	15th IEEE International Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	137-144
	Keywords
	Abstract	We present a novel method to auto-calibrate gaze estimators based on gaze patterns obtained from other viewers. Our method is based on the observation that the gaze patterns of humans are indicative of where a new viewer will look at [12]. When a new viewer is looking at a stimulus, we first estimate a topology of gaze points (initial gaze points). Next, these points are transformed so that they match the gaze patterns of other humans to find the correct gaze points. In a flexible uncalibrated setup with a web camera and no chin rest, the proposed method was tested on ten subjects and ten images. The method estimates the gaze points after looking at a stimulus for a few seconds with an average accuracy of 4.3 im. Although the reported performance is lower than what could be achieved with dedicated hardware or calibrated setup, the proposed method still provides a sufficient accuracy to trace the viewer attention. This is promising considering the fact that auto-calibration is done in a flexible setup , without the use of a chin rest, and based only on a few seconds of gaze initialization data. To the best of our knowledge, this is the first work to use human gaze patterns in order to auto-calibrate gaze estimators.
	Address	Sydney
	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	ICCV
	Notes	ALTRES;ISE			Approved	no
	Call Number	Admin @ si @ AGV2013			Serial	2365
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	Author	Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg
	Title	Painting-91: A Large Scale Database for Computational Painting Categorization			Type	Journal Article
	Year	2014	Publication	Machine Vision and Applications	Abbreviated Journal	MVAP
	Volume	25	Issue	6	Pages	1385-1397
	Keywords
	Abstract	Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0932-8092	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC; LAMP; 600.074; 600.079			Approved	no
	Call Number	Admin @ si @ KBW2014			Serial	2510
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	Author	Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen
	Title	Compact color texture description for texture classification			Type	Journal Article
	Year	2015	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	51	Issue		Pages	16-22
	Keywords
	Abstract	Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature. However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7:8%, 4:3% and 5:0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively.
	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; 600.068; 600.079;ADAS			Approved	no
	Call Number	Admin @ si @ KRW2015a			Serial	2587
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	Author	Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen
	Title	Deep semantic pyramids for human attributes and action recognition			Type	Conference Article
	Year	2015	Publication	Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015	Abbreviated Journal
	Volume	9127	Issue		Pages	341-353
	Keywords	Action recognition; Human attributes; Semantic pyramids
	Abstract	Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features. We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature.
	Address	Denmark; Copenhagen; June 2015
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-319-19664-0	Medium
	Area		Expedition		Conference	SCIA
	Notes	LAMP; 600.068; 600.079;ADAS			Approved	no
	Call Number	Admin @ si @ KRW2015b			Serial	2672
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	Author	Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez
	Title	Color Attributes for Object Detection			Type	Conference Article
	Year	2012	Publication	25th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3306-3313
	Keywords	pedestrian detection
	Abstract	State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification, leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe- art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.
	Address	Providence; Rhode Island; USA;
	Corporate Author				Thesis
	Publisher	IEEE Xplore	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1063-6919	ISBN	978-1-4673-1226-4	Medium
	Area		Expedition		Conference	CVPR
	Notes	ADAS; CIC;			Approved	no
	Call Number	Admin @ si @ KRW2012			Serial	1935
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	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
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	Author	Fahad Shahbaz Khan; Joost Van de Weijer; Sadiq Ali; Michael Felsberg
	Title	Evaluating the impact of color on texture recognition			Type	Conference Article
	Year	2013	Publication	15th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	8047	Issue		Pages	154-162
	Keywords	Color; Texture; image representation
	Abstract	State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task. In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets.
	Address	York; UK; August 2013
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-40260-9	Medium
	Area		Expedition		Conference	CAIP
	Notes	CIC; 600.048			Approved	no
	Call Number	Admin @ si @ KWA2013			Serial	2263
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