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Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
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Title |
Cross-Spectral Image Processing |
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Book Chapter |
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2022 |
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Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
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23-34 |
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Although this book is on IR computer vision and its main focus lies on IR image and video processing and analysis, a special attention is dedicated to cross-spectral image processing due to the increasing number of publications and applications in this domain. In these cross-spectral frameworks, IR information is used together with information from other spectral bands to tackle some specific problems by developing more robust solutions. Tasks considered for cross-spectral processing are for instance dehazing, segmentation, vegetation index estimation, or face recognition. This increasing number of applications is motivated by cross- and multi-spectral camera setups available already on the market like for example smartphones, remote sensing multispectral cameras, or multi-spectral cameras for automotive systems or drones. In this chapter, different cross-spectral image processing techniques will be reviewed together with possible applications. Initially, image registration approaches for the cross-spectral case are reviewed: the registration stage is the first image processing task, which is needed to align images acquired by different sensors within the same reference coordinate system. Then, recent cross-spectral image colorization approaches, which are intended to colorize infrared images for different applications are presented. Finally, the cross-spectral image enhancement problem is tackled by including guided super resolution techniques, image dehazing approaches, cross-spectral filtering and edge detection. Figure 3.1 illustrates cross-spectral image processing stages as well as their possible connections. Table 3.1 presents some of the available public cross-spectral datasets generally used as reference data to evaluate cross-spectral image registration, colorization, enhancement, or exploitation results. |
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SLCV |
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978-3-031-00698-2 |
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MSIAU; MACO |
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Admin @ si @ TSH2022b |
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3805 |
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Michael Teutsch; Angel Sappa; Riad I. Hammoud |
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Title |
Detection, Classification, and Tracking |
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2022 |
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Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
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35-58 |
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Automatic image and video exploitation or content analysis is a technique to extract higher-level information from a scene such as objects, behavior, (inter-)actions, environment, or even weather conditions. The relevant information is assumed to be contained in the two-dimensional signal provided in an image (width and height in pixels) or the three-dimensional signal provided in a video (width, height, and time). But also intermediate-level information such as object classes [196], locations [197], or motion [198] can help applications to fulfill certain tasks such as intelligent compression [199], video summarization [200], or video retrieval [201]. Usually, videos with their temporal dimension are a richer source of data compared to single images [202] and thus certain video content can be extracted from videos only such as object motion or object behavior. Often, machine learning or nowadays deep learning techniques are utilized to model prior knowledge about object or scene appearance using labeled training samples [203, 204]. After a learning phase, these models are then applied in real world applications, which is called inference. |
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SLCV |
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978-3-031-00698-2 |
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MSIAU; MACO |
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Admin @ si @ TSH2022c |
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3806 |
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Michael Teutsch; Angel Sappa; Riad I. Hammoud |
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Title |
Image and Video Enhancement |
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2022 |
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Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
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9-21 |
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Image and video enhancement aims at improving the signal quality relative to imaging artifacts such as noise and blur or atmospheric perturbations such as turbulence and haze. It is usually performed in order to assist humans in analyzing image and video content or simply to present humans visually appealing images and videos. However, image and video enhancement can also be used as a preprocessing technique to ease the task and thus improve the performance of subsequent automatic image content analysis algorithms: preceding dehazing can improve object detection as shown by [23] or explicit turbulence modeling can improve moving object detection as discussed by [24]. But it remains an open question whether image and video enhancement should rather be performed explicitly as a preprocessing step or implicitly for example by feeding affected images directly to a neural network for image content analysis like object detection [25]. Especially for real-time video processing at low latency it can be better to handle image perturbation implicitly in order to minimize the processing time of an algorithm. This can be achieved by making algorithms for image content analysis robust or even invariant to perturbations such as noise or blur. Additionally, mistakes of an individual preprocessing module can obviously affect the quality of the entire processing pipeline. |
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SLCV |
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MSIAU; MACO |
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no |
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Admin @ si @ TSH2022a |
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3807 |
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Author |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li |
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Title |
Face Presentation Attack Detection (PAD) Challenges |
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Book Chapter |
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2023 |
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Advances in Face Presentation Attack Detection |
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17–35 |
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In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges. |
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HUPBA |
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no |
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Admin @ si @ WGE2023b |
Serial |
3956 |
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Author |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li |
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Title |
Best Solutions Proposed in the Context of the Face Anti-spoofing Challenge Series |
Type |
Book Chapter |
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Year |
2023 |
Publication |
Advances in Face Presentation Attack Detection |
Abbreviated Journal |
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37–78 |
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The PAD competitions we organized attracted more than 835 teams from home and abroad, most of them from the industry, which shows that the topic of face anti-spoofing is closely related to daily life, and there is an urgent need for advanced algorithms to solve its application needs. Specifically, the Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round; the Chalearn Face Anti-spoofing Attack Detection Challenge attracted 340 teams in the development stage, and finally, 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively; the 3D High-Fidelity Mask Face Presentation Attack Detection Challenge attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. In this chapter, we briefly the methods developed by the teams participating in each competition, and introduce the algorithm details of the top-three ranked teams in detail. |
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HUPBA |
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Admin @ si @ WGE2023d |
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3958 |
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Author |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li |
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Title |
Face Anti-spoofing Progress Driven by Academic Challenges |
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Book Chapter |
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Year |
2023 |
Publication |
Advances in Face Presentation Attack Detection |
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1–15 |
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With the ubiquity of facial authentication systems and the prevalence of security cameras around the world, the impact that facial presentation attack techniques may have is huge. However, research progress in this field has been slowed by a number of factors, including the lack of appropriate and realistic datasets, ethical and privacy issues that prevent the recording and distribution of facial images, the little attention that the community has given to potential ethnic biases among others. This chapter provides an overview of contributions derived from the organization of academic challenges in the context of face anti-spoofing detection. Specifically, we discuss the limitations of benchmarks and summarize our efforts in trying to boost research by the community via the participation in academic challenges |
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SLCV |
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HUPBA |
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no |
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Admin @ si @ WGE2023c |
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3957 |
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Author |
Beata Megyesi; Alicia Fornes; Nils Kopal; Benedek Lang |
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Title |
Historical Cryptology |
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Book Chapter |
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2024 |
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Learning and Experiencing Cryptography with CrypTool and SageMath |
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Historical cryptology studies (original) encrypted manuscripts, often handwritten sources, produced in our history. These historical sources can be found in archives, often hidden without any indexing and therefore hard to locate. Once found they need to be digitized and turned into a machine-readable text format before they can be deciphered with computational methods. The focus of historical cryptology is not primarily the development of sophisticated algorithms for decipherment, but rather the entire process of analysis of the encrypted source from collection and digitization to transcription and decryption. The process also includes the interpretation and contextualization of the message set in its historical context. There are many challenges on the way, such as mistakes made by the scribe, errors made by the transcriber, damaged pages, handwriting styles that are difficult to interpret, historical languages from various time periods, and hidden underlying language of the message. Ciphertexts vary greatly in terms of their code system and symbol sets used with more or less distinguishable symbols. Ciphertexts can be embedded in clearly written text, or shorter or longer sequences of cleartext can be embedded in the ciphertext. The ciphers used mostly in historical times are substitutions (simple, homophonic, or polyphonic), with or without nomenclatures, encoded as digits or symbol sequences, with or without spaces. So the circumstances are different from those in modern cryptography which focuses on methods (algorithms) and their strengths and assumes that the algorithm is applied correctly. For both historical and modern cryptology, attack vectors outside the algorithm are applied like implementation flaws and side-channel attacks. In this chapter, we give an introduction to the field of historical cryptology and present an overview of how researchers today process historical encrypted sources. |
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DAG |
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Admin @ si @ MFK2024 |
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4020 |
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Author |
Josep Llados; Ernest Valveny; Enric Marti |
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Title |
Symbol Recognition in Document Image Analysis: Methods and Challenges |
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2000 |
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Recent Research Developments in Pattern Recognition, Transworld Research Network, |
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1 |
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151–178. |
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81-86846-61-1 |
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DAG;IAM |
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IAM @ iam @ LVM2000 |
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1575 |
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Jorge Bernal; Fernando Vilariño; F. Javier Sanchez |
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Towards Intelligent Systems for Colonoscopy |
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2011 |
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Colonoscopy |
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1 |
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257-282 |
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In this chapter we present tools that can be used to build intelligent systems for colonoscopy.
The idea is, by using methods based on computer vision and artificial intelligence, add significant value to the colonoscopy procedure. Intelligent systems are being used to assist in other medical interventions |
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Intech |
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Paul Miskovitz |
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978-953-307-568-6 |
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800 |
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MV;SIAI |
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IAM @ iam @ BVS2011 |
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1697 |
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Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo |
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From re-identification to identity inference: Labeling consistency by local similarity constraints |
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2014 |
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Person Re-Identification |
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2 |
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287-307 |
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re-identification; Identity inference; Conditional random fields; Video surveillance |
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In this chapter, we introduce the problem of identity inference as a generalization of person re-identification. It is most appropriate to distinguish identity inference from re-identification in situations where a large number of observations must be identified without knowing a priori that groups of test images represent the same individual. The standard single- and multishot person re-identification common in the literature are special cases of our formulation. We present an approach to solving identity inference by modeling it as a labeling problem in a Conditional Random Field (CRF). The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space. Experimental results are given on the ETHZ, i-LIDS and CAVIAR datasets. Our approach yields state-of-the-art performance for multishot re-identification, and our results on the more general identity inference problem demonstrate that we are able to infer the identity of very many examples even with very few labeled images in the gallery. |
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Springer London |
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2191-6586 |
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978-1-4471-6295-7 |
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LAMP; 600.079 |
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Admin @ si @KLB2014b |
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2521 |
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Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
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Exploiting Natural Language Generation in Scene Interpretation |
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2009 |
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Human–Centric Interfaces for Ambient Intelligence |
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4 |
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71–93 |
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Elsevier Science and Tech |
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ISE |
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ISE @ ise @ FBR2009 |
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1212 |
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Author |
V. Valev; Petia Radeva |
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Title |
Determining Structural Description by Boolean Formulas. |
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1992 |
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Advances in Structural and Syntactic Pattern Recognition |
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5 |
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131–140 |
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Pattern recognition is an active area of research with many applications, some of which have reached commercial maturity. Structural and syntactic methods are very powerful. They are based on symbolic data structures together with matching, parsing, and reasoning procedures that are able to infer interpretations of complex input patterns.
This book gives an overview of the latest developments and achievements in the field. |
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World Scientific |
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H. Bunke |
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Machine Perception and Artificial Intelligence: |
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978-981-279-791-9 |
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MILAB |
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BCNPCL @ bcnpcl @ VaR1992c |
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254 |
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Javier Marin; David Geronimo; David Vazquez; Antonio Lopez |
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Title |
Pedestrian Detection: Exploring Virtual Worlds |
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2012 |
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Handbook of Pattern Recognition: Methods and Application |
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5 |
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145-162 |
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Virtual worlds; Pedestrian Detection; Domain Adaptation |
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Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition. |
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iConcept Press |
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English |
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978-1-477554-82-1 |
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ADAS @ adas @ MGV2012 |
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1979 |
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Author |
Ariel Amato; Ivan Huerta; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez |
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Title |
Moving Cast Shadows Detection Methods for Video Surveillance Applications |
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Book Chapter |
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2014 |
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Augmented Vision and Reality |
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6 |
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23-47 |
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Abstract |
Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows). |
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Springer Berlin Heidelberg |
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2190-5916 |
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978-3-642-37840-9 |
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ISE; 605.203; 600.049; 302.018; 302.012; 600.078 |
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Admin @ si @ AHM2014 |
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2223 |
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Author |
Jose Manuel Alvarez; Antonio Lopez |
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Title |
Photometric Invariance by Machine Learning |
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Book Chapter |
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2012 |
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Color in Computer Vision: Fundamentals and Applications |
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7 |
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113-134 |
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road detection |
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iConcept Press Ltd |
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Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek |
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978-0-470-89084-4 |
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ADAS |
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Admin @ si @ AlL2012 |
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2186 |
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