Professor Miroslaw Bober

Research Interests

My research focuses on novel techniques in Signal Processing, Computer Vision and Machine Learning and their applications in industry, healthcare, big-data and security. I have a particular interest in image and video analysis & retrieval (visual search, object recognition, analysis of motion, shape & texture).  The broad research objective is to develop unique methods and technology solutions for visual content understanding that can dramatically improve on existing state-of-the art leading to new applications. My algorithms for shape analysis and image/video fingerprinting as well as visual search are considered world-leading and were selected for ISO International standards within MPEG and used by, e.g. Metropolitan Police.

Teaching

  • EEE3034 – Media Casting (Module Coordinator)
  • EEE3029 – Multimedia Systems and Component Technology
  • EEEM001 – Image and Video Compression
  • EEE3035 – Engineering Professional Studies

Departmental Duties

  • Programme Director for MSc in Multimedia Signal Processing and Communications
  • Industrial Tutor for undergraduate industrial placement year
  • Personal tutor for undergraduate students (L1, L2, L3, L4)
  • MSc-level tutor
  • Member of Faculty Research Degrees Committee (FRDC)
  • Member of the Departmental Industrial Advisory Board (IAB)

Affiliations

I have extensive collaboration links with universities and research institutions in Europe (UK, Switzerland, Germany, Poland, France, Spain), US, Japan and China. I have also worked with the following companies: the BBC (UK), Bang and Olufsen (DE), CEDEO (IT), Casio (JP), Ericsson (SE), Huawei (DE), Mitsubishi Electric (JP), RAI television (IT), Renesas Electronics (JP), Telecom Italia (IT), and Visual Atoms (UK).

Current Projects & Research Funding

I am the project coordinator and PI for the BRIDGET FP-7 project [5.28 M€], where my team is responsible for the development of ultra large-scale visual search and media analysis algorithms for the broadcast industry. The project aims to open new dimensions for multimedia content creation and consumption by bridging the gap between the Broadcast and Internet. Project partners include RAI television, Huawei, Telecom Italia and more.

CODAM is my latest project (PI) and is funded by the TSB creative media call [£1.05 M]. My team is working with the BBC and Visual Atoms to develop an advanced video asset management system with unique visual fingerprinting and visual search capabilities. It will aid content creation and deployment by enabling visual content tracking, identification and searching across multiple devices and platforms, and across diverse digital media ecosystems and markets. Where is the original version of the low-quality clip? Which video clip has been used most often in BBC programmes? Is it a stock shot of a red double decker bus, or an excerpt from a royal wedding? Is there other footage in the archive that shows the same event but can provide a fresh viewpoint? The CODAM system will answer these questions, track the origins of video clips across multi-platform productions and search for related material. It will take the form of a modular software system that can identify individual video clips in edited programmes, and perform object or scene recognition to find similar footage in an archive without relying on manually entered and often incomplete metadata.

Contact Me

E-mail:
Phone: 01483 68 4724

Find me on campus
Room: 29 BA 00

Publications

Journal articles

  • Madeo S, Bober M. (2016) 'Fast, Compact and Discriminative: Evaluation of Binary Descriptors for Mobile Applications'. IEEE Transactions on Multimedia, PP (99)

    Abstract

    © 2016 IEEE.Local feature descriptors underpin many diverse applications, supporting object recognition, image registration, database search, 3D reconstruction and more. The recent phenomenal growth in mobile devices and mobile computing in general has created demand for descriptors that are not only discriminative, but also compact in size and fast to extract and match. In response, a large number of binary descriptors have been proposed, each claiming to overcome some limitations of the predecessors. This paper provides a comprehensive evaluation of several promising binary designs. We show that existing evaluation methodologies are not sufficient to fully characterize descriptors' performance and propose a new evaluation protocol and a challenging dataset. In contrast to the previous reviews, we investigate the effects of the matching criteria, operating points and compaction methods, showing that they all have a major impact on the systems' design and performance. Finally, we provide descriptor extraction times for both general-purpose systems and mobile devices, in order to better understand the real complexity of the extraction task. The objective is to provide a comprehensive reference and a guide that will help in selection and design of the future descriptors.

  • Husain S, Bober M. (2016) 'Improving large-scale image retrieval through robust aggregation of local descriptors'. IEEE Transactions on Pattern Analysis and Machine Intelligence,

    Abstract

    Visual search and image retrieval underpin numerous applications, however the task is still challenging predominantly due to the variability of object appearance and ever increasing size of the databases, often exceeding billions of images. Prior art methods rely on aggregation of local scale-invariant descriptors, such as SIFT, via mechanisms including Bag of Visual Words (BoW), Vector of Locally Aggregated Descriptors (VLAD) and Fisher Vectors (FV). However, their performance is still short of what is required. This paper presents a novel method for deriving a compact and distinctive representation of image content called Robust Visual Descriptor with Whitening (RVD-W). It significantly advances the state of the art and delivers world-class performance. In our approach local descriptors are rank-assigned to multiple clusters. Residual vectors are then computed in each cluster, normalized using a direction-preserving normalization function and aggregated based on the neighborhood rank. Importantly, the residual vectors are de-correlated and whitened in each cluster before aggregation, leading to a balanced energy distribution in each dimension and significantly improved performance. We also propose a new post-PCA normalization approach which improves separability between the matching and non-matching global descriptors. This new normalization benefits not only our RVD-W descriptor but also improves existing approaches based on FV and VLAD aggregation. Furthermore, we show that the aggregation framework developed using hand-crafted SIFT features also performs exceptionally well with Convolutional Neural Network (CNN) based features. The RVD-W pipeline outperforms state-of-the-art global descriptors on both the Holidays and Oxford datasets. On the large scale datasets, Holidays1M and Oxford1M, SIFT-based RVD-W representation obtains a mAP of 45.1% and 35.1%, while CNN-based RVD-W achieve a mAP of 63.5% and 44.8%, all yielding superior performance to the state-of-the-art.

  • Ravi D, Bober M, Farinella GM, Guarnera M, Battiato S. (2016) 'Semantic segmentation of images exploiting DCT based features and random forest'. Pattern Recognition, 52, pp. 260-273.

    Abstract

    This paper presents an approach for generating class-specific image segmentation. We introduce two novel features that use the quantized data of the Discrete Cosine Transform (DCT) in a Semantic Texton Forest based framework (STF), by combining together colour and texture information for semantic segmentation purpose. The combination of multiple features in a segmentation system is not a straightforward process. The proposed system is designed to exploit complementary features in a computationally efficient manner. Our DCT based features describe complex textures represented in the frequency domain and not just simple textures obtained using differences between intensity of pixels as in the classic STF approach. Differently than existing methods (e.g., filter bank) just a limited amount of resources is required. The proposed method has been tested on two popular databases: CamVid and MSRC-v2. Comparison with respect to recent state-of-the-art methods shows improvement in terms of semantic segmentation accuracy.

  • Wang Z, Duan LY, Lin J, Huang T, Gao W, Bober M. (2015) 'Component hashing of variable-length binary aggregated descriptors for fast image search'. 2014 IEEE International Conference on Image Processing, ICIP 2014, , pp. 2217-2221.
  • Husain S, Bober M. (2015) 'Robust and scalable aggregation of local features for ultra large-scale retrieval'. 2014 IEEE International Conference on Image Processing, ICIP 2014, , pp. 2799-2803.
  • Cordara G, Bober M, Reznik Y. (2013) 'Special issue on visual search and augmented reality'. Signal Processing: Image Communication, 28 (4), pp. 309-310.
  • Paschalakis S, Iwamoto K, Sprljan N, Oami R, Nomura T, Yamada A, Bober M. (2012) 'The MPEG-7 Video Signature Tools for Content Identification'. IEEE IEEE Transactions on Circuits and Systems for Video Technology, 22 (7), pp. 1050-1063.

    Abstract

    This paper presents the core technologies of the Video Signature Tools recently standardized by ISO/IEC MPEG as an amendment to the MPEG-7 Standard (ISO/IEC 15938). The Video Signature is a high-performance content fingerprint which is suitable for desktop-scale to web-scale deployment and provides high levels of robustness to common video editing operations and high temporal localization accuracy at extremely low false alarm rates, achieving a detection rate in the order of 96% at a false alarm rate in the order of five false matches per million comparisons. The applications of the Video Signature are numerous and include rights management and monetization, distribution management, usage monitoring, metadata association, and corporate or personal database management. In this paper we review the prior work in the field, we explain the standardization process and status, and we provide the details and evaluation results of the Video Signature Tools.

  • Cordara G, Bober M, Reznik Y. (2012) 'Special issue on visual search and augmented reality'. Signal Processing: Image Communication,
  • Bober MZ, Paschalakis S. (2010) 'MPEG image and video signature'. , pp. 81-95.
  • Kucharski K, Skarbek W, Bober M. (2005) 'Feature space reduction for face recognition with dual Linear Discriminant Analysis'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3691 LNCS, pp. 587-595.
  • O'Callaghan R, Bober M. (2005) 'MPEG-7 visual-temporal clustering for digital image collections'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3689 LNCS, pp. 339-350.
  • Skarbek W, Kucharski K, Bober M. (2004) 'Dual LDA for face recognition'. Fundamenta Informaticae, 61 (3-4), pp. 303-334.
  • Paschalakis S, Bober M. (2004) 'Real-time face detection and tracking for mobile videoconferencing'. Real-Time Imaging, 10 (2), pp. 81-94.
  • Paschalakis S, Lee P, Bober M. (2003) 'An FPGA system for the high speed extraction, normalization and classification of moment descriptors'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2778, pp. 543-552.
  • Bober M, Kucharski K, Skarbek W. (2003) 'Face recognition by Fisher and scatter linear discriminant analysis'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2756, pp. 638-645.
  • Bober M. (2001) 'MPEG-7 visual shape descriptors'. IEEE Transactions on Circuits and Systems for Video Technology, 11 (6), pp. 716-719.
  • Badenas J, Bober M, Pla F. (2001) 'Segmenting traffic scenes from grey level and motion information'. Pattern Analysis and Applications, 4 (1), pp. 28-38.
  • Georgis N, Kittler J, Bober M. (2000) 'Accurate Recovery of Dense Depth Map for 3D Motion Based Coding'. European Transactions on Telecommunications, 11 (2), pp. 219-232.
  • Bober M, Price W, Atkinson J. (2000) 'Contour shape descriptor for MPEG-7 and its applications'. Digest of Technical Papers - IEEE International Conference on Consumer Electronics, , pp. 286-287.
  • Bober M, Petrou M, Kittler J. (1998) 'Nonlinear motion estimation using the supercoupling approach'. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (5), pp. 550-555.
  • Bober M, Georgis N, Kittler J. (1998) 'On Accurate and Robust Estimation of Fundamental Matrix'. Computer Vision and Image Understanding, 72 (1), pp. 39-53.
  • Zhang K, Bober M, Kittler J. (1997) 'Image sequence coding using multiple-level segmentation and affine motion estimation'. IEEE Journal on Selected Areas in Communications, 15 (9), pp. 1704-1713.
  • Cieplinski L, Bober M. (1997) 'Scalable image coding using Gaussian pyramid vector quantization with resolution-independent block size'. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 4, pp. 2949-2952.
  • Zhang K, Bober M, Kittler J. (1996) 'Hybrid codec for very low bit rate video coding'. IEEE International Conference on Image Processing, 1, pp. 641-644.
  • Zhang K, Bober M, Kittler J. (1995) 'Motion based image segmentation for video coding'. IEEE International Conference on Image Processing, 3, pp. 476-479.
  • Bober M, Kittler J. (1994) 'Robust motion analysis'. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, , pp. 947-952.
  • Bober M, Kittler J. (1994) 'Estimation of complex multimodal motion: an approach based on robust statistics and Hough transform'. Image and Vision Computing, 12 (10), pp. 661-668.

Conference papers

  • Ong EJ, Bober M. (2016) 'Improved Hamming distance search using variable length Hashing'. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-January, pp. 2000-2008.

    Abstract

    This paper addresses the problem of ultra-large-scale search in Hamming spaces. There has been considerable research on generating compact binary codes in vision, for example for visual search tasks. However the issue of efficient searching through huge sets of binary codes remains largely unsolved. To this end, we propose a novel, unsupervised approach to thresholded search in Hamming space, supporting long codes (e.g. 512-bits) with a wide-range of Hamming distance radii. Our method is capable of working efficiently with billions of codes delivering between one to three orders of magnitude acceleration, as compared to prior art. This is achieved by relaxing the equal-size constraint in the Multi-Index Hashing approach, leading to multiple hash-tables with variable length hash-keys. Based on the theoretical analysis of the retrieval probabilities of multiple hash-tables we propose a novel search algorithm for obtaining a suitable set of hash-key lengths. The resulting retrieval mechanism is shown empirically to improve the efficiency over the state-of-the-art, across a range of datasets, bit-depths and retrieval thresholds.

  • Husain S, Bober MZ. (2016) 'On Aggregation of local binary descriptors'. ICME MMC 2016 Proceedings, Seattle, USA: 3rd IEEE International Workshop on Mobile Multimedia Computing (MMC 2016)

    Abstract

    This paper addresses the problem of aggregating local binary descriptors for large scale image retrieval in mobile scenarios. Binary descriptors are becoming increasingly popular, especially in mobile applications, as they deliver high matching speed, have a small memory footprint and are fast to extract. However, little research has been done on how to efficiently aggregate binary descriptors. Direct application of methods developed for conventional descriptors, such as SIFT, results in unsatisfactory performance. In this paper we introduce and evaluate several algorithms to compress high-dimensional binary local descriptors, for efficient retrieval in large databases. In addition, we propose a robust global image representation; Binary Robust Visual Descriptor (B-RVD), with rank-based multi-assignment of local descriptors and direction-based aggregation, achieved by the use of L1-norm on residual vectors. The performance of the B-RVD is further improved by balancing the variances of residual vector directions in order to maximize the discriminatory power of the aggregated vectors. Standard datasets and measures have been used for evaluation showing significant improvement of around 4% mean Average Precision as compared to the state-of-the-art.

  • Messina A, Burgos FM, Preda M, Lepsoy S, Bober M, Bertola D, Paschalakis S. (2015) 'Making second screen sustainable in media production: The BRIDGET approach'. TVX 2015 - Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video, , pp. 155-160.
  • Tirunagari S, Poh N, Bober MZ, Windridge D . (2015) 'Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics.'. WIFS, IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1-6.

    Abstract

    Recent studies have shown that it is possible to attack a finger vein (FV) based biometric system using printed materials. In this study, we propose a novel method to detect spoofing of static finger vein images using Windowed Dynamic mode decomposition (W-DMD). This is an atemporal variant of the recently proposed Dynamic Mode Decomposition for image sequences. The proposed method achieves better results when compared to established methods such as local binary patterns (LBP), discrete wavelet transforms (DWT), histogram of gradients (HoG), and filter methods such as range-filters, standard deviation filters (STD) and entropy filters, when using SVM with a minimum intersection kernel. The overall pipeline which consists ofW-DMD and SVM, proves to be efficient, and convenient to use, given the absence of additional parameter tuning requirements. The effectiveness of our methodology is demonstrated using FV-Spoofing-Attack database which is publicly available. Our test results show that W-DMD can successfully detect printed finger vein images because they contain micro-level artefacts that not only differ in quality but also in light reflection properties compared to valid/live finger vein images.

  • Husain S, Bober M. (2014) 'Robust and scalable aggregation of local features for ultra large-scale retrieval'. 2014 IEEE International Conference on Image Processing, ICIP 2014, , pp. 2799-2803.
  • Paschalakis S, Wnukowicz K, Bober M. (2011) 'Low-cost hierarchical video segmentation for consumer electronics applications'. Digest of Technical Papers - IEEE International Conference on Consumer Electronics, , pp. 79-80.
  • Smith RS, Bober M, Windeatt T. (2011) 'A comparison of random forest with ECOC-based classifiers'. Springer Lecture Notes in Computer Science: Multiple Classifier Systems, Naples, Italy: 10th International Workshop, MCS 2011 6713, pp. 207-216.

    Abstract

    We compare experimentally the performance of three approaches to ensemble-based classification on general multi-class datasets. These are the methods of random forest, error-correcting output codes (ECOC) and ECOC enhanced by the use of bootstrapping and class-separability weighting (ECOC-BW). These experiments suggest that ECOC-BW yields better generalisation performance than either random forest or unmodified ECOC. A bias-variance analysis indicates that ECOC benefits from reduced bias, when compared to random forest, and that ECOC-BW benefits additionally from reduced variance. One disadvantage of ECOC-based algorithms, however, when compared with random forest, is that they impose a greater computational demand leading to longer training times.

  • Brasnett P, Paschalakis S, Bober M. (2010) 'Recent developments on standardisation of MPEG-7 visual signature tools'. 2010 IEEE International Conference on Multimedia and Expo, ICME 2010, , pp. 1347-1352.
  • Bober M, Brasnett P. (2009) 'MPEG-7 visual signature tools'. Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009, , pp. 1540-1543.
  • Jin Y, Mokhtarian F, Bober M, Illingworth J. (2008) 'Fuzzy chamfer distance and its probabilistic formulation for visual tracking'. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR,
  • Brasnett P, Bober M. (2008) 'Fast and robust image identification'. Proceedings - International Conference on Pattern Recognition,
  • Sibiryakov A, Bober M. (2007) 'Graph-based multiple panorama extraction from unordered image sets'. Proceedings of SPIE - The International Society for Optical Engineering, 6498
  • Sibiryakov A, Bober M. (2007) 'Low-complexity motion analysis for mobile video devices'. Digest of Technical Papers - IEEE International Conference on Consumer Electronics,
  • Sibiryakov A, Bober M. (2006) 'Image registration using RST-clustering and its application in remote sensing'. Proceedings of SPIE - The International Society for Optical Engineering, 6365
  • Sibiryakov A, Bober M. (2006) 'Real-time multi-frame analysis of dominant translation'. IEEE COMPUTER SOC 18th International Conference on Pattern Recognition, Vol 1, Proceedings, Hong Kong, PEOPLES R CHINA: 18th International Conference on Pattern Recognition (ICPR 2006), pp. 55-58.
  • Kucharski K, Skarbek W, Bober M. (2005) 'Dual LDA - An effective feature space reduction method for face recognition'. IEEE International Conference on Advanced Video and Signal Based Surveillance - Proceedings of AVSS 2005, 2005, pp. 336-341.
  • Sibiryakov A, Bober M. (2005) 'A method of statistical template matching and its application to face and facial feature detection'. WSEAS Transactions on Information Science and Applications, 2 (9), pp. 1285-1293.
  • Santamaria C, Bober M, Szajnowski W. (2004) 'Texture analysis using level-crossing statistics'. Proceedings - International Conference on Pattern Recognition, 2, pp. 712-715.
  • Santamaria C, Bober M, Szajnowski W, Aso N. (2004) 'Analysis of remotely-sensed imagery using the level-crossing statistics texture descriptor'. Proceedings of SPIE - The International Society for Optical Engineering, 5573, pp. 115-125.
  • Berriss WP, Price WG, Bober MZ. (2003) 'The use of MPEG-7 for intelligent analysis and retrieval in video surveillance'. IEE Colloquium (Digest), 3-10062, pp. 41-45.
  • Paschalakis S, Bober M. (2003) 'A low cost FPGA system for high speed face detection and tracking'. Proceedings - 2003 IEEE International Conference on Field-Programmable Technology, FPT 2003, , pp. 214-221.
  • Ghanbari S, Cieplinski L, Bober MZ. (2003) 'Recovery of lost motion vectors for error concealment in video coding'. Picture Coding Symposium, , pp. 239-242.
  • Berriss WP, Price WG, Bober MZ. (2002) '<VISS>™: A video intelligent surveillance system'. Proceedings of SPIE - The International Society for Optical Engineering, 4861, pp. 13-21.
  • Bober M. (2001) 'MPEG-7: Evolution or revolution?'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2124
  • Bober M, Asai K, Divakaran A. (2001) 'A MPEG-4/7 based internet video and still image browsing system'. SPIE-INT SOC OPTICAL ENGINEERING MULTIMEDIA SYSTEMS AND APPLICATIONS III, BOSTON, MA: Conference on Multimedia Systems and Applications III 4209, pp. 33-38.
  • Bober M. (1999) 'Motion analysis for video coding and retrieval'. IEE Colloquium (Digest), (103), pp. 51-56.
  • Pla F, Bober M. (1997) 'Estimating translation/deformation motion through phase correlation'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1310, pp. 653-660.
  • Badenas J, Bober M, Pla F. (1997) 'Motion and intensity-based segmentation and its application to traffice monitoring'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1310, pp. 502-509.
  • Bober M, Kittler J. (1996) 'Video coding for mobile communications - MPEG4 perspective'. IEE Colloquium (Digest), (248)
  • Zhang K, Bober M, Kittler J. (1996) 'Video coding using affine motion compensated prediction'. I E E E 1996 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, CONFERENCE PROCEEDINGS, VOLS 1-6, ATLANTA, GA: 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 96), pp. 1978-1981.
  • Bober M, Kittler J. (1996) 'Combining the hough transform and multiresolution MRF's for the robust motion estimation'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1035, pp. 91-100.
  • Kittler J, Matas J, Bober M, Nguyen L. (1995) 'Image interpretation: exploiting multiple cues'. IEE Conference Publication, (410), pp. 1-5.
  • ZHANG K, BOBER M, KITTLER J. (1995) 'VARIABLE BLOCK SIZE VIDEO CODING WITH MOTION PREDICTION AND MOTION SEGMENTATION'. SPIE - INT SOC OPTICAL ENGINEERING DIGITAL VIDEO COMPRESSION: ALGORITHMS AND TECHNOLOGIES 1995, SAN JOSE, CA: Conference on Digital Video Compression - Algorithms and Technologies 1995 2419, pp. 62-70.
  • ZHANG K, BOBER M, KITTLER J. (1994) 'ROBUST MOTION ESTIMATION AND MULTISTAGE VECTOR QUANTISATION FOR SEQUENCE COMPRESSION'. I E E E, COMPUTER SOC PRESS ICIP-94 - PROCEEDINGS, VOL II, AUSTIN, TX: 1994 IEEE International Conference on Image Processing (ICIP-94), pp. 452-456.
  • PETROU M, BOBER M, KITTLER J. (1994) 'MULTIRESOLUTION MOTION SEGMENTATION'. I E E E, COMPUTER SOC PRESS PROCEEDINGS OF THE 12TH IAPR INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION - CONFERENCE A: COMPUTER VISION & IMAGE PROCESSING, JERUSALEM, ISRAEL: Conference A on Computer Vision and Image Processing, at the 12th IAPR International Conference on Pattern Recognition, pp. 379-383.

Books

  • Mokhtarian F, Bober MZ. (2003) Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Springer Netherlands

Book chapters

  • Windridge D, Bober M. (2014) 'A Kernel-Based Framework for Medical Big-Data Analytics'. in Holzinger A, Jurisica I (eds.) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics Springer Berlin Heidelberg 8401, pp. 197-208-197-208.
  • Bober MZ, Paschalakis S. (2012) 'MPEG image and video signature'. in (ed.) The MPEG Representation of Digital Media 9781441961846, pp. 81-95.
  • Bober MZ, Preteux F, W-Y Kim . (2002) 'Shape Descriptors'. in Manjunath BS, Salembier P, Sikora T (eds.) Introduction to MPEG-7 John Wiley & Sons Inc

Patents

  • Bober MZ, Sibiryakov A. (2011) Robust image registration.
  • Bober MZ, Cooper J. (2011) Method and apparatus for representing and searching for an object in an image.
  • Bober MZ, Paschalakis S. (2010) Methods of representing and analysing images.
  • Bober MZ, Paschalakis S. (2010) Methods of representing and analysing images.
  • Bober MZ. (2010) Method and apparatus for representing and searching for an object using shape.
  • Bober MZ. (2010) Method and apparatus for representing and searching for an object using shape. US:
  • Bober MZ, Santamaria C. (2010) Algorithm for line tracking using a circular sector search window.
  • Bober MZ, Sibiryakov A. (2010) Dominant motion analysis. US:
  • Bober MZ. (2010) Method and apparatus for motion vector field encoding.
  • Bober MZ, Paschalakis S, Brasnett P. (2010) Video Identification. US: Article number 12/693,220
  • Bober MZ, Santamaria C. (2009) Direction-sensitive line detection operator for image data.
  • Bober MZ. (2009) Method, apparatus, computer program, computer system, and computer-readable storage medium for representing and searching for an object in an image.
  • Bober MZ, Zaharia R, Cieplinski L. (2009) Adaptive quantization of depth signal in 3D visual coding.
  • Bober MZ. (2009) Method, apparatus, computer program, computer system, and computer-readable storage medium for representing and searching for an object in an image.
  • Bober MZ, Brasnett P. (2009) SCALE ROBUST FEATURE-BASED IDENTIFIERS FOR IMAGE IDENTIFICATION. US: Article number 12/989,362
  • Bober MZ. (2009) Method, apparatus, computer program, computer system, and computer-readable storage medium for representing and searching for an object in an image.
  • Bober MZ, Skarbek W. (2008) Method and apparatus for processing image data. US:
  • Bober MZ, Szajnowski WJ. (2008) Method and apparatus for image processing.
  • Bober MZ, Szajnowski WJ. (2008) Determining statistical descriptors of a signal from a set of its samples. US:
  • Bober MZ, Brasnett P. (2008) HIGH PERFORMANCE IMAGE IDENTIFICATION. US: Article number 12/663,479
  • Bober MZ. (2007) Method, apparatus, computer program, computer system and computer-readable storage for representing and searching for an object in an image. US:
  • Bober MZ. (2007) Method and apparatus for representing and searching for an object using shape.
  • Bober MZ. (2007) Method and device for processing and for searching for an object by signals corresponding to images. US:
  • Bober MZ, Sibiryakov A. (2007) SPARSE INTEGRAL IMAGE DESCRIPTORS WITH APPLICATION TO MOTION ANALYSIS. US: Article number 12/375,998
  • Bober MZ, Sibiryakov A. (2007) Sparse Integral Image Descriptors with Application to Motion Analysis. Article number 12/375,998
  • Bober MZ. (2007) Method for efficient coding of shape descriptor parameters.
  • Bober MZ. (2007) Method and device for processing and for searching for an object by signals corresponding to images.
  • Bober MZ. (2007) Method and device for processing and for searching for an object by signals corresponding to images.
  • Bober MZ, Cooper J, Paschalakis S. (2007) Method and apparatus for detecting and/or tracking one or more colour regions in an image or sequence of images.
  • Bober MZ, Paschalakis S. (2006) Method and apparatus for video navigation. US: Article number 11/991,092
  • Bober MZ, O'Callaghan R. (2006) Mutual-Rank Similarity-Space for Navigating, Visualising and Clustering in Image Databases. Article number 11/990,452
  • Bober MZ, Szajnowski W. (2006) Image Analysis and representation. US: Article number 11/886,232
  • Bober MZ, Sibiryakov A. (2006) Fast Method of Object Detection by Statistical Template Matching. US: Article number 11/884,699
  • Bober MZ. (2005) Method and device for displaying or searching for object in image and computer-readable storage medium.
  • Bober MZ. (2005) Method and apparatus for motion vector field encoding.
  • Bober MZ, Zaharia R. (2004) Occupant monitoring apparatus. Article number 10/838,370
  • Bober MZ. (2002) Hough transform based method of estimating parameters.
  • Bober MZ. (2001) Method, apparatus, computer program, computer system and computer-readable storage for representing and searching for an object in an image.

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