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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Dario Figueira Loris Bazzani Hà Quang Minh Marco Cristani Alexandre Bernardino Vittorio Murino ISR-Instituto Superior Técnico Lisboa Portugal Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Genova Itely

Person re-identification is probably the open challenge for low-level video surveillance in the presence of a camera network with non-overlapped fields of view. A large number of direct approaches has emerged in the last five years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. On the other hand, learning-based methods are usually based on simpler features, and are trained on pairs of cameras to discriminate between individuals. In this paper, we present a method that joins these two ideas: given an arbitrary state-of-the-art set of features, no matter their number, dimensionality or descriptor, the proposed multi-class learning approach learns how to fuse them, ensuring that the features agree on the classification result. The approach consists of a semi-supervised multi-feature learning strategy, that requires at least a single image per person as training data. To validate our approach, we present results on different datasets, using several heterogeneous features, that set a new level of performance in the person re-identification problem.

关键词： Kernel Feature extraction Histograms Training Probes Optimized production technology Cameras

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Correlation filter cascade for facial landmark localization

Correlation filter cascade for facial landmark localization

引用

IEEE Workshop on Applications of computer vision (WACV)

作者： Hamed Kiani Galoogahi Terence Sim Pattern Analysis and Computer Vision Department Istituto Italiano di Tecnologia Genova Italy School of Computing National University of Singapore Singapore

ISBN: (纸本)9781509006427

The application of correlation filters for the task of facial landmark detection has been studied by many vision works. Their success, however, is limited by the presence of large pose variations, expression and occlusion in face images. Moreover, existing correlation filters may suffer from poor discrimination to distinguish visually similar landmarks such as the right and left eyes. In this work, we present a new framework, referred to as Correlation Filter Cascade, to address the above limitations. The proposed framework consists of a set of correlation filters with different spatial supports (sizes) which are connected together in a cascade form. More specifically, the size of filters decreases from the lower to upper levels. Filters at lower levels implicitly code face shape information since they are trained using large patches stemmed from face images. This avoids ambiguous detections caused by landmarks with similar appearance. Detections in these levels, however, may not be accurate and suffer from small localization errors, mainly caused by face pose, expression and occlusion. Therefore, locations detected by lower levels will be further used by the higher levels to narrow down their search regions. Since the filters at higher levels have smaller size, they are less affected by pose, expression and occlusion, and thus, can perform more accurately. The evaluation on BioID and LFPW shows the superiority of our method compared to prior correlation filters and leading facial landmark detectors.

关键词： Face Correlation Training Shape Robustness Feature extraction Testing

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On Moving Object Segmentation from Monocular Video with Transformers

arXiv

引用

arXiv 2024年

作者： Homeyer, Christian Schnörr, Christoph Robert Bosch GmbH Corporate Research Computer Vision Lab Hildesheim Germany Image and Pattern Analysis Group Heidelberg University Germany

Moving object detection and segmentation from a single moving camera is a challenging task, requiring an understanding of recognition, motion and 3D geometry. Combining both recognition and reconstruction boils down to a fusion problem, where appearance and motion features need to be combined for classification and segmentation. In this paper, we present a novel fusion architecture for monocular motion segmentation - M3Former, which leverages the strong performance of transformers for segmentation and multi-modal fusion. As reconstructing motion from monocular video is ill-posed, we systematically analyze different 2D and 3D motion representations for this problem and their importance for segmentation performance. Finally, we analyze the effect of training data and show that diverse datasets are required to achieve SotA performance on Kitti and Davis. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Complex-Object Visual Inspection: Empirical Studies on A Multiple Lighting Solution

Complex-Object Visual Inspection: Empirical Studies on A Mul...

引用

International Conference on pattern Recognition

作者： Maya Aghaei Matteo Bustreo Pietro Morerio Nicolò Carissimi Alessio Del Bue Vittorio Murino Pattern Analysis & Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy University of Genova Italy University of Verona Italy

The design of an automatic visual inspection system is usually performed in two stages. While the first stage consists in selecting the most suitable hardware setup for highlighting most effectively the defects on the surface to be inspected, the second stage concerns the development of algorithmic solutions to exploit the potentials offered by the collected data. In this paper, first, we present a novel illumination setup embedding four illumination configurations to resemble diffused, dark-field, and front lighting techniques. Second, we analyze the contributions brought by deploying the proposed setup in the training phase only, mimicking the scenario in which an already developed visual inspection system cannot be modified on the customer site. Along with an exhaustive set of experiments, in this paper, we demonstrate the suitability of the proposed setup for effective illumination of complex-objects, defined as manufactured items with variable surface characteristics that cannot be determined a priori. Eventually, we provide insights into the importance of multiple light configurations availability during training and their natural boosting effect which, without the need to modify the system design in the evaluation phase, lead to improvements in the overall system performance.

关键词： Training Visualization System performance Lighting Inspection Boosting Hardware

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MagiWrite: Towards touchless digit entry using 3D space around mobile devices

MagiWrite: Towards touchless digit entry using 3D space arou...

引用

12th International Conference on Human-computer Interaction with Mobile Devices and Services, Mobile HCI2010

作者： Ketabdar, Hamed Roshandel, Mehran Yüksel, Kamer Ali Quality and Usability Lab. Deutsche Telekom Laboratories TU Berlin Ernst-Reuter-Platz 7 10587 Berlin Germany Deutsche Telekom Laboratories Ernst-Reuter-Platz 7 10587 Berlin Germany Computer Vision and Pattern Analysis Lab. Sabanci University Istanbul Turkey

ISBN: (纸本)9781605588353

In this work, we present a new approach for text (mainly digit) entry based on digit shaped gestures created in 3D space around a mobile device. Some new mobile devices such as Apple iPhone 3GS and Google Android are equipped with magnetic (compass) sensor. The main idea is to influence the magnetic sensor using a magnet taken in hand. The user draws (writes) digits in the 3D space around the device using the magnet taken in hand. Movement of the magnet changes temporal pattern of magnetic field around the device which is sensed and registered by the magnetic (compass) sensor. The registered pattern is then compared against already recorded templates for different digits. Such a text (digit) entry approach can be especially useful for small mobile devices in which it is hard to operate small buttons or touch screen. Using our technique, the text entry space extends beyond physical boundaries of the device. A demonstrator for this approach is implemented on Apple iPhone 3GS platform. It demonstrates registering a few templates for different digits, and recognizing digits written in the space around the device.

关键词： Magnets

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BONE EXTRACTION IN X-RAY IMAGES BY analysis OF LINE FLUCTUATIONS

BONE EXTRACTION IN X-RAY IMAGES BY ANALYSIS OF LINE FLUCTUAT...

引用

IEEE International Conference on Image Processing

作者： S. Kazeminia N. Karimi B. Mirmahboub S. M. R. Soroushmehr S. Samavi K. Najarian ECE Department Isfahan University of Technology Department of Pattern Analysis & Computer Vision Italian Institute of Technology Department of Emergency Medicine University of Michigan

ISBN: (纸本)9781479983407

Segmentation of X-ray bone images is of concern in many medical applications such as detection of osteoporosis and bone fractures. Segmentation of such images is a challenging process. Varying brightness throughout the image makes it difficult to separate bones from background and soft tissue. Costume made as well as standard segmentation methods, such as active contour and region growing, have been applied to bone X-ray images. Although each method could perform well for some images, due to variety of bone structures and lighting conditions none of these methods can be considered as complete. In this paper we present a new bone segmentation method in which an image goes through preprocessing steps such as noise cancellation and edge detection. analysis of intensity fluctuations in all rows of the image results in more accurate segmentation of bone regions. Visual evaluation show that the proposed algorithm segments bones better than conventional and some recent bone segmentation approaches.

关键词： X-ray Bone segmentation Noise cancellation Edge detection Noise cancellation bone structure X-RAY IMAGE Edge detection images segmentation Segmentation X-rays Imagery (Psychotherapy)

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WEIGHTED BAG OF VISUAL WORDS FOR OBJECT RECOGNITION

WEIGHTED BAG OF VISUAL WORDS FOR OBJECT RECOGNITION

引用

IEEE International Conference on Image Processing

作者： Marco San Biagio Loris Bazzani Marco Cristani Vittorio Murino Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Genova Italy Departimento di Informatica Universita di Verona Verona Italy

Bag of Visual words (BoV) is one of the most successful strategy for object recognition, used to represent an image as a vector of counts using a learned vocabulary. This strategy assumes that the representation is built using patches that are either densely extracted or sampled from the images using feature detectors. However, the dense strategy captures also the noisy background information, whereas the feature detection strategy can lose important parts of the objects. In this paper we propose a solution in-between these two strategies, by densely extracting patches from the image, and weighting them accordingly to their salience. Intuitively, highly salient patches have an important role in describing an object, while those with low saliency are still taken with low emphasis, instead of discarding them. We embed this idea in the word encoding mechanism adopted in the BoV approaches. The technique is successfully applied to vector quantization and Fisher vector, on Caltech-101 and Caltech-256.

关键词： Visualization Feature extraction Object recognition Vectors Encoding Pipelines Training

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Leveraging category-level labels for instance-level image retrieval

Leveraging category-level labels for instance-level image re...

引用

Conference on computer vision and pattern Recognition (CVPR)

作者： Albert Gordoa José A. Rodríguez-Serrano Florent Perronnin Ernest Valveny Computer Vision Center Universitat Autònoma de Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Center Europe France

In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor which is subsequently encoded into a small number of bits. We note that most encoding techniques include an unsupervised dimensionality reduction step. Our goal in this work is to learn a better subspace in a supervised manner. We especially raise the following question: "can category-level labels be used to learn such a subspace?" To answer this question, we experiment with four learning techniques: the first one is based on a metric learning framework, the second one on attribute representations, the third one on Canonical Correlation analysis (CCA) and the fourth one on Joint Subspace and Classifier Learning (JSCL). While the first three approaches have been applied in the past to the image retrieval problem, we believe we are the first to show the usefulness of JSCL in this context. In our experiments, we use ImageNet as a source of category-level labels and report retrieval results on two standard dataseis: INRIA Holidays and the University of Kentucky benchmark. Our experimental study shows that metric learning and attributes do not lead to any significant improvement in retrieval accuracy, as opposed to CCA and JSCL. As an example, we report on Holidays an increase in accuracy from 39.3% to 48.6% with 32-dimensional representations. Overall JSCL is shown to yield the best results.

关键词： Measurement Vectors Principal component analysis Accuracy Encoding Correlation Image retrieval

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Handwritten Word Image Retrieval with Synthesized Typed Queries

Handwritten Word Image Retrieval with Synthesized Typed Quer...

引用

International Conference on Document analysis and Recognition

作者： José A. Rodríguez-Serrano Florent Perronnin Computer Vision Centre Universitat Authnòma de Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Centre Europe France

We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying. More precisely, a model is trained ldquoon the flyrdquo with images rendered from the searched words in one or multiple computer fonts. To reduce the mismatch between the typed-text prototypes and the candidate handwritten images, we make use of: (i) local gradient histogram(LGH) features, which were shown to model word shapes robustly, and (ii) semi-continuous hidden Markov models(SC-HMM), in which the typed-text models are constrained to a ldquovocabularyrdquo of handwritten shapes, thus learning a link between both types of data. Experiments show that the proposed method is effective in retrieving handwritten words, and the comparison to alternative methods reveals that the contribution of both the LGH features and the SCHMM is crucial. To the best of the authorspsila knowledge, this is the first work to address this issue in a non-trivial manner.

关键词： Image retrieval Hidden Markov models Prototypes Shape Robustness pattern analysis Handwriting recognition Rendering (computer graphics) Writing Text analysis

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A Bag-of-Pages Approach to Unordered Multi-page Document Classification

A Bag-of-Pages Approach to Unordered Multi-page Document Cla...

引用

International Conference on pattern Recognition

作者： Albert Gordo Florent Perronnin Computer Vision Center Universitat Autònoma Barcelona Spain Xerox Research Centre Europe Textual and Visual Pattern Analysis (TVPA) France

ISBN: (纸本)9781424475421

We consider the problem of classifying documents containing multiple unordered pages. For this purpose, we propose a novel bag-of-pages document representation. To represent a document, one assigns every page to a prototype in a codebook of pages. This leads to a histogram representation which can then be fed to any discriminative classifier. We also consider several refinements over this initial approach. We show on two challenging datasets that the proposed approach significantly outperforms a baseline system.

关键词： Histograms Training Accuracy Feature extraction Visualization Kernel Hidden Markov models

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