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| 2. |
- Chojnacki, W., et al.
(författare)
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The equivalence of two definitions of compatible homography matrices
- 2020
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Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655 .- 1872-7344. ; 135, s. 38-43
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Tidskriftsartikel (refereegranskat)abstract
- In many computer vision applications, one acquires images of planar surfaces from two different vantage points. One can use a projective transformation to map pixel coordinates associated with a particular planar surface from one image to another. The transformation, called a homography, can be represented by a unique, to within a scale factor, 3 × 3 matrix. One requires a different homography matrix, scale differences apart, for each planar surface whose two images one wants to relate. However, a collection of homography matrices forms a valid set only if the matrices satisfy consistency constraints implied by the rigidity of the motion and the scene. We explore what it means for a set of homography matrices to be compatible and show that two seemingly disparate definitions are in fact equivalent. Our insight lays the theoretical foundations upon which the derivation of various sets of homography consistency constraints can proceed. © 2020 Elsevier B.V.
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| 3. |
- Hedman, Pontus, et al.
(författare)
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On the effect of selfie beautification filters on face detection and recognition
- 2022
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Ingår i: Pattern Recognition Letters. - Amsterdam : Elsevier. - 0167-8655 .- 1872-7344. ; 163, s. 104-111
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Tidskriftsartikel (refereegranskat)abstract
- Beautification and augmented reality filters are very popular in applications that use selfie images. However, they can distort or modify biometric features, severely affecting the ability to recognise the individuals’ identity or even detect the face. Accordingly, we address the effect of such filters on the accuracy of automated face detection and recognition. The social media image filters studied modify the image contrast, illumination, or occlude parts of the face. We observe that the effect of some of these filters is harmful to face detection and identity recognition, especially if they obfuscate the eye or (to a lesser extent) the nose. To counteract such effect, we develop a method to reverse the applied manipulation with a modified version of the U-NET segmentation network. This method is observed to contribute to better face detection and recognition accuracy. From a recognition perspective, we employ distance measures and trained machine learning algorithms applied to features extracted using several CNN backbones. We also evaluate if incorporating filtered images into the training set of machine learning approaches is beneficial. Our results show good recognition when filters do not occlude important landmarks, especially the eyes. The combined effect of the proposed approaches also allows mitigating the impact produced by filters that occlude parts of the face. © 2022 The Authors. Published by Elsevier B.V.
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| 4. |
- Mokayed, Hamam, et al.
(författare)
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A New DCT-PCM Method for License Plate Number Detection in Drone Images
- 2021
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 148, s. 45-53
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Tidskriftsartikel (refereegranskat)abstract
- License plate number detection in drone images is a complex problem because the images are generally captured at oblique angles and pose several challenges like perspective distortion, non-uniform illumination effect, degradations, blur, occlusion, loss of visibility etc. Unlike, most existing methods that focus on images captured by orthogonal direction (head-on), the proposed work focuses on drone text images. Inspired by the Phase Congruency Model (PCM), which is invariant to non-uniform illuminations, contrast variations, geometric transformation and to some extent to distortion, we explore the combination of DCT and PCM (DCT-PCM) for detecting license plate number text in drone images. Motivated by the strong discriminative power of deep learning models, the proposed method exploits fully connected neural networks for eliminating false positives to achieve better detection results. Furthermore, the proposed work constructs working model that fits for real environment. To evaluate the proposed method, we use our own dataset captured by drones and benchmark license plate datasets, namely, Medialab for experimentation. We also demonstrate the effectiveness of the proposed method on benchmark natural scene text detection datasets, namely, SVT, MSRA-TD-500, ICDAR 2017 MLT and Total-Text.
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| 5. |
- Nyström, Ingela, 1967-, et al.
(författare)
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Editorial of special section on CIARP 2019
- 2021
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Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655 .- 1872-7344. ; 148, s. 82-83
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 6. |
- Salas, Julián, et al.
(författare)
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Swapping trajectories with a sufficient sanitizer
- 2020
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 131, s. 474-480
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Tidskriftsartikel (refereegranskat)abstract
- Real-time mobility data is useful for several applications such as planning transports in metropolitan areas or localizing services in towns. However, if such data is collected without any privacy protection it may reveal sensible locations and pose safety risks to an individual associated to it. Thus, mobility data must be anonymized preferably at the time of collection. In this paper, we consider the SwapMob algorithm that mitigates privacy risks by swapping partial trajectories. We formalize the concept of sufficient sanitizer and show that the SwapMob algorithm is a sufficient sanitizer for various statistical decision problems. That is, it preserves the aggregate information of the spatial database in the form of sufficient statistics and also provides privacy to the individuals. This may be used for personalized assistants taking advantage of users’ locations, so they can ensure user privacy while providing accurate response to the user requirements. We measure the privacy provided by SwapMob as the Adversary Information Gain, which measures the capability of an adversary to leverage his knowledge of exact data points to infer a larger segment of the sanitized trajectory. We test the utility of the data obtained after applying SwapMob sanitization in terms of Origin-Destination matrices, a fundamental tool in transportation modelling.
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| 7. |
- Souibgui, Mohamed Ali, et al.
(författare)
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Few shots are all you need : A progressive learning approach for low resource handwritten text recognition
- 2022
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 160, s. 43-49
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Tidskriftsartikel (refereegranskat)abstract
- Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort.
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| 8. |
- Öfverstedt, Johan, et al.
(författare)
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Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment
- 2022
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 159, s. 196-203
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Tidskriftsartikel (refereegranskat)abstract
- Multimodal image alignment is the process of finding spatial correspondences between images formed by different imaging techniques or under different conditions, to facilitate heterogeneous data fusion and correlative analysis. The information-theoretic concept of mutual information (MI) is widely used as a similarity measure to guide multimodal alignment processes, where most works have focused on local maximization of MI, which typically works well only for small displacements. This points to a need for global maximization of MI, which has previously been computationally infeasible due to the high run-time complexity of existing algorithms. We propose an efficient algorithm for computing MI for all discrete displacements (formalized as the cross-mutual information function (CMIF)), which is based on cross-correlation computed in the frequency domain. We show that the algorithm is equivalent to a direct method while superior in terms of run-time. Furthermore, we propose a method for multimodal image alignment for transformation models with few degrees of freedom (e.g., rigid) based on the proposed CMIF-algorithm. We evaluate the efficacy of the proposed method on three distinct benchmark datasets, containing remote sensing images, cytological images, and histological images, and we observe excellent success-rates (in recovering known rigid transformations), overall outperforming alternative methods, including local optimization of MI, as well as several recent deep learning-based approaches. We also evaluate the run-times of a GPU implementation of the proposed algorithm and observe speed-ups from 100 to more than 10,000 times for realistic image sizes compared to a GPU implementation of a direct method. Code is shared as open-source at github.com/MIDA-group/globalign.
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| 9. |
- Comin, Cesar H., et al.
(författare)
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Quantifying the regularity of a 3D set of points on the surface of an ellipsoidal object
- 2020
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Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655. ; 133, s. 1-7
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Tidskriftsartikel (refereegranskat)abstract
- Several natural and artificial structures, such as human skin and mammals cortices, exhibit a compound organization, with basic elements being distributed along a surface. The problem of quantifying the geometrical uniformity of this type of biological and physical compound structures is addressed in this work. This required the solution of several problems, including the detection, along the surface, of the borders of the compound system, defining the adjacency between the elements in the 3D space, and obtaining a reference of uniformity for calculating the polygonality. Specific approaches were devised and applied to address each of these difficulties, including connectivity criteria ensuring the adjacency to remain within the considered surface as well as the extension of the polygonality, originally suggested for 2D structures, to 3D compound systems. The potential of the so-obtained method is illustrated with respect to compound eyes of fungus gnats (small, forest dwelling flies), and interesting results are reported and discussed, including the fact that the uniformity tends to increase toward the center of the system, and the absence of correlation with two measurements traditionally used for characterizing this type of eyes.
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