Relevant bibliographies by topics / Re-identification and image enhancement

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  1. Journal articles
  2. Dissertations / Theses
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  5. Conference papers

Academic literature on the topic 'Re-identification and image enhancement'

Author: Grafiati
Published: 29 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Re-identification and image enhancement"

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Xiao, Ruoxiu, Jian Yang, Mahima Goyal, Yue Liu, and Yongtian Wang. "Automatic Vasculature Identification in Coronary Angiograms by Adaptive Geometrical Tracking." Computational and Mathematical Methods in Medicine 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/796342.

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As the uneven distribution of contrast agents and the perspective projection principle of X-ray, the vasculatures in angiographic image are with low contrast and are generally superposed with other organic tissues; therefore, it is very difficult to identify the vasculature and quantitatively estimate the blood flow directly from angiographic images. In this paper, we propose a fully automatic algorithm named adaptive geometrical vessel tracking (AGVT) for coronary artery identification in X-ray angiograms. Initially, the ridge enhancement (RE) image is obtained utilizing multiscale Hessian information. Then, automatic initialization procedures including seed points detection, and initial directions determination are performed on the RE image. The extracted ridge points can be adjusted to the geometrical centerline points adaptively through diameter estimation. Bifurcations are identified by discriminating connecting relationship of the tracked ridge points. Finally, all the tracked centerlines are merged and smoothed by classifying the connecting components on the vascular structures. Synthetic angiographic images and clinical angiograms are used to evaluate the performance of the proposed algorithm. The proposed algorithm is compared with other two vascular tracking techniques in terms of the efficiency and accuracy, which demonstrate successful applications of the proposed segmentation and extraction scheme in vasculature identification.
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Moler, Emilce, Virginia Ballarin, Franco Pessana, Sebastian Torres, and Dario Olmo. "Fingerprint Identification Using Image Enhancement Techniques." Journal of Forensic Sciences 43, no. 3 (May 1, 1998): 16202J. http://dx.doi.org/10.1520/jfs16202j.

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Wang, Yifeng, Zhijiang Zhang, Ning Zhang, and Dan Zeng. "Attention Modulated Multiple Object Tracking with Motion Enhancement and Dual Correlation." Symmetry 13, no. 2 (February 4, 2021): 266. http://dx.doi.org/10.3390/sym13020266.

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The one-shot multiple object tracking (MOT) framework has drawn more and more attention in the MOT research community due to its advantage in inference speed. However, the tracking accuracy of current one-shot approaches could lead to an inferior performance compared with their two-stage counterparts. The reasons are two-fold: one is that motion information is often neglected due to the single-image input. The other is that detection and re-identification (ReID) are two different tasks with different focuses. Joining detection and re-identification at the training stage could lead to a suboptimal performance. To alleviate the above limitations, we propose a one-shot network named Motion and Correlation-Multiple Object Tracking (MAC-MOT). MAC-MOT introduces a motion enhance attention module (MEA) and a dual correlation attention module (DCA). MEA performs differences on adjacent feature maps which enhances the motion-related features while suppressing irrelevant information. The DCA module focuses on decoupling the detection task and re-identification task to strike a balance and reduce the competition between these two tasks. Moreover, symmetry is a core design idea in our proposed framework which is reflected in Siamese-based deep learning backbone networks, the input of dual stream images, as well as a dual correlation attention module. Our proposed approach is evaluated on the popular multiple object tracking benchmarks MOT16 and MOT17. We demonstrate that the proposed MAC-MOT can achieve a better performance than the baseline state of the arts (SOTAs).
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Gupta, S., J. M. Solomon, T. A. Tasciyan, M. M. Cao, R. D. Stone, J. L. Ostuni, J. M. Ohayon, et al. "Interferon-beta-1b effects on re-enhancing lesions in patients with multiple sclerosis." Multiple Sclerosis Journal 11, no. 6 (December 2005): 658–68. http://dx.doi.org/10.1191/1352458505ms1229oa.

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Interferon-beta (IFNβ) reduces the number and load of new contrast-enhancing lesions (CELs) in patients with multiple sclerosis (MS). However, the ability of IFNβ to reduce lesion sizes and re-enhancements of pre-existing CELs has not been examined extensively. Activity of contrast re-enhancing lesions (Re-CELs) and contrast single-enhancing lesions (S-CELs) were monitored in ten patients with relapsingremitting (RR) MS. These patients underwent monthly post-contrast magnetic resonance imaging (MRIs) for an 18-month natural history phase and an 18-month therapy phase with subcutaneous IFNβ-1b, totaling 37 images per patient. The activity was analysed using the first image as a baseline and registering subsequent active monthly images to the baseline. There was a 76.4% reduction in the number of CELs with IFNβ therapy. The decrease was greater (P=0.003) for S-CELs (82.3%) than for Re-CELs (57.4%). S-CELs showed no changes in durations of enhancement and maximal lesion sizes with treatment. Exclusively for Re-CELs, IFNβ-1b significantly decreased maximal lesion sizes, total number of enhancement periods and total months of enhancement. Thus, IFNβ appears to be effective in reducing the degree of severity of inflammation among Re-CELs, as reflected by their reduced maximal lesion sizes and durations of enhancement.
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Yan, Lingyu, Jiarun Fu, Chunzhi Wang, Zhiwei Ye, Hongwei Chen, and Hefei Ling. "Enhanced network optimized generative adversarial network for image enhancement." Multimedia Tools and Applications 80, no. 9 (January 23, 2021): 14363–81. http://dx.doi.org/10.1007/s11042-020-10310-z.

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AbstractWith the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.
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DZULKIFLI, FAHMI AKMAL. "Identification of Suitable Contrast Enhancement Technique for Improving the Quality of Astrocytoma Histopathological Images." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 1 (May 27, 2021): 84–98. http://dx.doi.org/10.5565/rev/elcvia.1256.

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Contrast enhancement plays an important part in image processing. In histology, the application of a contrast enhancement technique is necessary since it can help pathologists in diagnosing the sample slides by increasing the visibility of the morphological and features of cells in an image. Various techniques have been proposed to enhance the contrast of microscopic images. Thus, this paper aimed to study the effectiveness of contrast enhancement techniques in enhancing the Ki67 images of astrocytoma. Three contrast enhancement techniques consist of contrast stretching, histogram equalization, and CLAHE techniques were proposed to enhance the sample images. The performance of each technique was compared by computing seven quantitative measures. The CLAHE technique was preferred for enhancing the contrast of the astrocytoma images. This technique produces good results especially in contrast enhancement, edge conservation and enhancement, brightness preservation, and minimum distortions to the enhanced images.
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Aijing, Luo, and Yin Jin. "Research on an Improved Medical Image Enhancement Algorithm Based on P-M Model." Open Biomedical Engineering Journal 9, no. 1 (August 31, 2015): 209–13. http://dx.doi.org/10.2174/1874120701509010209.

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Image enhancement can improve the detail of the image to achieve the purpose of the identification of the image. At present, the image enhancement is widely used in medical images, which can help doctor’s diagnosis. IEABPM (Image Enhancement Algorithm Based on P-M Model) is one of the most common image enhancement algorithms. However, it may cause the loss of the texture details and other features. To solve the problems, this paper proposes an IIEABPM (Improved Image Enhancement Algorithm Based on P-M Model). The simulation demonstrates that IIEABPM can effectively solve the problems of IEABPM, and improve image clarity, image contrast, and image brightness.
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Stankevich, Sergey, Oleh Maslenko, and Vitalii Andronov. "Neural network technology adaptation to the small-size objects identification in satellite images of insufficient resolution within the graphic reference images database." Ukrainian journal of remote sensing, no. 27 (December 10, 2020): 13–17. http://dx.doi.org/10.36023/ujrs.2020.27.175.

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A novel flowchart for small-size objects identification in satellite images of insufficient resolution within the graphic reference images database using neural network technology based on compromise contradiction, i.e. simultaneously the resolution enhancement of the object segment of input image and the resolution reduction of the reference image to joint resolution through the simulation of the imaging system has been proposed. This is necessary due to a significant discrepancy between the resolutions of the input image and the graphic reference images used for identification. The required level of resolution enhancement for satellite images, as a rule, is unattainable, and a significant coarsening of reference images is undesirable because of identification errors. Therefore, a certain intermediate spatial resolution is used for identification, which, on the one hand, can be obtained, and on the other the loss of information contained in the reference image is still acceptable. The intermediate resolution is determined by simulating the process of image acquisition with satellite imaging system. To facilitate such simulation, it is advisable to perform it in the frequency domain, where the advanced Fourier analysis is available and, as a rule, all the necessary transfer properties of the links of image formation chain are known. Three main functional elements are engaged for identification: an artificial neural network for the resolution enhancement of input images, a module of frequency-domain simulating of the graphical reference satellite imaging and an artificial neural network for comparing the enhanced object segment with the reference model images. The feasibility of the described approach is demonstrated by the example of successful identification of the sea vessel image in the SPOT-7 satellite image. Currently, the works are under way to compare the performance of a neural network platforms variety for small-size objects identification in satellite images aa well as to assess achievable accuracy.
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AILISTO, HEIKKI, MIKKO LINDHOLM, and PAULI TIKKANEN. "A REVIEW OF FINGERPRINT IMAGE ENHANCEMENT METHODS." International Journal of Image and Graphics 03, no. 03 (July 2003): 401–24. http://dx.doi.org/10.1142/s0219467803001081.

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Automatic fingerprint identification methods have become the most widely used technology in rapidly growing bioidentification applications. In this paper, different image enhancement approaches presented in the scientific literature are reviewed. Fingerprint verification can be divided into image acquisition, enhancement, feature extraction and matching steps. The enhancement step is needed to improve image quality prior to feature extraction. By far the most common approach relies on the filtering of the fingerprint images with filters adapted to local ridge orientation, but alternative approaches based on Fourier domain processing, direct ridge following and global features also exist. Methods of comparing the performance of enhancement methods are discussed. An example of the performance of different methods is given. Conclusions are made regarding the importance of effective enhancement, especially for noisy or low quality images.
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Kim, Changi, Junghun Han, Giwon Yoon, Dongjin Kim, and Sejung Yang. "Novel Framework for Knee Arthroscopic Image Enhancement." Journal of Medical Imaging and Health Informatics 10, no. 6 (June 1, 2020): 1459–65. http://dx.doi.org/10.1166/jmihi.2020.3070.

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An arthroscope is a tool for allowing an endoscope to be inserted directly into the inside of a joint to observe its structure, in contrast to X-rays, computed tomography, and magnetic resonance imaging, which directly capture pictures of a joint. Therefore, it can effectively treat joint diseases by identifying causes of pain that are not found by, e.g., computed tomography and magnetic resonance imaging. However, joint endoscopy has a very high cost, is very burdensome for patients, and has problems in regards to infection when being re-used. Thus, we developed disposable joint endoscopic camera modules for preventing re-use and infection, and researched approaches to reducing patient waiting times and cost burdens. In that regard, it is necessary to improve the brightness and color of the images, as they are used for compacting and disposal of the camera modules. In addition, we studied methods for improving automatic images, as image colors may vary (owing to blood or other foreign substances) when observed using the arthroscope. The proposed framework is divided into two sequences. First, we perform a histogram modification algorithm as an image enhancement technique. This results in a brightness optimization effect on the arthroscopic image. Second, we conduct a high saturation color mapping before proceeding to the next step. In particular, one of the reference points for diagnosing a disease is color information; thus, the improvement of color saturation is considered first in the color mapping. The proposed method provides better brightness values while preserving color information.
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Dissertations / Theses on the topic "Re-identification and image enhancement"

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Abdelkarim, Ahmad Ali. "Effect of JPEG2000 compression on landmark identification of lateral cephalometric digital radiographs a thesis /." San Antonio : UTHSC, 2008. http://learningobjects.library.uthscsa.edu/cdm4/item_viewer.php?CISOROOT=/theses&CISOPTR=57&CISOBOX=1&REC=16.

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Turesson, Eric. "Multi-camera Computer Vision for Object Tracking: A comparative study." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21810.

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Background: Video surveillance is a growing area where it can help with deterring crime, support investigation or to help gather statistics. These are just some areas where video surveillance can aid society. However, there is an improvement that could increase the efficiency of video surveillance by introducing tracking. More specifically, tracking between cameras in a network. Automating this process could reduce the need for humans to monitor and review since the tracking can track and inform the relevant people on its own. This has a wide array of usability areas, such as forensic investigation, crime alerting, or tracking down people who have disappeared. Objectives: What we want to investigate is the common setup of real-time multi-target multi-camera tracking (MTMCT) systems. Next up, we want to investigate how the components in an MTMCT system affect each other and the complete system. Lastly, we want to see how image enhancement can affect the MTMCT. Methods: To achieve our objectives, we have conducted a systematic literature review to gather information. Using the information, we implemented an MTMCT system where we evaluated the components to see how they interact in the complete system. Lastly, we implemented two image enhancement techniques to see how they affect the MTMCT. Results: As we have discovered, most often, MTMCT is constructed using a detection for discovering object, tracking to keep track of the objects in a single camera and a re-identification method to ensure that objects across cameras have the same ID. The different components have quite a considerable effect on each other where they can sabotage and improve each other. An example could be that the quality of the bounding boxes affect the data which re-identification can extract. We discovered that the image enhancement we used did not introduce any significant improvement. Conclusions: The most common structure for MTMCT are detection, tracking and re-identification. From our finding, we can see that all the component affect each other, but re-identification is the one that is mostly affected by the other components and the image enhancement. The two tested image enhancement techniques could not introduce enough improvement, but other image enhancement could be used to make the MTMCT perform better. The MTMCT system we constructed did not manage to reach real-time.
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Senses, Engin Utku. "Blur Estimation And Superresolution From Multiple Registered Images." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609929/index.pdf.

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Resolution is the most important criterion for the clarity of details on an image. Therefore, high resolution images are required in numerous areas. However, obtaining high resolution images has an evident technological cost and the value of these costs change with the quality of used optical systems. Image processing methods are used to obtain high resolution images with low costs. This kind of image improvement is named as superresolution image reconstruction. This thesis focuses on two main titles, one of which is the identification methods of blur parameters, one of the degradation operators, and the stochastic SR image reconstruction methods. The performances of different stochastic SR image reconstruction methods and blur identification methods are shown and compared. Then the identified blur parameters are used in superresolution algorithms and the results are shown.
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Dimitrov, Emanuil. "Fingerprints recognition." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-5522.

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Nowadays biometric identification is used in a variety of applications-administration, business and even home. Although there are a lot of biometric identifiers, fingerprints are the most widely spread due to their acceptance from the people and the cheap price of the hardware equipment. Fingerprint recognition is a complex image recognition problem and includes algorithms and procedures for image enhancement and binarization, extracting and matching features and sometimes classification. In this work the main approaches in the research area are discussed, demonstrated and tested in a sample application. The demonstration software application is developed by using Verifinger SDK and Microsoft Visual Studio platform. The fingerprint sensor for testing the application is AuthenTec AES2501.

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Kaufman, Jason R. "Spatial-Spectral Feature Extraction on Pansharpened Hyperspectral Imagery." Ohio University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1408706595.

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Ye, Mang. "Open-world person re-identification." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/688.

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With the increasing demand of intelligent video surveillance systems, person re-identification (re-ID) plays an important role in intelligent video analysis, which aims at matching person images across non-overlapping camera views. It has gained increasing attention in computer vision community. With the advanced deep neural networks, existing methods have achieved promising performance on the widely-used re-ID benchmarks, even outperform the human-level rank-1 matching accuracy. However, most of the research efforts are conducted on the closed-world settings, with large-scale well annotated training data and all the person images are from the same visible modality. As a prerequisite in practical video surveillance application, there is still a large gap between the closed-world research-oriented setting and the practical open-world settings. In this thesis, we try to narrow the gap by studying three important issues in open-world person re-identification, including 1) unsupervised learning with large-scale unlabelled training data; 2) learning robust re-ID model with label corrupted training data and 3) cross-modality visible-thermal person re-identification with multi-modality data. For unsupervised learning with unlabelled training data, we mainly focus on video-based person re-identification, since the video data is usually easily obtained by tracking algorithms and the video sequence provides rich weakly labelled samples by assuming the image frames within the tracked sequence belonging to the same person identity. Following the cross-camera label estimation approach, we formulate the cross-camera label estimation as a one-to-one graph matching problem, and then propose a novel dynamic graph matching framework to estimate cross-camera labels. However, in a practical wild scenario, the unlabelled training data usually cannot satisfy the one-to-one matching constraint, which would result in a large proportion of false positives. To address this issue, we further propose a novel robust anchor embedding method for unsupervised video re-ID. In the proposed method, some anchor sequences are firstly selected to initialize the CNN feature representation. Then a robust anchor embedding method is proposed to measure the relationship between the unlabelled sequences and anchor sequences, which considers both the scalability and efficiency. After that, a top-{dollar}k{dollar} counts label prediction strategy is proposed to predict the labels of unlabelled sequences. With the newly estimated sequences, the CNN representation could be further updated. For robust re-ID model learning with label corrupted training data, we propose a two-stage learning method to handle the label noise. Rather than simply filtering the falsely annotated samples, we propose a joint learning method by simultaneously refining the falsely annotated labels and optimizing the neural networks. To address the limited training samples for each identity, we further propose a novel hard-aware instance re-weighting strategy to fine-tune the learned model, which assigns larger weights to hard samples with correct labels. For cross-modality visible-thermal person re-identification, it addresses an important issue in night-time surveillance applications by matching person images across different modalities. We propose a dual-path network to learn the cross-modality feature representations, which learns the multi-modality sharable feature representations by simultaneously considering the modality discrepancy and commonness. To guide the feature representation learning process, we propose a dual-constrained top-ranking loss, which contains both cross-modality and intra-modality top-ranking constraints to reduce the large cross-modality and intra-modality variations. Besides the open-world person re-identification, we have also studied the unsupervised embedding learning problem for general image classification and retrieval. Motivated by supervised embedding learning, we propose a data augmentation invariant and instance spread-out feature. To learn the feature embedding, we propose a instance feature-based softmax embedding, which optimizes the embedding directly on top of the real-time instance features. It achieves much faster learning speed and better accuracy than existing methods. In short, the major contributions of this thesis are summarized as follows. l A dynamic graph matching framework is proposed to estimate cross-camera labels for unsupervised video-based person re-identification. l A robust anchor embedding method with top-{dollar}k{dollar} counts label prediction is proposed to efficiently estimate the cross-camera labels for unsupervised video-based person re-identification under wild settings. l A two-stage PurifyNet is introduced to handle the label noise problem in person re-identification, which jointly refines the falsely annotated labels and mines hard samples with correct labels. l A dual-constrained top-ranking loss with a dual-path network is proposed for cross-modality visible-thermal person re-identification, which simultaneously addresses the cross-modality and intra-modality variations. l A data augmentation invariant and instance spread-out feature is proposed for unsupervised embedding learning, which directly optimizes the learned embedding on top of real-time instance features with softmax function
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Boudjenouia, Fouad. "Restauration d’images avec critères orientés qualité." Thesis, Orléans, 2017. http://www.theses.fr/2017ORLE2031/document.

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Cette thèse concerne la restauration aveugle d’images (formulée comme un problème inverse mal-posé et mal-conditionné), en considérant particulièrement les systèmes SIMO. Dans un premier temps une technique d’identification aveugle de ce système où l’ordre du canal est inconnu (surestimé) est introduite. Nous introduisons d’abord une version simplifiée à coût réduit SCR de la méthode des relations croisées (CR). Ensuite, une version robuste R-SCR basée sur la recherche d’une solution parcimonieuse minimisant la fonction de coût CR est proposée. La restauration d’image est ensuite assurée par une nouvelle approche inspirée des techniques de décodage des signaux 1D et étendue ici aux cas de la restauration d’images en se basant sur une recherche arborescente efficace (algorithme ‘Stack’). Plusieurs améliorations de la méthode ‘Stack’ ont été introduites afin de réduire sa complexité et d’améliorer la qualité de restauration lorsque les images sont fortement bruitées. Ceci en utilisant une technique de régularisation et une approche d’optimisation all-at-once basée sur la descente du gradient qui permet de raffiner l’image estimée et mieux converger vers la solution optimale. Ensuite, les mesures de la qualité d’images sont utilisées comme fonctions de coûts (intégrées dans le critère global) et ce afin d’étudier leur potentiel pour améliorer les performances de restauration. Dans le contexte où l’image d’intérêt est corrompue par d’autres images interférentes, sa restauration nécessite le recours aux techniques de séparation aveugle de sources. Pour cela, une étude comparative de certaines techniques de séparation basées sur la propriété de décorrélation au second ordre et la parcimonie est réalisée
This thesis concerns the blind restoration of images (formulated as an ill-posed and illconditioned inverse problem), considering a SIMO system. Thus, a blind system identification technique in which the order of the channel is unknown (overestimated) is introduced. Firstly, a simplified version at reduced cost SCR of the cross relation (CR) method is introduced. Secondly, a robust version R-SCR based on the search for a sparse solution minimizing the CR cost function is proposed. Image restoration is then achieved by a new approach (inspired from 1D signal decoding techniques and extended here to the case of 2D images) based on an efficient tree search (Stack algorithm). Several improvements to the ‘Stack’ method have been introduced in order to reduce its complexity and to improve the restoration quality when the images are noisy. This is done using a regularization technique and an all-at-once optimization approach based on the gradient descent which refines the estimated image and improves the algorithm’s convergence towards the optimal solution. Then, image quality measurements are used as cost functions (integrated in the global criterion), in order to study their potential for improving restoration performance. In the context where the image of interest is corrupted by other interfering images, its restoration requires the use of blind sources separation techniques. In this sense, a comparative study of some separation techniques based on the property of second-order decorrelation and sparsity is performed
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Franco, Alexandre da Costa e. Silva. "On deeply learning features for automatic person image re-identification." Escola Politécnica / Instituto de Matemática, 2016. http://repositorio.ufba.br/ri/handle/ri/21639.

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The automatic person re-identification (re-id) problem resides in matching an unknown person image to a database of previously labeled images of people. Among several issues to cope with this research field, person re-id has to deal with person appearance and environment variations. As such, discriminative features to represent a person identity must be robust regardless those variations. Comparison among two image features is commonly accomplished by distance metrics. Although features and distance metrics can be handcrafted or trainable, the latter type has demonstrated more potential to breakthroughs in achieving state-of-the-art performance over public data sets. A recent paradigm that allows to work with trainable features is deep learning, which aims at learning features directly from raw image data. Although deep learning has recently achieved significant improvements in person re-identification, found on some few recent works, there is still room for learning strategies, which can be exploited to increase the current state-of-the-art performance. In this work a novel deep learning strategy is proposed, called here as coarse-to-fine learning (CFL), as well as a novel type of feature, called convolutional covariance features (CCF), for person re-identification. CFL is based on the human learning process. The core of CFL is a framework conceived to perform a cascade network training, learning person image features from generic-to-specific concepts about a person. Each network is comprised of a convolutional neural network (CNN) and a deep belief network denoising autoenconder (DBN-DAE). The CNN is responsible to learn local features, while the DBN-DAE learns global features, robust to illumination changing, certain image deformations, horizontal mirroring and image blurring. After extracting the convolutional features via CFL, those ones are then wrapped in covariance matrices, composing the CCF. CCF and flat features were combined to improve the performance of person re-identification in comparison with component features. The performance of the proposed framework was assessed comparatively against 18 state-of-the-art methods by using public data sets (VIPeR, i-LIDS, CUHK01 and CUHK03), cumulative matching characteristic curves and top ranking references. After a thorough analysis, our proposed framework demonstrated a superior performance.
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Wang, Xiangwen. "Photo-based Vendor Re-identification on Darknet Marketplaces using Deep Neural Networks." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83447.

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Darknet markets are online services behind Tor where cybercriminals trade illegal goods and stolen datasets. In recent years, security analysts and law enforcement start to investigate the darknet markets to study the cybercriminal networks and predict future incidents. However, vendors in these markets often create multiple accounts (i.e., Sybils), making it challenging to infer the relationships between cybercriminals and identify coordinated crimes. In this thesis, we present a novel approach to link the multiple accounts of the same darknet vendors through photo analytics. The core idea is that darknet vendors often have to take their own product photos to prove the possession of the illegal goods, which can reveal their distinct photography styles. To fingerprint vendors, we construct a series deep neural networks to model the photography styles. We apply transfer learning to the model training, which allows us to accurately fingerprint vendors with a limited number of photos. We evaluate the system using real-world datasets from 3 large darknet markets (7,641 vendors and 197,682 product photos). A ground-truth evaluation shows that the system achieves an accuracy of 97.5%, outperforming existing stylometry-based methods in both accuracy and coverage. In addition, our system identifies previously unknown Sybil accounts within the same markets (23) and across different markets (715 pairs). Further case studies reveal new insights into the coordinated Sybil activities such as price manipulation, buyer scam, and product stocking and reselling.
Master of Science
Taking advantage of the high anonymity of darknet, cybercriminals have set up underground trading websites such as darknet markets for trading illegal goods. To understand the relationships between cybercriminals and identify coordinated activities, it is necessary to identify the multiple accounts hold by the same vendor. Apart from manual investigation, previous studies have proposed methods for linking multiple accounts through analyzing the writing styles hidden in the users' online posts, which face key challenges in similar tasks on darknet markets. In this thesis, we propose a novel approach to link multiple identities within the same darknet market or across different markets by analyzing the product photos. We develop a system where a series of deep neural networks (DNNs) are used with transfer learning to extract distinct features from a vendor's photos automatically. Using real-world datasets from darknet markets, we evaluate the proposed system which shows clear advantages over the writing style based system. Further analysis of the results reported by the proposed system reveal new insights into coordinated activities such as price manipulation, buyer scam and product stocking and reselling for those vendors who hold multiple accounts.
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Ibn, Khedher Mohamed. "Ré-identification de personnes à partir des séquences vidéo." Thesis, Evry, Institut national des télécommunications, 2014. http://www.theses.fr/2014TELE0018/document.

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Cette thèse s'inscrit dans le contexte de la vidéo surveillance et s'intéresse à la ré-identification de personnes dans un réseau de caméras à champs disjoints. La ré-identification consiste à déterminer si une personne quitte le champ d'une caméra et réapparait dans une autre. Elle est particulièrement difficile car l'apparence de la personne change de manière significative à cause de différents facteurs. Nous proposons d'exploiter la complémentarité de l'apparence de la personne et son style de mouvement pour la décrire d'une manière appropriée aux facteurs de complexité. C'est une nouvelle approche car la ré-identification a été traitée par des approches d'apparence. Les contributions majeures proposées concernent: la description de la personne et l'appariement des primitives. Nous étudions deux scénarios de ré-identification : simple et complexe. Dans le scénario simple, nous étudions la faisabilité de deux approches : approche biométrique basée sur la démarche et approche d'apparence fondée sur des points d'intérêt (PI) spatiaux et des primitives de couleur. Dans le scénario complexe, nous proposons de fusionner des primitives d'apparence et de mouvement. Nous décrivons le mouvement par des Pis spatio-temporels et l'apparence par des PIs spatiaux. Pour l'appariement, nous utilisons la représentation parcimonieuse comme méthode d'appariement local entre les PIs. Le schéma de fusion est fondé sur le calcul de la somme pondérée des votes des PIs et ensuite l'application de la règle de vote majoritaire. Nous proposons également une analyse d'erreurs permettant d'identifier les sources d'erreurs de notre système pour dégager les pistes d'amélioration les plus prometteuses
This thesis focuses on the problem of hu man re-identification through a network of cameras with non overlapping fields of view. Human re-identification is defined as the task of determining if a persan leaving the field of one camera reappears in another. It is particularly difficult because of persons' significant appearance change within different cameras vision fields due to various factors. In this work, we propose to exploit the complementarity of the person's appearance and style of movement that leads to a description that is more robust with respect to various complexity factors. This is a new approach for the re-identification problem that is usually treated by appearance methods only. The major contributions proposed in this work include: person's description and features matching. First we study the re-identification problem and classify it into two scenarios: simple and complex. In the simple scenario, we study the feasibility of two approaches: a biometric approach based on gait and an appearance approach based on spatial Interest Points (IPs) and color features. In the complex scenario, we propose to exploit a fusion strategy of two complementary features provided by appearance and motion descriptions. We describe motion using spatiotemporal IPs, and use the spatial IPs for describing the appearance. For feature matching, we use sparse representation as a local matching method between IPs. The fusion strategy is based on the weighted sum of matched IPs votes and then applying the rule of majority vote. Moreover, we have carried out an error analysis to identify the sources of errors in our proposed system to identify the most promising areas for improvement
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Books on the topic "Re-identification and image enhancement"

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Gallagher, Thomas P. Image enhancement and feature extraction of benthic macroinvertebrates. 1996.

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Baldi, Cindi, Caroline Bartel, and Janet Dukerich. Fostering Stakeholder Identification Through Expressed Organizational Identities. Edited by Michael G. Pratt, Majken Schultz, Blake E. Ashforth, and Davide Ravasi. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199689576.013.1.

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A vital task for organizations is to communicate their organizational identity in ways that generate organizational images that are attractive to external stakeholders. Such favorable evaluations may generate stakeholder identification—a perception of belonging such that an organization becomes important to stakeholders’ sense of self. This chapter reviews and integrates prior research to elaborate how organizational communications via mass communication channels (e.g., company websites) can generate stakeholder identification. Several motivational pathways are outlined involving stakeholders’ needs for uncertainty reduction, self-continuity, and self enhancement. Association and dissociation communication strategies offer organizations means of conveying organizational images that directly address these stakeholder needs. How organizational communications aimed at stakeholder identification connects to other management practices such as corporate branding and corporate identity communication are discussed.
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Murphy, Patrick D. Battle of the Blogosphere. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252041037.003.0005.

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This chapter examines how the multinational agricultural biotechnology corporation Monsanto has attempted to re-brand itself from a chemical company to a food company through the elaboration of a highly interlaced, multi-platform on-line media strategy. This image enhancement operation is a response to its many critics—from citizen-based groups in India and Mexico to prominent food security activists like Michael Pollan and Vandana Shiva. At the center of analysis is how Monsanto has used the trope of “sustainability” to craft a proactive profile that is responsive to the challenges that the planet is facing. Foregrounding the issue of environmental agency, the chapter provides an assessment of what kinds of environmental discourses the company privileges through its media operations, and how these have been produced as a means to combat those who have challenged Monsanto’s vision of food production and “responsible” environmental stewardship.
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Book chapters on the topic "Re-identification and image enhancement"

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Kumar, Ajay. "3D Fingerprint Image Preprocessing and Enhancement." In Contactless 3D Fingerprint Identification, 63–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67681-4_5.

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Qiao, Jianping, and Ju Liu. "A SVM-Based Blur Identification Algorithm for Image Restoration and Resolution Enhancement." In Lecture Notes in Computer Science, 28–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893004_4.

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Simon-Zorita, D., J. Ortega-Garcia, S. Cruz-Llanas, J. L. Sanchez-Bote, and J. Glez-Rodriguez. "An Improved Image Enhancement Scheme for Fingerprint Minutiae Extraction in Biometric Identification." In Lecture Notes in Computer Science, 217–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45344-x_31.

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Vamsi Kiran Reddy, P., and V. V. Sajith Variyar. "Image Enhancement Using GAN (A Re-Modeling of SR-GAN for Noise Reduction)." In Information and Communication Technology for Competitive Strategies (ICTCS 2020), 721–29. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0882-7_64.

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Ma, Bingpeng, Yu Su, and Frédéric Jurie. "Discriminative Image Descriptors for Person Re-identification." In Person Re-Identification, 23–42. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6296-4_2.

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Gong, Shengrong, Chunping Liu, Yi Ji, Baojiang Zhong, Yonggang Li, and Husheng Dong. "Image Understanding-Person Re-identification." In Advanced Image and Video Processing Using MATLAB, 475–512. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77223-3_13.

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Hirzer, Martin, Csaba Beleznai, Peter M. Roth, and Horst Bischof. "Person Re-identification by Descriptive and Discriminative Classification." In Image Analysis, 91–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_9.

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Huang, Chung-Hsien, Yi-Ta Wu, and Ming-Yu Shih. "Unsupervised Pedestrian Re-identification for Loitering Detection." In Advances in Image and Video Technology, 771–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92957-4_67.

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Frontoni, Emanuele, Marina Paolanti, and Rocco Pietrini. "People Counting in Crowded Environment and Re-identification." In RGB-D Image Analysis and Processing, 397–425. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_18.

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Qian, Xuelin, Yanwei Fu, Tao Xiang, Wenxuan Wang, Jie Qiu, Yang Wu, Yu-Gang Jiang, and Xiangyang Xue. "Pose-Normalized Image Generation for Person Re-identification." In Computer Vision – ECCV 2018, 661–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01240-3_40.

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Conference papers on the topic "Re-identification and image enhancement"

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ding, Yong. "Pedestrian Re-identification Based on Image Enhancement and Over-fitting Solution Strategies." In 2018 5th International Conference on Systems and Informatics (ICSAI). IEEE, 2018. http://dx.doi.org/10.1109/icsai.2018.8599465.

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Fronthaler, H., K. Kollreider, and J. Bigun. "Pyramid-based Image Enhancement of Fingerprints." In 2007 IEEE Workshop on Automatic Identification Advanced Technologies. IEEE, 2007. http://dx.doi.org/10.1109/autoid.2007.380591.

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Sepasian, M., C. Mares, S. M. Azimi, and W. Balachandran. "Image enhancement for minutiae-based fingerprint identification." In 2008 37th IEEE Applied Imagery Pattern Recognition Workshop. IEEE, 2008. http://dx.doi.org/10.1109/aipr.2008.4906466.

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Hu, Yibo, Hongqing Hu, Yue Huang, Yuliang Tang, Yifeng Zhao, and Shurong Huang. "Research on a LED large screen adaptive image enhancement algorithm." In 2013 International Conference on Anti-Counterfeiting, Security and Identification (ASID). IEEE, 2013. http://dx.doi.org/10.1109/icasid.2013.6825316.

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Dela Cruz, Jennifer C., Ramon G. Garcia, Jian Chelly Czyrylle V. Cueto, Sherilyn C. Pante, and Christopher Glad V. Toral. "Automated Human Identification through Dental Image Enhancement and Analysis." In 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ). IEEE, 2019. http://dx.doi.org/10.1109/hnicem48295.2019.9072780.

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Paul, Anto, and R. Lourde. "A Study on Image Enhancement Techniques for Fingerprint Identification." In 2006 IEEE International Conference on Video and Signal Based Surveillance. IEEE, 2006. http://dx.doi.org/10.1109/avss.2006.14.

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Qiao, Shiquan, Kun Zhang, Xiaowen Zhang, and Hengcao Wang. "Research of Knee Infrared Image Noise Reduction and Enhancement." In 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI). IEEE, 2015. http://dx.doi.org/10.1109/iiki.2015.73.

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Hu, Hongqing, and Guoqiang Ni. "The improved algorithm for the defect of the Retinex image enhancement." In 2010 International Conference on Anti-Counterfeiting, Security and Identification (2010 ASID). IEEE, 2010. http://dx.doi.org/10.1109/icasid.2010.5551401.

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Singh, Tripty. "Foggy Image Enhancement and Object Identification by Extended Maxima Algorithm." In 2017 International Conference on Innovations in Control, Communication and Information Systems (ICICCI). IEEE, 2017. http://dx.doi.org/10.1109/iciccis.2017.8660851.

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Dim, J. R., H. Murakami, and M. Hori. "Use of satellite image enhancement procedures for global cloud identification." In IS&T/SPIE Electronic Imaging, edited by Jaakko T. Astola and Karen O. Egiazarian. SPIE, 2010. http://dx.doi.org/10.1117/12.839096.

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