An efficient framework for visible-infrared cross modality person re-identification

Visible-infrared cross-modality person re-identification (VI-ReId) is an essential task for video surveillance in poorly illuminated or dark environments. Despite many recent studies on person re-identification in the visible domain (ReId), there are few studies dealing specifically with VI-ReId. Besides challenges that are common for both ReId and VI-ReId such as pose/illumination variations, background clutter and occlusion, VI-ReId has additional challenges as color information is not available in infrared images. As a result, the performance of VI-ReId systems is typically lower than that of ReId systems. In this work, we propose a four-stream framework to improve VI-ReId performance. We train a separate deep convolutional neural network in each stream using different representations of input images. We expect that different and complementary features can be learned from each stream. In our framework, grayscale and infrared input images are used to train the ResNet in the first stream. In the second stream, RGB and three-channel infrared images (created by repeating the infrared channel) are used. In the remaining two streams, we use local pattern maps as input images. These maps are generated utilizing local Zernike moments transformation. Local pattern maps are obtained from grayscale and infrared images in the third stream and from RGB and three-channel infrared images in the last stream. We improve the performance of the proposed framework by employing a re-ranking algorithm for post-processing. Our results indicate that the proposed framework outperforms current state-of-the-art with a large margin by improving Rank-1/mAP by 29.79%/30.91% on SYSU-MM01 dataset, and by 9.73%/16.36% on RegDB dataset.WOS:000551127300017Scopus - Affiliation ID: 60105072Science Citation Index ExpandedQ2ArticleUluslararası işbirliği ile yapılmayan - HAYIREylül2020YÖK - 2020-2

An efficient multiscale scheme using local zernike moments for face recognition

In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.WOS:000437326800174Scopus - Affiliation ID: 60105072Science Citation Index ExpandedQ2 - Q3ArticleUluslararası işbirliği ile yapılmayan - HAYIRMayıs2018YÖK - 2017-1

Human semantic parsing for person re-identification

Person re-identification is a challenging task mainly dueto factors such as background clutter, pose, illuminationand camera point of view variations. These elements hinder the process of extracting robust and discriminative representations, hence preventing different identities from being successfully distinguished. To improve the representation learning, usually local features from human body partsare extracted. However, the common practice for such aprocess has been based on bounding box part detection.In this paper, we propose to adopt human semantic parsing which, due to its pixel-level accuracy and capabilityof modeling arbitrary contours, is naturally a better alternative. Our proposed SPReID integrates human semanticparsing in person re-identification and not only considerably outperforms its counter baseline, but achieves stateof-the-art performance. We also show that, by employinga simple yet effective training strategy, standard populardeep convolutional architectures such as Inception-V3 andResNet-152, with no modification, while operating solelyon full image, can dramatically outperform current stateof-the-art. Our proposed methods improve state-of-the-artperson re-identification on: Market-1501 [48] by ~17% inmAP and ~6% in rank-1, CUHK03 [24] by ~4% in rank-1and DukeMTMC-reID [50] by ~24% in mAP and ~10% inrank-1.Computer Vision FoundationWOS:000457843601020Scopus - Affiliation ID: 60105072Conference Proceedings Citation Index- ScienceProceedings PaperHaziran2018YÖK - 2017-1

Automated Nanofiber Diameter Measurement in SEM Images Using a Robust Image Analysis Method

Due to the high surface area, porosity, and rigidity, applications of nanofibers and nanosurfaces have developed in recent years. Nanofibers and nanosurfaces are typically produced by electrospinning method. In the production process, determination of average fiber diameter is crucial for quality assessment. Average fiber diameter is determined by manually measuring the diameters of randomly selected fibers on scanning electron microscopy (SEM) images. However, as the number of the images increases, manual fiber diameter determination becomes a tedious and time consuming task as well as being sensitive to human errors. Therefore, an automated fiber diameter measurement system is desired. In the literature, this task is achieved by using image analysis algorithms. Typically, these methods first isolate each fiber in the image and measure the diameter of each isolated fiber. Fiber isolation is an error-prone process. In this study, automated calculation of nanofiber diameter is achieved without fiber isolation using image processing and analysis algorithms. Performance of the proposed method was tested on real data. The effectiveness of the proposed method is shown by comparing automatically and manually measured nanofiber diameter values

Statistical methods for reconstruction of parametric images from dynamic PET data

In this thesis, we demonstrate and evaluate a new and efficient method for direct voxel-wise reconstruction of kinetic parameter images using all frames of the PET data. The direct parametric image reconstruction is formulated in a Bayesian framework, and uses the parametric iterative coordinate descent (PICD) algorithm to solve the resulting optimization problem. The PICD algorithm is computationally efficient and is implemented with spatial regularization in the domain of the physiologically relevant parameters. Our experimental simulations of a rat head imaged in a working small animal scanner indicate that direct parametric reconstruction can substantially reduce estimation error in kinetic parameters, as compared to indirect methods, without appreciably increasing computation. The PICD algorithm is also used to reconstruct the parametric images of a monkey brain directly from the EXACT HR+ sinograms. PICD reconstructions are compared with the estimates using image domain methods. The results have shown that the proposed direct reconstruction algorithm can produce higher resolution parametric reconstructions. We simultaneously estimate both the kinetic parameters at each voxel and the model-based plasma input function directly from the sinogram data. The plasma model parameters are initialized with an image domain method to avoid local minima, and multiresolution optimization is used to perform the required reconstruction. Good initial guesses for the plasma parameters are required for the algorithm to converge to the correct answer. This method can estimate some of the kinetic parameters (k2, k3, k4, BP), but it can only estimate the others (k1, VD) within a scale factor