On Designing Deep Learning Approaches for Classification of Football Jersey Images in the Wild

Internet shopping has spread wide and into social networking. Someone may want to buy a shirt, accessories, etc., in a random picture or a streaming video. In this thesis, the problem of automatic classification was taken upon, constraining the target to jerseys in the wild, assuming the object is detected.;A dataset of 7,840 jersey images, namely the JerseyXIV is created, containing images of 14 categories of various football jersey types (Home and Alternate) belonging to 10 teams of 2015 Big 12 Conference football season. The quality of images varies in terms of pose, standoff distance, level of occlusion and illumination. Due to copyright restrictions on certain images, unaltered original images with appropriate credits can be provided upon request.;While various conventional and deep learning based classification approaches were empirically designed, optimized and tested, a solution that resulted in the highest accuracy in terms of classification was achieved by a train-time fused Convolutional Neural Network (CNN) architecture, namely CNN-F, with 92.61% accuracy. The final solution combines three different CNNs through score level average fusion achieving 96.90% test accuracy. To test these trained CNN models on a larger, application oriented scale, a video dataset is created, which may present an addition of higher rate of occlusion and elements of transmission noise. It consists of 14 videos, one for each class, totaling to 3,584 frames, with 2,188 frames containing the object of interest. With manual detection, the score level average fusion has achieved the highest classification accuracy of 81.31%.;In addition, three Image Quality Assessment techniques were tested to assess the drop in accuracy of the average-fusion method on the video dataset. The Natural Image Quality Evaluator (NIQE) index by Bovik et al. with a threshold of 0.40 on input images improved the test accuracy of the average fusion model on the video dataset to 86.36% by removing the low quality input images before it reaches the CNN.;The thesis concludes that the recommended solution for the classification is composed of data augmentation and fusion of networks, while for application of trained models on videos, an image quality metric would aid in performance increase with a trade-off in loss of input data

One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment

The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Given other advances in hardware-accelerated encoding, quality assessment is emerging as a significant bottleneck in video compression pipelines. Towards alleviating this burden, we propose a novel Fusion of Unified Quality Evaluators (FUNQUE) framework, by enabling computation sharing and by using a transform that is sensitive to visual perception to boost accuracy. Further, we expand the FUNQUE framework to define a collection of improved low-complexity fused-feature models that advance the state-of-the-art of video quality performance with respect to both accuracy, by 4.2\% to 5.3\%, and computational efficiency, by factors of 3.8 to 11 times!Comment: Version

Multi-exposure microscopic image fusion-based detail enhancement algorithm

[EN] Traditional microscope imaging techniques are unable to retrieve the complete dynamic range of a diatom species with complex silica-based cell walls and multi-scale patterns. In order to extract details from the diatom, multi-exposure images are captured at variable exposure settings using microscopy techniques. A recent innovation shows that image fusion overcomes the limitations of standard digital cameras to capture details from high dynamic range scene or specimen photographed using microscopy imaging techniques. In this paper, we present a cell-region sensitive exposure fusion (CS-EF) approach to produce well-exposed fused images that can be presented directly on conventional display devices. The ambition is to preserve details in poorly and brightly illuminated regions of 3-D transparent diatom shells. The aforesaid objective is achieved by taking into account local information measures, which select well-exposed regions across input exposures. In addition, a modified histogram equalization is introduced to improve uniformity of input multi-exposure image prior to fusion. Quantitative and qualitative assessment of proposed fusion results reveal better performance than several state-of-the-art algorithms that substantiate the method’s validitySIThis work was supported in part by the Spanish Government, Spain under the AQUALITAS-retos project (Ref.CTM2014-51907-C2-2-R-MINECO) and by Junta de Comunidades de Castilla-La Mancha, Spain under project HIPERDEEP (Ref. SBPLY/19/180501/000273). The funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519