Application Analyses of Visual Information Processing Techniques in E-Commerce

Digital visual information plays a very important role in E-Commerce (EC). Their usage brings forth many novel research topics for digital visual information processing skills and software. Some issues of application analysis of image/video information processing techniques suitable for EC are described in the paper. Visual design for goods or services trading, image retrieval based on visual contents, applications of images to the trade safety on the Internet, 3-dimensional display, virtual reality for goods browsing, inquiry based on image and video contents, trade safety and copyright protection of digital works based on digital watermarking are mainly discussed which are considered as the technological solutions that could enhance EC

Feature-Guided Black-Box Safety Testing of Deep Neural Networks

Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. Most existing approaches for crafting adversarial examples necessitate some knowledge (architecture, parameters, etc.) of the network at hand. In this paper, we focus on image classifiers and propose a feature-guided black-box approach to test the safety of deep neural networks that requires no such knowledge. Our algorithm employs object detection techniques such as SIFT (Scale Invariant Feature Transform) to extract features from an image. These features are converted into a mutable saliency distribution, where high probability is assigned to pixels that affect the composition of the image with respect to the human visual system. We formulate the crafting of adversarial examples as a two-player turn-based stochastic game, where the first player's objective is to minimise the distance to an adversarial example by manipulating the features, and the second player can be cooperative, adversarial, or random. We show that, theoretically, the two-player game can con- verge to the optimal strategy, and that the optimal strategy represents a globally minimal adversarial image. For Lipschitz networks, we also identify conditions that provide safety guarantees that no adversarial examples exist. Using Monte Carlo tree search we gradually explore the game state space to search for adversarial examples. Our experiments show that, despite the black-box setting, manipulations guided by a perception-based saliency distribution are competitive with state-of-the-art methods that rely on white-box saliency matrices or sophisticated optimization procedures. Finally, we show how our method can be used to evaluate robustness of neural networks in safety-critical applications such as traffic sign recognition in self-driving cars.Comment: 35 pages, 5 tables, 23 figure

Surgical data science for safe cholecystectomy: a protocol for segmentation of hepatocystic anatomy and assessment of the critical view of safety

Minimally invasive image-guided surgery heavily relies on vision. Deep learning models for surgical video analysis could therefore support visual tasks such as assessing the critical view of safety (CVS) in laparoscopic cholecystectomy (LC), potentially contributing to surgical safety and efficiency. However, the performance, reliability and reproducibility of such models are deeply dependent on the quality of data and annotations used in their development. Here, we present a protocol, checklists, and visual examples to promote consistent annotation of hepatocystic anatomy and CVS criteria. We believe that sharing annotation guidelines can help build trustworthy multicentric datasets for assessing generalizability of performance, thus accelerating the clinical translation of deep learning models for surgical video analysis.Comment: 24 pages, 34 figure

The impact of specialty settings on the perceived quality of medical ultrasound video

Health care professionals are increasingly viewing medical images and videos in a variety of environments. The perception of medical visual information across all specialties, career stages, and practice settings are critical to patient care and patient safety. Visual signal distortions, such as various types of noise and artifacts arising in medical imaging, affect the perceptual quality of visual content and potentially impact diagnoses. To optimize clinical practice, it is of fundamental importance to understand the way medical experts perceive visual quality. Psychophysical studies have been undertaken to evaluate the impact of visual distortions on the perceived quality of medical images and videos. However, very little research has been conducted on how speciality settings affect the perception of visual quality. In this paper, we investigate whether and how radiologists and sonographers differently perceive the quality of compressed ultrasound videos, via a dedicated subjective experiment. The findings can be used to develop useful solutions for improved visual experience and better image-based diagnoses