In this work, we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained convolutional neural networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task. Our best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple subregions of the original image. Experimental results on the LIVE In the Wild Image Quality Challenge Database show that DeepBIQ outperforms the state-of-the-art methods compared, having a linear correlation coefficient with human subjective scores of almost 0.91. These results are further confirmed also on four benchmark databases of synthetically distorted images: LIVE, CSIQ, TID2008, and TID2013.

On the use of deep learning for blind image quality assessment

Fabric defect detection systems and methods—A systematic literature review

DOI: 10.1002/admt.201900837 high-cost, and unsuitable for interaction with humans as well as intricate environments.[1–3] To overcome these challenges, great efforts have been taken to develop soft robots that are primarily made of compliant elastomers and polymers (silicone rubber, dielectric elastomers, liquidcrystalline elastomers and hydrogels, etc.) and possess unique properties such as lightweight, mechanical compliance, infinite degrees of freedom, continuous deformation, low cost, and easy fabrication.[2–6] Among these soft robotics, the class of soft crawling robots inspired from biological creatures has attracted increasing attention owing to their anticipated effective interaction with humans and uncertain environments, as well as potential capabilities of completing a variety of tasks, like search and rescue, infrastructure inspection, surveillance, drug delivery, and human assistance.[2,5] Various novel actuations and locomotion designs have been explored to drive soft crawling robots, with some of them even equipped with capabilities of the autonomous motion,[7] environment accommodation[4,8] and decision making.[9,10] For instance, soft crawling robots inspired by octopuses, worms, and jellyfishes have been designed to achieve complex motions with multiple gaits at low cost.[10,11] While there have been several excellent review papers on the topic of soft robotics,[3,4,12,13] there has not been a review paper that covers recent advances in soft crawling robots in depth. This review paper aims to fill that void. The locomotion mode, crawling speed, and working efficiency of soft crawling robots are mainly determined by the soft actuators they employed.[14] Generally, the external stimuli drive the actuators to generate the desired strains and/or deformations, and the induced strain and/or deformation will then supply the necessary propulsion for the robots to crawl. Up to now, a few actuation approaches have been successfully employed to drive the soft crawling robots, mainly consisting of pneumatic/hydraulic pressure,[15] chemical reaction,[16,18] and stimuli responses of soft active materials, such as dielectric elastomers (DEs),[19] shape memory alloys (SMAs),[20] magnetoactive elastomers (MAEs),[21] liquid-crystalline elastomers (LCEs),[22] piezoelectric materials (PEMs),[23] ionic polymer– metal composites (IPMCs),[24] and twisted and coiled polymers (TCPs).[25] Despite the inherent drawbacks of these actuation approaches, continuous efforts have been devoted to designing soft crawling robots capable of performing vivid, multigait, effective, and intelligent locomotion. Over the past decade, Soft crawling robots have attracted great attention due to their anticipated effective interactions with humans and uncertain environments, as well as their potential capabilities of completing a variety of tasks encompassing search and rescue, infrastructure inspection, surveillance, drug delivery, and human assistance. Herein, a comprehensive survey on recent advances of soft crawling robots categorized by their major actuation mechanisms is provided, including pneumatic/hydraulic pressure, chemical reaction, and soft active material-based actuations, which include dielectric elastomers, shape memory alloys, magnetoactive elastomers, liquid crystalline elastomers, piezoelectric materials, ionic polymer–metal composites, and twisted and coiled polymers. For each type of actuation, the prevalent modes of locomotion adopted in representative robots, the design, working principle and performance of their soft actuators, and the performance of each locomotion approach, as well as the advantages and drawbacks of each design are discussed. This review summarizes the state-of-the-art progresses and the critical knowledge in designing soft crawling robots and offers a guidance and insightful outlook for the future development of soft robots.

Soft Crawling Robots: Design, Actuation, and Locomotion

Digital image colorimetry on smartphone for chemical analysis: A review

Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Beca ...

/pdf/automated-taxonomic-identification-of-insects-with-expert-34kapksv60.pdf

Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks

Generating a group of category-independent proposals of objects in an image within a very short time is an effective approach to accelerate traditional sliding window search, which has been widely used in preprocessing step of object recognition. In this article, we propose a novel object proposals generation method to produce an order set of candidate windows covering most of object instances. With combination of gradient and local binary pattern, our approach achieves better performance than BING in finding occluded objects and objects in dim lighting conditions. In experiments on the challenging PASCAL VOC 2007 data set, we show that our approach is significantly more accurate than BING. In particular, using 2000 proposals, we achieve 97.6% object detection rate and 69.3% mean average best overlap. Moreover, our proposed method is very efficient and takes only about 0.006 s per image on a laptop central processing unit. The detection speed and high accuracy of proposed method mean that it can be applied to recognizing specific objects in robot visions.

https://journals.sagepub.com/doi/pdf/10.1177/1729881417724679

Wenxuan Shi

Papers

An improved real-time object proposals generation method based on local binary pattern