This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-dither block truncation coding (ODBTC) for the generation of image ...content descriptor. In the encoding step, ODBTC compresses an image block into corresponding quantizers and bitmap image. Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the ODBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system.
This paper presents an effective image retrieval method by combining high-level features from convolutional neural network (CNN) model and low-level features from dot-diffused block truncation coding ...(DDBTC). The low-level features, e.g., texture and color, are constructed by vector quantization -indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate and average recall rate (ARR), are employed to examine various data sets. As documented in the experimental results, the proposed schemes can achieve superior performance compared with the state-of-the-art methods with either low-or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.
We propose an effective method to boost the accuracy of multi-person pose estimation in images. Initially, the three-layer fractal network was constructed to regress multi-person joints location ...heatmap that can help to enhance an image region with receptive field and capture more joints local-contextual feature information, thereby producing keypoints heatmap intermediate prediction to optimize human body joints regression results. Subsequently, the hierarchical bi-directional inference algorithm was proposed to calculate the degree of relatedness (call it Kinship) for adjacent joints, and it combines the Kinship between adjacent joints with the spatial constraints, which we refer to as joints kinship pattern matching mechanism, to determine the best matched joints pair. We iterate the above-mentioned joints matching process layer by layer until all joints are assigned to a corresponding individual. Comprehensive experiments demonstrate that the proposed approach outperforms the state-of-the-art schemes and achieves about 1% and 0.6% increase in mAP on MPII multi-person subset and MSCOCO 2016 keypoints challenge.
•The Local Binary Pattern (LBP)-based feature has drawback in capturing the color information of an image.•This paper overcomes this problem by incorporating CHF on the LBP-based image retrieval and ...classification.•The hybrid CHF and LBP-based feature yield a promising result and outperform the former existing methods.
The Local Binary Pattern (LBP) operator and its variants play an important role as the image feature extractor in the textural image retrieval and classification. The LBP-based operator extracts the textural information of an image by considering the neighboring pixel values. A single or join histogram can be derived from the LBP code which can be used as an image feature descriptor in some applications. However, the LBP-based feature is not a good candidate in capturing the color information of an image, making it is less suitable for measuring the similarity of color images with rich color information. This work overcomes this problem by adding an additional color feature, namely Color Information Feature (CIF), along with the LBP-based feature in the image retrieval and classification systems. The CIF and LBP-based feature adequately represent the color and texture features. As documented in the experimental result, the hybrid CIF and LBP-based feature presents a promising result and outperforms the existing methods over several image databases. Thus, it can be a very competitive candidate in retrieval and classification application.
Abstract
Chiral aldehyde catalysis is a burgeoning strategy for the catalytic asymmetric α-functionalization of aminomethyl compounds. However, the reaction types are limited and to date include no ...examples of stereodivergent catalysis. In this work, we disclose two chiral aldehyde-catalysed diastereodivergent reactions: a 1,6-conjugate addition of amino acids to
para
-quinone methides and a bio-inspired Mannich reaction of pyridinylmethanamines and imines. Both the
syn
- and
anti
-products of these two reactions can be obtained in moderate to high yields, diastereo- and enantioselectivities. Four potential reaction models produced by DFT calculations are proposed to explain the observed stereoselective control. Our work shows that chiral aldehyde catalysis based on a reversible imine formation principle is applicable for the α-functionalization of both amino acids and aryl methylamines, and holds potential to promote a range of asymmetric transformations diastereoselectively.
Drowsiness and fatigue of the drivers are amongst the significant causes of the car accidents. Every year the number of deaths and fatalities are tremendously increasing due to multifaceted issues ...and henceforth requires an intelligent processing system for accident avoidance. In relevant with this, an effective driver drowsiness detection system is proposed. The main challenges are robustness of the algorithm towards variation of the human face and real-time processing capability. The first challenge pertaining to the facial variation has been handled well using conventional image processing and hand-craft features of computer vision algorithms. Yet, variations such as facial expression, lighting condition, intra-class variation, and pose variation are additional issues that conventional method failed to address. Deep learning is an alternative solution which provides a better performance by learning features automatically. Thus, this paper proposed a new concept for handling the real-time driver drowsiness detection using the hybrid of convolutional neural network (CNN) and long short-term memory (LSTM). The performance of the system has been tested using the public drowsy driver dataset from ACCV 2016 competition. The results show that it can outperform the former schemes in the literature.
An Efficient Fusion-Based Defogging Jing-Ming Guo; Jin-yu Syue; Radzicki, Vincent R. ...
IEEE transactions on image processing,
2017-Sept., 2017-Sep, 2017-9-00, 20170901, Letnik:
26, Številka:
9
Journal Article
Recenzirano
Degradation in visibility is often introduced to images captured in poor weather conditions, such as fog or haze. To overcome this problem, conventional approaches focus mainly on the enhancement of ...the overall image contrast. However, because of the unspecified light-source distribution or unsuitable mathematical constraints of the cost functions, it is often difficult to achieve quality results. In this paper, a fusion-based transmission estimation method is introduced to adaptively combine two different transmission models. Specifically, the new fusion weighting scheme and the atmospheric light computed from the Gaussian-based dark channel method improve the estimation of the locations of the light sources. To reduce the flickering effect introduced during the process of frame-based dehazing, a flicker-free module is formulated to alleviate the impacts. The systematic assessments show that this approach is capable of achieving superior defogging and dehazing performance, compared with superior defogging and dehazing performance, compared with the state-of-the-art methods, both quantitatively and qualitatively.
Visual encryption and show through watermarking are widely used techniques to hide secret data in halftone images. The secret watermark can be quickly revealed when the halftone images are printed in ...transparency and overlaid on each other. The present studies emphasize developing a show-through watermarking technique for the clustered-dot halftone types. The proposed method exploits the properties of various configurations of dither array screens constructed using distinct Gaussian filters to embed watermarks. Two approaches are proposed, i.e., adjacent dither array pairs and the dither array translations. The optimal configuration of dither array parameters is developed to obtain maximum contrast and imperceptibility. Moreover, a new edge screen dithering is proposed to eliminate the edge artifacts, resulting in smooth screen transitions. As efficient ordered dithering is adopted for the watermark embedding, the joint watermarking and halftoning can be performed without additional computations. In comparison to the existing state-of-the-art show-through watermarking techniques, the proposed method can present superior image quality, computational simplicity, and decoded watermarks can be perceived with clear contrast when the images are overlaid with each other.
Road crack is one of the prominent problems that can frequently occur in highways and main roads. The manual road crack evaluation is laborious, time-consuming, inaccurate, and it has several ...implementation issues. Conversely, the computer vision-based solution is very challenging due to the complex ambient conditions, including illumination, shadow, dust, and crack shape. Most of the cracks exist as irregular edge patterns and are the most important features for detection purpose. Recent advances in deep learning adopt a convolutional neural network as the base model to detect and localize crack with a single RGB image. Yet, this approach has an inaccurate boundary for crack localization, resulting in thicker and blurry edges. To overcome this problem, the study proposes a novel and robust road crack detection based on deep learning which also considers the original edge of the image as the additional feature. The main contribution of this work is adapting the original image gradient with the coarse crack detection result and refining it to produce more precise crack boundaries. Extensive experimental results have shown that the proposed method outperforms the former state-of-the-art methods in terms of the detection accuracy.
Kushen （Radix Sophorae Flavescentis） has a long history of use for the treatment of tumors, inflammation and other diseases in traditional Chinese medicine. Compound Kushen Injection （CKI） is a ...mixture of natural compounds extracted from Kushen and Baituling （Rhizoma Smilacis Glabrae）. The main principles of CKI are matrine （MT） and oxymatrine （OMT） that exhibit a variety of pharmacological activities, including anti-inflammatory, anti-allergic, anti-viral, anti-fibrotic and cardiovascular protective effects. Recent evidence shows that these compounds also produce anti-cancer actions, such as inhibiting cancer cell proliferation, inducing cell cycle arrest, accelerating apoptosis, restraining angiogenesis, inducing cell differentiation, inhibiting cancer metastasis and invasion, reversing multidrug resistance, and preventing or reducing chemotherapy- and/or radiotherapy-induced toxicity when combined with chemotherapeutic drugs. In this review, we summarize recent progress in studying the anti-cancer activities of MT, OMT and CKI and their potential molecular targets, which provide clues and references for further study.
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