Modelling of chromatic contrast for retrieval of wallpaper images

Colour remains one of the key factors in presenting an object and, consequently, has been widely applied in retrieval of images based on their visual contents. However, a colour appearance changes with the change of viewing surroundings, the phenomenon that has not been paid attention yet while performing colour‐based image retrieval. To comprehend this effect, in this article, a chromatic contrast model, CAMcc, is developed for the application of retrieval of colour intensive images, cementing the gap that most of existing colour models lack to fill by taking simultaneous colour contrast into account. Subsequently, the model is applied to the retrieval task on a collection of museum wallpapers of colour‐rich images. In comparison with current popular colour models including CIECAM02, HSI and RGB, with respect to both foreground and background colours, CAMcc appears to outperform the others with retrieved results being closer to query images. In addition, CAMcc focuses more on foreground colours, especially by maintaining the balance between both foreground and background colours, while the rest of existing models take on dominant colours that are perceived the most, usually background tones. Significantly, the contribution of the investigation lies in not only the improvement of the accuracy of colour‐based image retrieval but also the development of colour contrast model that warrants an important place in colour and computer vision theory, leading to deciphering the insight of this age‐old topic of chromatic contrast in colour science. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 361–373, 2015     

Wavelet-based hybrid natural image modeling using generalized Gaussian and α-stable distributions

Natural image is characterized by its highly kurtotic and heavy-tailed distribution in wavelet domain. These typical non-Gaussian statistics are commonly described by generalized Gaussian density (GGD) or α-stable distribution. However, each of the two models has its own deficiency to capture the variety and complexity of real world scenes. Considering the statistical properties of GGD and α-stable distributions respectively, in this paper we propose a hybrid statistical model of natural image’s wavelet coefficients which is better in describing the leptokurtosis and heavy tails simultaneously. Based on a clever fusion of GGD and α-stable functions, we establish the optimal parametric hybrid model, and a close-formed Kullback–Leibler divergence of the hybrid model is derived for evaluating model accuracy. Experiment results and comparative studies demonstrate that the proposed hybrid model is closer to the true distribution of natural image’s wavelet coefficients than the single modeling using GGD or α-stable, while is beneficial for applications such as image comparison.     

Underwater image quality enhancement through integrated color model with Rayleigh distribution

The physical properties of water cause light-induced degradation of underwater images. Light rapidly loses intensity as it travels in water, depending on the color spectrum wavelength. Visible light is absorbed at the longest wavelength first. Red and blue are the most and least absorbed, respectively. Underwater images with low contrast are captured due to the degradation effects of light spectrum. Therefore, the valuable information from these images cannot be fully extracted for further processing. The current study proposes a new method to improve the contrast and reduce the noise of underwater images. The proposed method integrates the modification of image histogram into two main color models, Red–Green–Blue (RGB) and Hue-Saturation-Value (HSV). In the RGB color model, the histogram of the dominant color channel (i.e., blue channel) is stretched toward the lower level, with a maximum limit of 95%, whereas the inferior color channel (i.e., red channel) is stretched toward the upper level, with a minimum limit of 5%. The color channel between the dominant and inferior color channels (i.e., green channel) is stretched to both directions within the whole dynamic range. All stretching processes in the RGB color model are shaped to follow the Rayleigh distribution. The image is converted into the HSV color model, wherein the S and V components are modified within the limit of 1% from the minimum and maximum values. Qualitative analysis reveals that the proposed method significantly enhances the image contrast, reduces the blue-green effect, and minimizes under- and over-enhanced areas in the output image. For quantitative analysis, the test with 300 underwater images shows that the proposed method produces average mean square error (MSE) and peak signal to noise ratio (PSNR) of 76.76 and 31.13, respectively, which outperform six state-of-the-art methods.     

Frame detection using gradients fuzzy logic and morphological processing for distant color eye images in an intelligent iris recognition system

The capture of an eye image with the occlusion of spectacles in a non-cooperative environment compromises the accuracy in identifying a person in an iris recognition system. This is due to the obstruction of the iris by the frame which tends to produce an incorrect estimation of the initial center of the iris and the pupil during the iris segmentation process. In addition, it also causes incorrect localization of the upper eyelid during the process of iris segmentation and sometimes, the edges of the frame are wrongly identified as the edges of the upper eyelid. A frame detection method which involves the combination of two gradients, namely the Sobel operator and high pass filter, followed by fuzzy logic and the dilation operation of morphological processing is proposed to identify the frame on the basis of different frame factors in the capture of a distant eye image. In addition, a different color space is applied and only a single channel is used for the process of frame detection. The proposed frame detection method provides the highest frame detection rate compared to the other methods, with a detection rate of more than 80.0%. For the accuracy of the iris localization, upper eyelid localization and iris recognition system, the proposed method gives more than 96.5% accuracy compared to the other methods. The index of decidability showed that the proposed method gives more than 2.35 index compared to the existing methods.     

基于DSP的电子行走辅助系统的设计与实现

为帮助视障者独立行走，基于DSP设计一套电子行走辅助系统。介绍系统的功能和基本结构，详细描述各模块的硬件设计和系统软件工作流程。该系统以实时图像处理为基础，能够完全脱离 PC机实现视频图像的采集、处理和语音提示等功能，帮助视障碍者独立行走。该系统具有小巧、便携、使用方便的特点，与传统助盲设备相比提高了导盲精度，与大型电子盲道或全球定位系统相比节省了成本，实验验证了其有效性。     

Synergistic effects of anisotropy and image force on TEM diffraction contrast of dislocation loops

Based on column approximation (CA) assumption, many-beam Schaeublin–Stadelmann diffraction equations are employed for simulating the transmission electron microscopy (TEM) diffraction image contrast of dislocation loops within thin TEM foil of finite thickness, and two beam and many beam diffraction conditions are compared. Moreover, the effects of materials anisotropy and free surface relaxation induced elastic fields distortion of dislocation loops on the black-white image contrast are specially focused. It is found that anisotropy has a remarkable impact on the TEM image contrast of dislocation loop, and free surface relaxation induced image forces can change the black-white contrast features when dislocation loops are near TEM foil free surfaces. Thus, in order to make reliable judgment on the nature of defects, effects of free surface and anisotropy should be included when analysing irradiation induced dislocation loops and other type of defects in in-situ electron, proton, heavy-ion irradiation experiments under TEM environments.     

SDN中基于KMOBPSO的高可靠性控制器部署算法

针对SDN中控制器系统的单节点故障问题,兼顾系统成本和系统时延,应用N+1冗余备份模型来提高SDN控制器部署的可靠性,并将其抽象为多目标优化问题.同时,提出了一种融合K-means聚类算法和遗传算子的多目标二进制粒子群算法——KMOBPSO算法,以求解SDN控制器高可靠性部署问题的解.仿真结果表明,所提算法具有求解精度高、分布均匀、沿Pareto前沿面覆盖广的特点,能够显著提高SDN中控制器部署的可靠性.     

Real-world single image super-resolution: A brief review

Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) observation, has been an active research topic in the area of image processing in recent decades. Particularly, deep learning-based super-resolution (SR) approaches have drawn much attention and have greatly improved the reconstruction performance on synthetic data. However, recent studies show that simulation results on synthetic data usually overestimate the capacity to super-resolve real-world images. In this context, more and more researchers devote themselves to develop SR approaches for realistic images. This article aims to make a comprehensive review on real-world single image super-resolution (RSISR). More specifically, this review covers the critical publicly available datasets and assessment metrics for RSISR, and four major categories of RSISR methods, namely the degradation modeling-based RSISR, image pairs-based RSISR, domain translation-based RSISR, and self-learning-based RSISR. Comparisons are also made among representative RSISR methods on benchmark datasets, in terms of both reconstruction quality and computational efficiency. Besides, we discuss challenges and promising research topics on RSISR.     

Influence of physical and biological variability and solution methods in fruit and vegetable quality nondestructive inspection by using imaging and near-infrared spectroscopy techniques: A review

Over the past decades, imaging and spectroscopy techniques have been rapidly developing and widely applied in nondestructive fruit and vegetable quality assessment. The physical properties (including size, shape, color, position, and temperature) and biological properties (including cultivar, season, maturity level and geographical origin) of fruits and vegetables vary from one to another. A great variety of physical and biological properties of agricultural products influence the optical propagation properties and interaction behaviors with incident light, thus decreasing the quality inspection accuracy. Many attempts have been made in image correction and spectral compensation methods to improve the inspection accuracy. This paper gives a detailed summary about influence of physical and biological variability, as well as the correction and compensation methods for eliminating or reducing the effects in fruit and vegetable quality nondestructive inspection by using imaging and spectroscopy techniques. The advantages and disadvantages of the solution methods are discussed and summarized. Additionally, the future challenges and potential trends are also reported.     

融合约束学习的图像字幕生成方法

目的 图像字幕生成是一个涉及计算机视觉和自然语言处理的热门研究领域，其目的是生成可以准确表达图片内容的句子。在已经提出的方法中，生成的句子存在描述不准确、缺乏连贯性的问题。为此，提出一种基于编码器-解码器框架和生成式对抗网络的融合训练新方法。通过对生成字幕整体和局部分别进行优化，提高生成句子的准确性和连贯性。方法 使用卷积神经网络作为编码器提取图像特征，并将得到的特征和图像对应的真实描述共同作为解码器的输入。使用长短时记忆网络作为解码器进行图像字幕生成。在字幕生成的每个时刻，分别使用真实描述和前一时刻生成的字幕作为下一时刻的输入，同时生成两组字幕。计算使用真实描述生成的字幕和真实描述本身之间的相似性，以及使用前一时刻的输出生成的字幕通过判别器得到的分数。将二者组合成一个新的融合优化函数指导生成器的训练。结果 在CUB-200数据集上，与未使用约束器的方法相比，本文方法在BLEU-4、BLEU-3、BLEI-2、BLEU-1、ROUGE-L和METEOR等6个评价指标上的得分分别提升了0.8%、1.2%、1.6%、0.9%、1.8%和1.0%。在Oxford-102数据集上，与未使用约束器的方法相比，本文方法在CIDEr、BLEU-4、BLEU-3、BLEU-2、BLEU-1、ROUGE-L和METEOR等7个评价指标上的得分分别提升了3.8%、1.5%、1.7%、1.4%、1.5%、0.5%和0.1%。在MSCOCO数据集上，本文方法在BLEU-2和BLEU-3两项评价指标上取得了最优值，分别为50.4%和36.8%。结论 本文方法将图像字幕中单词前后的使用关系纳入考虑范围，并使用约束器对字幕局部信息进行优化，有效解决了之前方法生成的字幕准确度和连贯度不高的问题，可以很好地用于图像理解和图像字幕生成。