视错觉及其应用

视觉足人类获取外部信息的最重要方式。但由于人类自身的生理、心理因素以及外界光、形、色等因素的干扰,会产生视觉误差,即视错觉。本章系统的分析了视错觉产生原因,按不同的现象和成因把视错觉分成病理视错觉、动态视错觉、几何视错觉、颜色视错觉、轮廓错觉等5类。在此基础上介绍了视错觉的应用,特别介绍了其在图像融合中的应用,并用matlab实现了图像的融合。     

透明加解密系统原理与实现分析

介绍透明加解密系统产生的背景、原理以及实现技术,同时还对对称加密算法进行简要的介绍,对于加解密系统的使用提出了独特见解。     

图形图像制作软件中的色彩模式分析

色彩模式是在数字处理过程中处理色彩的基本标准,其是以颜色模型为基础,利用数字定量对颜色进行描述的方法。针对图形图像制作软件中的色彩模式进行分析。     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.     

Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation

Medical image segmentation is the most complex and important task in the field of medical image processing and analysis, as it is linked to disease diagnosis accuracy. However, due to the medical image's high complexity and noise, segmentation performance is limited. We propose a novel quadratic polynomial guided fuzzy C-means and dual attention mechanism composite network model architecture to address the aforementioned issues (QPFC-DA). It has mechanisms for channel and spatial edge attention, which guide the content and edge segmentation branches, respectively. The bi-directional long short-term memory network was added after the two content segmentation branches to better integrate multi-scale features and prevent the loss of important features. Furthermore, the fuzzy C-means algorithm guided by the quadratic polynomial can better distinguish the image's weak edge regions and has a degree of noise resistance, resulting in a membership matrix with less ambiguity and a more reliable segmentation result. We also conducted comparison and ablation experiments on three medical data sets. The experimental results show that this method is superior to several other well-known methods.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

基于SPOT-5多光谱影像的矿区塌塘水体提取方法研究

针对基于遥感影像的水体提取方法存在水体提取不完整和误提的现象,提出了一种基于SPOT-5多光谱影像的矿区塌塘水体提取方法。在利用波段合成增加一个可用波段的基础上对已有的水体提取方法进行适当的改进,并基于决策树分类器和改进后的方法进行矿区水体的四级提取,保证了水体提取的完整性,同时减少了误提率;最后利用实测数据对水体提取的精度进行了评定。试验结果表明,基于决策树分类器的水体提取方法具有较高的精度,能满足矿区实际应用的需要。     

高灰度级TFT-LCD显示系统的实现

提出了一种以FPGA为核心控制器的高灰度级TFT-LCD显示系统的设计方案。该系统采用幅值-帧速率控制调制-空间混合灰度调制方法,首先将10 bit的R、G、B信号分割成高8 bit数据和低2 bit数据,低2 bit数据经过空间处理后平均分到4个相邻的8 bit数据上,然后把这8 bit数据继续分割成高6 bit数据和低2 bit数据,其中高6 bit数据用于幅值调制,低2 bit数据用于帧速率控制调制,从而实现1 024个灰度等级显示。试验结果表明,该系统具有良好的显示效果和较高的显示灰度等级。     

煤矿监控图像增强算法的分析与实现

针对煤矿井下粉尘多、光照差的恶劣环境使得矿井监控图像偏暗、对比度低、视觉效果差的特点,提出了一种基于小波变换和模糊理论的图像增强算法。该算法选择小波变换为工具分解图像,应用新的模糊隶属度和增强算子对高频信息进行模糊处理,利用直方图均衡化对低频信息进行处理,最后对图像进行重构。处理结果较好地增强了图像细节信息,从整体上改善了图像效果。     

强力输送带接头识别算法

针对现有的强力输送带故障实时检测方法存在故障定位困难的问题,以接头为基准参考点,提出了一种基于图像处理技术的强力输送带接头识别算法。该算法根据接头点的特征,把接头点用一个数值表示出来,并根据这个数值,以一种简单的方式区分出上、下接头点;然后根据接头的特征,并通过修改最长公共子序列算法的匹配方式,对接头进行识别。实际应用表明,该算法精度高、速度快,可有效识别强力输送带的接头,满足了强力输送带故障检测中接头定位的要求。