Colorization of Line Drawings with Empty Pupils

Automatic image colorization is to generate a colorful image from a given gray image automatically. It is an ill-conditioned task and remains a challenging problem in computer vision. One main challenge in example-based image colorization is how to find the correct correspondence between the grayscale image and the reference color image. In this paper, we propose a novel automatic example-based image colorization method via weighted sparse matching. First, we segment the images into superpixels, and operates at the superpixel level rather than pixels. Then we extract intensity features and texture features for each superpixel, which are then concatenated to form its descriptor. The feature descriptors collected from the reference image composes the representation dictionary. Finally, the correspondence between target grayscale image and the colorful reference image is built by solving a weighted sparse representation learning problem, and the target superpixels are colorized based on the chrominance information from the corresponding reference superpixels. Experimental results demonstrate that our colorization method outperforms several state-of-the-art methods.

Multimodal emotion recognition has become a hot topic in human-computer interaction and intelligent healthcare fields. However, combining information from different human different modalities for emotion computation is still challenging. In this paper, we propose a three-dimensional convolutional recurrent neural network model (referred to as 3FACRNN network) based on multimodal fusion and attention mechanism. The 3FACRNN network model consists of a visual network and an EEG network. The visual network is composed of a cascaded convolutional neural network-time convolutional network (CNN-TCN). In the EEG network, the 3D feature building module was added to integrate band information, spatial information and temporal information of the EEG signal, and the band attention and self-attention modules were added to the convolutional recurrent neural network (CRNN). The former explores the effect of different frequency bands on network recognition performance, while the latter is to obtain the intrinsic similarity of different EEG samples. To investigate the effect of different frequency bands on the experiment, we obtained the average attention mask for all subjects in different frequency bands. The distribution of the attention masks across the different frequency bands suggests that signals more relevant to human emotions may be active in the high frequency bands γ (31-50 Hz). Finally, we try to use the multi-task loss function Lc to force the approximation of the intermediate feature vectors of the visual and EEG modalities, with the aim of using the knowledge of the visual modalities to improve the performance of the EEG network model. The mean recognition accuracy and standard deviation of the proposed method on the two multimodal sentiment datasets DEAP and MAHNOB-HCI (arousal, valence) were 96.75 ± 1.75, 96.86 ± 1.33; 97.55 ± 1.51, 98.37 ± 1.07, better than those of the state-of-the-art multimodal recognition approaches. The experimental results show that starting from the multimodal information, the facial video frames and electroencephalogram (EEG) signals of the subjects are used as inputs to the emotion recognition network, which can enhance the stability of the emotion network and improve the recognition accuracy of the emotion network. In addition, in future work, we will try to utilize sparse matrix methods and deep convolutional networks to improve the performance of multimodal emotion networks.

Current multispectral night vision (NV) colorization techniques can manipulate images to produce colorized images that closely resemble natural scenes. The colorized NV images can enhance human perception by improving observer object classification and reaction times especially for low light conditions. This paper focuses on the qualitative (subjective) evaluations and comparisons of six NV colorization methods. The multispectral images include visible (Red-Green- Blue), near infrared (NIR), and long wave infrared (LWIR) images. The six colorization methods are channel-based color fusion (CBCF), statistic matching (SM), histogram matching (HM), joint-histogram matching (JHM), statistic matching then joint-histogram matching (SM-JHM), and the lookup table (LUT). Four categries of quality measurements are used for the qualitative evaluations, which are contrast, detail, colorfulness, and overall quality. The score of each measurement is rated from 1 to 3 scale to represent low, average, and high quality, respectively. Specifically, high contrast (of rated score 3) means an adequate level of brightness and contrast. The high detail represents high clarity of detailed contents while maintaining low artifacts. The high colorfulness preserves more natural colors (i.e., closely resembles the daylight image). Overall quality is determined from the NV image compared to the reference image. Nine sets of multispectral NV images were used in our experiments. For each set, the six colorized NV images (produced from NIR and LWIR images) are concurrently presented to users along with the reference color (RGB) image (taken at daytime). A total of 67 subjects passed a screening test (“Ishihara Color Blindness Test”) and were asked to evaluate the 9-set colorized images. The experimental results showed the quality order of colorization methods from the best to the worst: CBCF < SM < SM-JHM < LUT < JHM < HM. It is anticipated that this work will provide a benchmark for NV colorization and for quantitative evaluation using an objective metric such as objective evaluation index (OEI).

Gesture recognition is a key research field in the human-computer interaction. At present, most of researchers focus on one-handed gesture recognition, but do not pay much attention to bimanual (two hands) gesture recognition. This paper presents a deep learning-based solution to tackle the self-occlusion and self-similarity. To solve this problem, this paper uses Kinect to collect many colour and depth images of different gestures, and each gesture contains multiple sample individuals. Colour images and depth images are used to train the recognition model of bimanual gesture respectively, and then the colour image and depth image are fused, and the bimanual gesture recognition model is trained based on colour image and depth image fusion. Then, the bimanual recognition effects of the three models are compared. The experimental results show that, regardless of the single gesture precision or the mean average precision, the bimanual gesture recognition effect of the fused model is better than the gesture recognition models based on either colour image or depth image.

Prediction of PM2.5 concentration in urban agglomeration of China by hybrid network model

In recent years, deep learning has made good progress and has been applied to face recognition, video monitoring, image processing, and other fields. In this big data background, deep convolution neural network has also received more and more attention. In order to extract the ancient Chinese characters effectively, the paper will discuss the structure model, pool process, and network training of deep convolution neural network and compare the algorithm with the traditional machine learning algorithm. The results show that the accuracy and recall rate of the Chinese characters in the plaque of Ming Dynasty can reach the peak, 81.38% and 81.31%, respectively. When the number of training samples increases to 50, the recognition rate of MFA is 99.72%, which is much higher than other algorithms. This shows that the algorithm based on deep convolution neural network and big data analysis has excellent performance and can effectively identify the Chinese characters under different dynasties, different sample sizes, and different interference factors, which can provide a powerful reference for the extraction of ancient Chinese characters.

The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.

Among the list of dreadful diseases, 'Brain Tumor and Brain Cancer' is one of them. According to survival rate, after the detection of cancerous Brain or Central Nervous System (CNS) tumor, 34% of men and 36% of women survive at least 5 years. Therefore, 'Early Detection of tumor' can be a savior for so many lives and artificial intelligence will make the way very accurate and appropriate. A new non-invasive approach to detect brain tumors in early-stage, cost-efficiently with the lowest risk factor is-'Liquid Biopsy'. It is a technique for analyzing non-solid biological tissue, i.e. - blood, plasma, cerebrospinal fluid (CSF). As human inspection maybe error full and inaccurate, in this paper, we have used 'k-means clustering' in 'Image Processing' as visual perception of artificial intelligence for detection of 'CTC' and we have used the technique 'Convolutional Neural Network' (CNN) for ctDNA detection and 'Support Vector Machine'(SVM) algorithm for classification of ctDNA and normal cell-free DNA (cfDNA). 'ctDNA' could provide clues for growth factor of cancerous cells, type of tumors, location, etc. Therefore, our work provides a new way to early detection and a new prospect for early cure.

Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading. During colorization, the artist usually takes an existing cartoon image as color guidance, particularly when colorizing related characters or an animation sequence. Reference-guided colorization is more intuitive than colorization with other hints, such as color points or scribbles, or text-based hints. Unfortunately, reference-guided colorization is challenging since the style of the colorized image should match the style of the reference image in terms of both global color composition and local color shading. In this paper, we propose a novel learning-based framework which colorizes a sketch based on a color style feature extracted from a reference color image. Our framework contains a color style extractor to extract the color feature from a color image, a colorization network to generate multi-scale output images by combining a sketch and a color feature, and a multi-scale discriminator to improve the reality of the output image. Extensive qualitative and quantitative evaluations show that our method outperforms existing methods, providing both superior visual quality and style reference consistency in the task of reference-based colorization.

Convolutional neural networks (CNNs) have lately proven to be extremely effective in image recognition. Besides CNN, hidden Markov chains (HMCs) are probabilistic models widely used in image processing. This paper presents a new hybrid model composed of both CNNs and HMCs. The CNN model is used for feature extraction and dimensionality reduction and the HMC model for classification. In the new model, named CNN-HMC, convolutional and pooling layers of the CNN model are applied to extract features maps. Also a Peano scan is applied to obtain several HMCs. Expectation–Maximization (EM) algorithm is used to estimate HMC’s parameters and to make the Bayesian Maximum Posterior Mode (MPM) classification method used unsupervised. The objective is to enhance the performances of the CNN models for the image classification task. To evaluate the performance of our proposal, it is compared to six models in two series of experiments. In the first series, we consider two CNN-HMC and compare them to two CNNs, 4Conv and Mini AlexNet, respectively. The results show that CNN-HMC model outperforms the classical CNN model, and significantly improves the accuracy of the Mini AlexNet. In the second series, it is compared to four models CNN-SVMs, CNN-LSTMs, CNN-RFs, and CNN-gcForests, which only differ from CNN-HMC by the second classification step. Based on five datasets and four metrics recall, precision, F1-score, and accuracy, results of these comparisons show again the interest of the proposed CNN-HMC. In particular, with a CNN model of 71% of accuracy, the CNN-HMC gives an accuracy ranging between 81.63% and 92.5%.

Automatic colorization is generally classified into two groups: propagation-based methods and reference-based methods. In reference-based automatic colorization methods, color image(s) are used as reference(s) to reconstruct original color of a gray target image. The most important task here is to find the best matching pairs for all pixels between reference and target images in order to transfer color information from reference to target pixels. A lot of attractive local feature-based image matching methods have already been developed for the last two decades. Unfortunately, as far as we know, there are no optimal matching methods for automatic colorization because the requirements for pixel matching in automatic colorization are wholly different from those for traditional image matching. To design an efficient matching algorithm for automatic colorization, clustering pixel with low computational cost and generating descriptive feature vector are the most important challenges to be solved. In this paper, we present a novel method to address these two problems. In particular, our work concentrates on solving the second problem (designing a descriptive feature vector); namely, we will discuss how to learn a descriptive texture feature using scaled sparse texture feature combining with a nonlinear transformation to construct an optimal feature descriptor. Our experimental results show our proposed method outperforms the state-of-the-art methods in terms of robustness for color reconstruction for automatic colorization applications.

In recent years, convolutional neural networks have been widely used in image processing and have shown good results. Particularly useful was their ability to automatically extract image features (textures and shapes of objects). The article proposes a method that improves the accuracy and speed of recognition of an ultra-precise neural network based on image recognition of people’s faces. At first, a recurrent neural network is introduced into the convolutional neural network, thereby studying the characteristics of the image more deeply. Deep image characteristics are studied in parallel using a convolutional and recurrent neural network. In line with the idea of skipping the ResNet convolution layer, a new ShortCut3- ResNet residual module is built. A double optimization model is created to fully optimize the convolution process. A study of the influence of various parameters of a convolutional neural network on network performance is demonstrated, also analyzed using simulation experiments. As a result, the optimal parameters of the convolutional neural network are established. Ex- periments show that the method presented in this paper can study various images of people’s faces regardless of age, gender, and also improves the accuracy of feature extraction and image recognition ability.

Exemplar-based colorization is a challenging task, which attempts to add colors to the target grayscale image with the aid of a reference color image, so as to keep the target semantic content while with the reference color style. In order to achieve visually plausible chromatic results, it is important to sufficiently exploit the global color style and the semantic color information of the reference color image. However, existing methods are either clumsy in exploiting the semantic color information, or lack of the dedicated fusion mechanism to decorate the target grayscale image with the reference semantic color information. Besides, these methods usually use a single-stage encoder-decoder architecture, which results in the loss of spatial details. To remedy these problems, we propose an effective exemplar colorization strategy based on pyramid dual non-local attention network to exploit the long-range dependency as well as multi-scale correlation. Specifically, two symmetrical branches of pyramid non-local attention block are tailored to achieve alignments from the target feature to the reference feature and from the reference feature to the target feature respectively. The bidirectional non-local fusion strategy is further applied to get a sufficient fusion feature that achieves full semantic consistency between multi-modal information. To train the network, we propose an unsupervised learning manner, which employs the hybrid supervision including the pseudo paired supervision from the reference color images and unpaired supervision from both the target grayscale and reference color images. Extensive experimental results are provided to demonstrate that our method achieves better photo-realistic colorization performance than the state-of-the-art methods. The source code is available at https://github.com/wd1511/PDNLA-Net.

An improved image texture synthesis based on algorithm convolution neural network

Abstract: This research aims to develop a method for detecting leaf spot disease in oil palm seedlings using Convolutional Neural Network (CNN). Leaf spot disease in oil palm seedlings can hinder growth and production. CNN has proven effective in image processing and classification, particularly in plant disease detection. In this study, we utilized a dataset of images containing oil palm seedling leaves infected with leaf spot disease and healthy leaves. We performed data processing, built a CNN model, and conducted hyperparameter tuning. The test results demonstrate that the developed CNN model achieves high accuracy in recognizing and distinguishing between oil palm seedling leaves infected with leaf spot disease and healthy ones. This research contributes to the development of plant disease detection technology that can support economic growth in the oil palm plantation sector. Keywords: Convolutional Neural Network, image processing, leaf spot disease detection, oil palm seedlings. Abstrak: Penelitian ini bertujuan untuk mengembangkan metode deteksi penyakit bercak pada bibit kelapa sawit menggunakan Convolutional Neural Network (CNN). Bibit kelapa sawit yang terinfeksi penyakit bercak dapat menghambat pertumbuhan dan produksi kelapa sawit. Metode CNN telah terbukti efektif dalam pengolahan citra dan klasifikasi, khususnya dalam deteksi penyakit pada tanaman. Dalam penelitian ini, kami menggunakan dataset citra daun bibit kelapa sawit yang terinfeksi penyakit bercak dan yang normal. Kami melakukan processing data, membangun model CNN, dan melakukan tuning hyperparameter. Hasil pengujian menunjukkan bahwa model CNN yang dikembangkan memiliki akurasi yang tinggi dalam mengenali dan membedakan citra daun bibit kelapa sawit yang terinfeksi penyakit bercak dan yang normal. Penelitian ini memberikan kontribusi dalam pengembangan teknologi deteksi penyakit tanaman yang dapat mendukung pertumbuhan ekonomi di sektor perkebunan kelapa sawit. Kata kunci: bibit kelapa sawit, Convolutional Neural Network, deteksi penyakit bercak, pengolahan citra.