Deep Convolutional Nets Learning Classification for Artistic Style Transfer

Humans have mastered the skill of creativity for many decades. The process of replicating this mechanism is introduced recently by using neural networks which replicate the functioning of human brain, where each unit in the neural network represents a neuron, which transmits the messages from one neuron to other, to perform subconscious tasks. Usually, there are methods to render an input image in the style of famous art works. This issue of generating art is normally called nonphotorealistic rendering. Previous approaches rely on directly manipulating the pixel representation of the image. While using deep neural networks which are constructed using image recognition, this paper carries out implementations in feature space representing the higher levels of the content image. Previously, deep neural networks are used for object recognition and style recognition to categorize the artworks consistent with the creation time. This paper uses Visual Geometry Group (VGG16) neural network to replicate this dormant task performed by humans. Here, the images are input where one is the content image which contains the features you want to retain in the output image and the style reference image which contains patterns or images of famous paintings and the input image which needs to be style and blend them together to produce a new image where the input image is transformed to look like the content image but “sketched” to look like the style image.

CNN-Based Embroidery Style Rendering

Nonphotorealistic rendering (NPR) techniques are used to transform real-world images into high-quality aesthetic styles automatically. NPR mainly focuses on transfer hand-painted styles to other content images, and simulates pencil drawing, watercolor painting, sketch painting, Chinese monochromes, calligraphy and, so on. However, digital simulation of Chinese embroidery style has not attracted researcher’s much attention. This study proposes an embroidery style transfer method from a 2D image on the basis of a convolutional neural network (CNN) and evaluates the relevant rendering features. The primary novelty of the rendering technique is that the strokes and needle textures are produced by the CNN and the results can display embroidery styles. The proposed method can not only embody delicate strokes and needle textures but also realize stereoscopic effects to achieve real embroidery features. First, using conditional random fields (CRF), the algorithm segments the target content and the embroidery style images through a semantic segmentation network. Then, the binary mask image is generated to guide the embroidery style transfer for different regions. Next, CNN is used to extract the strokes and texture features from the real embroidery images, and transfer these features to the content images. Finally, the simulating image is generated to show the features of the real embroidery styles. To demonstrate the performance of the proposed method, the simulations are compared with real embroidery artwork and other methods. In addition, the quality evaluation method is used to evaluate the quality of the results. In all the cases, the proposed method is found to achieve needle visual quality of the embroidery styles, thereby laying a foundation for the research and preservation of embroidery works.

Human Emotional Care Purposed Automatic Remote Portrait Drawing Generation and Display System Using Wearable Heart Rate Sensor and Smartphone Camera with Depth Perception

We propose a system that automatically generates portrait drawings for the purpose of human emotional care. Our system comprises two parts: a smartphone application and a server. The smartphone application enables the user to take photographs throughout the day while acquiring heart rates from the smartwatch worn by the user. The server collects the photographs and heart rates and displays portrait drawings automatically stylized from the photograph for the most exciting moment of the day. In the system, the user can recall the exciting and happy moment of the day through admiring the drawings and heal the emotion accordingly. To stylize photographs as portrait drawings, we employ nonphotorealistic rendering (NPR) methods, including a portrait etude stylization proposed in this paper. Finally, the effectiveness of our system is demonstrated through user studies.

Simulating Chalk Art Style Painting

Different kinds of illustrations and artistic imagery can be generated or simulated through the nonphotorealistic rendering (NPR) technique. However, designing and simulating new NPR artistic styles remains extremely challenging. Chalk art style is a very famous artistic work all over the world, and few algorithms have been put forward to illustrate this style. This paper presents a novel NPR technique which generates a chalk art drawing from a 2D photograph automatically. We aim at obtaining a set of lines surface with coarse appearance and generating stroke textures of the real chalk painting. Firstly, the edge of the source image is extracted by difference-of-Gaussian filter method. To simulate chalk painting’s lines, image diffusion and enhancement techniques are proposed to produce coarse and rough lines. Secondly, we developed an improved line integral convolution and dilation operation methods to produce the chalk stroke texture. Finally, the edge image, stroke texture image and color image will be mapped to another background image to generate the chalk art drawing. Experimental results are presented to show the effectiveness of our method in producing the color chalk stylistic illustrations, and the methods can simulate the characters of the real chalk art painting. The proposed method of this paper will enlarge the research and application fields of NPR. Meanwhile, it provides a tool for the user to create chalk art paintings via computers even without painting skill.