Accurate and fast recognition of ink symbols can enhance the perception ability of intelligent ink symbols in the environment, provide information input for the operation of intelligent ink symbols, and improve the perfection of ink painting. This paper proposes an end-to-end deep neural network model based on YOLOv3 for overall and individual recognition of ink symbols from a computer vision perspective. Ink symbol images generated by the simulation software are used for learning the overall and individual ink symbol detection models. Experiments demonstrate that the YOLOv3 detection algorithm has a good detection effect on ink symbolic targets, and the recognition of individual ink symbols has higher accuracy and flexibility, which provides a preliminary solution idea to solve the ink symbol information perception problem.

Application of Emotional Factors of Ink Symbols Evaluated by Network Model in Modern Visual Image Design

A bio-inspired visual neural network named ICMNN was adopted to extract image structural information for image scrambling evaluation. In order to describe image structure more effectively with ICMNN, image bitmap was introduced into ICMNN input field. First, the original image was decomposed into eight binary images and each was scrambled with Arnold transformation without loss of generality. Then, ICMNN was adopted to extract the structural feature sequence of bitmap images and their corresponding scrambled ones. Last, L1 norm of the structure change sequence between them was calculated to evaluate the scrambling degree of the scrambling images. Results show that combining image bitmap decomposition with ICMNN can effectively evaluate image scrambling degree and describe the change of the structure information, which agrees with human visual perception. This evaluation algorithm is also independent of the scrambling algorithm and has a good versatility.

Randomness Evaluation for Scrambled Image Using Intersecting Cortical Model Neural Network

Local binary pattern (LBP) spectrum is a powerful feature for texture image, which is invariant to local illumination changes Pulse coupled neural network (PCNN) is a biologically inspired algorithm, which is well suited for image processing and can generate rotation, scale, translation invariant image signature This paper proposed an image retrieval tool named LBP-PCNN which combined the advantages of LBP and PCNN First, images are mapped into local structural domain with rotation invariant LBP Then, the simplified PCNN was adapted to extract the image signature in structural domain At last, texture image retrieval was completed by measuring the signature similarity of input sample texture image and texture database Experimental results on Brodatz texture gallery show that LBP-PCNN overbears LBP and PCNN in texture image retrieval and has potential applications

Bang Jun Lei

Papers

Randomness Evaluation for Scrambled Image Using Intersecting Cortical Model Neural Network

Using LBP to Improve PCNN Performance for Texture Image Retrieval