Voice Guidance System for Color Recognition Based on IoT

We often need to identify the colors of such objects as buildings,clothing,photos,and traffic signs in everyday life.Some people cannot distinguish among such colors.This study proposes a method to help people,including colorblind people,identify colors.We establish a platform for color identification that is connected by the Internet of Things(IoT).The Espressif Systems 32(ESP32)single chip is connected to a Wi-Fi communication network to transmit data to the color identification platform.The system interface is displayed in the form of color code and color name display.The speech synthesis module Speech Synthesizer Node 6288(SYN6288)is used to broadcast color-related information.Since there are many color conversion technologies in modern times,or other color recognition methods are not applicable to the guide-blind function.Therefore,the authors use the method of reading out color-related information to directly enable colorblind people to conveniently identify the colors of objects.After experiments:the accuracy of various colors,the accuracy of environmental impact,the comparison of the original sensing experiment and the effect of adding interference light sources,the time required for color sensing to identify various colors,and the interference test results of any color light source,it is proved that the research mentioned in this research.The proposed method can not only improve the rate of recognition of the system in different environments,but can also accurately identify a variety of colors.The platform is integrated with the IoT to allow users to quickly monitor the displayed data.

Vehicle color recognition based on smooth modulation neural network with multi-scale feature fusion

Vehicle Color Recognition(VCR)plays a vital role in intelligent traffic management and criminal investigation assistance.However,the existing vehicle color datasets only cover 13 classes,which can not meet the current actual demand.Besides,although lots of efforts are devoted to VCR,they suffer from the problem of class imbalance in datasets.To address these challenges,in this paper,we propose a novel VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion(SMNN-MSFF).Specifically,to construct the benchmark of model training and evaluation,we first present a new VCR dataset with 24 vehicle classes,Vehicle Color-24,consisting of 10091 vehicle images from a 100-hour urban road surveillance video.Then,to tackle the problem of long-tail distribution and improve the recognition performance,we propose the SMNN-MSFF model with multiscale feature fusion and smooth modulation.The former aims to extract feature information from local to global,and the latter could increase the loss of the images of tail class instances for training with class-imbalance.Finally,comprehensive experimental evaluation on Vehicle Color-24 and previously three representative datasets demonstrate that our proposed SMNN-MSFF outperformed state-of-the-art VCR methods.And extensive ablation studies also demonstrate that each module of our method is effective,especially,the smooth modulation efficiently help feature learning of the minority or tail classes.Vehicle Color-24 and the code of SMNN-MSFF are publicly available and can contact the author to obtain.

Illumination Invariant Recognition of Three-Dimensional Texture in Color Images

In this paper, illumination-affine invariant methods are presented based onaffine moment normalization techniques, Zernike moments, and multiband correlation functions. Themethods are suitable for the illumination invariant recognition of 3D color texture. Complex valuedmoments (i.e., Zernike moments) and affine moment normalization are used in the derivation ofillumination affine invariants where the real valued affine moment invariants fail to provide affineinvariants that are independent of illumination changes. Three different moment normalizationmethods have been used, two of which are based on affine moment normalization technique and thethird is based on reducing the affine transformation to a Euclidian transform. It is shown that fora change of illumination and orientation, the affinely normalized Zernike moment matrices arerelated by a linear transform. Experimental results are obtained in two tests: the first is usedwith textures of outdoor scenes while the second is performed on the well-known CUReT texturedatabase. Both tests show high recognition efficiency of the proposed recognition methods.

Quality assessment of processed Eucommiae Cortex based on the color and tensile force

Eucommiae Cortex(EC),the dried stem bark of Eucommia ulmoides Oliv,has been traditionally used to strengthen the muscle and bone tissues and improve liver and kidney functions in East Asian countries,including China,Japan,and Korea.Salty-fried EC(SFEC)is made by using mixing EC and saline together based on the protocol of Chinese Materia Medica Processing(CMMP)for clinical use.However,the clinical effectiveness of SFEC is directly impacted by the frying temperature and time.But precise techniques for evaluating the caliber of SFEC have yet to be developed.Thus,this study aimed to establish a fast and accurate quality-check method for SFEC decoction pieces.According to the frying temperature and time,four different categories of SFEC had been got according to the Chinese Pharmacopoeia method as Raw(R),Under(U),Moderately(M),and Overly(O).The red(R),green(G),blue(B),and light(L)color values of the decoction pieces were quantitated using the Photoshop software to determine the standard value range of the L color(raw[104.44±15.06],under[67.28±8.20],moderately[39.94±6.40],and over[15.02±5.03]).Additionally,the tensile strengths and pinoresinol diglucoside(PDG)levels of EC gum-silks were measured using the mechanical tensile test and HPLC,respectively.We also conducted Pearson correlation analysis on the L color value,EC gum-silk tension(FbcN),and PDG level,and established the following muliple-linear regression equation:the decline in PDG level(w)=0.829-0.001×FbeN-0.009×I.In conclusion,the L color value and FbeN could be used for cluster analysis of four categories of SFEC.Additinally,based on the correlation among the L color value,gum-silk tensile test,and PDG level,a rapid and accurate quality-control method for SFEC decoction pieces with different frying temperatures and durations was estab-lished.This method facilitates the manufacturing of efficacious SFEC.