Logo Image Research Articles

Branding consistency across product portfolios in the wine industry

PurposeThe purpose of this study is to examine branding consistency for wine labels. The front label on wine bottles is important for identifying the brand and aiding purchase. Many brands are part of brand families, with the sub-brands linked to the overall brand family. This research provides an overview of how the front label varies across product portfolios of wine brands, noting the importance placed on branding elements and the level of consistency in their use across the brand portfolio.Design/methodology/approachThe authors propose and test a new method to measure branding consistency on labels from the same brand family. Two coding frameworks were created. The first recorded the incidence of brand elements and wine attributes. The second coded wine labels within a company’s portfolio based on the consistency of various brand elements. A total of 3,000 branding elements and wine attributes from 300 wine labels were examined across 60 wine brands from a list of Australian wineries.FindingsGrape variety, brand name and region are used across >90% of wine labels. Branding is presented more prominently than wine attributes. Sub-brand, region, price and variety did not influence branding consistency. Logo presence, logo image on label and colour elements contribute to the greatest variation in branding consistency across a product portfolio.Originality/valueThis study proposes and tests a novel method to measure branding consistency on wine labels and explores the extent to which consistent branding is used across product portfolios. Descriptive research is the first step to theory building. This study provides industry norms for attribute use and a measure of branding consistency for product portfolios giving valuable descriptive knowledge.

Robust zero-watermarking algorithm for multi-medical images based on FFST-Schur and Tent mapping

Aiming at issues such as the difficulty of centralized protection of multiple medical images and the high storage cost caused by repetitive operations, we proposed a robust zero-watermarking algorithm for multiple medical images based on fast finite Shearlet transform (FFST)-Schur and Tent mapping. First, multiple medical images were normalized, and the processed images were fused using a gray-weighted average fusion method for image fusion. Second, an FFST is applied to the fused image to divide the low-frequency sub-bands into blocks of the same size and perform Schur decomposition, which generates a feature image using the magnitude between the largest eigenvalue of each block and the overall average value. Finally, an image encryption algorithm based on Tent mapping is proposed to encrypt the logo image, and the encrypted image and feature image are then subjected to an exclusive OR operation to produce a zero-watermarking image. Experimental results demonstrate that the proposed approach effectively safeguards against standard image processing and geometric and combinatorial attacks. The average performance of the anti-attack algorithm was improved by approximately 3.2 % compared to similar algorithms, indicating the superior robustness of the proposed algorithm.

Multi‐label logo recognition and retrieval based on weighted fusion of neural features

AbstractClassifying logo images is a challenging task as they contain elements such as text or shapes that can represent anything from known objects to abstract shapes. While the current state of the art for logo classification addresses the problem as a multi‐class task focusing on a single characteristic, logos can have several simultaneous labels, such as different colours. This work proposes a method that allows visually similar logos to be classified and searched from a set of data according to their shape, colour, commercial sector, semantics, general characteristics, or a combination of features selected by the user. Unlike previous approaches, the proposal employs a series of multi‐label deep neural networks specialized in specific attributes and combines the obtained features to perform the similarity search. To delve into the classification system, different existing logo topologies are compared and some of their problems are analysed, such as the incomplete labelling that trademark registration databases usually contain. The proposal is evaluated considering 76,000 logos (seven times more than previous approaches) from the European Union Trademarks dataset, which is organized hierarchically using the Vienna ontology. Overall, experimentation attains reliable quantitative and qualitative results, reducing the normalized average rank error of the state‐of‐the‐art from 0.040 to 0.018 for the Trademark Image Retrieval task. Finally, given that the semantics of logos can often be subjective, graphic design students and professionals were surveyed. Results show that the proposed methodology provides better labelling than a human expert operator, improving the label ranking average precision from 0.53 to 0.68.

Logo recognition of vehicles based on deep convolutional generative adversarial networks

Vehicle logo recognition plays a critical role in enhancing the efficiency of intelligent transportation systems by enabling accurate vehicle identification and tracking. Despite advancements in image recognition technologies, accurately detecting and classifying vehicle logos in diverse and dynamically changing environments remains a significant challenge. This research introduces an innovative approach utilizing a Deep Convolutional Generative Adversarial Network (DCGAN) framework, tailored specifically for the complex task of vehicle logo recognition. Unlike traditional methods, which heavily rely on manual feature extraction and pre-defined image processing techniques, our method employs a novel DCGAN architecture. This architecture automatically learns the distinctive features of vehicle logos directly from data, enabling more robust and accurate recognition across various conditions. Furthermore, we propose a refined training strategy for both the generator and discriminator components of our DCGAN, optimized through extensive experimentation, to enhance the model’s ability to generate high-fidelity vehicle logo images for improved training efficacy. The technical core of our approach lies in the strategic integration of transfer learning techniques. These techniques significantly boost classification accuracy by leveraging pre-learned features from vast image datasets, thereby addressing the challenge of limited labeled data in the vehicle logo domain. Our experimental results demonstrate a substantial improvement in logo detection and classification accuracy, achieving an Intersection over Union (IoU) ratio of 42.67 % and a classification accuracy of 99.78 %, which markedly surpasses the performance of existing methods. This research not only advances the field of vehicle logo recognition but also contributes to the broader domain of measurement science and technology, offering a technically sound and logically coherent solution to a complex problem.

Building a Strong Image Logo Design: Human Centered Design Approach in Logo Design for SMEs in Pematang Serai Village

This article delves into the pivotal role of Human-Centered Design (HCD) in logo development, specifically tailored for Small and Medium-sized Enterprises (SMEs) situated in Pematang Serai Village. Recognizing the essence of building a robust brand image, the study explores how HCD principles influence the entire process of logo design, from conceptualization to implementation. The exploration begins by emphasizing the iterative and user-centric nature of HCD, which ensures continuous refinement of logos based on user feedback. Visual elements are dissected for their impact on audience perception, acknowledging logos as significant visual cues influencing consumer behavior. Cultural considerations take center stage as the study draws insights from cross-national logo evaluation analysis, emphasizing the need to tailor logos to specific cultural contexts. This is particularly relevant for SMEs in Pematang Serai Village, ensuring resonance with the local community. The article concludes by offering actionable recommendations for SMEs, advocating for a continued user-centric approach, emphasis on visual appeal, cultural sensitivity, iterative refinement, and strategic logo implementation. It underscores the importance of these principles in creating logos that not only visually captivate but also emotionally connect with the target audience, thereby contributing to sustained business success

Willingness to Purchase Co-Branded Products: Gender and Age Differences

This study explores the impact of co-branding on consumers’ purchase intention (PI), focusing on the 2023 trend of joint branding in the milk tea industry. The research adopts a questionnaire to analyze factors influencing PI, with a specific emphasis on gender and age differences. Previous research investigated at how joint branding affected consumption, examining factors such as the connection between perceived risk, perceived value, and popularity. The study reveals a noteworthy positive correlation between the perceived value and the participation of opinion leaders, which can positively influence consumers and lower their perceived risk. The study also looks at age and gender variations in co-brand product willingness to buy. Women participate in joint branding activities at a higher rate than men, which suggests that they are more willing. Age-wise, teens have a greater preference for milk tea, but older people participate in joint branding activities at a higher rate. The results imply that consumer willingness to purchase co-branded goods is strongly influenced by the cultural and logo images of those products. This study suggests that co-branded products' cultural and logo images significantly impact consumers’ PI. The author suggests increasing the number of participants and combining offline and online data collection techniques in order to improve the calibre of the research. As a result, the study clarifies the intricate dynamics influencing consumer decisions in the changing co-branded product market.

A binarization method for logos

In the field of digital image processing, the binarization of logo images presents a significant challenge due to the diversity in their color distribution and graphical structures. Logos, as distinctive visual symbols, are integral to brand recognition and commercial advertising. Traditional binarization methods often struggle with this diversity, leading to difficulties in preserving the original color and details of the images. To address this issue, this paper introduces a novel binarization approach specifically designed for logo images. The method comprises three stages: preprocessing, binarization, and noise removal. In the binarization stage, the Otsu method is adapted from a global to a local thresholding technique, combining both global and local thresholds for each pixel. The efficacy of this method is demonstrated through comparative experiments with logos of varying colors and structures, showing superior performance in maintaining image color and detail restoration over traditional methods. Additionally, the preprocessing and noise removal steps further enhance the method's effectiveness in handling logos with rich background colors and complex details. Overall, this approach proves to be highly adaptable across a wide range of logo types.

PERLINDUNGAN HUKUM ATAS CIPTAAN LOGO PROVINSI PAPUA BARAT MENURUT UNDANG-UNDANG HAK CIPTA

The West Papua Province logo drawn by Pieter Mambor as an order from the Government of West Papua Provincial, Abraham Octavianus Atururi. However, the West Papua Provincial Government does not provide compensation to Pieter Mambor as the creator of the logo image. The main problem in this articles is how is the legal protection of the creation of the West Papua Province logo based on Law No. 28 of 2014 on Copyright? and whether the decision of the Makassar Commercial Court Number: 3/Pdt.Sus-HKI/Cipta/2022/PN Niaga Mks is accordance Law No. 28 of 2014 on Copyright? This research is normative type, descriptive nature, the main data is secondary data supported by interviews, data collection methods with literature studies, analyzed qualitatively and conclusions are drawn using deductive logic. The result of the research is the legal protection of the creation of the West Papua Province logo which is included in the category of fine art images regulated in Article 40 paragraph (1) of the UUHC and Decision 3/Pdt.Sus-HKI/Cipta/2022/PN Niaga Mks is not accordance with Article 9 paragraph (2) of Law No. 28 of 2014 concerning Copyright. The conclusion is that the logo of Papua province is included in the creation protected by copyright.

Unsupervised Logo Detection Using Adversarial Learning From Synthetic to Real Images

Most existing deep learning-based methods for logo detection require a large amount of object-level annotated training data. However, it takes a lot of time and effort to create a good object-level annotated logo detection dataset. Many researchers have tried to utilize synthesized images with automatically generated annotations for training to deal with data annotation problem. However, in real applications, model trained using synthesized images suffer from performance loss due to domain shift between synthesized and real-world images. To this end, in this paper, we propose an adversarial learning-based unsupervised domain adaptation method for logo detection. We only use labeled synthesized logo images for model training and adapt target domain knowledge using unlabeled real-world logo images. We propose entropy minimization of mid-level output feature maps in order to effectively align the domain gap between synthetic and real images for the logo detection task. Additionally, we have generated coherent synthesized logo images with automatically constructed bounding box annotations for different datasets to perform unsupervised training for the experiments. Our experiments show that the proposed method improves performance on different logo datasets compared to direct transfer from source to target domain (synthetic-to-real images) without any labeling cost and increasing network parameters.

Car Logo Image Extraction and Recognition using K-Medoids, Daubechies Wavelets, and DCT Transforms

Recognizing cars is a highly difficult task due to the wide variety in the appearance of cars from the same car manufacturer. Therefore, the car logo is the most prominent indicator of the car manufacturer. The captured logo image suffers from several problems, such as a complex background, differences in size and shape, the appearance of noise, and lighting circumstances. To solve these problems, this paper presents an effective technique for extracting and recognizing a logo that identifies a car. Our proposed method includes four stages: First, we apply the k-medoids clustering method to extract the logo and remove the background and noise. Secondly, the logo image is converted to grayscale and also converted to a binary image using Otsu's method. Thirdly, the Daubechies wavelet with DCT transforms is applied to extract a feature vector for each image. Finally, the Canberra distance is used to match the tested image's feature vector to all feature vectors in the database. The test results indicate the highest CRR, accuracy, and precision at 99.37%, 99.39%, and 99.80%, respectively. This system is applicable to intelligent surveillance systems.

Research on the Development Status of Baoan Waist Dagger under the Strategy of Rural Revitalization and Practical Teaching Research on Brand Innovation and Design

Based on the background of the current rural revitalization strategy, this thesis discusses the importance of brand design for the Bonan waist dagger through the analysis of the development status quo of the “Bonan Waist Dagger”. The brand image design is of great significance to enhance the benefit of the Bonan waist dagger products. The brand image design can solve the problem of the loss of the “non-genetic inheritance”. The brand image design is of great significance to the inheritance and development of Bonan waist dagger. The brand image design can solve the problem of “non-heritage bearers”. The brand image design is of great significance to the inheritance and development of the Bonan waist dagger. Finally, through the specific brand image Logo design and IP image design, we can improve the brand image of the Bonan waist dagger, promote the inheritance and development of the Bonan waist dagger, and promote the revitalization of the countryside.

Pengaruh Produk Halal terhadap Pilihan Konsumen dalam Berbelanja di Indonesia

Halal is an essential factor for Muslim consumers in choosing products and services. Indonesia is a country with the largest Muslim population in the world. Producers must understand the factors that influence consumer buying behavior towards halal products. Sales of halal products are increasing, especially demand from Muslim consumers. This study aims to determine consumer buying behavior when buying halal products in Indonesia. This study involves the variables of halal awareness, halal logo, attitude, halal marketing, and halal image as independent variables, and buying behavior as the dependent variable. This study uses quantitative research methodology. The research data used primary data. Primary data was collected through a survey using a structured questionnaire. The questionnaire was created using Google Forms and distributed online to respondents. The data collection technique uses a probability sampling technique. The samples are selected randomly. The sample data was 320 respondents. The data analysis method uses SEM (Structural Equation Modeling) to predict the effect of independent variables on the dependent variable. The results of this study indicate that halal attitudes and images influence consumer behavior when buying halal products. Halal awareness, halal logo, and halal marketing do not influence consumer buying behavior when buying halal products. The findings of this study have implications for marketers and manufacturers in developing marketing strategies for halal products in Indonesia. Marketers need to understand the needs and preferences of Muslim consumers by building a positive attitude and halal image on the products sold. The availability of halal products in the market can help drive more buying behavior toward halal products.

You eye what you eat: BMI, consumption patterns, and dieting status predict temporal attentional bias to food-associated images

People know that overconsumption of high-fat high-sugar (HFHS) foods have negative consequences for physical and cognitive wellbeing but continue to consume these foods in excess, leading to recent proposals to model obesity as an addiction disorder. The current experiment tested, in a large undergraduate sample (N = 306), the hypothesis that obesity and overconsumption is linked with an oversensitivity to rewards that drives attentional biases towards foods and food-associated cues. Using a modified emotion-induced blindness task with food-related distractors, we examined the extent to which attentional biases to images of HFHS foods were accounted for by BMI, HFHS food intake, self-reported hunger, time since last meal, diet status, food preferences, and attentional control. We also examined whether the same individual differences predicted attentional priority to cues that have a learned association with HFHS foods (i.e., images of food logos). Contrary to our predictions, higher BMI predicted less attentional priority for images of food and food logos. At the same time, increased consumption of HFHS foods predicted increased attentional priority for food images, whereas dieting predicted increased attentional priority for food logo images. Our results suggest that different people may preferentially attend to food versus food logo imagery based on their relationships with food. More broadly, our results support the theoretical perspective that attentional biases to food-associated stimuli can be affected by various competing, state-related factors.

Automatic Jordanian License Plate Detection and Recognition System Using Deep Learning Techniques.

Recently, the number of vehicles on the road, especially in urban centres, has increased dramatically due to the increasing trend of individuals towards urbanisation. As a result, manual detection and recognition of vehicles (i.e., license plates and vehicle manufacturers) become an arduous task and beyond human capabilities. In this paper, we have developed a system using transfer learning-based deep learning (DL) techniques to identify Jordanian vehicles automatically. The YOLOv3 (You Only Look Once) model was re-trained using transfer learning to accomplish license plate detection, character recognition, and vehicle logo detection. In contrast, the VGG16 (Visual Geometry Group) model was re-trained to accomplish the vehicle logo recognition. To train and test these models, four datasets have been collected. The first dataset consists of 7035 Jordanian vehicle images, the second dataset consists of 7176 Jordanian license plates, and the third dataset consists of 8271 Jordanian vehicle images. These datasets have been used to train and test the YOLOv3 model for Jordanian license plate detection, character recognition, and vehicle logo detection. In comparison, the fourth dataset consists of 158,230 vehicle logo images used to train and test the VGG16 model for vehicle logo recognition. Text measures were used to evaluate the performance of our developed system. Moreover, the mean average precision (mAP) measure was used to assess the YOLOv3 model of the detection tasks (i.e., license plate detection and vehicle logo detection). For license plate detection, the precision, recall, F-measure, and mAP were 99.6%, 100%, 99.8%, and 99.9%, respectively. While for character recognition, the precision, recall, and F-measure were 100%, 99.9%, and 99.95%, respectively. The performance of the license plate recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 99.8%, 99.8%, and 99.8%, respectively. Furthermore, for vehicle logo detection, the precision, recall, F-measure, and mAP were 99%, 99.6%, 99.3%, and 99.1%, respectively, while for vehicle logo recognition, the precision, recall, and F-measure were 98%, 98%, and 98%, respectively. The performance of the vehicle logo recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 95.3%, 99.5%, and 97.4%, respectively.

A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT

Currently, algorithms to embed watermarks into digital images are increasing exponentially, for example in image copyright protection. However, when a watermarking algorithm is applied, the preservation of the image’s quality is of utmost importance, for example in medical images, where improper embedding of the watermark could change the patient’s diagnosis. On the other hand, in digital images distributed over the Internet, the owner of the images must also be protected. In this work, an imperceptible, robust, secure, and hybrid watermarking algorithm is presented for copyright protection. It is based on the Hermite Transform (HT) and the Discrete Cosine Transform (DCT) as a spatial–frequency representation of a grayscale image. Besides, it uses a block-based strategy and a perfectibility analysis of the best embedding regions inspired by the Human Vision System (HVS), giving the imperceptibility of the watermark, and a Singular-Value Decomposition (SVD) approach improved robustness against attacks. In addition, the proposed method can embed two watermarks, a digital binary image (LOGO) and information about the owner and the technical data of the original image in text format (MetaData). To secure both watermarks, the proposed method uses the Jigsaw Transform (JST) and the Elementary Cellular Automaton (ECA) to encrypt the image LOGO and a random sequence generator and the XOR operation to encrypt the image MetaData. On the other hand, the proposed method was tested using a public dataset of 49 grayscale images to assess the effectiveness of the watermark embedding and extraction procedures. Furthermore, the proposed watermarking algorithm was evaluated under several processing and geometric algorithms to demonstrate its robustness to the majority, intentional or unintentional, attacks, and a comparison was made with several state-of-the-art techniques. The proposed method obtained average values of PSNR = 40.2051 dB, NCC = 0.9987, SSIM = 0.9999, and MSSIM = 0.9994 for the watermarked image. In the case of the extraction of the LOGO, the proposal gave MSE = 0, PSNR ≫ 60 dB, NCC = 1, SSIM = 1, and MSSIM = 1, whereas, for the image MetaData extracted, it gave BER = 0% and Berror=0. Finally, the proposed encryption method presented a large key space (K=1.2689×1089) for the LOGO image.

An attitude estimation algorithm for the telescopic arm of the boarding bridge based on YOLOv5 and EPnP

Aiming at the attitude estimation of the telescopic arm of the boarding bridge in the process of docking with the offshore wind turbine, an attitude estimation algorithm for the telescopic arm of the boarding bridge based on YOLOv5 and EPnP is proposed in this paper. YOLOv5 algorithm is used to detect four marks in the offshore wind turbine logo image, and then the EPnP algorithm is used to solve attitude angles of the telescopic arm relative to the landing port of the offshore wind turbine according to the 2D pixel coordinates of the center point of each mark. The experimental results show that the attitude estimation error of the proposed algorithm is less than 0.4° and the data update frequency of the algorithm implemented in NVIDIA Jetson AGX Xavier is 25Hz, which meets the real-time and accuracy requirements for the attitude detection of the boarding bridge telescopic arm.

Multi Watermarking for Medical Images using Fractal Encoding and Concept of UNET

Abstract: The Internet of Medical Things, smart healthcare systems are becoming ubiquitous in our daily lives. Patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of electronic health records , medical images such as scanning reports X-rays for successful treatment and management of different ailments. A Multi watermarking method is proposed for medical images based on quantum random walk and optimization algorithm. A logo image is used to verifying medical image integrity is embedding in region of interest and text data are embedded in the region of non interest to conceal private hospital and patient informations. This method improves the authenticity and integrity of medical images. A number of experiments are conducted to validate the security ,robustness and capacity of the proposed multi watermarking scheme.

A robust semi-fragile watermarking system using Pseudo-Zernike moments and dual tree complex wavelet transform for social media content authentication.

With the growing use of mobile devices and Online Social Networks (OSNs), sharing digital content, especially digital images is extremely high as well as popular. This made us convenient to handle the ongoing COVID-19 crisis which has brought about years of change in the sharing of digital content online. On the other hand, the digital image processing tools which are powerful enough to make the perfect image duplication compromises the privacy of the transmitted digital content. Therefore, content authentication, proof of ownership, and integrity of digital images are considered crucial problems in the world of digital that can be accomplished by employing a digital watermarking technique. On contrary, watermarking issues are to triumph trade-offs among imperceptibility, robustness, and payload. However, most existing systems are unable to handle the problem of tamper detection and recovery in case of intentional and unintentional attacks concerning these trade-offs. Also, the existing system fails to withstand the geometrical attacks. To resolve the above shortcomings, this proposed work offers a new multi-biometric based semi-fragile watermarking system using Dual-Tree Complex Wavelet Transform (DTCWT) and pseudo-Zernike moments (PZM) for content authentication of social media data. In this research work, the DTCWT-based coefficients are used for achieving maximum embedding capacity. The Rotation and noise invariance properties of Pseudo Zernike moments make the system attain the highest level of robustness when compared to conventional watermarking systems. To achieve authentication and proof of identity, the watermarks of about four numbers are used for embedding as a replacement for a single watermark image in traditional systems. Among four watermarks, three are the biometric images namely Logo or unique image of the user, fingerprint biometric of the owner, and the metadata of the original media to be transmitted. In addition, to achieve the tamper localization property, the Pseudo Zernike moments of the original cover image are obtained as a feature vector and also embedded as a watermark. To attain a better level of security, each watermark is converted into Zernike moments, Arnold scrambled image, and SHA outputs respectively. Then, to sustain the trade-off among the watermarking parameters, the optimal embedding location is determined. Moreover, the watermarked image is also signed by the owner's other biometric namely digital signature, and converted into Public key matrix Pkm and embedded onto the higher frequency subband namely, HL of the 1-level DWT. The proposed system also accomplishes a multi-level authentication, among that the first level is attained by the decryption of the extracted multiple watermark images with the help of the appropriate decryption mechanism which is followed by the comparison of the authentication key which is extracted using the key which is regenerated at the receiver's end. The simulation outcomes evident that the proposed system shows superior performance towards content authentication, to most remarkable intentional and unintentional attacks among the existing watermarking systems.

Watermarking Technique for Verifying Medical Images in IOMT Using Fractal Encoding and Concept of UNET

The Internet of Medical Things, smart healthcare systems are becoming ubiquitous in our daily lives. Patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of electronic health records , medical images such as scanning reports X-rays for successful treatment and management of different ailments. A Multi watermarking method is proposed for medical images based on quantum random walk and optimization algorithm. A logo image is used to verifying medical image integrity is embedding in region of interest and text data are embedded in the region of non interest to conceal private hospital and patient informations. This method improves the authenticity and integrity of medical images. A number of experiments are conducted to validate the security ,robustness and capacity of the proposed multi watermarking scheme.

Watermarking In Medical Images

Abstract—The Internet of Medical Things, smart healthcare systems are becoming ubiquitous in our daily lives. Patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of electronic health records , medical images such as scanning reports X-rays for successful treatment and management of different ailments. A Multi watermarking method is proposed for medical images based on quantum random walk and optimization algorithm. A logo image is used to verifying medical image integrity is embedding in region of interest and text data are embedded in the region of non interest to conceal private hospital and patient informations. This method improves the authenticity and integrity of medical images. A number of experiments are conducted to validate the security ,robustness and capacity of the proposed multi watermarking scheme. Keywords—medical image, quantum random walk, optimization algorithm, Internet of Medical Things