aZIBO Shape Descriptor for Monitoring Tool Wear in Milling

El objetivo de este trabajo es estimar eficientemente el desgaste del mecanizado de metales y mejorar las operaciones de sustitución de la herramienta. El procesamiento de imágenes y la clasificación se utilizan para automatizar la toma de decisiones sobre el tiempo adecuado para el reemplazo dela herramienta. Específicamente, el descriptor de forma aZIBO (momentos absolutos de Zernike con orientación de contorno invariable) se ha utilizado para caracterizar el desgaste de la plaquita y garantizar su uso óptimo. Se ha creado un conjunto de datos compuesto por 577 regiones con diferentes niveles de desgaste. Se han llevado a cabo dos procesos de clasificación diferentes: el primero con tres clases diferentes (desgaste bajo, medio y alto -L, M y H, respectivamente) y el segundo con sólo dos clases: Low (L) y High (H). La clasificación se llevó a cabo utilizando por un lado kNN con cinco distancias diferentes y cinco valores de k y, por otra parte, una máquina de vectores de soporte (SVM). El rendimiento de aZIBO se ha comparado con descriptores de forma clásicos como los momentos de Hu y Flusser. Los supera, obteniendo tasas de éxito de hasta el 91,33% para la clasificación L-H y 90,12% para la clasificación L-M-H

Temu Kembali Berbasis Citra untuk Menemukan Kemiripan Merek Menggunakan Algoritma SIFT dan SURF

Abstract. Image-Based Retrieval to Find Trademark Similarities Using SIFT and SURF Algorithms. In the world of trade in products and services, brands are essential. Every company wants to register a unique trademark for its products and services. Registration and evaluation to find the uniqueness of a trademark is challenging. Trademark image registration is one of the critical application areas of Content-BasedRetrieval (CBIR), which compares new brands with existing ones to ensure no dispute in the community. This study used SIFT and SURF algorithms to build a content-based brand image retrieval system. The research data used trademark data dispute cases that were decided in court. The features extracted from the SIFT and SURF algorithms are used to find similarities between the query image and the image in the database. Furthermore, the k-Nearest Neighbors algorithm with Euclidean distance measurements was used to sort the database images that were most similar to the query image. Experiments were conducted to find the algorithm and sequencing with the highest precision and recall values.Keywords: Trademark, SIFT, SURF, K-Nearest Neighbors, Euclidean. Abstrak. Dalam dunia perdagangan produk dan jasa, merek menjadi sangat penting. Setiap perusahaan ingin mendaftarkan merek dagang yang unik untuk produk dan jasanya. Pendaftaran dan evaluasi untuk menemukan kekhasan suatu merek dagang menjadi suatu pekerjaan yang sangat sulit. Pendaftaran citra merek dagang adalah salah satu area aplikasi penting Content Based Information Retrieval (CBIR) yang membandingkan merek baru dengan merek yang ada untuk memastikan tidak ada sengketa di masyarakat. Penelitian ini menggunakan algoritma SIFT dan SURF untuk membangun sistem temu kembali citra merek berbasis konten . Data penelitian menggunakan kasus sengketa data merek yang diputuskan di pengadilan. Fitur hasil ekstraksi algoritma SIFT dan SURF digunakan untuk mencari kemiripan citra query dan citra dalam basis data. Selanjutnya algoritma k-Nearest Neighbors dengan pengukuran jarak Euclidean digunakan untuk mengurutkan citra basis data yang paling mirip dengan citra query. Eksperimen dilakukan untuk mengetahui algoritma dan pengurutan dengan nilai presisi dan recall tertinggi. Kata Kunci: Merek, SIFT, SURF, K-Nearest Neighbors, Euclidean

A Comparative Analysis of the Zernike Moments for Single Object Retrieval

تم استخدام لحظات زينرك بشكل شائع في العديد من دراسات استرجاع الصور المبنية على الأشكال بسبب تمثيلها القوي للأشكال.ومع ذلك لم يتم تسليط الضوء بوضوح على نقاط قوتها وضعفها في الدراسات السابقة. وبالتالي، لا يمكن استغلال تمثيلها القوي بشكل كامل.في هذه االدراسة، يتم تنفيذ طريقة لالتقاط خصائص تمثيل الأشكال في لحظات زينرك بالكامل وتطبيقها على كائن واحد للحصول على صورثنائية ومستوى رمادي. تعمل الطريقة المقترحة عن طريق تحديد حدود كائن الشكل ثم تغيير حجم شكل الكائن إلى حدود الصورة. تم إجراءثلاث دراسات حالة. الحالة 1 هي تطبيق لحظات زينرك على صورة كائن الشكل الأصلي. في الحالة 2 ، يتم نقل النقطة الوسطى لصورةكائن الشكل في الحالة 1 إلى مركز الصورة. في الحالة 3 ، تقوم الطريقة المقترحة أولاً باكتشاف الحدود الخارجية لشكل الكائن ثم تغيير حجمالكائن إلى حد الصورة. تم إجراء تحقيقات تجريبية باستخدام مجموعتي بيانات صورتين قياسيتين أظهرت أن الطريقة المقترحة في الحالة 3قد تم توضيحها لتوفير أفضل أداء لاسترجاع الصور مقارنةً بكل من 1 و 2 . الخلاصة تتلخص ان لالتقاط الصورة بشكل كامل خصائص تمثيلالشكل القوية للحظة زينرك ، يجب تغيير حجم كائن الشكل إلى حدود الصورة .Zernike Moments has been popularly used in many shape-based image retrieval studies due to its powerful shape representation. However its strength and weaknesses have not been clearly highlighted in the previous studies. Thus, its powerful shape representation could not be fully utilized. In this paper, a method to fully capture the shape representation properties of Zernike Moments is implemented and tested on a single object for binary and grey level images. The proposed method works by determining the boundary of the shape object and then resizing the object shape to the boundary of the image. Three case studies were made. Case 1 is the Zernike Moments implementation on the original shape object image. In Case 2, the centroid of the shape object image in Case 1 is relocated to the center of the image. In Case 3, the proposed method first detect the outer boundary of the shape object and then resizing the object to the boundary of the image. Experimental investigations were made by using two benchmark shape image datasets showed that the proposed method in Case 3 had demonstrated to provide the most superior image retrieval performances as compared to both the Case 1 and Case 2. As a conlusion, to fully capture the powerful shape representation properties of the Zernike moment, a shape object should be resized to the boundary of the image

Multi-faceted Assessment of Trademark Similarity

Trademarks are intellectual property assets with potentially high reputational value. Their infringement may lead to lost revenue, lower profits and damages to brand reputation. A test normally conducted to check whether a trademark is highly likely to infringe other existing, already registered, trademarks is called a likelihood of confusion test. One of the most influential factors in this test is establishing similarity in appearance, meaning or sound. However, even though the trademark registration process suggests a multi-faceted similarity assessment, relevant research in expert systems mainly focuses on computing individual aspects of similarity between trademarks. Therefore, this paper contributes to the knowledge in this field by proposing a method, which, similar to the way people perceive trademarks, blends together the three fundamental aspects of trademark similarity and produces an aggregated score based on the individual visual, semantic and phonetic assessments. In particular, semantic similarity is a new aspect, which has not been considered by other researchers in approaches aimed at providing decision support in trademark similarity assessment. Another specific scientific contribution of this paper is the innovative integration, using a fuzzy engine, of three independent assessments, which collectively provide a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments. The work described in the paper is evaluated using a database comprising 1,400 trademarks compiled from a collection of real legal cases of trademark disputes. The evaluation involved two experiments. The first experiment employed information retrieval measures to test the classification accuracy of the proposed method while the second used human collective opinion to examine correlations between the trademark scoring/rating and the ranking of the proposed method, and human judgment. In the first experiment, the proposed method improved the F-score, precision and accuracy of classification by 12.5%, 35% and 8.3%, respectively, against the best score computed using individual similarity. In the second experiment, the proposed method produced a perfect positive Spearman rank correlation score of 1.00 in the ranking task and a pairwise Pearson correlation score of 0.92 in the rating task. The test of significance conducted on both scores rejected the null hypotheses of the experiment and showed that both scores correlated well with collective human judgment. The combined overall assessment could add value to existing support systems and be beneficial for both trademark examiners and trademark applicants. The method could be further used in addressing recent cyberspace phenomena related to trademark infringement such as customer hijacking and cybersquatting. Keywords—Trademark assessment, trademark infringement, trademark retrieval, degree of similarity, fuzzy aggregation, semantic similarity, phonetic similarity, visual similarity

Semantic retrieval of trademarks based on conceptual similarity

Trademarks are signs of high reputational value. Thus, they require protection. This paper studies conceptual similarities between trademarks, which occurs when two or more trademarks evoke identical or analogous semantic content. This paper advances the state-of-the-art by proposing a computational approach based on semantics that can be used to compare trademarks for conceptual similarity. A trademark retrieval algorithm is developed that employs natural language processing techniques and an external knowledge source in the form of a lexical ontology. The search and indexing technique developed uses similarity distance, which is derived using Tversky's theory of similarity. The proposed retrieval algorithm is validated using two resources: a trademark database of 1400 disputed cases and a database of 378,943 company names. The accuracy of the algorithm is estimated using measures from two different domains: the R-precision score, which is commonly used in information retrieval and human judgment/collective human opinion, which is used in human-machine systems

Trademark image retrieval using an integrated shape descriptor

Trademarks are distinctive visual symbols with high reputational value, due to the perception of quality and innovation associated with them. They are important reputational assets used as a marketing tool to convey a certain assurance of quality, innovation, and the standards, which the manufacturer seeks to maintain. This motivates the need for trademark protection by providing a solution to prevent infringement. This problem can be addressed by developing retrieval systems capable of comparing the visual similarity of trademarks. This paper contributes to the research in this field by proposing an innovative trademark retrieval technique with improved retrieval performance due to the integration of global and local descriptors. The global descriptor employed is the Zernike moment’s coefficients. The local descriptor is the edge-gradient co-occurrence matrix, derived from the contour information that is considered very important in human perception of visual similarity. The proposed retrieval technique is tested using the standard MPEG-7 shape database of 1400 images and the MPEG-7 trademark database of 3260 images. The results show 5% precision/recall improvement in the case of the MPEG-7 shape database, as well as 2.35% Bull’s eye score improvement and 19.8% NMRR score improvement for the 10 randomly selected trademarks from the MPEG-7 trademarks database

Trade mark similarity assessment support system

Trade marks are valuable intangible intellectual property (IP) assets with potentially high reputational value that can be protected. Similarity between trade marks may potentially lead to infringement. That similarity is normally assessed based on the visual, conceptual and phonetic aspects of the trade marks in question. Hence, this thesis addresses this issue by proposing a trade mark similarity assessment support system that uses the three main aspects of trade mark similarity as a mechanism to avoid future infringement. A conceptual model of the proposed trade mark similarity assessment support system is first proposed and developed based on the similarity assessment criteria outlined in a trade mark manual. The proposed model is the first contribution of this study, and it consists of visual, conceptual, phonetic and inference engine modules. The second contribution of this work is an algorithm that compares trade marks based on their visual similarity. The algorithm performs a similarity assessment using content-based image retrieval (CBIR) technology and an integrated visual descriptor derived using the low-level image feature, i.e. the shape feature. The performance of the algorithm is then assessed using information retrieval based measures. The obtained result demonstrates better retrieval performance in comparison to the state of the art algorithm. The conceptual aspect of trade mark similarity is then examined and analysed using a proposed algorithm that employs semantic technology in the conceptual module. This contribution enables the computation of the conceptual similarity between trade marks, with the utilisation of an external knowledge source in the form of a lexical ontology, together with natural language processing and set similarity theory. The proposed algorithm is evaluated using both information VI retrieval and human collective opinion measures. The retrieval result produced by the proposed algorithm outperforms the traditional string similarity comparison algorithm in both measures. The phonetic module examines the phonetic similarity of trade marks using another proposed algorithm that utilises phoneme analysis. This algorithm employs phonological features, which are extracted based on human speech articulation. In addition, the algorithm also provides a mechanism to compare the phonetic aspect of trade marks with typographic characters. The proposed algorithm is the fourth contribution of this study. It is evaluated using an information retrieval based measure. The result shows better retrieval performance in comparison to the traditional string similarity algorithm. The final contribution of this study is a methodology to aggregate the overall similarity score between trade marks. It is motivated by the understanding that trade mark similarity should be assessed holistically; that is, the visual, conceptual and phonetic aspects should be considered together. The proposed method is developed in the inference engine module; it utilises fuzzy logic for the inference process. A set of fuzzy rules, which consists of several membership functions, is also derived in this study based on the trade mark manual and a collection of trade mark disputed cases is analysed. The method is then evaluated using both information retrieval and human collective opinion. The proposed method improves the retrieval accuracy and the experiment also proves that the aggregated similarity score correlates well with the score produced from human collective opinion. The evaluations performed in the course of this study employ the following datasets: the MPEG-7 shape dataset, the MPEG-7 trade marks dataset, a collection of 1400 trade marks from real trade mark dispute cases, and a collection of 378,943 company names

Actas de las XXXIV Jornadas de Automática

Postprint (published version