Detecting Multiple Copies in Tampered Images

Copy-move forgeries are parts of the image that are duplicated elsewhere into the same image, often after being modified by geometrical transformations. In this paper we present a method to detect these image alterations, using a SIFT-based approach. First we describe a state of the art SIFT-point matching method, which inspired our algorithm, then we compare it with our SIFT-based approach, which consists of three parts: keypoint clustering, cluster matching, and texture analysis. The goal is to find copies of the same object, i.e. clusters of points, rather than points that match. Cluster matching proves to give better results than single point matching, since it returns a complete and coherent comparison between copied objects. At last, textures of matching areas are analyzed and compared to validate results and to eliminate false positives

Well-Known brands recognition by automated classifiers using local and global features

From color and type to patterns and illustrations, brands sense to be recognizable and convey their values and personality. Here patterns and color are key elements, as they can play a vital role in brand recognition. The images used for brand classification were handpicked and collectively named as HKDataset. We have explored various feature extractors used for classification and used automated classifiers named Linear SVM to achieve higher accuracy while tuning the model parameters to achieve optimal performance. It has been observed that Support Vector Machines performs better when using GIST descriptors combined with Bag of SIFT features. We hope to apply deep learning and other sophisticated classifiers to much-expanded categories of brands in the future

Correlation-Based Burstiness for Logo Retrieval

International audienceDetecting logos in photos is challenging. A reason is that logos locally resemble patterns frequently seen in random images. We propose to learn a statistical model for the distribution of incorrect detections output by an image matching algorithm. It results in a novel scoring criterion in which the weight of correlated keypoint matches is reduced, penalizing irrelevant logo detections. In experiments on two very diff erent logo retrieval benchmarks, our approach largely improves over the standard matching criterion as well as other state-of-the-art approaches

Diseño de herramientas de apoyo para la detección de logotipos en secuencias de video

Este Trabajo Fin de Grado se ha realizado usando herramientas y conceptos de visión por ordenador para poder desarrollar métodos analíticos que permitan procesar una secuencia de video y obtener distintos tipos de parámetros o datos que de forma independiente o encadenados puedan llevar a realizar detecciones de logos (precargados o no) en los distintos fotogramas de la secuencia a procesar. El trabajo no se realiza sólo sobre un concepto dentro de la visión por ordenador y el procesado de imagen, sino que se intentan abarcar el máximo de herramientas y conceptos que pueden ser utilizados para detectar un logo, ya sean de color o forma. El método comienza definiendo tres pasos de pre-procesado que, motivados por las heurísticas del diseño, determinan las áreas donde un logo es más susceptible de ser localizado. Específicamente, los métodos usados son estrategias basadas en técnicas estructurales, saliencia y color que vayan reduciendo las zonas donde se ejecutarán las tareas de detección. Además, una detección de regiones estáticas en la secuencia evita detecciones en éstas áreas. En este proyecto, la detección de logotipos se logra mediante una serie de pasos, siendo el primero y más innovador el preprocesado, seguido del uso de segmentado de la imagen y matching de puntos de interés para alcanzar el reconocimiento correcto de un logotipo, que luego será revisado por varias técnicas incluyendo un módulo de perspectiva que detecta si el match está en la perspectiva general de la toma. Los logos se detectan midiendo el grado de similitud entre la plantilla transformada y el área candidata. Los resultados experimentales en una serie de secuencias elegidas validan parcialmente el diseño y método para transmisiones futbolísticas. Aunque por otro lado, los resultados muestran las limitaciones y problemas del método al analizar secuencias de otros deportes. Además, también se incluyen experimentos preliminares del uso de éste método en la generación de estadísticas enfocadas al análisis publicitario, dando resultados prometedores. En términos generales, los resultados sugieren que el uso de técnicas de pre-procesado puede ayudar en la labor de detección automática de logotipos.This work describes an automatic method for the detection of brand logos in sport sequences. The work starts by studying the solutions existing in the state-of-the art in the topic. From this study a set of conclusions is derived, and these are used to define the design of the proposed method. The method starts by defining three pre-processing methods which—motivated by design-heuristics—determine the spatial areas on which a logo is prone to be placed. Specifically, the methods use colour, structural and saliency based strategies to constrain the areas on which the logo detection process takes place. On the candidate areas—those prone to contain a logo—, a classical point-of-interest matching strategy is used to relate the candidate instances with a preload logo template. From these matches, an affine correction of the template is derived. Logos are detected by measuring the similarity between the transformed template and the candidate areas. Experimental results on a set of candidate sequences partially validate the design and development of the method for soccer sequences. However, results also illustrate the method’s drawbacks and limitations when analysing sequences of alternative sports. Furthermore, preliminary experiments on the use of the method for the generation of publicity statistics are also included, obtaining promising results. In overall, results suggest that the use of pre-processing techniques may help in the task of automatic logo detection

Diseño, análisis y optimización de un sistema de reconocimiento de imágenes basadas en contenido para imagen publicitaria

Juan Albarracín, J. (2011). Diseño, análisis y optimización de un sistema de reconocimiento de imágenes basadas en contenido para imagen publicitaria. http://hdl.handle.net/10251/13959.Archivo delegad

Trademark matching and retrieval in sports video databases

In this paper we describe a system for detection and retrieval of trademarks appearing in sports videos. We propose a compact representation of trademarks and video frame content based on SIFT feature points. This representation can be used to robustly detect, localize, and retrieve trademarks as they appear in a variety of different sports video types. Classification of trademarks is performed by matching a set of SIFT feature descriptors for each trademark instance against the set of SIFT features detected in each frame of the video. Localization is performed through robust clustering of matched feature points in the video frame. Experimental results are provided, along with an analysis of the precision and recall. Results show that the our proposed technique is efficient and effectively detects and classifies trademarks

Trademark Matching and Retrieval in Sports Video Databases

In this paper we describe a system for detection and retrieval of trademarks appearing in sports videos. We propose a compact representation of trademarks and video frame content based on SIFT feature points. This representation can be used to robustly detect, localize, and retrieve trademarks as they appear in a variety of different sports video types. Classification of trademarks is performed by matching a set of SIFT feature descriptors for each trademark instance against the set of SIFT features detected in each frame of the video. Localization is performed through robust clustering of matched feature points in the video frame. Experimental results are provided, along with an analysis of the precision and recall. Results show that the our proposed technique is efficient and effectively detects and classifies trademarks