Composite median wiener filter based technique for image enhancement

Image processing begins with image enhancement to improve the quality of the information existing in images for further processing. Noise is any unwanted object that affects the quality of original images. This always happened during the acquisition of images, which cause gaussian noise via photoelectric sensor. Also, impulse noise as well is introduced during transferring of some images from one place to another because of unstable network. Hence, these noises combine to form mixed noise in some images, which change the form and loss of information in the images. Filtering techniques are usually used in smoothing and sharpness of images, extraction the useful information and prepare an image for analysis processing. In this research, a novel technique of hybrid filter for enhancing images degraded by mixed noise has been exhibited. The proposed model of the novel filter uses the concept of two element composite filter. This technique improved the fusion of Median filter and Wiener filter to eliminate mixed form of noise from digital image created during image acquisition process. Composite Median Wiener(CMW) is not two filters in series, yet it can remove the blurredness, keep the image edges, and eliminate the mixed noise from the image. The result of CMW filter application on noisy image shows that it is an effective filter in enhancing the quality image

Target tracking with composite linear filters on noisy scenes

A tracking system using a bank of adaptive linear filters is proposed. Tracking is carried out by means of multiple target detections. The linear filters are designed from multiple views of a target using synthetic discriminant functions. For each view an optimum filter is derived from noisy reference image and disjoint background model. An iterative algorithm is used to improve the performance of the synthesized filters. The number of filters in the bank can be controlled to guarantee a prescribed tracking accuracy. Computer simulation results show that the proposed algorithm is able to precisely track a target.This work was supported the Russian Science Foundation grant №15-19-10010

Real-time tracking of multiple objects with locally adaptive correlation filters

A tracking algorithm using locally adaptive correlation filtering is proposed. The algorithm is designed to track multiple objects withinvariancetopose,occlusion,clutter,andilluminationvariations. Thealgorithmemploysapredictionschemeandcomposite correlationfilters. Thefiltersaresynthesizedwiththehelpofaniterativealgorithm,whichoptimizesdiscriminationcapabilityfor each target. The filters are adapted online to targets changes using information of current and past scene frames. Results obtained with the proposed algorithm using real-life scenes, are presented and compared with those obtained with state-of-the-art tracking methods in terms of detection efficiency, tracking accuracy, and speed of processing.This work was supported by the Russian Science Foundation, grant no. 15-19-10010

UNA NUEVA METRICA PARA UTILIZAR FILTROS DE CORRELACION EN EL RECONOCIMIENTO DE OBJETOS CON EL ROBOT HUMANOIDE NAO (A NOVEL METRIC TO USE CORRELATION FILTERS IN OBJECT RECOGNITION WITH THE NAO HUMANOID ROBOT)

ResumenEn este trabajo, se propone una nueva métrica para el uso del filtro de función discriminante sintética (Synthetic Discriminant Function, SDF por sus siglas en inglés) en el problema de reconocimiento de objetos. Se realiza una serie de experimentos con el filtro SDF en la plataforma de programación del robot humanoide NAO, que permiten observar un comportamiento de la nueva métrica (Peak to Neighboring Values, PNV por sus siglas en inglés) y predecir comportamientos futuros en situaciones similares. Con los experimentos realizados se concluye que la métrica PNV mejora notablemente la medición del desempeño del filtro, generando mejores resultados que las métricas convencionales, específicamente en los objetos que tienen variaciones en su apariencia, como cambios de escala y de rotación. Calificaciones altas en el desempeño brindan una mayor seguridad para determinar que el objeto ha sido reconocido.Palabras Claves: Peak to Neighboring Values, Reconocimiento de objetos, filtros de correlación, robot NAO. AbstractIn this paper, a new metric is proposed for the use of the Synthetic Discriminant Function (SDF) in the problem of object recognition. A series of experiments are carried out with the SDF filter in the programming platform of the NAO humanoid robot, which allow observing a behavior of the new metric (Peak to Neighboring Values, PNV) and predicting future behaviors in similar situations. With the experiments carried out, it is concluded that the PNV metric significantly improves the measurement of the filter's performance, generating better results than conventional metrics, specifically on objects that have variations in their appearance, such as changes in scale and rotation. High performance ratings provide greater security to determine that the object has been recognized.Keywords: Peak to Neighboring Values, Object Recognition, correlation filters, NAO robot

Improved methods for finger vein identification using composite median-wiener filter and hierarchical centroid features extraction

Finger vein identification is a potential new area in biometric systems. Finger vein patterns contain highly discriminative characteristics, which are difficult to be forged because they reside underneath the skin of the finger and require a specific device to capture them. Research have been carried out in this field but there is still an unresolved issue related to low-quality data due to data capturing and processing. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. To address this issue, a new image enhancement and feature extraction methods were developed to improve finger vein identification. The image enhancement, Composite Median-Wiener (CMW) filter would improve image quality and preserve the edges of the finger vein image. Next, the feature extraction method, Hierarchical Centroid Feature Method (HCM) was fused with statistical pixel-based distribution feature method at the feature-level fusion to improve the performance of finger vein identification. These methods were evaluated on public SDUMLA-HMT and FV-USM finger vein databases. Each database was divided into training and testing sets. The average result of the experiments conducted was taken to ensure the accuracy of the measurements. The k-Nearest Neighbor classifier with city block distance to match the features was implemented. Both these methods produced accuracy as high as 97.64% for identification rate and 1.11% of equal error rate (EER) for measures verification rate. These showed that the accuracy of the proposed finger vein identification method is higher than the one reported in the literature. As a conclusion, the results have proven that the CMW filter and HCM have significantly improved the accuracy of finger vein identification