Recent advances in technology have made tremendous amount of multimedia information available to the general population. To access the needed information in this scenario there is a need for automatic tools to filter and present information summary. Summarization techniques will give a choice to users to browse and select the multimedia documents of their choice for complete viewing later. In this work a new summarization technique to collect frames of importance in a video is presented. The method is based on selection of frames typically different from their immediate neighbors as key frames from group of similar frames. It uses the process of clustering, where visually similar frames are collected into one group using Fuzzy C means clustering algorithm. When clusters are formed, the frames that exhibit a change ratio which is a measure of the content variation, greater than the average value of the cluster are treated as Key frames. The summary is created by merging Key frames on the basis of their timeline. This method ensures that video summary represents the most unique frames of the input video and gives equal attention to preserving continuity of the summarized video. The robustness of the algorithm is validated by average values of performance parameters. The average compression ratio of 92% is indication of higher conciseness. The average fidelity of 95% is an indicative of comprehensive representation of video by the key frames selected using proposed algorithm.
{"title":"Entropy Based Fuzzy C Means Clustering and Key Frame Extraction for Sports Video Summarization","authors":"S. Angadi, Vilas Naik","doi":"10.1109/ICSIP.2014.49","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.49","url":null,"abstract":"Recent advances in technology have made tremendous amount of multimedia information available to the general population. To access the needed information in this scenario there is a need for automatic tools to filter and present information summary. Summarization techniques will give a choice to users to browse and select the multimedia documents of their choice for complete viewing later. In this work a new summarization technique to collect frames of importance in a video is presented. The method is based on selection of frames typically different from their immediate neighbors as key frames from group of similar frames. It uses the process of clustering, where visually similar frames are collected into one group using Fuzzy C means clustering algorithm. When clusters are formed, the frames that exhibit a change ratio which is a measure of the content variation, greater than the average value of the cluster are treated as Key frames. The summary is created by merging Key frames on the basis of their timeline. This method ensures that video summary represents the most unique frames of the input video and gives equal attention to preserving continuity of the summarized video. The robustness of the algorithm is validated by average values of performance parameters. The average compression ratio of 92% is indication of higher conciseness. The average fidelity of 95% is an indicative of comprehensive representation of video by the key frames selected using proposed algorithm.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131918181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. S. Bhat, Namrata S. Prasad, Amith V S, M. Murali
Over the past decade, a lot of research has been done in audio content analysis for extracting various kinds of information, especially the moods it denotes, from an audio signal, because music expresses emotions in a concise and succinct way, yet in an effective way. People select music in congruence to their moods and emotions, making the need to classify music in accordance to moods more of a demand. Since different individuals have different perceptions about classifying music according to mood, it becomes a much more difficult task. This paper proposes an automated and efficient method to perceive the mood of any given music piece, or the "emotions" related to it, by drawing out a link between the spectral and harmonic features and human perception of music and moods. Features such as rhythm, harmony, spectral feature, and so on, are studied in order to classify the songs according to its mood, based on Thayer's model. The values of the quantified features are then compared against the threshold value using neural networks before classifying them according to different mood labels. The method analyzes many different features of the music piece, including spectra of beat and roughness, before classifying it under any mood. A total of 8 different moods are considered. In particular, the paper classifies both western and Indian Hindi film music, taking into consideration, a database of over 100 songs in total. The efficiency of this method was found to reach 94.44% at the best.
{"title":"An Efficient Classification Algorithm for Music Mood Detection in Western and Hindi Music Using Audio Feature Extraction","authors":"A. S. Bhat, Namrata S. Prasad, Amith V S, M. Murali","doi":"10.1109/ICSIP.2014.63","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.63","url":null,"abstract":"Over the past decade, a lot of research has been done in audio content analysis for extracting various kinds of information, especially the moods it denotes, from an audio signal, because music expresses emotions in a concise and succinct way, yet in an effective way. People select music in congruence to their moods and emotions, making the need to classify music in accordance to moods more of a demand. Since different individuals have different perceptions about classifying music according to mood, it becomes a much more difficult task. This paper proposes an automated and efficient method to perceive the mood of any given music piece, or the \"emotions\" related to it, by drawing out a link between the spectral and harmonic features and human perception of music and moods. Features such as rhythm, harmony, spectral feature, and so on, are studied in order to classify the songs according to its mood, based on Thayer's model. The values of the quantified features are then compared against the threshold value using neural networks before classifying them according to different mood labels. The method analyzes many different features of the music piece, including spectra of beat and roughness, before classifying it under any mood. A total of 8 different moods are considered. In particular, the paper classifies both western and Indian Hindi film music, taking into consideration, a database of over 100 songs in total. The efficiency of this method was found to reach 94.44% at the best.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121919487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With high volumes of Multimedia data transmission becoming a reality, most of the networks are capable of meeting only bare requirements of video information transfer. An important aspect of video transfer is to deal with User's satisfaction in terms of quality of service. Hence new methods are being proposed to meet user's criteria. In this paper a frame work has been described based on Artificial Neural Networks for real time video quality assessment.
{"title":"Automated Real Time Video Quality Measurement Using Levenberg-Marquardt Backpropagation Algorithm","authors":"P. Archana, S. Kulkarni","doi":"10.1109/ICSIP.2014.61","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.61","url":null,"abstract":"With high volumes of Multimedia data transmission becoming a reality, most of the networks are capable of meeting only bare requirements of video information transfer. An important aspect of video transfer is to deal with User's satisfaction in terms of quality of service. Hence new methods are being proposed to meet user's criteria. In this paper a frame work has been described based on Artificial Neural Networks for real time video quality assessment.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122970226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stereo Image rectification is more popular due to its robustness and vast amount of search space reduction in stereo correspondence problem. It has wide range of applications in 3d medical imaging, robot navigation, virtual reality, and entertainment etc. Rectified Images includes more distortion which will affect 3d imaging quality. In this paper, we choose time stamped stereo frames from stereo video streams from heterogeneous cameras. These stereo image pairs are verified for suitability for calibration using error pair elimination method. Heterogeneous cameras are calibrated from the selected stereo image pairs and followed by rectification of stereo image pairs by compensating the focal lengths of the cameras. The resultant rectified image has lesser number of hollow pixels as compared to conventional algorithm. Lesser number of hollow pixels results in accurate 3D co-ordinate estimation compared with conventional algorithm.
{"title":"Stereo Image Rectification Using Focal Length Adjustment","authors":"M. S. Kumar, N. Avinash","doi":"10.1109/ICSIP.2014.45","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.45","url":null,"abstract":"Stereo Image rectification is more popular due to its robustness and vast amount of search space reduction in stereo correspondence problem. It has wide range of applications in 3d medical imaging, robot navigation, virtual reality, and entertainment etc. Rectified Images includes more distortion which will affect 3d imaging quality. In this paper, we choose time stamped stereo frames from stereo video streams from heterogeneous cameras. These stereo image pairs are verified for suitability for calibration using error pair elimination method. Heterogeneous cameras are calibrated from the selected stereo image pairs and followed by rectification of stereo image pairs by compensating the focal lengths of the cameras. The resultant rectified image has lesser number of hollow pixels as compared to conventional algorithm. Lesser number of hollow pixels results in accurate 3D co-ordinate estimation compared with conventional algorithm.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122723518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Prasanth, K. K, Mayank Kumar, Bhargav B V S, Mrinmoy Ghorai
In this paper, an integrated methodology is proposed to virtually enhance the mural images by taking the weighted average of original image with the mean image. The algorithm consists of four major steps as described in the paper. A new line detection and extraction technique using correlation followed by convolution with different templates is implemented and explained. The synthesis of the templates is also explained in detail. Toggle filter is used to enhance the lines. This step is followed by K-means clustering, averaging pixels and weighted average. An idea on recovery of degraded patch is also presented. The results of our experiment are found to be good and may be used to restore deteriorated digital mural images.
{"title":"Digital Restoration of Deteriorated Mural Images","authors":"M. Prasanth, K. K, Mayank Kumar, Bhargav B V S, Mrinmoy Ghorai","doi":"10.1109/ICSIP.2014.10","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.10","url":null,"abstract":"In this paper, an integrated methodology is proposed to virtually enhance the mural images by taking the weighted average of original image with the mean image. The algorithm consists of four major steps as described in the paper. A new line detection and extraction technique using correlation followed by convolution with different templates is implemented and explained. The synthesis of the templates is also explained in detail. Toggle filter is used to enhance the lines. This step is followed by K-means clustering, averaging pixels and weighted average. An idea on recovery of degraded patch is also presented. The results of our experiment are found to be good and may be used to restore deteriorated digital mural images.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130925582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper details about segmentation of iris region for iris recognition as a biometrical personal identification and verification. Human iris is unique and differs from one individual to another. Just as finger prints, biomedical proves human irises are distinct. Also, iris can be easily accessed from any visual capturing device. The two dimensional structure of iris further assists the technology. This paper describes the extraction of iris region from an image of the human eye. The proposed algorithm defines a new method to segment Iris from the image. It's a new technique for circular edge detection particularly for Iris recognition. An image undergoes various operations like black and white conversion, edge detection and filtering. The fact that the intensity of iris lies between the intensities of pupil and rest of the eye is the key here to extract iris. A simple vertical and horizontal scan is done over the image to get the tangents of the circles. A mathematical analysis is done on the images to get the radius and the center of the circle and hence the inner and outer circles of the iris are drawn or Hough transform can be done using the obtained values for more accuracy. We are constructed the circles after obtaining the values.
{"title":"A Novel Approach to Circular Edge Detection for Iris Image Segmentation","authors":"Shashidhara H R, A. Aswath","doi":"10.1109/ICSIP.2014.56","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.56","url":null,"abstract":"This paper details about segmentation of iris region for iris recognition as a biometrical personal identification and verification. Human iris is unique and differs from one individual to another. Just as finger prints, biomedical proves human irises are distinct. Also, iris can be easily accessed from any visual capturing device. The two dimensional structure of iris further assists the technology. This paper describes the extraction of iris region from an image of the human eye. The proposed algorithm defines a new method to segment Iris from the image. It's a new technique for circular edge detection particularly for Iris recognition. An image undergoes various operations like black and white conversion, edge detection and filtering. The fact that the intensity of iris lies between the intensities of pupil and rest of the eye is the key here to extract iris. A simple vertical and horizontal scan is done over the image to get the tangents of the circles. A mathematical analysis is done on the images to get the radius and the center of the circle and hence the inner and outer circles of the iris are drawn or Hough transform can be done using the obtained values for more accuracy. We are constructed the circles after obtaining the values.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"48 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126844515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we propose a novel approach for content-based image retrieval with relevance feedback, which is based on Riemannian Manifold learning algorithm. This method uses positive and negative (relevant/irrelevant) images labeled by the user at every feedback iteration. In this paper, we pre-computed the cost adjacency matrix and its eigenvectors corresponding to the smallest eigen values for effectiveness and efficiency of the retrieval system. Then we apply the Riemannian Manifolds learning concept to estimate the boundary between positive and negative images. Experimental results of the proposed method have been compared with earlier approaches, which show the superiority of the proposed method.
{"title":"Content Based Image Retrieval with Relevance Feedback Using Riemannian Manifolds","authors":"Pushpa B. Patil, M. Kokare","doi":"10.1109/ICSIP.2014.9","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.9","url":null,"abstract":"In this paper we propose a novel approach for content-based image retrieval with relevance feedback, which is based on Riemannian Manifold learning algorithm. This method uses positive and negative (relevant/irrelevant) images labeled by the user at every feedback iteration. In this paper, we pre-computed the cost adjacency matrix and its eigenvectors corresponding to the smallest eigen values for effectiveness and efficiency of the retrieval system. Then we apply the Riemannian Manifolds learning concept to estimate the boundary between positive and negative images. Experimental results of the proposed method have been compared with earlier approaches, which show the superiority of the proposed method.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115217759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gazal Garg, P. Mondal, S. M. Aswatha, Jit Mukherjee, Tapas Maji, J. Mukherjee
In this paper, a secured web based online subjective image evaluation system has been proposed to assess different image processing algorithms. Since many image processing algorithms are designed to enhance the human perception of available image cues, subjective evaluation plays an important role in the assessment of the same. The proposed technique assesses several similar processes by accumulation of votes by individual human evaluators through pair wise comparisons of their outputs. Three tournament strategies are used for the pair wise image comparisons, namely knockout, challenging, and round-robin. The experiments show a satisfactory result in evaluation of accumulated ensemble of evaluators' votes, which is validated using Berkeley boundary detection benchmark dataset.
{"title":"VIMARSHAK -- A Web Based Subjective Image Evaluation System","authors":"Gazal Garg, P. Mondal, S. M. Aswatha, Jit Mukherjee, Tapas Maji, J. Mukherjee","doi":"10.1109/ICSIP.2014.16","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.16","url":null,"abstract":"In this paper, a secured web based online subjective image evaluation system has been proposed to assess different image processing algorithms. Since many image processing algorithms are designed to enhance the human perception of available image cues, subjective evaluation plays an important role in the assessment of the same. The proposed technique assesses several similar processes by accumulation of votes by individual human evaluators through pair wise comparisons of their outputs. Three tournament strategies are used for the pair wise image comparisons, namely knockout, challenging, and round-robin. The experiments show a satisfactory result in evaluation of accumulated ensemble of evaluators' votes, which is validated using Berkeley boundary detection benchmark dataset.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128704875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image segmentation is a fundamental task in image analysis which is responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours. The goal of medical image segmentation is to partition a medical image in to separate regions, usually anatomic structures that are meaningful for a specific task. In many medical applications, such as diagnosis, surgery planning, and radiation treatment planning determining of the volume and position of an anatomic structure is required and plays a critical role in the treatment outcome.
{"title":"An Active Contour Method for MR Image Segmentation of Anterior Cruciate Ligament (ACL)","authors":"N. A. Vinay, H. Vinay, T. Narendra","doi":"10.1109/ICSIP.2014.28","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.28","url":null,"abstract":"Image segmentation is a fundamental task in image analysis which is responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours. The goal of medical image segmentation is to partition a medical image in to separate regions, usually anatomic structures that are meaningful for a specific task. In many medical applications, such as diagnosis, surgery planning, and radiation treatment planning determining of the volume and position of an anatomic structure is required and plays a critical role in the treatment outcome.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122364273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data Acquisition (DAQ) and Telemetry are part of the winning formula of any race team or vehicle manufacturer. It is vital to the development phase of a vehicle, so that designs can be validated and tunable parameters adjusted to increase performance and efficiency. Existing DAQ systems fail as they are of universal application type and turn out to be extremely costly and power hungry. Also, the lack of filtering stages is an issue for sensor data coming from a moving vehicle. The system discussed in this paper was designed specifically for automotive application, taking into account the size, cost and performance, while also taking care of the noise factor by including digital filters. The system was found to be 2.2 times more cost effective than current modules, with a data rate of 9600Hz and 10 bit resolution for DAQ and the telemetry system working at a serial data baud rate of 9600 transmitted wirelessly through a ZigBee network.
{"title":"Vehicle Data Acquisition and Telemetry","authors":"J. Chandiramani, Sanjam Bhandari, S. Hariprasad","doi":"10.1109/ICSIP.2014.35","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.35","url":null,"abstract":"Data Acquisition (DAQ) and Telemetry are part of the winning formula of any race team or vehicle manufacturer. It is vital to the development phase of a vehicle, so that designs can be validated and tunable parameters adjusted to increase performance and efficiency. Existing DAQ systems fail as they are of universal application type and turn out to be extremely costly and power hungry. Also, the lack of filtering stages is an issue for sensor data coming from a moving vehicle. The system discussed in this paper was designed specifically for automotive application, taking into account the size, cost and performance, while also taking care of the noise factor by including digital filters. The system was found to be 2.2 times more cost effective than current modules, with a data rate of 9600Hz and 10 bit resolution for DAQ and the telemetry system working at a serial data baud rate of 9600 transmitted wirelessly through a ZigBee network.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130471796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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