Pub Date : 2017-05-22DOI: 10.1109/ATSIP.2017.8075539
Sara Belmil, M. Charif-Chefchaouni
In this paper, we present an approach of the generalization of the Spatially-Variant Morphological Operators to the color images, that preserves the concept of the structuring function. Two methods are suggested. The first method is based on total ordering and the second on marginal treatment of each component of the image. For each method, we define the notion of Spatially-Variant (SV) structuring elements, the basic color operators (dilation, erosion, opening and closing). The former operators allow the construction of morphological filters obtained by infimum, supremum and composition operations. Examples are provided through simulations to show the potential power of the defined operators for image filtering.
{"title":"Spatially-variant mathematical morphology for color images","authors":"Sara Belmil, M. Charif-Chefchaouni","doi":"10.1109/ATSIP.2017.8075539","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075539","url":null,"abstract":"In this paper, we present an approach of the generalization of the Spatially-Variant Morphological Operators to the color images, that preserves the concept of the structuring function. Two methods are suggested. The first method is based on total ordering and the second on marginal treatment of each component of the image. For each method, we define the notion of Spatially-Variant (SV) structuring elements, the basic color operators (dilation, erosion, opening and closing). The former operators allow the construction of morphological filters obtained by infimum, supremum and composition operations. Examples are provided through simulations to show the potential power of the defined operators for image filtering.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134583795","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}
Pub Date : 2017-05-22DOI: 10.1109/ATSIP.2017.8075596
Hiba Ramadan, H. Tairi
A new algorithm for automatic segmentation of moving objects in video based on spatio-temporal saliency and Neutro-Connectedness is presented in this paper. First, we propose a simple model to compute video saliency by combining initial saliency maps computed in spatial and temporal domains. Then, based on the detected spatiotemporal saliency map and temporal superpixels, initial background and foreground regions can be detected and taken as input of our proposed boundary connectedness based video cut (BC-video cut) to achieve moving object segmentation. Our model predicts jointly appearance models, Neutro-Connectedness, and pixel labels via an iterative energy minimization framework. Experiments show a good performance of our algorithm to segment moving objects on benchmark datasets.
{"title":"Boundary connectedness based video cut for moving object segmentation","authors":"Hiba Ramadan, H. Tairi","doi":"10.1109/ATSIP.2017.8075596","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075596","url":null,"abstract":"A new algorithm for automatic segmentation of moving objects in video based on spatio-temporal saliency and Neutro-Connectedness is presented in this paper. First, we propose a simple model to compute video saliency by combining initial saliency maps computed in spatial and temporal domains. Then, based on the detected spatiotemporal saliency map and temporal superpixels, initial background and foreground regions can be detected and taken as input of our proposed boundary connectedness based video cut (BC-video cut) to achieve moving object segmentation. Our model predicts jointly appearance models, Neutro-Connectedness, and pixel labels via an iterative energy minimization framework. Experiments show a good performance of our algorithm to segment moving objects on benchmark datasets.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121179999","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}
Pub Date : 2017-05-22DOI: 10.1109/ATSIP.2017.8075610
J. Cexus, A. Toumi, Orian Couderc
This paper proposes to adapt the Empirical Mode Decomposition Time-Frequency Distribution (EMD-TFD) to non-analytic complex-valued signals. This original method employs the Non uniformly Sampled Bivariate Empirical Mode Decomposition (NSBEMD) to design a filter in the ambiguity domain and clean the Time-Frequency Distribution (TFD) of the signal. This new approach is called NSBEMD-TFD. The suggested adaptation is used in the generation of Inverse Synthetic Aperture Radar (ISAR) image and compared to other Time-Frequency Representation (TFR) such as Spectrogram, Wigner-Ville Distribution (WVD)…Furthermore, two criteria to qualify TFD are adjusted to be perform on ISAR images generated by TFD. This method, called NSBEMD-TFD, and those criteria are tested on simulated data and also on data acquired from an anechoic chamber.
{"title":"Quantitative measures in ISAR image formation based on time-frequency representations","authors":"J. Cexus, A. Toumi, Orian Couderc","doi":"10.1109/ATSIP.2017.8075610","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075610","url":null,"abstract":"This paper proposes to adapt the Empirical Mode Decomposition Time-Frequency Distribution (EMD-TFD) to non-analytic complex-valued signals. This original method employs the Non uniformly Sampled Bivariate Empirical Mode Decomposition (NSBEMD) to design a filter in the ambiguity domain and clean the Time-Frequency Distribution (TFD) of the signal. This new approach is called NSBEMD-TFD. The suggested adaptation is used in the generation of Inverse Synthetic Aperture Radar (ISAR) image and compared to other Time-Frequency Representation (TFR) such as Spectrogram, Wigner-Ville Distribution (WVD)…Furthermore, two criteria to qualify TFD are adjusted to be perform on ISAR images generated by TFD. This method, called NSBEMD-TFD, and those criteria are tested on simulated data and also on data acquired from an anechoic chamber.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"243 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122721795","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}
Pub Date : 2017-05-22DOI: 10.1109/ATSIP.2017.8075533
Khaoula Belhaj Soulami, Mohamed Nabil Saidi, A. Tamtaoui
The detection of abnormalities in the breast at an early stage can be so helpful for breast cancer treatment. Currently, mammography is the cheapest and the most efficient technique in terms of identifying the suspicious lesions in the breast. However, the interpretation of this screening remains so hard and could lead to inaccurate detection known as false positive and false negative. Dense breast category mammograms particularly, are difficult to read, because it may contain abnormal structures that are similar to the normal breast tissue. In this paper, we introduce an effecient Computer-Aided-Diagnosis system for the detection and classification of the ambiguous areas in dense breast mammograms. After noise and artifacts removal from the images using 2D Median filtering and labeling, we isolate the abnormalities using the metaheuristic algorithm Electromagnetism-like Optimization (EML), then we extract shape-based descriptors from the region of interest(ROI) using Zernike Moments. The detected abormal regions were classified into normal and abnormal based on the extracted shape features and through the Support Vector Machine(SVM) classification.
{"title":"A CAD system for the detection and classification of abnormalities in dense mammograms using electromagnetism-like optimization algorithm","authors":"Khaoula Belhaj Soulami, Mohamed Nabil Saidi, A. Tamtaoui","doi":"10.1109/ATSIP.2017.8075533","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075533","url":null,"abstract":"The detection of abnormalities in the breast at an early stage can be so helpful for breast cancer treatment. Currently, mammography is the cheapest and the most efficient technique in terms of identifying the suspicious lesions in the breast. However, the interpretation of this screening remains so hard and could lead to inaccurate detection known as false positive and false negative. Dense breast category mammograms particularly, are difficult to read, because it may contain abnormal structures that are similar to the normal breast tissue. In this paper, we introduce an effecient Computer-Aided-Diagnosis system for the detection and classification of the ambiguous areas in dense breast mammograms. After noise and artifacts removal from the images using 2D Median filtering and labeling, we isolate the abnormalities using the metaheuristic algorithm Electromagnetism-like Optimization (EML), then we extract shape-based descriptors from the region of interest(ROI) using Zernike Moments. The detected abormal regions were classified into normal and abnormal based on the extracted shape features and through the Support Vector Machine(SVM) classification.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321226","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}
Pub Date : 2017-05-22DOI: 10.1109/ATSIP.2017.8075570
U. Noreen, A. Bounceur, L. Clavier
LoRa™ is low power wide area wireless network (LPWAN) protocol for Internet of Things (IoTs) applications. LPWAN has been enabling technology of large scale wireless sensor networks (WSNs). Effective cost, long range and energy efficiency of LPWANs make them most suitable candidates for smart city applications. These technologies offer novel communication paradigm to address discrete IoT's applications. LoRa is a recently proposed LPWAN technology based on spread spectrum technique with a wider band. LoRa uses the entire channel bandwidth to broadcast a signal which makes it resistant to channel noise, long term relative frequency, doppler effects and fading. This paper focuses on the emerging transmission technologies dedicated to IoT networks. Characteristics of LoRa are based on three basic parameters: Code Rate (CR), Spreading Factor (SF) and Bandwidth (BW). This paper provides in depth analysis of the impact of these three parameters on the data rate and time on air.
{"title":"A study of LoRa low power and wide area network technology","authors":"U. Noreen, A. Bounceur, L. Clavier","doi":"10.1109/ATSIP.2017.8075570","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075570","url":null,"abstract":"LoRa™ is low power wide area wireless network (LPWAN) protocol for Internet of Things (IoTs) applications. LPWAN has been enabling technology of large scale wireless sensor networks (WSNs). Effective cost, long range and energy efficiency of LPWANs make them most suitable candidates for smart city applications. These technologies offer novel communication paradigm to address discrete IoT's applications. LoRa is a recently proposed LPWAN technology based on spread spectrum technique with a wider band. LoRa uses the entire channel bandwidth to broadcast a signal which makes it resistant to channel noise, long term relative frequency, doppler effects and fading. This paper focuses on the emerging transmission technologies dedicated to IoT networks. Characteristics of LoRa are based on three basic parameters: Code Rate (CR), Spreading Factor (SF) and Bandwidth (BW). This paper provides in depth analysis of the impact of these three parameters on the data rate and time on air.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121119735","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}
Pub Date : 2017-05-22DOI: 10.1109/ATSIP.2017.8075567
Laetitia Jeancolas, H. Benali, B. Benkelfat, G. Mangone, J. Corvol, M. Vidailhet, S. Lehéricy, D. Petrovska-Delacrétaz
Vocal impairments are one of the earliest disrupted modalities in Parkinson's disease (PD). Most of the studies whose aim was to detect Parkinson's disease through acoustic analysis use global parameters. In the meantime, in speaker and speech recognition, analyses are carried out by short-term parameters, and more precisely by Mel-Frequency Cepstral Coefficients (MFCC), combined with Gaussian Mixture Models (GMM). This paper presents an adaptation of the classical methodology used in speaker recognition to the detection of early stages of Parkinson's disease. Automatic analyses were performed during 4 tasks: sustained vowels, fast syllable repetitions, free speech and reading. Men and women were considered separately in order to improve the classification performance. Leave one subject out cross validation exhibits accuracies ranging from 60% to 91% depending on the speech task and on the gender. Best performances are reached during the reading task (91% for men). This accuracy, obtained with a simple and fast methodology, is in line with the best classification results in early PD detection found in literature, obtained with more complex methods.
{"title":"Automatic detection of early stages of Parkinson's disease through acoustic voice analysis with mel-frequency cepstral coefficients","authors":"Laetitia Jeancolas, H. Benali, B. Benkelfat, G. Mangone, J. Corvol, M. Vidailhet, S. Lehéricy, D. Petrovska-Delacrétaz","doi":"10.1109/ATSIP.2017.8075567","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075567","url":null,"abstract":"Vocal impairments are one of the earliest disrupted modalities in Parkinson's disease (PD). Most of the studies whose aim was to detect Parkinson's disease through acoustic analysis use global parameters. In the meantime, in speaker and speech recognition, analyses are carried out by short-term parameters, and more precisely by Mel-Frequency Cepstral Coefficients (MFCC), combined with Gaussian Mixture Models (GMM). This paper presents an adaptation of the classical methodology used in speaker recognition to the detection of early stages of Parkinson's disease. Automatic analyses were performed during 4 tasks: sustained vowels, fast syllable repetitions, free speech and reading. Men and women were considered separately in order to improve the classification performance. Leave one subject out cross validation exhibits accuracies ranging from 60% to 91% depending on the speech task and on the gender. Best performances are reached during the reading task (91% for men). This accuracy, obtained with a simple and fast methodology, is in line with the best classification results in early PD detection found in literature, obtained with more complex methods.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129870374","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}
Pub Date : 2017-05-01DOI: 10.1109/ATSIP.2017.8075545
Y. Filali, A. Ennouni, M. A. Sabri, A. Aarab
Skin cancer is one of the most deadly cancers in the world. If not diagnosed in early stages it might be hard to cure. This paper suggests a new approach for automatic segmentation and classification of skin lesion for dermoscopic images. The segmentation is based on a pre-processing; using the color structure-texture image decomposition to decompose a textured image into texture and geometrical components. Geometrical component is used in the lesion segmentation and the texture component is used to extract the lesion texture features. Feature classification is performed using the Support Vector Machine (SVM) classifier. The efficiency and the performance of the proposed approach are evaluated in comparison with recent and robust dermoscopic approaches from literature.
{"title":"Multiscale approach for skin lesion analysis and classification","authors":"Y. Filali, A. Ennouni, M. A. Sabri, A. Aarab","doi":"10.1109/ATSIP.2017.8075545","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075545","url":null,"abstract":"Skin cancer is one of the most deadly cancers in the world. If not diagnosed in early stages it might be hard to cure. This paper suggests a new approach for automatic segmentation and classification of skin lesion for dermoscopic images. The segmentation is based on a pre-processing; using the color structure-texture image decomposition to decompose a textured image into texture and geometrical components. Geometrical component is used in the lesion segmentation and the texture component is used to extract the lesion texture features. Feature classification is performed using the Support Vector Machine (SVM) classifier. The efficiency and the performance of the proposed approach are evaluated in comparison with recent and robust dermoscopic approaches from literature.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116457450","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}
Pub Date : 2017-05-01DOI: 10.1109/ATSIP.2017.8075594
Feriel Boudamous, H. Nemmour, Yasmine Serdouk, Y. Chibani
Offline signature identification and verification systems encounter several challenges such as the diversity of signatories and the limited number of references. To address these problems we propose a new writer-independent system for signature identification and verification. Besides, a new feature generation scheme is proposed by using the Histogram Of Templates (HOT). The identification and verification step is performed by SVM. Experiments are conducted on a standard dataset which contains off-line signatures of 55 persons. The results obtained are very promising.
{"title":"An-open system for off-line handwritten signature identification and verification using histogram of templates and SVM","authors":"Feriel Boudamous, H. Nemmour, Yasmine Serdouk, Y. Chibani","doi":"10.1109/ATSIP.2017.8075594","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075594","url":null,"abstract":"Offline signature identification and verification systems encounter several challenges such as the diversity of signatories and the limited number of references. To address these problems we propose a new writer-independent system for signature identification and verification. Besides, a new feature generation scheme is proposed by using the Histogram Of Templates (HOT). The identification and verification step is performed by SVM. Experiments are conducted on a standard dataset which contains off-line signatures of 55 persons. The results obtained are very promising.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122763968","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}
Pub Date : 2017-05-01DOI: 10.1109/ATSIP.2017.8075584
Nuhu Aliyu Shuaibu, A. Malik, I. Faye, Y. Ali
Recently the traditional video surveillance systems of crowd scenes have been deployed in various areas of applications; health monitoring, security etc. Monitoring crowds and identifying their behaviors is one of the most interesting applications of visual surveillance as it is very difficult to assess crowds by human experts. In this paper, we present inter-group and intra-group properties of crowd scene; namely, we investigated collectiveness, stability, uniformity and conflict properties of crowds. A collective transition algorithm is used for crowd scene detection and segmentation. Based on this algorithm, a set of visual descriptors are extracted to quantify the group properties. The descriptors convey deeper scene information and can be effectively deploy in large crowd scene. Experiments on hundreds of crowd scenes videos were carried out on publicly available datasets. Quantitative evaluation shows that linear SVM display superior accuracy, precision, recall and F-measure in classifying human behaviors when compared to a k-nearest neighbor (kNN), and Decision Tree (DT) classifiers.
{"title":"Pedestrian group attributes detection in crowded scenes","authors":"Nuhu Aliyu Shuaibu, A. Malik, I. Faye, Y. Ali","doi":"10.1109/ATSIP.2017.8075584","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075584","url":null,"abstract":"Recently the traditional video surveillance systems of crowd scenes have been deployed in various areas of applications; health monitoring, security etc. Monitoring crowds and identifying their behaviors is one of the most interesting applications of visual surveillance as it is very difficult to assess crowds by human experts. In this paper, we present inter-group and intra-group properties of crowd scene; namely, we investigated collectiveness, stability, uniformity and conflict properties of crowds. A collective transition algorithm is used for crowd scene detection and segmentation. Based on this algorithm, a set of visual descriptors are extracted to quantify the group properties. The descriptors convey deeper scene information and can be effectively deploy in large crowd scene. Experiments on hundreds of crowd scenes videos were carried out on publicly available datasets. Quantitative evaluation shows that linear SVM display superior accuracy, precision, recall and F-measure in classifying human behaviors when compared to a k-nearest neighbor (kNN), and Decision Tree (DT) classifiers.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132802621","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}
Pub Date : 2017-05-01DOI: 10.1109/ATSIP.2017.8075577
Adnane Ouazzani Chahdi, A. Halli, Anouar Ragragui, K. Satori
The cone tracing is one of the best techniques for real time rendering of microreliefs. To find the intersection between the viewing ray and the microrelief, this technique use the empty space encoded in the form of top-opened cones. Cones are calculated in a preprocessing stage and are stored in a texture. There are two versions of this technique, the first one uses a conservative cone and the second uses a relaxed one. In this paper, we present the third version that uses the hybrid cone situated between the two already mentioned cones.
{"title":"Per-pixel displacement mapping using hybrid cone approach","authors":"Adnane Ouazzani Chahdi, A. Halli, Anouar Ragragui, K. Satori","doi":"10.1109/ATSIP.2017.8075577","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075577","url":null,"abstract":"The cone tracing is one of the best techniques for real time rendering of microreliefs. To find the intersection between the viewing ray and the microrelief, this technique use the empty space encoded in the form of top-opened cones. Cones are calculated in a preprocessing stage and are stored in a texture. There are two versions of this technique, the first one uses a conservative cone and the second uses a relaxed one. In this paper, we present the third version that uses the hybrid cone situated between the two already mentioned cones.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115793352","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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