Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073577
S. Urbán, A. Tanács
Organ segmentation in the head and neck region is very challenging due to the large variability of the shape and size of organs among patients. Accurate and consistent segmentation of the organ-at-risk (OAR) regions is important in radiation treatment planning. This paper presents a fully automated atlas- and learning-based method for segmenting three OARs (trachea, spinal cord, parotid glands) in multimodal head-and-neck MRI images. The proposed method consists of three main parts. First, a probabilistic atlas is generated. Then, a Random Forest classifier that incorporates the atlas as well as various image features of the multimodal images is applied globally and locally in order to handle local variations. The method was trained and tested on 30 multimodal MRI examinations including T2w, T1w and T1w fat saturated images. Manually defined contours were used as reference. The presented results show good correlation with the reference using DICE similarity measurements. Based on these preliminary results the proposed method can be adapted to other organs of the head-and-neck region.
{"title":"Atlas-based global and local RF segmentation of head and neck organs on multimodal MRI images","authors":"S. Urbán, A. Tanács","doi":"10.1109/ISPA.2017.8073577","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073577","url":null,"abstract":"Organ segmentation in the head and neck region is very challenging due to the large variability of the shape and size of organs among patients. Accurate and consistent segmentation of the organ-at-risk (OAR) regions is important in radiation treatment planning. This paper presents a fully automated atlas- and learning-based method for segmenting three OARs (trachea, spinal cord, parotid glands) in multimodal head-and-neck MRI images. The proposed method consists of three main parts. First, a probabilistic atlas is generated. Then, a Random Forest classifier that incorporates the atlas as well as various image features of the multimodal images is applied globally and locally in order to handle local variations. The method was trained and tested on 30 multimodal MRI examinations including T2w, T1w and T1w fat saturated images. Manually defined contours were used as reference. The presented results show good correlation with the reference using DICE similarity measurements. Based on these preliminary results the proposed method can be adapted to other organs of the head-and-neck region.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116589456","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 : 2017-09-01DOI: 10.1109/ISPA.2017.8073578
Nikola Banić, S. Lončarić
Retinex theory was among the first to introduce a model for simultaneous brightness adjustment and removal of illumination influence on image colors by supposedly emulating some aspects of the human visual system's behaviour. The main idea of most Retinex methods is to readjust color channel values of individual pixels with respect to their local white references. Recently the Smart Light Random Memory Sprays Retinex (SLRMSR) method with a O(1) per-pixel complexity was proposed. Although theoretically fast, like with many other Retinex methods, the problem is that its local pixel sampling scheme and some of its local maximum calculation structures common to other Retinex methods as well are not particularly hardware-friendly. In this paper a reduced sampling scheme and an approximated local maxima calculation are proposed and included into a modified SLRMSR. While the resulting images are visually very similar to the ones obtained by the original SLRMSR, the modified SLRMSR is structurally simpler and more hardware friendly. The results are presented and discussed.
{"title":"Towards hardware-friendly retinex algorithms","authors":"Nikola Banić, S. Lončarić","doi":"10.1109/ISPA.2017.8073578","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073578","url":null,"abstract":"Retinex theory was among the first to introduce a model for simultaneous brightness adjustment and removal of illumination influence on image colors by supposedly emulating some aspects of the human visual system's behaviour. The main idea of most Retinex methods is to readjust color channel values of individual pixels with respect to their local white references. Recently the Smart Light Random Memory Sprays Retinex (SLRMSR) method with a O(1) per-pixel complexity was proposed. Although theoretically fast, like with many other Retinex methods, the problem is that its local pixel sampling scheme and some of its local maximum calculation structures common to other Retinex methods as well are not particularly hardware-friendly. In this paper a reduced sampling scheme and an approximated local maxima calculation are proposed and included into a modified SLRMSR. While the resulting images are visually very similar to the ones obtained by the original SLRMSR, the modified SLRMSR is structurally simpler and more hardware friendly. The results are presented and discussed.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129051080","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 : 2017-09-01DOI: 10.1109/ISPA.2017.8073569
V. Antsiperov
The report substantiates the concept of the analytic spectrum and the synthesis of time-frequency representations of signals based on it. A number of properties of the analytic spectrum are considered and their comparison with the corresponding properties of the analytic signal is carried out. On the basis of this comparison, key features of similarity and dissimilarity between these dual concepts are formulated. The report discusses the relation between the analytic spectra of the local past and local future of the signal with the Page and Levin instantaneous spectra concepts. The report also presents the relation of analytic spectra of the signal's local past and future with the popular quadratic cone-shaped (Zhao-Atlas-Marks) time-frequency representations.
{"title":"Analytic spectrum as a tool for time-frequency signal analysis","authors":"V. Antsiperov","doi":"10.1109/ISPA.2017.8073569","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073569","url":null,"abstract":"The report substantiates the concept of the analytic spectrum and the synthesis of time-frequency representations of signals based on it. A number of properties of the analytic spectrum are considered and their comparison with the corresponding properties of the analytic signal is carried out. On the basis of this comparison, key features of similarity and dissimilarity between these dual concepts are formulated. The report discusses the relation between the analytic spectra of the local past and local future of the signal with the Page and Levin instantaneous spectra concepts. The report also presents the relation of analytic spectra of the signal's local past and future with the popular quadratic cone-shaped (Zhao-Atlas-Marks) time-frequency representations.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"92 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120886016","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 : 2017-09-01DOI: 10.1109/ISPA.2017.8073593
P. Skulimowski, Mateusz Owczarek, P. Strumiłło
In this paper we propose a fast method for detecting doors in images of 3D scenes. First, the equation estimating the orientation and location of the ground surface is computed. This information is used in further processing steps of the algorithm. Then, the edge image is calculated (using the Canny edge detector) and line segments justifying predefined conditions are searched for by applying the Probabilistic Hough Transform method. Pairs of parallel line segments perpendicular to the ground surface located at a distance range 80–110 cm are identified. The detection performance has been also enhanced by detecting door handles. The proposed method was successfully verified on the recorded indoor RGB-D video sequences acquired by a vision based Electronic Travel Aid (ETA) for the blind. The achieved door detection performance for the tested sequences is at a level of 63% for sensitivity and 84% for positive predictivity values.
{"title":"Door detection in images of 3D scenes in an electronic travel aid for the blind","authors":"P. Skulimowski, Mateusz Owczarek, P. Strumiłło","doi":"10.1109/ISPA.2017.8073593","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073593","url":null,"abstract":"In this paper we propose a fast method for detecting doors in images of 3D scenes. First, the equation estimating the orientation and location of the ground surface is computed. This information is used in further processing steps of the algorithm. Then, the edge image is calculated (using the Canny edge detector) and line segments justifying predefined conditions are searched for by applying the Probabilistic Hough Transform method. Pairs of parallel line segments perpendicular to the ground surface located at a distance range 80–110 cm are identified. The detection performance has been also enhanced by detecting door handles. The proposed method was successfully verified on the recorded indoor RGB-D video sequences acquired by a vision based Electronic Travel Aid (ETA) for the blind. The achieved door detection performance for the tested sequences is at a level of 63% for sensitivity and 84% for positive predictivity values.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132837626","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 : 2017-09-01DOI: 10.1109/ISPA.2017.8073575
Attila Tiba, B. Harangi, A. Hajdu
Co-occurrence matrices as sources of second order statistical descriptors are commonly used in texture classification tasks. To generate such a matrix, we need a position vector to check possible intensity frequencies in its endpoints. In this paper, we propose an efficient algorithm to locate such position vectors according which the pattern of the texture repeats and thus, the descriptors (Haralick features) derived from the co-occurrence matrix are capable to characterize the regularity of the pattern. The essence of our approach is to look for vectors that span well-approximating grids defined by reference points obtained by quantizing the input image. To extract such grids we use the LLL algorithm, which has a polynomial running time. Thus, we have a much more efficient solution than e.g. a brute force based search. Our results show that the proposed approach is capable to suggest position vectors for an efficient co-occurrence matrix based texture analysis.
{"title":"Efficient texture regularity estimation for second order statistical descriptors","authors":"Attila Tiba, B. Harangi, A. Hajdu","doi":"10.1109/ISPA.2017.8073575","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073575","url":null,"abstract":"Co-occurrence matrices as sources of second order statistical descriptors are commonly used in texture classification tasks. To generate such a matrix, we need a position vector to check possible intensity frequencies in its endpoints. In this paper, we propose an efficient algorithm to locate such position vectors according which the pattern of the texture repeats and thus, the descriptors (Haralick features) derived from the co-occurrence matrix are capable to characterize the regularity of the pattern. The essence of our approach is to look for vectors that span well-approximating grids defined by reference points obtained by quantizing the input image. To extract such grids we use the LLL algorithm, which has a polynomial running time. Thus, we have a much more efficient solution than e.g. a brute force based search. Our results show that the proposed approach is capable to suggest position vectors for an efficient co-occurrence matrix based texture analysis.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133445219","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 : 2017-09-01DOI: 10.1109/ISPA.2017.8073565
David Kupas, B. Harangi, Gyorgy Czifra, G. Andrassy
Current diagnosis of neurological disorders is an expensive and time-consuming task. Our goal is to make this procedure easier and more accurate using a digital eye scanner. Our system can help in making diagnoses, assists in the practice and shortens the time needed to find the appropriate treatment. First and foremost we collect all important visual effects in the field of neurological examination and create a video to make possible the testing of the eye movement of the patient during the video. Their gaze data is collected by an appropriate eye tracker, then we analyze the gaze information in order to evaluate the mental state of the patient using machine learning based algorithms. According to the experimental results, our proposed technique can separate the healthy and ill patients from each other using their gaze data.
{"title":"Decision support system for the diagnosis of neurological disorders based on gaze tracking","authors":"David Kupas, B. Harangi, Gyorgy Czifra, G. Andrassy","doi":"10.1109/ISPA.2017.8073565","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073565","url":null,"abstract":"Current diagnosis of neurological disorders is an expensive and time-consuming task. Our goal is to make this procedure easier and more accurate using a digital eye scanner. Our system can help in making diagnoses, assists in the practice and shortens the time needed to find the appropriate treatment. First and foremost we collect all important visual effects in the field of neurological examination and create a video to make possible the testing of the eye movement of the patient during the video. Their gaze data is collected by an appropriate eye tracker, then we analyze the gaze information in order to evaluate the mental state of the patient using machine learning based algorithms. According to the experimental results, our proposed technique can separate the healthy and ill patients from each other using their gaze data.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133647540","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 : 2017-09-01DOI: 10.1109/ISPA.2017.8073589
Z. Su, Hong Zheng, Guorui Song
Salient object detection has become a valuable tool in image processing. In this paper, we propose a novel approach to get full-resolution saliency maps. The input image is segmented into superpixels, each of them presents an irregular but homogenous area of the image thus can be treated as an image unit. Intuitively, superpixels touching the image borders will have the potential to capture the background information. Therefore, pixels belong to those superpixels are collected as background samples to train a Gaussian mixture model. The saliency of each superpixel is then defined by computing the weighted probability density of the Gaussian mixture model followed by an enhancement and smoothness step. At the end, a dense conditional random field based refinement tool or cellular automata is selected by an adaptive threshold to remove the false salient regions or find other potential saliency regions to get a more accurate result in pixel-level. We compare our method to five saliency detection algorithms which are classic or similar to ours but published in recent years on a commonly used challenging dataset ECSSD. Experiments show that our approach outperforms others well.
{"title":"Gaussian mixture background for salient object detection","authors":"Z. Su, Hong Zheng, Guorui Song","doi":"10.1109/ISPA.2017.8073589","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073589","url":null,"abstract":"Salient object detection has become a valuable tool in image processing. In this paper, we propose a novel approach to get full-resolution saliency maps. The input image is segmented into superpixels, each of them presents an irregular but homogenous area of the image thus can be treated as an image unit. Intuitively, superpixels touching the image borders will have the potential to capture the background information. Therefore, pixels belong to those superpixels are collected as background samples to train a Gaussian mixture model. The saliency of each superpixel is then defined by computing the weighted probability density of the Gaussian mixture model followed by an enhancement and smoothness step. At the end, a dense conditional random field based refinement tool or cellular automata is selected by an adaptive threshold to remove the false salient regions or find other potential saliency regions to get a more accurate result in pixel-level. We compare our method to five saliency detection algorithms which are classic or similar to ours but published in recent years on a commonly used challenging dataset ECSSD. Experiments show that our approach outperforms others well.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115821108","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 : 2017-09-01DOI: 10.1109/ISPA.2017.8073563
Luka Cular, Mia Tomaic, M. Subašić, T. Saric, Viktorija Sajkovic, M. Vodanović
This paper presents an application of computer vision methods to dental age estimation based on the lower third right molar in panoramic X-ray images. For this purpose, two statistical computer vision models are adjusted and applied: Active Shape Model and Active Appearance Model. Both models use shape and appearance of the object to find the outer contour, with the only difference being in the way appearance is used. Statistical models are used to extract features describing the selected tooth, and neural network is used to provide dental age estimation using the features as input. Our own dataset was created, consisting of panoramic X-ray images with known age. A manual segmentation of the selected tooth has been performed for each image in the training set, and the obtained outer contours were used to train both models. Promising preliminary results are presented.
{"title":"Dental age estimation from panoramic X-ray images using statistical models","authors":"Luka Cular, Mia Tomaic, M. Subašić, T. Saric, Viktorija Sajkovic, M. Vodanović","doi":"10.1109/ISPA.2017.8073563","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073563","url":null,"abstract":"This paper presents an application of computer vision methods to dental age estimation based on the lower third right molar in panoramic X-ray images. For this purpose, two statistical computer vision models are adjusted and applied: Active Shape Model and Active Appearance Model. Both models use shape and appearance of the object to find the outer contour, with the only difference being in the way appearance is used. Statistical models are used to extract features describing the selected tooth, and neural network is used to provide dental age estimation using the features as input. Our own dataset was created, consisting of panoramic X-ray images with known age. A manual segmentation of the selected tooth has been performed for each image in the training set, and the obtained outer contours were used to train both models. Promising preliminary results are presented.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129130372","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 : 2017-09-01DOI: 10.1109/ISPA.2017.8073595
A. Tanács
Segmenting organs in MRI images is a common task in medical practice where image registration techniques can be used in the preprocessing steps to reduce the required interactivity. This is especially true in the head and neck region where large variability of shape and size of organs is present among patients. When an image database of MRI images and segmented organ contours are available, these can be used to build probability atlases in a selected reference frame. The atlas data can then be transformed to the coordinate systems of studies to be segmented applying the transformations in the inverse direction. In this paper two registration approaches for atlas building are evaluated and compared. Separate atlases for 6 organs (spinal cord, trachea, carotis, jugularis, parotis, sternocleidomastoid muscle — SCM) are built from 15 MRI T2 weighted Fast Relaxation Fast Spin Echo (FRFSE) studies using expert segmented organ contours and evaluated using further 15 such studies. The evaluation takes into account the overlap of the expert segmented organ regions and the transformed probability atlases, the discrimination capabilities of the atlases in the carotis-jugularis region, and the errors induced by the inverse registration approach. The results show the superiority of the multiresolution B-Spline transformation implemented by the elastix package against a less flexible, composite transformation formed using scaled rigid + single resolution B-Spline approach. The presented framework can be used for e.g., determining regions of interests (ROIs) as a preprocessing step of learning based fully automatic segmentation approaches.
{"title":"Generation and evaluation of an MRI statistical organ atlas in the head-neck region","authors":"A. Tanács","doi":"10.1109/ISPA.2017.8073595","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073595","url":null,"abstract":"Segmenting organs in MRI images is a common task in medical practice where image registration techniques can be used in the preprocessing steps to reduce the required interactivity. This is especially true in the head and neck region where large variability of shape and size of organs is present among patients. When an image database of MRI images and segmented organ contours are available, these can be used to build probability atlases in a selected reference frame. The atlas data can then be transformed to the coordinate systems of studies to be segmented applying the transformations in the inverse direction. In this paper two registration approaches for atlas building are evaluated and compared. Separate atlases for 6 organs (spinal cord, trachea, carotis, jugularis, parotis, sternocleidomastoid muscle — SCM) are built from 15 MRI T2 weighted Fast Relaxation Fast Spin Echo (FRFSE) studies using expert segmented organ contours and evaluated using further 15 such studies. The evaluation takes into account the overlap of the expert segmented organ regions and the transformed probability atlases, the discrimination capabilities of the atlases in the carotis-jugularis region, and the errors induced by the inverse registration approach. The results show the superiority of the multiresolution B-Spline transformation implemented by the elastix package against a less flexible, composite transformation formed using scaled rigid + single resolution B-Spline approach. The presented framework can be used for e.g., determining regions of interests (ROIs) as a preprocessing step of learning based fully automatic segmentation approaches.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122312409","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 : 2017-09-01DOI: 10.1109/ISPA.2017.8073583
V. Kitanovski, Marius Pedersen
This paper addresses the visual masking that occurs in the chrominance channels of natural images. We present results from a psychophysical experiment designed to obtain local thresholds of just noticeable log-Gabor distortion in the Cr and Cb channels of natural images. We analyzed the data and investigated the correlation between several low-level image features and the collected thresholds. As expected, features like variance, entropy, or edge density were correlated relatively high with the thresholds. We evaluated the performance of linear and non-linear regression (using neural networks and support vector machines) for thresholds prediction from multiple global image features; we also fitted a modified Watson-Solomon's computational model (based on log-Gabor features) for thresholds prediction. The evaluation showed that neural networks and support vector machines are most suitable for thresholds prediction. The computational model performed reasonably well, with further prospects of its improvement.
{"title":"Masking in chrominance channels of natural images — Data, analysis, and prediction","authors":"V. Kitanovski, Marius Pedersen","doi":"10.1109/ISPA.2017.8073583","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073583","url":null,"abstract":"This paper addresses the visual masking that occurs in the chrominance channels of natural images. We present results from a psychophysical experiment designed to obtain local thresholds of just noticeable log-Gabor distortion in the Cr and Cb channels of natural images. We analyzed the data and investigated the correlation between several low-level image features and the collected thresholds. As expected, features like variance, entropy, or edge density were correlated relatively high with the thresholds. We evaluated the performance of linear and non-linear regression (using neural networks and support vector machines) for thresholds prediction from multiple global image features; we also fitted a modified Watson-Solomon's computational model (based on log-Gabor features) for thresholds prediction. The evaluation showed that neural networks and support vector machines are most suitable for thresholds prediction. The computational model performed reasonably well, with further prospects of its improvement.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133737372","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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