Pub Date : 2011-11-01DOI: 10.1109/WNYIPW.2011.6122884
K. Adelsberger, J. Zavislan
Wavefront coding is successful at decreasing the focus dependence of an optical system. These systems require image processing and additional optical surfaces. We develop a phase surface placed near the image plane to engineer the point spread function into a similar shape. The resulting system contains a beam shaping optic that utilizes the already-present detector window and provides more flexibility to enhance resolution in systems that are inherently aberrated.
{"title":"Engineered phase window for extended depth of focus","authors":"K. Adelsberger, J. Zavislan","doi":"10.1109/WNYIPW.2011.6122884","DOIUrl":"https://doi.org/10.1109/WNYIPW.2011.6122884","url":null,"abstract":"Wavefront coding is successful at decreasing the focus dependence of an optical system. These systems require image processing and additional optical surfaces. We develop a phase surface placed near the image plane to engineer the point spread function into a similar shape. The resulting system contains a beam shaping optic that utilizes the already-present detector window and provides more flexibility to enhance resolution in systems that are inherently aberrated.","PeriodicalId":257464,"journal":{"name":"2011 Western New York Image Processing Workshop","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114129429","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 : 2011-11-01DOI: 10.1109/WNYIPW.2011.6122883
Colin P. Bellmore, R. Ptucha, A. Savakis
This paper introduces an interactive display system guided by a human observer's gesture, facial pose, and facial expression. The Kinect depth sensor is used to detect and track an observer's skeletal joints while the RGB camera is used for detailed facial analysis. The display consists of active regions that the observer can manipulate with body gestures and secluded regions that are activated through head pose and facial expression. The observer receives realtime feedback allowing for intuitive navigation of the interface. A storefront interactive display was created and feedback was collected from over one hundred subjects. Promising results demonstrate the potential of the proposed approach for human-computer interaction applications.
{"title":"Interactive display using depth and RGB sensors for face and gesture control","authors":"Colin P. Bellmore, R. Ptucha, A. Savakis","doi":"10.1109/WNYIPW.2011.6122883","DOIUrl":"https://doi.org/10.1109/WNYIPW.2011.6122883","url":null,"abstract":"This paper introduces an interactive display system guided by a human observer's gesture, facial pose, and facial expression. The Kinect depth sensor is used to detect and track an observer's skeletal joints while the RGB camera is used for detailed facial analysis. The display consists of active regions that the observer can manipulate with body gestures and secluded regions that are activated through head pose and facial expression. The observer receives realtime feedback allowing for intuitive navigation of the interface. A storefront interactive display was created and feedback was collected from over one hundred subjects. Promising results demonstrate the potential of the proposed approach for human-computer interaction applications.","PeriodicalId":257464,"journal":{"name":"2011 Western New York Image Processing Workshop","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121046575","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 : 2011-11-01DOI: 10.1109/WNYIPW.2011.6122886
M. Fernández
This paper presents an adaptive Order-Statistic Filter (OSF) that maximizes the gain in image SNR. In particular, this distribution-independent non-linear filter approximates the optimal filter when the noise is not Gaussian (e.g., speckle-type clutter, Gamma noise, etc.). Simulation results quantitatively demonstrate the superior performance of the adaptive OSF over popular linear and non-linear alternatives in the presence of non-Gaussian noise.
{"title":"Adaptive Order-Statistic Filters for optimal non-Gaussian noise suppression","authors":"M. Fernández","doi":"10.1109/WNYIPW.2011.6122886","DOIUrl":"https://doi.org/10.1109/WNYIPW.2011.6122886","url":null,"abstract":"This paper presents an adaptive Order-Statistic Filter (OSF) that maximizes the gain in image SNR. In particular, this distribution-independent non-linear filter approximates the optimal filter when the noise is not Gaussian (e.g., speckle-type clutter, Gamma noise, etc.). Simulation results quantitatively demonstrate the superior performance of the adaptive OSF over popular linear and non-linear alternatives in the presence of non-Gaussian noise.","PeriodicalId":257464,"journal":{"name":"2011 Western New York Image Processing Workshop","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122712249","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 : 2011-11-01DOI: 10.1109/WNYIPW.2011.6122885
Michael D'Angelo, R. Linares
This paper describes a path toward the development of theory for using a photon counting camera as a star tracker for spacecraft attitude estimation. The benefit of using a photon counting camera is that star data can be sampled at a faster rate while allowing one to measure very dim stars, increasing the number of stars available for attitude estimation. The development of a noise model is discussed and an algorithm to process raw data is shown. An attitude estimation method is discussed and simulated data is shown. A simulated star tracker for attitude estimation is shown and attitude estimation results are shown.
{"title":"Attitude determination using a photon counting star tracker","authors":"Michael D'Angelo, R. Linares","doi":"10.1109/WNYIPW.2011.6122885","DOIUrl":"https://doi.org/10.1109/WNYIPW.2011.6122885","url":null,"abstract":"This paper describes a path toward the development of theory for using a photon counting camera as a star tracker for spacecraft attitude estimation. The benefit of using a photon counting camera is that star data can be sampled at a faster rate while allowing one to measure very dim stars, increasing the number of stars available for attitude estimation. The development of a noise model is discussed and an algorithm to process raw data is shown. An attitude estimation method is discussed and simulated data is shown. A simulated star tracker for attitude estimation is shown and attitude estimation results are shown.","PeriodicalId":257464,"journal":{"name":"2011 Western New York Image Processing Workshop","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130811142","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 : 2011-11-01DOI: 10.1109/WNYIPW.2011.6122882
R. Gaborski, Yuheng Wang
Video classification and retrieval is currently performed manually by individuals adding semantic annotation or creating a description of the videos. Current algorithmic methods often suffer from semantic gap between visual content and human interpretation. This paper proposes a biologically inspired system that automatically cluster videos based on visual attributes. For feature extraction, each video frame is processed with a multi-scale, multi-orientation Gabor filter. The resulting Gabor-filtered sub-band images are down-sampled on a regular grid to achieve global representation of the image. For clustering, the system employs an unsupervised, adaptive algorithm, the Self-Organizing Map, resulting in the automatic discovery of video content. SOM's are single layer, two-dimensional neural networks that use the delta update rule and competition based on-line learning scheme to learn internal relationship of input data without supervision. The baseline framework is deployed and evaluated using a small dataset. Initial system results reveal effective mapping of input video frames and topological regions on SOM.
{"title":"Unsupervised learning of video content using Self-Organizing Maps","authors":"R. Gaborski, Yuheng Wang","doi":"10.1109/WNYIPW.2011.6122882","DOIUrl":"https://doi.org/10.1109/WNYIPW.2011.6122882","url":null,"abstract":"Video classification and retrieval is currently performed manually by individuals adding semantic annotation or creating a description of the videos. Current algorithmic methods often suffer from semantic gap between visual content and human interpretation. This paper proposes a biologically inspired system that automatically cluster videos based on visual attributes. For feature extraction, each video frame is processed with a multi-scale, multi-orientation Gabor filter. The resulting Gabor-filtered sub-band images are down-sampled on a regular grid to achieve global representation of the image. For clustering, the system employs an unsupervised, adaptive algorithm, the Self-Organizing Map, resulting in the automatic discovery of video content. SOM's are single layer, two-dimensional neural networks that use the delta update rule and competition based on-line learning scheme to learn internal relationship of input data without supervision. The baseline framework is deployed and evaluated using a small dataset. Initial system results reveal effective mapping of input video frames and topological regions on SOM.","PeriodicalId":257464,"journal":{"name":"2011 Western New York Image Processing Workshop","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134350911","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 : 2011-11-01DOI: 10.1109/WNYIPW.2011.6122887
B. Koc, Z. Arnavut
Color-mapped images are widely used in many applications, especially in WWW, and are usually compressed with Graphic Interchange Format (GIF) without any loss. In our recent work, we showed that further compression gains can be achieved for color-mapped images over GIF when a structured arithmetic coder is used along with the pseudo-distance metric, instead of a Huffman coder as suggested by others. In this work, we show that further compression gains are possible when block-sorting transformations are employed along with the pseudo-distance technique.
{"title":"Block-sorting transformations with pseudo-distance technique for lossless compression of color-mapped images","authors":"B. Koc, Z. Arnavut","doi":"10.1109/WNYIPW.2011.6122887","DOIUrl":"https://doi.org/10.1109/WNYIPW.2011.6122887","url":null,"abstract":"Color-mapped images are widely used in many applications, especially in WWW, and are usually compressed with Graphic Interchange Format (GIF) without any loss. In our recent work, we showed that further compression gains can be achieved for color-mapped images over GIF when a structured arithmetic coder is used along with the pseudo-distance metric, instead of a Huffman coder as suggested by others. In this work, we show that further compression gains are possible when block-sorting transformations are employed along with the pseudo-distance technique.","PeriodicalId":257464,"journal":{"name":"2011 Western New York Image Processing Workshop","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133484197","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}
Graduate students wishing to do research in areas within the purview of image processing can pursue doctorates in a variety of programs, including electrical engineering, bio-medical engineering, computer engineering, computer science, imaging science, and applied mathematics. Each of these programs has a distinct focus and provides PhD recipients with different skill sets. Panelists will discuss the aims of these programs and how their goals align with the requirements and objectives for different research careers in industry and academia. Panelists will also describe their perspectives on the key values and skills necessary for a successful career in research. The roles of academia, industrial research organizations, and professional associations such as IEEE and IS&T towards furthering research in the community will also be discussed. Panelists: 1) Dr. Nancy Ferris, Director of Eastman Kodak Research Labs. 2) Dr. Robert R. Buckley, NewMarket Imaging and Univ. of Rochester (former Xerox Research Fellow). 3) Prof. Gaurav Sharma, Dept. of Electrical Eng. at Univ. of Rochester. 4) Prof. Reneta Barneva, Chair of Dept. of Computer Science at SUNY Fredonia. 5) Prof. Jiebo Luo, Dept. of Computer Science at Univ. of Rochester. 6) Dr. Robert D. Fiete, Chief Technologist at ITT Geospatial Systems.
希望在图像处理领域进行研究的研究生可以攻读各种专业的博士学位,包括电气工程、生物医学工程、计算机工程、计算机科学、成像科学和应用数学。每个项目都有不同的重点,并为博士学位获得者提供不同的技能。小组成员将讨论这些项目的目的,以及他们的目标如何与工业和学术界不同研究职业的要求和目标相一致。小组成员还将描述他们对成功的研究事业所必需的关键价值观和技能的看法。会议还将讨论学术界、工业研究组织和专业协会(如IEEE和IS&T)对社区进一步研究的作用。小组成员:1)伊士曼柯达研究实验室主任Nancy Ferris博士2)Robert R. Buckley博士,NewMarket Imaging和罗切斯特大学(前施乐研究员)3)高拉夫·夏尔马教授,电气工程系4)纽约州立大学弗雷多尼亚分校计算机科学系主任Reneta Barneva教授,5)罗切斯特大学计算机科学系主任Jiebo Luo教授,6)ITT地理空间系统首席技术专家Robert D. Fiete博士。
{"title":"Front back","authors":"W. Freeman","doi":"10.4324/9780080454504","DOIUrl":"https://doi.org/10.4324/9780080454504","url":null,"abstract":"Graduate students wishing to do research in areas within the purview of image processing can pursue doctorates in a variety of programs, including electrical engineering, bio-medical engineering, computer engineering, computer science, imaging science, and applied mathematics. Each of these programs has a distinct focus and provides PhD recipients with different skill sets. Panelists will discuss the aims of these programs and how their goals align with the requirements and objectives for different research careers in industry and academia. Panelists will also describe their perspectives on the key values and skills necessary for a successful career in research. The roles of academia, industrial research organizations, and professional associations such as IEEE and IS&T towards furthering research in the community will also be discussed. Panelists: 1) Dr. Nancy Ferris, Director of Eastman Kodak Research Labs. 2) Dr. Robert R. Buckley, NewMarket Imaging and Univ. of Rochester (former Xerox Research Fellow). 3) Prof. Gaurav Sharma, Dept. of Electrical Eng. at Univ. of Rochester. 4) Prof. Reneta Barneva, Chair of Dept. of Computer Science at SUNY Fredonia. 5) Prof. Jiebo Luo, Dept. of Computer Science at Univ. of Rochester. 6) Dr. Robert D. Fiete, Chief Technologist at ITT Geospatial Systems.","PeriodicalId":257464,"journal":{"name":"2011 Western New York Image Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129251833","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}