Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001最新文献

{"title":"Segmentation of the left ventricle in cardiac MR images","authors":"M. Jolly, N. Duta, G. Funka-Lea","doi":"10.1109/ICCV.2001.937558","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937558","url":null,"abstract":"This paper describes a segmentation technique to automatically extract the myocardium in 4D cardiac MR images for quantitative cardiac analysis and the diagnosis of patients. Three different modules are presented. The automatic localization algorithm is able to approximately locate the left ventricle in an image using a maximum discrimination technique. Then, the local deformation algorithm can deform active contours so that they align to the edges in the image to produce the desired outlining of the myocardium. Finally, the global localization algorithm is able to propagate segmented contours from one image in the data set to all the others. We have experimented with the proposed method on a large number of patients and present some examples to show the strengths and pitfalls of our algorithm.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124386457","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}

{"title":"Photometric image-based rendering for image generation in arbitrary illumination","authors":"Y. Mukaigawa, H. Miyaki, Sadahiko Mihashi, Takeshi Shakunaga","doi":"10.1109/ICCV.2001.937688","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937688","url":null,"abstract":"A Photometric Image-Based Rendering (PIER) concept is proposed that facilitates the generation of an image with an arbitrary illumination. Based on this concept, we aim to generate both diffuse and specular reflections. It is not necessary to explicitly recover 3D shape and reflection properties of the scene. In order to control appearance changes caused by modifications in the lighting conditions, we utilize a set of real images taken, in a variety of lighting conditions. Since the diffuse and specular reflection components have different characteristics, we separate these components and apply different methods to each. A photometric linearization is introduced to control diffuse reflections as well as for separating the other components. This also facilitates the treatment of attached shadows as a part of the diffuse reflection. A morphing technique is utilized to generate specular reflections. This is an effective technique for dealing with glossy objects, even when the light shape is clearly observed in the specular reflection. Experimental results show that realistic images can be successfully generated using this technique.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124918896","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}

{"title":"Plane-based projective reconstruction","authors":"R. Kaucic, R. Hartley, N. Y. Dano","doi":"10.1109/ICCV.2001.937548","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937548","url":null,"abstract":"A linear method for computing a projective reconstruction from a large number of images is presented and then evaluated. The method uses planar homographies between views to linearize the resecting of the cameras. Constraints based on the fundamental matrix, trifocus tensor or quadrifocal tensor are used to derive relationship between the position vectors of all the cameras at once. The resulting set of equations are solved using a SVD. The algorithm is computationally efficient as it is linear in the number of matched points used. A key feature of the algorithm is that all of the images are processed simultaneously, as in the Sturm-Triggs factorization method, but it differs in not requiring that all points be visible in all views. An additional advantage is that it works with any mixture of line and point correspondence through the constraints these impose on the multilinear tensors. Experiments on both synthetic and real data confirm the method's utility.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124980102","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}

{"title":"Real-time video phase-locked loops","authors":"J. Boyd, M. Sayles","doi":"10.1109/ICCV.2001.937704","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937704","url":null,"abstract":"In the perception of gaits, timing is everything; specifically, the relative timing of the individual motions in a gait, and when events occur periodically, as they do in a gait, then relative timing is equivalent to phase. The importance of phase in gaits appears in the medical, psychology, and computer vision literature. The video phase locked loop (vPLL) [3] is a novel system that perceives gaits, is sensitive to the phases of the component motions of the gait, and is model-free. vPLLs provide a mechanism to perform two critical tasks in gait perception: frequency entrainment and phase locking [1]. A vPLL can lock on oscillations in pixels that arise because of oscillatory motion. In doing so, the vPLL matches its internal oscillators to the oscillations in pixel intensities, thus performing frequency entrainment. Phase locking occurs as individual phased-locked loops at each pixel site lock simultaneously. The abundance of data extracted by the vPLL makes gait recognition possible. In this demonstration we show a vPLL operating in realtime. The vPLL locks to oscillations in the gait of a person walking on a treadmill and also detects translational motion. As the vPLL system extracts phase information in the form of a phasor configuration, the configuration is also displayed in real time. Best [2] provides an excellent introduction to phaselocked loops. Their application to vPLLs is found in Boyd [3]. Figure 1(a) shows, as superimposed frames, an image sequences of an oscillatory motion, a person walking on a treadmill. The vPLL processes the sequence locking on the oscillations. Figure 1(b) shows the result as the magnitude of the oscillations as determined by the vPLL. While we have chosen to display the magnitude signal, the vPLL also captures frequency and phase information. There is ample information derived by a vPLL to recognize various oscillatory motions. We use Procrustes shape analysis to perform recognition-related tasks [4], such as averaging and matching the phase patterns that emerge from the vPLL. Our demonstration includes a real-time display of the captured phase patterns. The variations in phase that arise from different motions is evident as the phase config(a)","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126095699","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}

{"title":"A novel modeling algorithm for shape recovery of unknown topology","authors":"Y. Duan, Hong Qin","doi":"10.1109/ICCV.2001.937546","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937546","url":null,"abstract":"This paper presents a novel modeling algorithm that is capable of simultaneously recovering correct shape geometry as well as its unknown topology from arbitrarily complicated datasets. Our algorithm starts from a simple seed model (of genus zero) that can be arbitrarily initiated by users within any dataset. The deformable behavior of our model is governed by a locally defined objective function associated with each vertex of the model. Through the numerical computation of function optimization, our algorithm can adaptively subdivide the model geometry, automatically detect self-collision of the model, properly modify its topology (because of the occurrence of self-collision), continuously evolve the model towards the object boundary, and reduce fitting error and improve fitting quality via global subdivision. Commonly used mesh optimization techniques are employed throughout the geometric deformation and topological variation in order to ensure the model both locally smooth and globally well conditioned. We have applied our algorithm to various real/synthetic range data as well as volumetric image data in order to empirically verify and validate its usefulness. Based on our experiments, the new modeling algorithm proves to be very powerful and extremely valuable for shape recovery in computer vision, reverse engineering in computer graphics, and iso-surface extraction in visualization.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123555569","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}

{"title":"Incorporating process knowledge into object recognition for assemblies","authors":"Elke Braun, J. Fritsch, G. Sagerer","doi":"10.1109/ICCV.2001.937597","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937597","url":null,"abstract":"In this paper we present an object recognition framework integrating several recognition paradigms and context information from the scene history to recognize elementary parts contained in assemblies. We use a symbolic approach to detect actions based on the object changes in the scene to monitor the construction process. The information about the elements used to construct a new assembly serves as additional source of information for recognition. Process knowledge is exploited also for selecting the best interpretation out of several alternatives for a single scene which result from contradictions and uncertainties during integration of the different cues.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125311933","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}

{"title":"Efficient sequential karhunen-loeve basis extraction","authors":"A. Levy, M. Lindenbaum","doi":"10.1109/ICCV.2001.937701","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937701","url":null,"abstract":"The Karhunen-Loeve (KL) transform is an optimal method for approximating a set of vectors or images by a low dimensional subspace. The method provides the optimal partial KL basis, which minimizes the MSE between the given set of vectors and their projections on this basis. In computer vision it is used for a variety of tasks such as object recognition, motion estimation, visual learning and object tracking. Calculating the IU basis for N images of size M , where M >> N , requires roughly O ( M N 2 ) operations and O ( M N ) units of memory. In many applications, this large computational demands may be prohibitive. Here, we suggest an approach to reduce the computational effort, relying on the relatively small dimension (denoted K ) of the partial KL basis, that is usually needed. We propose an algorithm that does not require to store the entire set of input images before proceeding to the calculation of the KL basis. Rather, it takes the images in small blocks and updates the required KL basis sequentially.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117090285","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}

{"title":"Human tracking in multiple cameras","authors":"Sohaib Khan, O. Javed, Z. Rasheed, M. Shah","doi":"10.1109/ICCV.2001.937537","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937537","url":null,"abstract":"Multiple cameras are needed to cover large environments for monitoring activity. To track people successfully in multiple perspective imagery, one needs to establish correspondence between objects captured in multiple cameras. We present a system for tracking people in multiple uncalibrated cameras. The system is able to discover spatial relationships between the camera fields of view and use this information to correspond between different perspective views of the same person. We employ the novel approach of finding the limits of field of view (FOV) of a camera as visible in the other cameras. Using this information, when a person is seen in one camera, we are able to predict all the other cameras in which this person will be visible. Moreover, we apply the FOV constraint to disambiguate between possible candidates of correspondence. We present results on sequences of up to three cameras with multiple people. The proposed approach is very fast compared to camera calibration based approaches.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129843564","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}

{"title":"Performance evaluation of stereo for tele-presence","authors":"J. Mulligan, Volkan Isler, Kostas Daniilidis","doi":"10.1109/ICCV.2001.937675","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937675","url":null,"abstract":"In an immersive tele-presence environment a 3D remote real scene is projected from the viewpoint of the local user. This 3D world is acquired through stereo reconstruction at the remote site. In this paper we start a performance analysis of stereo algorithms with respect to the task of immersive visualization. As opposed to usual monocular image based rendering, we are also interested in the depth error in novel views because our rendering is stereoscopic. We describe an evaluation test-bed which provides a world-wide first available set of registered dense \"ground-truth\" laser data and image data from multiple views. We establish metrics for novel depth views that reflect discrepancies both in the image and in 3D-space. It is well known that stereo performance is affected by both erroneous matching as well as incorrect depth triangulation. We experimentally study the effects of occlusion and low texture on the distributions of the error metrics. Then, we algebraically predict the behavior of depth and novel projection error as a function of the camera set-up and the error in the disparity. These are first steps towards building a laboratory for psychophysical judgement of depth estimates which is the ultimate performance test of tele-presence stereo.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128495689","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}

{"title":"Finding \"anomalies\" in an arbitrary image","authors":"Toshifumi Honda, S. Nayar","doi":"10.1109/ICCV.2001.937669","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937669","url":null,"abstract":"A fast and general method to extract \"anomalies\" in an arbitrary image is proposed. The basic idea is to compute a probability density for sub-regions in an image, conditioned upon the areas surrounding the sub-regions. Linear estimation and Independent Component Analysis (ICA) are combined to obtain the probability estimates. Pseudo non-parametric correlation is used to group sets of similar surrounding patterns, from which a probability for the occurrence of a given sub-region is derived. A carefully designed multi-dimensional histogram, based on compressed vector representations, enables efficient and high-resolution extraction of anomalies from the image. Our current (unoptimized) implementation performs anomaly extraction in about 30 seconds for a 640/spl times/480 image using a 700 MHz PC. Experimental results are included that demonstrate the performance of the proposed method.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128757510","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}