We propose a novel approach to generate a high quality photometric compensated projection which, to our knowledge, is the first one, which does not require a radiometrical pre-calibration of cameras or projectors. Thi...
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ISBN:
(纸本)9780769549903
We propose a novel approach to generate a high quality photometric compensated projection which, to our knowledge, is the first one, which does not require a radiometrical pre-calibration of cameras or projectors. This improves the compensation quality using devices which cannot be easily linearized, such as single chip DLP projectors with complex color processing. In addition, the simple workflow significantly simplifies the compensation image generation. Our approach consists of a sparse sampling of the projector's color gamut and a scattered data interpolation to generate the per-pixel mapping from projector to camera colors in real-time. To avoid out-of-gamut artifacts, the input image is automatically scaled locally in an optional off-line optimization step maximizing the achievable luminance and contrast while still preserving smooth input gradients without significant clipping errors.
A description is given of a technique for generating a skeleton of a ribbon-like object using sequential data for all or part of the boundary. It shows how one may use local geometric information derived from the cont...
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A description is given of a technique for generating a skeleton of a ribbon-like object using sequential data for all or part of the boundary. It shows how one may use local geometric information derived from the contour to aid in the generation of a skeleton. For contours or curves of length n, this may be accomplished with a computation time of order n, while previous algorithms generally require order n**2 and require a two-dimensional matrix for their working representation.
A map-guided approach to interpretation of remotely sensed imagery is described, with emphasis on applications involving continuous monitoring of predetermined ground sites. Geometric correspondence between a sensed i...
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A map-guided approach to interpretation of remotely sensed imagery is described, with emphasis on applications involving continuous monitoring of predetermined ground sites. Geometric correspondence between a sensed image and a symbolic reference map is established in an initial stage of processing.by adjusting parameters of a sensor model so that image features predicted from the map optimally match corresponding features extracted from the sensed image. Information in the map is then used to constrain where to look in an image and what to look for. With such constraints, previously intractable remote sensing tasks can become feasible, even easy, to automate.
image monitoring, the process of locating and identifying significant changes or new activities, is one of the most important imagery exploitation tasks. A site model supported image monitoring system which utilizes i...
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ISBN:
(纸本)0818658258
image monitoring, the process of locating and identifying significant changes or new activities, is one of the most important imagery exploitation tasks. A site model supported image monitoring system which utilizes image understanding techniques driven by an underlying site model is presented. In our approach, we first register the image to be monitored to an existing site model, which is constructed using the RADIUS Common Development Environment;the regions of interest are then delineated based on site information, camera acquisition parameters, and goals of the image analyst;object extraction is then done using constraints on size, shape, orientation, and shadow of the target object derived from known information about image resolution, 3-D shape of the object, camera viewing and illuminant directions. The results of object detection are used for monitoring changes.
Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Concurrently, significant progress has be...
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ISBN:
(纸本)9798350365474
Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Concurrently, significant progress has been made in multimodal representation learning for text and image data. This paper explores a novel research direction that aims to connect the NeRF modality with other modalities, similar to established methodologies for images and text. To this end, we propose a simple framework that exploits pre-trained models for NeRF representations alongside multimodal models for text and imageprocessing. Our framework learns a bidirectional mapping between NeRF embeddings and those obtained from corresponding images and text. This mapping unlocks several novel and useful applications, including NeRF zero-shot classification and NeRF retrieval from images or text.
A segmentation is done on a monochrome 256 multiplied by 256 image using only texture information generated by local extreme measurements. The number of extrema of various sizes are counted over a local region and the...
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A segmentation is done on a monochrome 256 multiplied by 256 image using only texture information generated by local extreme measurements. The number of extrema of various sizes are counted over a local region and these numbers are used to separate the regions. The use of texture edge information in the local averaging process is discussed.
Hyperspectral cameras are used to preserve fine spectral details of scenes that are not captured by traditional RGB cameras that comprehensively quantizes radiance in RGB images. Spectral details provide additional in...
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ISBN:
(数字)9781538661000
ISBN:
(纸本)9781538661000
Hyperspectral cameras are used to preserve fine spectral details of scenes that are not captured by traditional RGB cameras that comprehensively quantizes radiance in RGB images. Spectral details provide additional information that improves the performance of numerous image based analytic applications, but due to high hyperspectral hardware cost and associated physical constraints, hyperspectral images are not easily available for further processing. Motivated by the performance of deep learning for various computer vision applications, we propose a 2D convolution neural network and a 3D convolution neural network based approaches for hyperspectral image reconstruction from RGB images. A 2D-CNN model primarily focuses on extracting spectral data by considering only spatial correlation of the channels in the image, while in 3D-CNN model the inter-channel co-relation is also exploited to refine the extraction of spectral data. Our 3D-CNN based architecture achieves very good performance in terms of MRAE and RMSE. In contrast to 3D-CNN, our 2D-CNN based architecture also achieves comparable performance with very less computational complexity.
This paper presents a novel representation for dynamic scenes composed of multiple rigid objects that may undergo different motions and be observed by a moving camera. Multi-view constraints associated with groups of ...
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ISBN:
(纸本)0769521584
This paper presents a novel representation for dynamic scenes composed of multiple rigid objects that may undergo different motions and be observed by a moving camera. Multi-view constraints associated with groups of affine-invariant scene patches and a normalized description of their appearance are used to segment a scene into its rigid parts, construct three-dimensional projective, affine, and Euclidean models of these parts, and match instances of models recovered from different image sequences. The proposed approach has been implemented, and it is applied to the detection and recognition of moving objects in video sequences and the identification of shots that depict the same scene in a video clip (shot matching).
A simple image smoothing scheme for improving the quality of noisy pictures is proposed. This scheme employs a 3 by 3 mask in which the weighting coefficients re the normalized gradient inverses. The smoothing operati...
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A simple image smoothing scheme for improving the quality of noisy pictures is proposed. This scheme employs a 3 by 3 mask in which the weighting coefficients re the normalized gradient inverses. The smoothing operation will clean out noises inside a region without blurring its boundary. Simulation studies show that this method tends to reduce the gray level variance within a region, and keep its mean relatively unchanged. Results of applications to several real world images are presented.
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