The proceedings contain 38 papers. The topics discussed include: automatic produce classification from images using color, texture and appearance cues;face recognition aiding historical photographs indexing using a tw...
ISBN:
(纸本)9780769533582
The proceedings contain 38 papers. The topics discussed include: automatic produce classification from images using color, texture and appearance cues;face recognition aiding historical photographs indexing using a two-stage training scheme and an enhanced distance measure;a new training algorithm for pattern recognition technique based on straight line segments;crop type recognition based on Hidden Markov Models of plant phenology;synchronizing video cameras with non-overlapping fields of view;people detection under occlusion in multiple camera views;occlusion handling for object tracking using a fast level set method;efficient text color modulation for printed side communications and data hiding;estimating the skew angle of scanned document through background area information;and benchmark for quantitative evaluation of assisted object segmentation methods to image sequences.
We present an architecture for rendering multiple views efficiently on a cluster of GPUs. The original scene is sampled by virtual cameras which are used later to reconstruct the desired views. We show that this image...
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ISBN:
(纸本)9780769533582
We present an architecture for rendering multiple views efficiently on a cluster of GPUs. The original scene is sampled by virtual cameras which are used later to reconstruct the desired views. We show that this image-based approach can be very scalable and support rendering at interactive rates.
This paper presents a comparative study of color descriptors for content-based image retrieval on the Web. Several image descriptors were compared theoretically and the most relevant ones were implemented and tested i...
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ISBN:
(纸本)9780769533582
This paper presents a comparative study of color descriptors for content-based image retrieval on the Web. Several image descriptors were compared theoretically and the most relevant ones were implemented and tested in two different databases. The main goal was to find out the best descriptors for Web image retrieval. Descriptors are compared according to the extraction and distance functions complexities, the compactness of feature vectors, and the ability to retrieve relevant images.
This paper presents a new relevance feedback method for content-based image retrieval using local image features. This method adopts a genetic programming approach to learn user preferences and combine the region simi...
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ISBN:
(纸本)9780769533582
This paper presents a new relevance feedback method for content-based image retrieval using local image features. This method adopts a genetic programming approach to learn user preferences and combine the region similarity values in a query session. Experiments demonstrate that the proposed method yields more effective results than the Local Aggregation Pattern (LAP)-based relevance feedback technique.
We propose a system to solve a multi-class produce categorization problem. For that, we use statistical color, texture, and structural appearance descriptors (bag-of-features). As the best combination setup is not kno...
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ISBN:
(纸本)9780769533582
We propose a system to solve a multi-class produce categorization problem. For that, we use statistical color, texture, and structural appearance descriptors (bag-of-features). As the best combination setup is not known for our problem, we combine several individual features from the state-of-the-art in many different ways to assess how they interact to improve the overall accuracy of the system. We validate the system using an image data set collected on our local fruits and vegetables distribution center.
Evaluation of segmentation methods applied to image sequences consists in the analysis of such methods according to quantitative and/or qualitative criteria, usually driven to some application. Literature proposes sev...
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ISBN:
(纸本)9780769533582
Evaluation of segmentation methods applied to image sequences consists in the analysis of such methods according to quantitative and/or qualitative criteria, usually driven to some application. Literature proposes several metrics for quantitative evaluation of object segmentation methods to image sequences, but it is still considered an open problem, since no one of the proposed metrics is considered the standard one. More, as the best of our knowledge, there is no method in literature that does computational quantitative evaluation of assisted methods to object segmentation in image sequence. This paper introduces a benchmark to do such quantitative evaluation. This evaluation is done according to several criteria such as the robustness of segmentation and the easiness to segment the objects through the sequence. Experimental results also evaluates the robustness of the watershed from propagated markers technique.
The parallel and discrete nature of many image-based techniques, such as interpolation methods, are highly suitable for GPU implementations. An important advantage is their bounded complexity by the screen resolution ...
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ISBN:
(纸本)9780769533582
The parallel and discrete nature of many image-based techniques, such as interpolation methods, are highly suitable for GPU implementations. An important advantage is their bounded complexity by the screen resolution as opposed to the data size. Consequently, the direct surface reconstruction of points using image-based methods is an attractive solution to interactively render large data-sets. In this paper we propose a novel approach that raises the robustness and quality standards of previous related works. The algorithm achieves quality equivalent to Surface Splatting techniques, while maintaining high performance rates compatible with previous image-based methods. A hierarchical pixel structure is employed to render the projected samples efficiently with minimum artifacts. One of the main advantages of the method is the independency from the number of samples during the surface reconstruction. Furthermore, no extra object space data structure is needed, making the approach also memory efficient.
Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces a methodology and a software toot called PEx-image - ...
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ISBN:
(纸本)9780769533582
Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces a methodology and a software toot called PEx-image - Projection, Explorer for image for analysis and exploration of image collections employing visualizations. The visual mappings proposed here are similarity-based multidimensional projections and point placements, which layout the data on a plane for visual exploration. The proposed approach supports various image analysis tasks such as feature selection and classification, improving data exploration capabilities. We also illustrate how it can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.
This paper presents a new method for bilateral asymmetry analysis of breast MR images that uses directional statistics of the breast parenchymal edges, obtained from a multiresolution local energy edge detector and im...
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ISBN:
(纸本)9780769533582
This paper presents a new method for bilateral asymmetry analysis of breast MR images that uses directional statistics of the breast parenchymal edges, obtained from a multiresolution local energy edge detector and image texture information derived from local energy maps, obtained by using a bank of log-Gabor filters. Classification of MRI scans into cancer and non-cancer categories was performed by, linear discriminant analysis and the leave-one-out methodology. A total of 40 cases, 20 normal/benign (BI-RADS 1 and 2) and 20 malignant, taken from a high risk screening population, were used in this pilot study. Average classification accuracy of 70% (kappa = 0.45 +/- 0.14) with sensitivity and specificity of 75% and 65%, respectively, was achieved. The results obtained support the idea that bilateral asymmetry analysis of breast MR images can provide additional information for detection of breast tissue changes arising from diseases.
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