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Titlebook: Image Analysis and Recognition; 4th International Co Mohamed Kamel,Aurélio Campilho Conference proceedings 2007 Springer-Verlag Berlin Heid

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21#
發(fā)表于 2025-3-25 06:39:44 | 只看該作者
Reconstruction of Low-Resolution Images Using Adaptive Bimodal Priorsrior. The bimodal prior is based on the fact that an edge pixel has a colour value that is typically a mixture of the colours of two connected regions, each having a dominant colour distribution. Local adaptation of the parameters of the bimodal prior is made to handle neighbourhoods which have typi
22#
發(fā)表于 2025-3-25 11:26:28 | 只看該作者
23#
發(fā)表于 2025-3-25 13:19:24 | 只看該作者
An Approach for Extracting Illumination-Independent Texture Featuresm. In many cases the researchers have to face an extra problem: to study the environmental conditions of the facilities where the application will run, the light technology and the wattage of the chosen lamps, nowadays we are moving to LED technology due to the increased life and absence of flicker,
24#
發(fā)表于 2025-3-25 18:12:59 | 只看該作者
25#
發(fā)表于 2025-3-25 22:36:33 | 只看該作者
26#
發(fā)表于 2025-3-26 00:40:07 | 只看該作者
A Run-Based Two-Scan Labeling Algorithmn image one by one, this paper presents an efficient run-based two-scan labeling algorithm: the run data obtained during the scan are recorded in a queue, and are used for detecting the connectivity later. Moreover, unlike conventional label-equivalence-based algorithms which resolve label equivalen
27#
發(fā)表于 2025-3-26 07:05:43 | 只看該作者
28#
發(fā)表于 2025-3-26 11:35:18 | 只看該作者
29#
發(fā)表于 2025-3-26 12:40:33 | 只看該作者
Landmark-Based Non-rigid Registration Via Graph Cuts. The main contribution of our method is the formulation of landmarks in the graph cut minimization framework. In the graph cut method, we add a penalty cost based on landmarks to the data energy. In the presence of a landmark, we adjust the . weights to cut strategic links. Our formulation also all
30#
發(fā)表于 2025-3-26 17:14:44 | 只看該作者
New Hypothesis Distinctiveness Measure for Better Ellipse Extractiontinctiveness measurement to rank the hypotheses and determine a suitable candidate. The method is aimed at improving HT robustness to image noise and quantization effects. Experiments on images that demonstrate the method’s applicability are also presented.
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