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81.
An innovative application focused on the segmentation of decay zones from images of stone materials is presented. The adopted numerical approach to extract decay regions from the color images of monuments provides a tool that helps experts analyze degraded regions by contouring them. In this way even if the results of the proposed procedure depend on the evaluation of experts, the approach can be a contribution to improving the efficiency of the boundary detection process. The segmentation is a process that allows an image to be divided into disjoint zones so that partitioned zones contain homogeneous characteristics. The numerical method, used to segment color images, is based on the theory of interface evolution, which is described by the eikonal equation. We adopted the fast marching technique to solve the upwind finite difference approximation of the eikonal equation. The fast marching starts from a seed point in the region of interest and generates a front which evolves according to a specific speed function until the boundary of the region is identified. We describe the segmentation results obtained with two speed functions, attained by the image gradient computation and color information about the object of interest. Moreover, we present the extension of the working modality of the method by introducing the possibility to extract the regions not only in a local way but also in a global way on the entire image. In this case, in order to improve the segmentation efficiency the application of the fast marching technique starts with more seed points defined as seed regions. The study case concerns the impressive remains of the Roman Theatre in the city of Aosta (Italy). In the image segmentation process the color space L∗a∗b∗ is utilized. 相似文献
82.
83.
Hyun Soo Kim 《Computer aided design》2009,41(1):47-591
This paper presents n-dimensional feature recognition of triangular meshes that can handle both geometric properties and additional attributes such as color information of a physical object. Our method is based on a tensor voting technique for classifying features and integrates a clustering and region growing methodology for segmenting a mesh into sub-patches. We classify a feature into a corner, a sharp edge and a face. Then, finally we detect features via region merging and cleaning processes. Our feature detection shows good performance with efficiency for various dimensional models. 相似文献
84.
G. Louloudis B. Gatos I. Pratikakis C. HalatsisAuthor vitae 《Pattern recognition》2009,42(12):3169-3183
In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents. 相似文献
85.
We present a machine learning tool for automatic texton-based joint classification and segmentation of mitochondria in MNT-1 cells imaged using ion-abrasion scanning electron microscopy (IA-SEM). For diagnosing signatures that may be unique to cellular states such as cancer, automatic tools with minimal user intervention need to be developed for analysis and mining of high-throughput data from these large volume data sets (typically ). Challenges for such a tool in 3D electron microscopy arise due to low contrast and signal-to-noise ratios (SNR) inherent to biological imaging. Our approach is based on block-wise classification of images into a trained list of regions. Given manually labeled images, our goal is to learn models that can localize novel instances of the regions in test datasets. Since datasets obtained using electron microscopes are intrinsically noisy, we improve the SNR of the data for automatic segmentation by implementing a 2D texture-preserving filter on each slice of the 3D dataset. We investigate texton-based region features in this work. Classification is performed by k-nearest neighbor (k-NN) classifier, support vector machines (SVMs), adaptive boosting (AdaBoost) and histogram matching using a NN classifier. In addition, we study the computational complexity vs. segmentation accuracy tradeoff of these classifiers. Segmentation results demonstrate that our approach using minimal training data performs close to semi-automatic methods using the variational level-set method and manual segmentation carried out by an experienced user. Using our method, which we show to have minimal user intervention and high classification accuracy, we investigate quantitative parameters such as volume of the cytoplasm occupied by mitochondria, differences between the surface area of inner and outer membranes and mean mitochondrial width which are quantities potentially relevant to distinguishing cancer cells from normal cells. To test the accuracy of our approach, these quantities are compared against manually computed counterparts. We also demonstrate extension of these methods to segment 3D images obtained using electron tomography. 相似文献
86.
We propose in this paper a segmentation process that can deal with noisy discrete objects. A flexible approach considering arithmetic discrete planes with a variable width is used to avoid the over-segmentation that might happen when classical segmentation algorithms based on regular discrete planes are used to decompose the surface of the object. A method to choose a seed and different segmentation strategies according to the shape of the surface is also proposed, as well as an application to smooth the border of convex noisy discrete objects. 相似文献
87.
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture,Layout, and Context 总被引:5,自引:0,他引:5
Jamie Shotton John Winn Carsten Rother Antonio Criminisi 《International Journal of Computer Vision》2009,81(1):2-23
This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and
context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of
photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification
and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number
of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which
(i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific
object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection
and piecewise training methods.
High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing
photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset
and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition,
vol. 2, pp. 695–702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results
for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and
even articulated (body, cow, etc.).
J. Shotton is now working at Toshiba Corporate Research & Development Center, Kawasaki, Japan. 相似文献
88.
Topological sensitivity analysis is performed for the piecewise constant Mumford-Shah functional. Topological and shape derivatives
are combined in order to derive an algorithm for image segmentation with fully automatized initialization. Segmentation of
2D and 3D data is presented. Further, a generalized Mumford-Shah functional is proposed and numerically investigated for the
segmentation of images modulated due to, e.g., coil sensitivities.
相似文献
M. HintermüllerEmail: |
89.
Per Bjesse 《Formal Methods in System Design》2009,35(1):56-72
In this paper we present a word-level model checking method that attempts to speed up safety property checking of industrial
netlists. Our aim is to construct an algorithm that allows us to check both bounded and unbounded properties using standard
bit-level model checking methods as back-end decision procedures, while incurring minimum runtime penalties for designs that
are unsuited to our analysis. We do this by combining modifications of several previously known techniques into a static abstraction
algorithm which is guaranteed to produce bit-level netlists that are as small or smaller than the original bitblasted designs.
We evaluate our algorithm on several challenging hardware components. 相似文献
90.