The invention relates to a method and arrangement for detecting free fiber ends in a paper surface. The method comprises illuminating a target sample (6) surface, which comprises free fiber ends, from at least two directions one at the time, with at least one light source (1). Original reflectance images are obtained for the target sample (6) surface with an imaging device (4), and a surface normal is estimated for each image pixel of the original reflectance image. Thus it is possible to reconstruct a reconstructed reflectance image from the estimated surface normals, and to compare the reconstructed reflectance image and the corresponding original reflectance image and to construct a difference image, where the differences represent shadow objects of the free fiber ends in a paper surface.

The invention relates to a method and arrangement for detecting free fiber ends in a paper surface. The method comprises illuminating a target sample (6) surface, which comprises free fiber ends, from at least two directions one at the time, with at least one light source (1). Original reflectance images are obtained for the target sample (6) surface with an imaging device (4), and a surface normal is estimated for each image pixel of the original reflectance image. Thus it is possible to reconstruct a reconstructed reflectance image from the estimated surface normals, and to compare the reconstructed reflectance image and the corresponding original reflectance image and to construct a difference image, where the differences represent shadow objects of the free fiber ends in a paper surface.

[PROBLEMS TO BE SOLVED] It is an object to provide a marker and a marker generating and detecting technology that can automatically design a diagrammatic marker that is not similar to any patterns to appear during the reproduction of background video images. [MEANS FOR SOLVING THE PROBLEMS] A marker generating system is characterized in having a special feature extracting means that extracts a portion, as a special feature, including a distinctive pattern in a video image not including a marker; a unique special feature selecting means that, based on the extracted special feature, selects a special feature of an image, as a unique special feature, that does not appear on the video image; and a marker generating means that generates a marker based on the unique special feature.

[PROBLEMS TO BE SOLVED] It is an object to provide a marker and a marker generating and detecting technology that can automatically design a diagrammatic marker that is not similar to any patterns to appear during the reproduction of background video images. [MEANS FOR SOLVING THE PROBLEMS] A marker generating system is characterized in having a special feature extracting means that extracts a portion, as a special feature, including a distinctive pattern in a video image not including a marker; a unique special feature selecting means that, based on the extracted special feature, selects a special feature of an image, as a unique special feature, that does not appear on the video image; and a marker generating means that generates a marker based on the unique special feature.

A method automatically parameterizes error detection of an image inspection system by a computer. The method includes digitizing a reference image in order to determine desired values and subdividing the reference image into homogeneous image regions with few edges, and inhomogeneous image regions with strongly structured image areas and many edges. Lower tolerance values for the homogeneous image regions, and higher tolerance values for the inhomogeneous image regions of the digitized reference image are determined by statistical image analyses. The determined tolerances are assigned to their respective desired values in dependence on a position of the desired values in homogeneous or inhomogeneous image regions. An inspection sensitivity is calculated from desired values and their respective tolerances. The parameters of the image inspection system are set with the aid of the inspection sensitivity configuration of the image inspection system using the parameters.

A method automatically parameterizes error detection of an image inspection system by a computer. The method includes digitizing a reference image in order to determine desired values and subdividing the reference image into homogeneous image regions with few edges, and inhomogeneous image regions with strongly structured image areas and many edges. Lower tolerance values for the homogeneous image regions, and higher tolerance values for the inhomogeneous image regions of the digitized reference image are determined by statistical image analyses. The determined tolerances are assigned to their respective desired values in dependence on a position of the desired values in homogeneous or inhomogeneous image regions. An inspection sensitivity is calculated from desired values and their respective tolerances. The parameters of the image inspection system are set with the aid of the inspection sensitivity configuration of the image inspection system using the parameters.

A method for performing cellular classification includes generating a plurality of local dense Scale Invariant Feature Transform (SIFT) features based on a set of input images and converting the plurality of local dense SIFT features into a multi-dimensional code using a feature coding process. A first classification component is used to generate first output confidence values based on the multi-dimensional code and a plurality of global Local Binary Pattern Histogram (LBP-H) features are generated based on the set of input images. A second classification component is used to generate second output confidence values based on the plurality of LBP-H features and the first output confidence values and the second output confidence values are merged. Each of the set of input images may then be classified as one of a plurality of cell types using the merged output confidence values.

A method for recognizing different object-categories within images based on texture classification of the different categories, which is implemented in an electronic device, includes extracting texture features from block images segmented from original images according to at least one Gabor filter; determining a grayscale level co-occurrence matrix of each block image according to the texture features; calculating texture feature statistics of each block image according to the grayscale level co-occurrence matrix; training and generating an object recognition model using the texture features and the texture feature statistics; and recognizing and classifying at least one object in original image according to the object recognition model.

A method for recognizing different object-categories within images based on texture classification of the different categories, which is implemented in an electronic device, includes extracting texture features from block images segmented from original images according to at least one Gabor filter; determining a grayscale level co-occurrence matrix of each block image according to the texture features; calculating texture feature statistics of each block image according to the grayscale level co-occurrence matrix; training and generating an object recognition model using the texture features and the texture feature statistics; and recognizing and classifying at least one object in original image according to the object recognition model.

The invention discloses an unsupervised content-preserved domain adaptation method for multiple CT lung texture recognition, which belongs to the field of image processing and computer vision. This method enables the deep network model of lung texture recognition trained in advance on one type of CT data (on the source domain), when applied to another CT image (on the target domain), under the premise of only obtaining target domain CT image and not requiring manually label the typical lung texture, the adversarial learning mechanism and the specially designed content consistency network module can be used to fine-tune the deep network model to maintain high performance in lung texture recognition on the target domain. This method not only saves development labor and time costs, but also is easy to implement and has high practicability.