The present invention relates to an image analysis method for providing information for supporting illness development prediction regarding a neoplasm in a human or animal body. The method includes receiving for the neoplasm first and second image data at a first and second moment in time, and deriving for a plurality of image features a first and a second image feature parameter value from the first and second image data. These feature parameter values being a quantitative representation of a respective image feature. Further, calculating an image feature difference value by calculating a difference between the first and second image feature parameter value, and based on a prediction model deriving a predictive value associated with the neoplasm for supporting treatment thereof. The prediction model includes a plurality of multiplier values associated with image features. For calculating the predictive value the method includes multiplying each image feature difference value with its associated multiplier value and combining the multiplied image feature difference values.

An image classifier comprises a first classifier and a second classifier. The first classifier comprises L individual classifiers, which are trained at different, respective image resolutions from a first full-resolution level to a lowest-resolution level. Outputs of the first set of classifiers are used to train the second classifier at the full-resolution level. Accordingly, the second classifier exploits contextual information at multiple different image resolutions. The classifiers may be trained to optimize a joint posterior probability at multiple resolutions.

A method is provided and includes acquiring a digital slide comprising objects of at least two sizes, the objects including a first object of a first size and a second object of a second size different from the first size, for each of the first object and the second object, acquiring images of a corresponding object in at least two scales based on the digital slide, where the acquired images of the corresponding object in the at least two scales include an image of a larger scale and an image of a smaller scale, the image of the larger scale having a smaller image size and a higher image resolution than the image of the smaller scale, and determining, from the images of the corresponding object in the at least two scales, an image having an image size that corresponds to a size of the corresponding object.

Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings. For high-level visual tasks, such low-level image representations are potentially not enough. The present invention provides a high-level image representation where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors, blind to the testing dataset or visual task. Leveraging on this representation, superior performances on high-level visual recognition tasks are achieved with relatively classifiers such as logistic regression and linear SVM classifiers.

There is provided a framework including systems and methods for analyzing skin parameters from images or videos showing skin. Using a series of Hierarchical Differential Image Filters (HDIF), it becomes possible to detect different skin features such as wrinkles, spots, and roughness. The hierarchical differential image filter computes two enhancements to an image showing skin at two different levels of enhancement, determines a differential image using the two enhancements and computes the skin analysis rating using the differential image. These skin ratings are comparably accurate to actual ratings by dermatologists.

In examples, image data representative of an image of a field of view of at least one sensor may be received. Source areas may be defined that correspond to a region of the image. Areas and/or dimensions of at least some of the source areas may decrease along at least one direction relative to a perspective of the at least one sensor. A downsampled version of the region (e.g., a downsampled image or feature map of a neural network) may be generated from the source areas based at least in part on mapping the source areas to cells of the downsampled version of the region. Resolutions of the region that are captured by the cells may correspond to the areas of the source areas, such that certain portions of the region (e.g., portions at a far distance from the sensor) retain higher resolution than others.

Implementations described and claimed herein provide localization systems and methods using surface imaging. In one implementation, a raw image of a target surface is captured using at least one imager. The raw image is encoded into a template using at least one transform. The template specifies a course direction and an intensity gradient at one or more spatial frequencies of a pattern of the target surface. The template is compared to a subset of reference templates selected from a gallery stored in one or more storage media. A location of the target surface is identified when the template matches a reference template in the subset.

The present application relates to a method for measuring the similarity of images/image blocks, which comprises: S1: acquiring two three-dimensional airspace images V and W; S2: decomposing the images V and W to obtain a plurality of sub-bands; S3: calculating a Laplacian probability corresponding to each high-frequency sub-band of V and W, weighting the high-frequency sub-hand; S4: marking two image blocks as X and Y, taking out data blocks corresponding to the image blocks X and Y, and calculating the statistics of the data blocks; S5: calculating the similarities of X and Yin each channel of each sub-band according to the statistics of the data blocks; S6: calculating an average value of the similarities of X and Y in each channel of each sub-band, and taking the average value as the similarity between X and Y.

A computer-implemented method for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the method comprising determining a set of samples characterising a location in an image by sampling scale-space data representative of the image, the scale-space data comprising data representative of the image at a plurality of length scales; and generating a feature descriptor in dependence on the determined set of samples.

An image processing apparatus includes a change detection unit configured to detect phase changes in multiple predetermined directions from among phase changes in a luminance image in units of mutually different resolutions, and a reliability estimation unit configured to estimate reliability of the detected phase change based on temporal amplitude change information in the multiple directions determined in the luminance image. The reliability estimation unit may estimate the reliability using an amplitude change of multiple resolutions and using a value of an amplitude change equal to or greater than a predetermined threshold value among images having multiple resolutions. The reliability may become a greater value as the amplitude change becomes larger.