Abstract: One exemplary embodiment involves receiving a test image generating, by a plurality of maps for the test image based on a plurality of object images. Each of the object images comprises an object of a same object type, e.g., each comprising a different face. Each of the plurality of maps is generated to provide information about the similarity of at least a portion of a respective object image to each of a plurality of portions of the test image. The exemplary embodiment further comprises detecting a test image object within the test image based at least in part on the plurality of maps.

Abstract: One exemplary embodiment involves receiving, at a computing device comprising a processor, a test image having a candidate object and a set of object images detected to depict a similar object as the test image. The embodiment involves localizing the object depicted in each one of the object images based on the candidate object in the test image to determine a location of the object in each respective object image and then generating a validation score for the candidate object in the test image based at least in part on the determined location of the object in the respective object image and known location of the object in the same respective object image. The embodiment also involves computing a final detection score for the candidate object based on the validation score that indicates a confidence level that the object in the test image is located as indicated by the candidate object.

Abstract: One exemplary embodiment involves identifying feature matches between each of a plurality of object images and a test image, each of the feature matches between a feature of a respective object image and a matching feature of the test image, wherein there is a spatial relationship between each respective object image feature and a first landmark of the object image, the first landmark at a known location in the object image. The embodiment additionally involves estimating a plurality of locations for a second landmark for the test image, the estimated locations based at least in part on the feature matches and the spatial relationships, and estimating a final location for the second landmark from the plurality of locations for the second landmark for the test image.

Abstract: One exemplary embodiment involves identifying feature matches between each of a plurality of object images and a test image, each feature matches between a feature of a respective object image and a matching feature of the test image, wherein there is a spatial relationship between each respective object image feature and a test image feature, and wherein the object depicted in the test image comprises a plurality of attributes. Additionally, the embodiment involves estimating, for each attribute in the test image, an attribute value based at least in part on information stored in a metadata associated with each of the object images.

Abstract: Systems and methods are provided for providing patch size adaptation for patch-based image enhancement operations. In one embodiment, an image manipulation application receives an input image. The image manipulation application compares a value for an attribute of at least one input patch of the input image to a threshold value. Based on comparing the value for the to the threshold value, the image manipulation application adjusts a first patch size of the input patch to a second patch size that improves performance of a patch-based image enhancement operation as compared to the first patch size. The image manipulation application performs the patch-based image enhancement operation based on one or more input patches of the input image having the second patch size.

Abstract: Systems and methods are provided for providing learned, piece-wise patch regression for image enhancement. In one embodiment, an image manipulation application generates training patch pairs that include training input patches and training output patches. Each training patch pair includes a respective training input patch from a training input image and a respective training output patch from a training output image. The training input image and the training output image include at least some of the same image content. The image manipulation application determines patch-pair functions from at least some of the training patch pairs. Each patch-pair function corresponds to a modification to a respective training input patch to generate a respective training output patch. The image manipulation application receives an input image generates an output image from the input image by applying at least some of the patch-pair functions based on at least some input patches of the input image.

Abstract: Methods, systems, and computer program products used to locate a feature in an image.

Abstract: A light-emitting system is provided which is removably attachable to headgear for personal illumination to enhance visibility of the user to others. The light-emitting system includes a housing that defines a receiving aperture and is configured to surround a portion of the headgear when the light-emitting system is removably attached to the headgear for use. The light-emitting system further includes at least one lens and a plurality of lighting elements coupled to the annular housing which are configured to selectively generate a halo or at least a partial halo of light that radiates outwardly away from the annular housing through the at least one lens to provide enhanced personal illumination.

Abstract: An approach is described for automatically tagging a single image or multiple images. The approach, in one example embodiment, is based on a graph-based framework that exploits both visual similarity between images and tag correlation within individual images. The problem is formulated in the context of semi-supervised learning, where a graph modeled as a Gaussian Markov Random Field (MRF) is solved by minimizing an objective function (the image tag score function) using an iterative approach. The iterative approach, in one embodiment, comprises: (1) fixing tags and propagating image tag likelihood values from labeled images to unlabeled images, and (2) fixing images and propagating image tag likelihood based on tag correlation.

Abstract: Techniques for facilitating a similarity search of digital assets (e.g., audio files, image files, video files, etc.) are described. Consistent with some embodiments, a cloud-based search service manages one or more search tree data structures for use in organizing digital assets to make the digital assets searchable. Each digital asset is associated with a feature vector based on the various attributes and/or characteristics of the digital asset. The digital assets are then assigned to leaf nodes in one or more search tree data structures based on a measure of the distance between the feature vector of the digital asset and a virtual feature vector associated with a leaf node. When a search for similar digital assets is invoked, a prioritized breadth first search of a search tree is performed to identify the digital assets having the feature vectors closest in distance to the reference digital asset.

Abstract: A system maintains data from different resolution levels of an image in textures of a graphics processing unit (GPU). Image data is organized into multiple resolution layers of an image. Data from the lower resolution level(s) is used to process an image while higher resolution data is loaded. In one embodiment, a first resolution representation of the image having a lowest resolution level using data resident in the GPU memory is drawn prior to drawing a portion of a second resolution representation having a higher resolution level using data resident in the GPU memory.

Abstract: An image search method includes receiving a first query, the first query providing a first image constraint. A first search of a plurality of images is performed, responsive to the first query, to identify a first set of images satisfying the first constraint. A first search result, which includes the first set of images identified as satisfying the first constraint, is presented. A second query is received, the second query providing a second image constraint with reference to a first image of the first set of images. A second search of the plurality of images is performed, responsive to the second query, to identify a second set of images that satisfy the second constraint. A second search result, which includes the second set of images identified as satisfying the second constraint, is presented.

Abstract: A light-emitting system is provided which is removably attachable to headgear for personal illumination to enhance visibility of the user to others. The light-emitting system includes a housing that defines a receiving aperture and is configured to surround a portion of the headgear when the light-emitting system is removably attached to the headgear for use. The light-emitting system further includes at least one lens and a plurality of lighting elements coupled to the annular housing which are configured to selectively generate a halo or at least a partial halo of light that radiates outwardly away from the annular housing through the at least one lens to provide enhanced personal illumination.

Abstract: A method, system, and computer-readable storage medium are disclosed for determining one or more symmetries in an image comprising a plurality of pixels. A symmetry value may be automatically determined for each of the plurality of pixels. The symmetry value may indicate the strength of one or more symmetries in the image for the respective pixel. The symmetry value may be stored for each of the pixels.

Abstract: An image search method includes receiving a first query, the first query providing a first image constraint. A first search of a plurality of images is performed, responsive to the first query, to identify a first set of images satisfying the first constraint. A first search result, which includes the first set of images identified as satisfying the first constraint, is presented. A second query is received, the second query providing a second image constraint with reference to a first image of the first set of images. A second search of the plurality of images is performed, responsive to the second query, to identify a second set of images that satisfy the second constraint. A second search result, which includes the second set of images identified as satisfying the second constraint, is presented.

Abstract: Methods and apparatus, including computer program products, implementing techniques for training an attentional cascade. An attentional cascade is an ordered sequence of detector functions, where the detector functions are functions that examine a target image and return a positive result if the target image resembles an object of interest and a negative result if the target image does not resemble the object of interest. A positive result from one detector function leads to consideration of the target image by the next detector function, and a negative result from any detector function leads to rejection of the target image. The techniques include training each detector function in the attentional cascade in sequence starting with the first detector function. Training a detector function includes selecting a counter-example set. Selecting a counter-example set includes selecting only images that are at least a minimum difference from an example set.

Abstract: Methods, systems, and computer program products used to locate a feature in an image.

Abstract: Methods, systems, and computer program products used to locate a feature in an image, including identifying one or more candidate features in an image, where each candidate feature is a group of pixels in the image that satisfies a pattern-matching criterion. A best candidate feature is selected from the one or more candidate features, and a parameterized shape is fit to the image in the region of the best candidate feature to compute a feature shape corresponding to the best candidate feature. Particular implentations can include one or more of the following features. The candidate feature is a candidate pupil and the feature shape is an ellipse. Fitting the parameterizes shape to the mage includes applying an iterative process varying shape parameters.

Abstract: A method, system, and computer-readable storage medium are disclosed for performing an operation on single-channel input using a plurality of execution channels of a graphics processing unit (GPU). A single-channel input may be divided into a plurality of sub-inputs, wherein each of the plurality of sub-inputs comprises a portion of the single-channel input. Borders between the sub-inputs may overlap. Program instructions may be executed on the GPU to perform the operation. At least a subset of the program instructions may be executed on each of the plurality of execution channels in parallel for a corresponding one of the plurality of sub-inputs. In one embodiment, the single-channel input may comprise a single-channel input image for an image-processing operation. In one embodiment, the GPU may comprise four execution channels.

Abstract: Methods, systems and apparatus, including computer program products, for processing electronic images. An electronic image is transformed to generate a transformed image according to a first coordinate transformation. The transformed image is filtered, and a second coordinate transformation is applied to the filtered image to align the filtered image with an accumulation array that includes multiple accumulation elements. Each accumulation element is updated based on a respective value in the aligned image. The operations of generating a transformed image according to a first transformation, filtering the transformed image, applying a second transformation and updating each accumulation element are iterated for one or more further first coordinate transformations. These techniques can be implemented to detect features in the image using a graphics processing unit.