Abstract: An optical code scanner is presented for assembling an image of a document from multiple images captured by the optical code scanner. A document is presented to the optical code scanner which captures multiple images where each image includes only a portion of the document. Once every part of the document has been captured in at least one image, the optical code scanner processes the images to generate a single image of the entire document.

Abstract: A character string detection device for detecting a character string including at least one character in an image has a clustering unit that defines at least one cluster including at least a fixed number of pixel groups having similar colors based on color information included in each pixel configuring the image, a clipping unit that partitions the image into a region of the pixel groups belonging to the cluster defined by the clustering unit and a region of other pixel groups, and generating a clipped image excluding the region of the other pixel groups from the image, and a character string detection unit that detects each character in a detection target character string to be detected so as to detect the detection target character string in the clipped image generated by the clipping unit.

Abstract: Techniques are disclosed herein that enable digital images to be segmented based on a user's semantic input. In other words, given an input image of a person walking a dog adjacent to a tree, a user can simply provide the semantic input “dog” and the system will segment the dog from the other elements in the image. If the user provides other semantic input, such as “person” or “tree”, the system will segment the person or the tree, respectively, from the same image. Using semantic input advantageously eliminates any need for a user to directly interact with the input image through a tedious process of painting brush strokes, tracing boundaries, clicking target points, and/or drawing bounding boxes. Thus semantic input represents an easier and more intuitive way for users to interact with an image segmentation interface, thereby enabling novice users to take advantage of advanced image segmentation techniques.

Abstract: This invention provides a system and method for determining correspondence between camera assemblies in a 3D vision system implementation having a plurality of cameras arranged at different orientations with respect to a scene involving microscopic and near microscopic objects under manufacture moved by a manipulator, so as to acquire contemporaneous images of a runtime object and determine the pose of the object for the purpose of guiding manipulator motion. At least one of the camera assemblies includes a non-perspective lens. The searched 2D object features of the acquired non-perspective image, corresponding to trained object features in the non-perspective camera assembly can be combined with the searched 2D object features in images of other camera assemblies, based on their trained object features to generate a set of 3D features and thereby determine a 3D pose of the object.

Abstract: An image processing device that identifies a characteristic of a lip from a face image including a mouth of a person has a representative skin color determination unit that determines a representative color of a skin in the face image, a candidate color determination unit that sets a plurality of regions in the face image such that at least one of the regions contains a part of the lip, and determines representative colors of the regions as candidate colors, and a representative lip color determination unit that determines a representative color of the lip from the plurality of candidate colors, in accordance with a difference in hue and saturation between the representative color of the skin and each candidate color.

Abstract: In an image processing performing local tone correction on an image, tone correction on an object (face) area can be suppressed from becoming nonuniform. The apparatus is provided with an acquisition unit (102) that acquires coordinate information on an object area, a division unit (103) that divides the inputted image into a plurality of divided areas, a first decision unit (105) that decides atone correction coefficient for each of the divided areas, a second decision unit (106) that decides a tone correction coefficient for the object area based on coordinate information on the object area and the tone correction coefficients for the plurality of divided areas, and an image processing unit (107) that performs tone correction processing on the object area by applying thereto the tone correction coefficient decided by the second decision unit in a uniform manner without depending on coordinates thereof.

Abstract: An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.

Abstract: An image processing apparatus includes: a decomposing section that decomposes a luminance component of a given original image into a plurality of band images in respective bands; a band intensity calculating section that calculates band intensities of the respective band images; and a determining section that determines band selection information indicating an emphasis band and band emphasis information to be used for controlling a degree of image emphasis based on the band intensities of the respective bands.

Abstract: Method, apparatus, and computer program product are provided. The method includes detecting a face portion in a frame of a plurality of frames of a multi-media content. The method further includes tracking the face portion in at least one subsequent frame of the frame. A color-tracking of the face portion is performed on losing a track of the face portion in the at least one subsequent frame. The color-tracking is performed for re-tracking the face portion in the at least one subsequent frame.

Abstract: A method comprising determining an image histogram associated with an image, determining a linear image histogram based, at least in part, on the image histogram, and causing display of a linear representation of the image histogram and, at least part of, the image is disclosed.

Abstract: A method for interpolating an image field with image processing circuitry includes identifying, from a first row in the image field, a pixel to be interpolated. Edge direction vectors from the identified pixel and a plurality of neighboring pixels in a second row of the image field are determined. The edge direction vectors may then be combined to produce an interpolated pixel. The edge direction vectors may be combined such that the pixel is interpolated along each edge direction vector to obtain multiple interpolated pixel values. The pixel may be interpolated based on a weighted average of the interpolated pixel values.

Abstract: A method of increasing resolution of an image includes generating vector contours associated with a first image and scaling the vector contours to a second resolution. The first image has a first resolution, and the second resolution is greater than the first resolution. The vector contours are rendered to generate a guiding image at the second resolution, and a second image is generated from the first image based on the guiding image, by using joint upsampling. The second image is generated at the second resolution. The vector contours can be generated by bitmap tracing binary images at different quantization levels. An apparatus and computer readable device implementing the method of increasing the resolution of an image are also provided.

Abstract: A method and apparatus for image compression. The method includes: calculating a total bit length that is needed for compressing any one segment line and an ideal bit length that is needed for compressing any one segment; for each segment in any one segment line, acquiring a quantization parameter and a compression mode for compressing the segment and an actual bit length that is needed for compressing the segment by using the acquired quantization parameter and compression mode; distributing a redundancy bit length of one or more simple segments in the segment line to one or more complex segments in the segment line, and re-acquiring quantization parameters and compression modes for compressing every complex segment of the one or more complex segments; and compressing every segment in the segment line by using the acquired quantization parameters and compression modes.

Abstract: A computerized method for recognition of a logo is described herein. The method comprises obtaining a plurality of feed frame of a feed video wherein the feed video has a logo embedded therein. At least one feed frame from the plurality of feed frames is compared with each template from a plurality of templates. For each template compared with the feed frame, a correlation parameter is computed and the logo is recognized based on the computing.

Abstract: A contextual local image recognition module of a device retrieves a primary content dataset from a server and then generates and updates a contextual content dataset based on an image captured with the device. The device stores the primary content dataset and the contextual content dataset. The primary content dataset comprises a first set of images and corresponding virtual object models. The contextual content dataset comprises a second set of images and corresponding virtual object models retrieved from the server.

Abstract: An information processing apparatus includes an acquisition section and a correction section. The acquisition section acquires information of lightness distribution of a first image captured by an imaging section capable of capturing an image of an observed area provided on an optical path of an optical system of an optical microscope, the image of the observed area being obtained by the optical microscope, the first image being an image of the observed area in a state where no sample is placed therein, the lightness distribution resulting from the optical system of the optical microscope. The correction section corrects, based on the information of lightness distribution acquired by the acquisition section, lightness unevenness of a second image captured by the imaging section, the second image being an image of the observed area in a state where the sample is placed therein.

Abstract: An image resizing method includes at least the following steps: receiving at least one input image; performing an image content analysis upon at least one image selected from the at least one input image to obtain an image content analysis result; and creating a target image with a target image resolution by scaling the at least one input image according to the image content analysis result, wherein the target image resolution is different from an image resolution of the at least one input image.

Abstract: A system receives a two-dimensional digital image of an aerial industrial plant area. Based on requirements of image processing, the image is zoomed in to different sub-images, that are referred to as first images. The system identifies circular tanks, vegetation areas, process areas, and buildings in the first image. The system formulates a second digital image by concatenating the first images. The system creates one or more polygons of the regions segmented in the second digital image. Each polygon encompasses a tank area, a vegetation area, a process area, or a building area in the second digital image, which is a concatenated image of the individual regions. The system displays the second digital image on a computer display device.

Abstract: An image processing apparatus includes: an adjusting unit that adjusts a color characteristic in relation to a color image; a conversion unit that converts the color image of which color characteristic is adjusted with the adjusting unit into a luminance-chromaticity image; a band image generating unit that generates a band image that represents a predetermined band from a luminance image of the luminance-chromaticity image; an emphasizing unit that obtains a band emphasized image that emphasizes a band using the luminance image and the band image; and a setting unit that sets an emphasized degree of the band performed by using the emphasizing unit and an adjusted degree of the color characteristic performed by using the adjusting unit.

Abstract: Systems and methods are provided for processing images (or other such instances of content) to detect which of the images exhibit artifacts when modified, such as by applying standard transformation algorithms to modify the images. Such techniques enable transformation algorithms to be applied to the detected images to minimize or prevent artifacts. In some embodiments, the headers of the detected images can be tagged with transformative instructions that indicate which transformation algorithms to apply. Responsive to a request from a web client to modify and render one of the detected images, embodiments obtain the requested image, read the transformative instructions in the header, apply the transformation algorithm specified in the header to modify the image so as to minimize or prevent artifacts, and render the modified image.