Abstract: A method of labeling pixels in an image in which pixels in the image that represent human skin of one or more people are detected and one or more regions in the image are identified, where each region in the one or more regions includes all or a portion of a human face of a person in the one or people in the image. Pixels that represent each face in the image are identified using the pixels that represent skin and the regions that include faces of the people, thereby identifying a position of each face in the image. From this, a face mask for each face and a rough body map corresponding to each face is determined using the positions of the identified faces. Further still, a torso map corresponding to each face is determined using determined face positions. Then, the extracted face masks and the torso maps are used to refine a skin map. A person or people map is determined using the skin map and the rough body map.

Abstract: A method of labeling pixels in an image in which pixels in the image that represent human skin of one or more people are detected and one or more regions in the image are identified, where each region in the one or more regions includes all or a portion of a human face of a person in the one or people in the image. Pixels that represent each face in the image are identified using the pixels that represent skin and the regions that include faces of the people, thereby identifying a position of each face in the image. From this, a face mask for each face and a rough body map corresponding to each face is determined using the positions of the identified faces. Further still, a torso map corresponding to each face is determined using determined face positions. Then, the extracted face masks and the torso maps are used to refine a skin map. A person or people map is determined using the skin map and the rough body map.

Abstract: Systems for segmenting human hairs and faces in color images are disclosed, with methods and processes for making and using the same. The image may be cropped around the face area and roughly centered. Optionally, the illumination environment of the input image may be determined. If the image is taken under dark environment or the contrast between the face and hair regions and background is low, an extra image enhancement may be applied. Sub-processes for identifying the pose angle and chin contours may be performed. A preliminary mask for the face by using multiple cues, such as skin color, pose angle, face shape and contour information can be represented. An initial hair mask by using the abovementioned multiple cues plus texture and hair shape information may be created. The preliminary face and hair masks are globally refined using multiple techniques.

Abstract: A method of labeling pixels in an image that represent human skin are detected and one or more regions in the image are identified. Each region may include all or a portion of a human face. Pixels that represent each face in the image are identified using the pixels that represent skin and the regions that include faces of the people, thereby identifying a position of each face in the image. A face mask and a torso map corresponding to each face is determined using determined face positions. The extracted face masks and the torso maps are used to refine a skin map. A person or people map is determined using the skin map and the rough body map.

Abstract: Systems for refinement of a segmentation of an image using spray-paint markup are disclosed, with methods and processes for making and using the same. Spray-paint markup allows for easy markup of errors in a segmentation. The markup's data may be correlated or harmonized with the representation of the segmentation, such that it may be applied to the segmentation. The markup's data is utilized to refine the segmentation errors. To assist in the refinement process, the resolutions may be scaled down so as to exert less computation burden during the process.

Abstract: Systems for segmenting human hairs and faces in color images are disclosed, with methods and processes for making and using the same. The image may be cropped around the face area and roughly centered. Optionally, the illumination environment of the input image may be determined. If the image is taken under dark environment or the contrast between the face and hair regions and background is low, an extra image enhancement may be applied. Sub-processes for identifying the pose angle and chin contours may be performed. A preliminary mask for the face by using multiple cues, such as skin color, pose angle, face shape and contour information can be represented. An initial hair mask by using the abovementioned multiple cues plus texture and hair shape information may be created. The preliminary face and hair masks are globally refined using multiple techniques.

Abstract: A method for distinguishing a foreground object from a monochromatic background in a digital image depicting a foreground object in front of said monochromatic background. A outer edge intensity map is created of the foreground object to which a tracing process is conducted. A continuous edge map is created by utilizing the traced edges in the outer edge intensity map. By selecting a path on the continuous edge map based on average edge intensity, a final edge map is created. The pixels comprised within the final edge map are distinguished as the foreground object. Optionally, the final edge map may be refined utilizing certain techniques.

Abstract: Systems, methods, and computer readable media for forming a mugshot from a digital color image are provided in which a dominant face is determined using the digital color image. Person segmentation is also performed using the digital color image. An image and a mask are cropped based on the dominant face thereby forming a cropped image. Rough segmentation is performed on the cropped image. A mask is averaged in projection space based on the cropped image. The mask is refined mask and prepared for the mugshot.

Abstract: A system for improved display of tuned multi-scaled regions of an image with local and global control and methods for making and using same. To assist the novice user of image processing tool, less input parameters should be required. Further, it will assist the user if results are diplayed in a shorter period of time. Utilizing a hierarchical bottom-up approach provides for the advantage of being able to utilize intermediate results to gather more details. The systems and methods disclosed provide for the grouping of contiguous pixels which have similar properties. Further, the disclosed embodiments provide for the user the ability to see all levels of detail of segmentation either globally or locally. The scale-space is tuned to the information in the image.

Abstract: An initial figure mask estimation of an image is generated using a figure ground segmentation system, thereby initially assigning each pixel in the image with a first attribute value or a second attribute value. A JigCut region segmentation of the image is generated. The figure mask estimation is processed with the JigCut region segmentation by (i) classifying the pixels of the image in each respective JigCut region in the JigCut region segmentation with the first attribute value when a predetermined number or a predetermined percentage of the pixels within the respective JigCut region have been initially assigned the first attribute value by the initial figure mask estimation and (ii) classifying the pixels of the image in each respective JigCut region in the JigCut region segmentation with the second attribute value otherwise.

Abstract: Systems for segmenting an image based on perceptual information and methods for making and using same. According to one embodiment, input channels from an image are derived and analyzed by heuristic metrics to create categorical estimates. Examples of categorical estimates include a foreground channel estimate and a background channel estimate. Once created, the categorical estimates are merged to create a final channel estimate. The final channel estimate may represent the foreground or background of an image. Optionally, noise removal will also be conducted to improve the segmentation.