Abstract: Red-eye detection based on red region detection with eye confirmation initially identifies pixels that correspond to the color of red-eye within an image. A determination is then made as to whether these identified pixels and surrounding areas are part of an eye or not part of an eye. Those identified pixels that are determined to be part of an eye are the detected red-eye regions.

Abstract: Red-eye detection based on red region detection with eye confirmation initially identifies pixels that correspond to the color of red-eye within an image. A determination is then made as to whether these identified pixels and surrounding areas are part of an eye or not part of an eye. Those identified pixels that are determined to be part of an eye are the detected red-eye regions.

Abstract: Shape suppression is used to identify areas of images that include particular shapes. According to one embodiment, a Vector Quantization (VQ)-based shape classifier is designed to identify the vertical edges of a set of shapes (e.g., English letters and numbers). A shape suppression filter is applied to the candidate areas, which are identified from a vertical edge map according to the edge density, to remove the vertical edges which are not classified as characteristic of shapes. Areas with high enough edge density after the filtering are identified as potential areas of the image that include one or more of the set of shapes.

Abstract: An apparatus is provided for detecting blur in an image. The apparatus includes an image generator that is configured to generate a plurality of corresponding different resolution images based on a base image. The plurality of corresponding different resolution images is provided to an edge detector. The edge detector detects edge transitions in each of the plurality of corresponding different resolution images and provides edge maps to an edge parameter comparator. The edge parameter comparator compares corresponding edge parameters as detected by the edge detector and provides a result map to a blur calculator. The blur calculator determines at least one blur parameter based on the result map and provides the blur parameter to a blur detector. The blur detector then determines if the base image is blurred based on a comparison of the blur parameter with at least one blur parameter threshold.

Abstract: An apparatus is provided for detecting blur in an image. The apparatus includes an image generator that is configured to generate a plurality of corresponding different resolution images based on a base image. The plurality of corresponding different resolution images is provided to an edge detector. The edge detector detects edge transitions in each of the plurality of corresponding different resolution images and provides edge maps to an edge parameter comparator. The edge parameter comparator compares corresponding edge parameters as detected by the edge detector and provides a result map to a blur calculator. The blur calculator determines at least one blur parameter based on the result map and provides the blur parameter to a blur detector. The blur detector then determines if the base image is blurred based on a comparison of the blur parameter with at least one blur parameter threshold.

Abstract: An apparatus is provided for detecting blur in an image. The apparatus includes an image generator that is configured to generate a plurality of corresponding different resolution images based on a base image. The plurality of corresponding different resolution images is provided to an edge detector. The edge detector detects edge transitions in each of the plurality of corresponding different resolution images and provides edge maps to an edge parameter comparator. The edge parameter comparator compares corresponding edge parameters as detected by the edge detector and provides a result map to a blur calculator. The blur calculator determines at least one blur parameter based on the result map and provides the blur parameter to a blur detector. The blur detector then determines if the base image is blurred based on a comparison of the blur parameter with at least one blur parameter threshold.

Abstract: Red-eye detection based on red region detection with eye confirmation initially identifies pixels that correspond to the color of red-eye within an image. A determination is then made as to whether these identified pixels and surrounding areas are part of an eye or not part of an eye. Those identified pixels that are determined to be part of an eye are the detected red-eye regions.