Object segmentation is important in image analysis for imaging tasks such as image rendering and image retrieval. Pet
owners have been known to be quite vocal about how important it is to render their pets perfectly. We present here an
algorithm for pet (mammal) fur color classification and an algorithm for pet (animal) fur texture classification. Per fur
color classification can be applied as a necessary condition for identifying the regions in an image that may contain pets
much like the skin tone classification for human flesh detection. As a result of the evolution, fur coloration of all
mammals is caused by a natural organic pigment called Melanin and Melanin has only very limited color ranges. We
have conducted a statistical analysis and concluded that mammal fur colors can be only in levels of gray or in two
colors after the proper color quantization. This pet fur color classification algorithm has been applied for peteye
detection. We also present here an algorithm for animal fur texture classification using the recently developed multi-resolution
directional sub-band Contourlet transform. The experimental results are very promising as these transforms
can identify regions of an image that may contain fur of mammals, scale of reptiles and feather of birds, etc. Combining
the color and texture classification, one can have a set of strong classifiers for identifying possible animals in an image.
KEYWORDS: Data mining, Cameras, Computed tomography, Photography, Knowledge discovery, Databases, Scene classification, Digital photography, Image retrieval, Digital cameras
A photograph captured by a digital camera usually includes camera metadata in which sensor readings, camera settings
and other capture pipeline information are recorded. The camera metadata, typically stored in an EXIF header,
contains a rich set of information reflecting the conditions under which the photograph was captured. This set of rich
information can be potentially useful for improvement in digital photography but its multi-dimensionality and
heterogeneous data structure make it difficult to be useful. Knowledge discovery, on the other hand, is usually
associated with data mining to extract potentially useful information from complex data sets. In this paper we use a
knowledge discovery framework based on data mining to automatically associate combinations of high-dimensional,
heterogeneous metadata with scene types. In this way, we can perform very simple and efficient scene classification for
certain types of photographs. We have also provided an interactive user interface in which a user can type in a query on
metadata and the system will retrieve from our image database the images that satisfy the query and display them. We
have used this approach to associate EXIF metadata with specific scene types like back-lit scenes, night scenes and snow
scenes. To improve the classification results, we have combined an initial classification based only on the metadata with
a simple, histogram based analysis for quick verification of the discovered knowledge. The classification results, in turn,
can be used to better manage, assess, or enhance the photographs.
Redeyes are caused by the camera flash light reflecting off the retina. Peteyes refer to similar artifacts in the eyes of
other mammals caused by camera flash. In this paper we present a peteye removal algorithm for detecting and
correcting peteye artifacts in digital images. Peteye removal for animals is significantly more difficult than redeye
removal for humans, because peteyes can be any of a variety of colors, and human face detection cannot be used to
localize the animal eyes. In many animals, including dogs and cats, the retina has a special reflective layer that can
cause a variety of peteye colors, depending on the animal's breed, age, or fur color, etc. This makes the peteye
correction more challenging. We have developed a semi-automatic algorithm for peteye removal that can detect peteyes
based on the cursor position provided by the user and correct them by neutralizing the colors with glare reduction and
glint retention.
In managing large collections of digital photographs, there have been many research efforts to compute low level image features such as texture and color to aid different managing tasks (e.g. query-by-example applications or scene classification for image clustering). In this paper, we focus on the assessment of image quality as a complementary feature to improve the manageability of images. Specifically, we propose an effective and efficient algorithm to analyze the focus quality of the photographs and provide quantitative measurement of the assessment. In this algorithm, global figure-of-merits are computed from matrices of the local image statistics such as sharpness, brightness and color saturation. The global figure-of-merits represent how well each image meets the prior assumptions about focus quality of natural images. Then, a collection of the global figure-of-merits are used to decide how well-focused an image is. Experimental results show that the method can detect 90% of the out-of-focus photographs labeled by experts while producing 11% of false positives. We further apply this quantitative measure in image management tasks, including image content filtering/sorting based on the focus quality and image retrieval.
A Visually significant two-dimensional barcode (VSB) developed by Shaked et. al. is a method used to design an information carrying two-dimensional barcode, which has the appearance of a given graphical entity such as a company logo. The encoding and decoding of information using the VSB, uses a base image with very few graylevels (typically only two). This typically requires the image histogram to be bi-modal. For continuous-tone images such as digital photographs of individuals, the representation of tone or "shades of gray" is not only important to obtain a pleasing rendition of the face, but in most cases, the VSB renders these images unrecognizable due to its inability to represent true gray-tone variations. This paper extends the concept of a VSB to an image bar code (IBC). We enable the encoding and subsequent decoding of information embedded in the hardcopy version of continuous-tone base-images such as those acquired with a digital camera. The encoding-decoding process is modeled by robust data transmission through a noisy print-scan channel that is explicitly modeled. The IBC supports a high information capacity that differentiates it from common hardcopy watermarks. The reason for the improved image quality over the VSB is a joint encoding/halftoning strategy based on a modified version of block error diffusion. Encoder stability, image quality vs. information capacity tradeoffs and decoding issues with and without explicit knowledge of the base-image are discussed.
Presented here is a method for reducing the computational complexity of two-dimensional linear convolutions used in image processing like binary image scaling. This method is a hybrid of convolving at run-time and convolving by table lookup. The convolution step in image processing usually calculates a weighted average of an area of the input image by calculating the entry-by-entry multiplication of the input pixels with a weight table. This method partitions the calculations in the convolution step and stores pre-calculated partial results in lookup tables. When the convolution step takes place, a binary indexing is used to retrieve the partial results and the final result is obtained by summing up the partial results. A line cache and a double buffering scheme are designed to reduce memory access in table lookup. Space and time complexities are analyzed and compared to the conventional two-dimensional linear convolutions. We demonstrate that an order of magnitude reduction in the computational cost can be achieved. Examples, test images and performance data are provided.
Presented here is an algorithm for scaling binary images based on piecewise polynomial interpolation. The algorithm defines a set of convolution kernels which can be used to scale the original image data by an arbitrary scaling factor and to reduce or remove the aliasing artifacts. The convolution kernels are derived from a surface geometry that is mathematically defined over the original data. This algorithm solves a quantization error problem which had prohibited practical applications of any polynomial as an interpolant for image scaling. Its microscopic behavior has been analyzed in a software simulation testbed. It can be applied for scaling binary images in the areas of facsimile imaging and font scaling. It has been fully tested and implemented in a commercial product.
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