Jonathan Yen

An efficient automatic redeye detection and correction algorithm

A fully automatic redeye detection and correction algorithm is presented to address the redeye artifacts in digital photos. The algorithm contains a redeye detection part and a correction part. The detection part is modeled as a feature based object detection problem. Adaboost is used to simultaneously select features and train the classifier. A new feature set is designed to address the orientation-dependency problem associated with the Haar-like features commonly used for object detection design. For each detected redeye, a correction algorithm is applied to do adaptive desaturation and darkening over the redeye region.

Hardcopy image barcodes via block-error diffusion

Error diffusion halftoning is a popular method of producing frequency modulated (FM) halftones for printing and display. FM halftoning fixes the dot size (e.g., to one pixel in conventional error diffusion) and varies the dot frequency according to the intensity of the original grayscale image. We generalize error diffusion to produce FM halftones with user-controlled dot size and shape by using block quantization and block filtering. As a key application, we show how block-error diffusion may be applied to embed information in hardcopy using dot shape modulation. We enable the encoding and subsequent decoding of information embedded in the hardcopy version of continuous-tone base images. The encoding-decoding process is modeled by robust data transmission through a noisy print-scan channel that is explicitly modeled. We refer to the encoded printed version as an image barcode due to its high information capacity that differentiates it from common hardcopy watermarks. The encoding/halftoning strategy is based on a modified version of block-error diffusion. Encoder stability, image quality versus information capacity tradeoffs, and decoding issues with and without explicit knowledge of the base image are discussed.

Parallel boxing in B-spline intersection

A modified formulation of oriented boxing called oriented slab boxing is presented. It almost doubles the speed of the oriented boxing component in B-spline intersection. The method used to accelerate B-spline intersection includes algorithmic improvements and parallelization of the algorithm at different levels of granularity to find an optimum solution on a network of parallel processors. The software testbed is linked to a B-spline-based library, which is a prototype of what may be used in an actual engineering design environment. A parallel implementation of the algorithm on a network of three processors achieves an additional twofold improvement in speed.<<ETX>>

Complexity reduction on two-dimensional convolutions for image processing

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.

Automatic focus quality analysis for managing large collection of digital photographs

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.