International Journal of Pattern Recognition and Artificial Intelligence

In this paper, we present a novel method to solve the inter-frame interpolation problem in image morphing. We use our improved snake model that is associated with the gravitational force field to locate control points in the object contours. Afterwards, we apply the greedy algorithm in free-form deformations to achieve optimal warps among feature point pairs in starting and ending frames. The new method uses an energy-minimization function under the influence of inter-frames. The energy serves to impose frame-wise and curve-wise constraints among the interpolated frames.

This paper proposes an efficient approach for human face detection and exact facial features location in a head-and-shoulder image. This method searches for the eye pair candidate as a base line by using the characteristic of the high intensity contrast between the iris and the sclera. To discover other facial features, the algorithm uses geometric knowledge of the human face based on the obtained eye pair candidate. The human face is finally verified with these unclosed facial features. Due to the merits of applying the Prune-and-Search and simple filtering techniques, we have shown that the proposed method indeed achieves very promising performance of face detection and facial feature location.

A two-stage face recognition method is presented in this paper. In the first stage, the set of candidate patterns is narrowed down with the global similarity being taken into account. In the second stage, synergetic approach is employed to perform further recognition. Face image is segmented into meaningful regions, each of which is represented as a prototype vector. The similarity between a given region of the test pattern and a stored sample is obtained as the order parameter which serves as an element of the order vector. Finally, a modified definition of the potential function is given, and the dynamic model of recognition is derived from it. The effectiveness of the proposed method is experimentally confirmed.

This paper deals with the problem of matching curves and structures extracted from 2D images that are subject to translation, rotation, scaling and other geometric transformations. We present in this paper a novel approach, Connected Equi-Length Line Segments (CELLS), for curve representation and matching. In our framework, a curve is represented by a number of connected equi-length line segments and a new matrix called Orientation Difference Matrix (ODM) is constructed for the curve, which reflects the distribution of the rest of the line segments with respect to the current one using orientation differences between them. The representation is invariant to rotation, scaling and translation. The problem of structure matching is also considered in this paper and is solved based on CELLS. The matching of structures is performed by (1) detecting tri-junctions and quad-junctions on the structures, (2) representing each arch using CELLS. A practical use of the proposed approach is demonstrated by registering a SAR image of a certain area to a map.

We describe the desire for a black box approach to pattern classification: a generic Autonomous Pattern Recognizer, which is capable of self-adapting to specific alphabets without human intervention. The CellNet software system is introduced, an evolutionary system that optimizes a set of pattern-recognizing agents relative to a provided set of features and a given pattern database. CellNet utilizes a new genetic operator designed to facilitate a canalization of development: Merger. CellNet utilizes our own set of arbitrarily chosen features, and is applied to the CEDAR Database of handwritten Latin characters, as well as to a database of handwritten Indian digits provided by CENPARMI. CellNet's cooperative co-evolutionary approach shows significant improvement over a more standard Genetic Algorithm, both in terms of efficiency and in nearly eliminating over-fitting (to the training set). Additionally, the binary classifiers autonomously evolved by CellNet return validation accuracies approaching 98% for both Latin and Indian digits, with no global changes to the system between the two trials.

XML has been widely used as metadata for image retrieval. As a standard, it makes it easier to index and retrieve information across different platforms. However, how to automatically convert an image into XML format remains a challenge. In this paper, a system for generating structured document in XML from digitally captured document images is presented. The system is aimed at providing an easy to use tool for average users without requiring depth of knowledge in the document processing areas. Further, a XML/XSL generator is developed to accurately represent a document in a XML structure, yet in a representation that reflects its original layout.

In this paper, splicing on images of rectangular arrays is introduced as a simple and effective extension of the operation of splicing on strings extensively studied in the context of DNA computing. A comparison of the resulting class of splicing array languages with the local two-dimensional array languages and two-dimensional right linear languages is made. Certain closure properties are obtained. Furthermore, the notion of self cross-over of strings is extended to arrays with respect to the splicing of arrays introduced here.

An object-based image retrieval method is addressed in this paper. For that purpose, a new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and textural features are extracted from each pixel in the image and these features are used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terms of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In the retrieval case, two comparing schemes are proposed. Comparisons between one query object and multi-objects of a database image and comparisons between multi-query objects and multi-objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into the database.

In this paper, a novel gray-level image-hiding scheme is proposed. The goal of this scheme is to hide multiple important gray-level images into another meaningful gray-level image. The secret images to be protected are first compressed using the vector quantization scheme. Then, the DES cryptosystem is conducted on the VQ indices and related parameters to generate the encrypted message. Finally, the encrypted message is embedded into the rightmost two bits of each pixel in the cover image.

According to the experimental results, average image qualities of 44.320 dB and 30.885 dB are achieved for the embedded images and the retrieved secret images, respectively. In other words, multiple secret images can be effectively hidden into one host image of the same size. In addition, the proposed scheme strengthens the protection of the secret images by conducting the DES cryptosystem on the related parameters and the VQ indices of the compressed secret images. Therefore, the proposed scheme provides a secure approach to embed multiple important images into another meaningful image of the same size.