Showing papers on "Sketch recognition published in 1993"

Handbook of Pattern Recognition and Computer Vision

Abstract: Both pattern recognition and computer vision have experienced rapid progress in the last twenty-five years. This book provides the latest advances on pattern recognition and computer vision along with their many applications. It features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers. The book is divided into five parts: basic methods in pattern recognition, basic methods in computer vision and image processing, recognition applications, life science and human identification, and systems and technology.

Three-Dimensional Object Recognition Systems

Abstract: Contributors. 3-D object recognition: Inspirations and lessons from biological vision. Range sensing for computer vision. Feature extraction for 3-D model building and object recognition. Three-dimensional surface reconstruction: Theory and implementation. CAD-based object recognition in range images using pre-compiled strategy trees. Active 3-D object models. Image prediction for computer vision. Tools for 3-D object location from geometrical features by monocular vision. Part-based modeling and qualitative recognition. Appearance-based vision and the automatic generation of object recognition programs. Recognizing 3-D objects using constrained search. Recognition of superquadric models in dense range data. Recognition by alignment. Representations and algorithms for 3-D curved object recognition. Structural indexing: efficient three dimensional object recognition. Building a 3-D world model for outdoor scenes from multiple sensor data. Understanding object configurations. Modal descriptions for modeling, recognition, and tracking. Function-based generic recognition for multiple object categories.

Connectionist architectural learning for high performance character and speech recognition

Abstract: The authors applied an automatic structure optimization (ASO) algorithm to the optimization of multistate time-delay neural networks (MSTDNNs), an extension of the TDNN. These networks allow the recognition of sequences of ordered events that have to be observed jointly. For example, in many speech recognition systems the recognition of words is decomposed into the recognition of sequences of phonemes or phonemelike units. In handwritten character recognition the recognition of characters can be decomposed into the joined recognition of characteristic strokes, etc. The combination of the proposed ASO algorithm with the MSTDNN was applied successfully to speech recognition and handwritten character recognition tasks with varying amounts of training data. >

Multi-modal HCI: combination of gesture and speech recognition

Abstract: Multi-modal interfaces can achieve more natural and effective human-computer interaction by integrating a variety of signal% or modalities, by which humans usually convey information. The integration of multiple input modalities permits greater expressiveness tim complementary information sources, and greater reliability due to redundancies across modalities. This paper describes a text editor developed at Carnegie Mellon, featuring a multi-modal interface that allows users to manipulate text using a combination of speech and pen-based gestures. The implementation of this multi-modal text editor also illustrates a framework on which more general joint interpretation of multiple modalities can be based

Application of high-resolution computer graphics to pattern recognition analysis

Abstract: False color data imaging has proven to be a powerful technique for the analysis and visualization of satellite data. This success suggests that high-resolution computer graphics can also play an important role in the analysis of multivariate data obtained from chemical instruments. The development of a graphics tool for visualization of multivariate data is described in this paper. The proposed graphics tool utilizes the fuzzy c-varieties clustering algorithm, principal components analysis and false color data imaging to generate maps of high informational density of the data space. The graphics tool has been tested successfully using data sets from the literature representative of the pattern recognition problems encountered by chemists.

2-stage character recognition by detection and correction of erroneously-identified characters

Abstract: The authors propose a two-stage text recognition system with high recognition rate. In the first stage, characters recognized as different recognition results by two matching modules are rejected, since they are recognized incorrectly by at least one of the two matching modules. The rejected characters are then processed by a Markov language model in the second stage. Since most of the input characters are recognized in the first stage, the computation cost of the language model is low and the recognition rate of the language model is excellent. By using this erroneously-identified text recognition approach, a system with high character recognition accuracy can be achieved. Our text recognition system can be extended to a multiple-stage recognition system to further improve the recognition accuracy. In each stage, various matching modules can be used. The recognized result of an input character will be accepted only when all matching modules produce the same result. Rejected characters will be fed into the next stage for further processing. >

Recognising hand-written Japanese sentences

Abstract: This paper makes a case for handwriting recognition compared to other input methods for communication with machines. A comparison is made with voice recognition and keyboard input systems for both western languages and for Japanese. Both single word recognition and whole sentence recognition are considered. A case is made for handwriting recognition for a language with a large character set and many homonyms, such as the Japanese language. For such a language, a fundamental problem exists for both keyboard input and for voice recognition. Both these systems need to convert a phonetic representation into Kanji, and this requires extensive knowledge of the meaning of the text if it is to be automatic. AI research has yet to deliver fast, competent text understanding systems. Consequently, both voice and keyboard input methods need to present the user with alternative choices during recognition, and this makes these methods slow and unnatural. A system is described here which is designed for accurate, fast sentence recognition of both western scripts and Japanese. The system is designed for whole sentence recognition, with the user allowed to write in a natural way. There is considerable flexibility allowed in terms of size and shape of the writing. The distinguishing characteristic of the system, is the use of a unified recognition technique applied to character, word and sentence recognition. This technique is an adaptation of chart parsing, used extensively in natural language processing in AI. Here the technique has been developed to allow weighted multiple hypotheses during recognition. This is important for a system that allows the user to write naturally. This approach to sentence recognition, allows mistakes made during low level processing to be corrected at higher levels. Knowledge of the vocabulary and allowable sentence structures are incorporated in the system in a unified way. A useful additional result of this approach, is the ability to produce a syntactic parse of the sentence recognised. Provisional results are presented for recognition of Japanese Hiragana characters and for English capital letters. The users were given considerable freedom on the style of writing used. The results show recognition rates of over 80% at present, for a variety of users. Improvements in this performance are anticipated when lexical and syntactic modules are added. Further improvements are anticipated by incorporating learning into the system, so that the knowledge base will be tuned for each user. >

Novel approach to human face recognition

Abstract: This paper presents a new method of human face recognition based on a novel algebraic feature extraction method. An input human face image is First transformed into a standard image; Then, the projective feature vectors of the standard image are extracted by projecting it onto the optimal discriminant projection vectors; Finally, face image recognition is completed by classifying these projective feature vectors. Experimental results showed that the present method is effective.

A hybrid system for handprinted word recognition

Abstract: The authors describe an alternative approach to handprinted word recognition using a hybrid of procedural and connectionist techniques. They utilize two connectionist components, which are to concurrently make recognition and segmentation hypotheses, and to perform refined recognition of segmented characters. Both networks are governed by a procedural controller which incorporates systematic domain knowledge and procedural algorithms to guide recognition. A recognition method is presented whereby an image is processed over time by a spatiotemporal connectionist network. The scheme offers several attractive features including shift-invariance and retention of local spatial relationships along the dimensions being temporalized, a reduction in the number of free parameters, and the ability to process arbitrarily long images. Recognition results on a set of real-world isolated zip code digits are comparable to the best reported to date with a 96.0% recognition rate. A pilot implementation of the complete system, and results on overlapping and touching pairs of zip code digits are reported. >

Curvature Based Analysis of Handwritten Symbols

Abstract: Pattern recognition is an important branch of computer vision and intelligent robotics. This paper explores local curve theory for different pattern recognition applications: recognition of good handwritten symbols (Fig 1 ), analysis of the hierarchical structure of 2-D patterns and simple 3-D object recognition. In the case of 2-D patterns, our technique is compared with a recent related method based on generalized Hough transforms and it is found that it is more robust with respect to complex image outlines.