Image recognition

An offline cursive handwriting recognition system, based on hybrid of Neural Networks (NN) and Hidden Markov Models (HMM), is described in this paper. Applying SegRec principle, the recognizer does not make hard decision at the character segmentation process. Instead, it delays the character segmentation to the recognition stage by generating a segmentation graph that describes all possible ways to segment a word into letters. To recognize a word, the NN computes the observation probabilities for each segmentation candidates (SCs) in the segmentation graph. Then, using concatenated letter-HMMs, a likelihood is computed for each word in the lexicon by multiplying the probabilities over the best paths through the graph. We present in detail two approaches to train the word recognizer: 1). character-level training 2). word-level training. The recognition performances of the two systems are discussed. I.

Aim  Automatic CT dataset classification is important to efficiently create reliable database annotations, especially when large collections of scans must be analyzed. Method  An automated segmentation and labeling algorithm was developed based on a fast patient segmentation and extraction of statistical density class features from the CT data. The method also delivers classifications of image noise level and patient size. The approach

This paper presents the mathematical framework of radial Tchebichef moment invariants, and investigates their feature representation capabilities for pattern recognition applications. The radial Tchebichef moments are constructed using the discrete ...

We describe a method of generating and utilizing visual landmarks that is well suited for SLAM applications. The landmarks created are highly distinctive and reliably detected, virtually eliminating the data association problem present in other landmark schemes. Upon subsequent detections of a landmark, a 3-D pose can be estimated. The scheme requires a single camera.

A digital watermark is an invisible mark embedded in a digital image which may be used for Copyright Protection. This paper proposes that Fourier-Mellin transform-based invariants can be used for digital image watermarking. The embedded marks may be designed to be una ected by any combination of rotation, scale and translation transformations. The original image is not required for extracting the embedded

Image recognition and understanding is one of the most interesting fields of researches. Its main idea is to bridge the gap between the high level human image understanding and the low level machine image representation. Quite a lot of applications have been suggested in different fields like medicine, industry, robotics, satellite imagery and other applications. This paper proposes a new approach of traffic signs image recognition and understanding using computational intelligent techniques and the application of this approach on intelligent cars which can recognize the traffic signs and take a decision according to the signs it reads. Supervised machine learning has been selected since the algorithm does not need to classify the images but to identify their precise meaning. Different neural networks have been trained and used in this paper. The best neural network has been selected, which uses genetic algorithms in its training, and is known as evolutionary training neural network...

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed.

Optical Character Recognition (OCR) is an extensive research field in image processing and pattern recognition. Traditional character recognition methods cannot distinguish a character or a word from a scanned image. This paper proposes a system, which is to develop a method that uses a personal computer, a portable scanner and an application program that would automatically correct the handwritten answer sheets. For handwritten character recognition, the scanned images are fed through a machine learning classifier known as the Convolutional Neural Network (CNN). Two CNN models were proposed and trained on 250 images that were collected from students at Prince Mohammad Bin Fahd University. The proposed system will finally output the final score of the student by comparing each classified answer with the correct answer. The experimental results exhibited that the proposed system performed a high testing accuracy of 92.86%. The system can be used by the instructors in several educational institutions to automatically grade the handwritten answer sheets of students effectively.

Selection of a good feature extraction method is the most important factor in achieving the higher recognition rate in face recognition. This paper presents the analysis of two moment based feature extraction methods namely Zernike moments (ZMs) and Complex Zernike moments (CZMs) in application to face image recognition. We have intensively analyzed these methods in terms of their reconstruction ability and invariance to rotation, scale and size. Almost all existing methods use only magnitude component of the moments as invariant features in recognition task. Recently it is proposed that the phase component of moments also captures useful information for image representation. In this paper, we have analyzed the performance of both magnitude and phase coefficients of ZMs and call it CZMs. These methods are tested separately on suitable databases. The databases used are UMIST pose database for rotation variation, JAFFE expression database for size and scale variations, and popular ORL and FERET databases for comparison of recognition results. It can be concluded from the experimental results that the performance of CZMs is not only better than ZMs but also it is the descriptor that gives best recognition rate amongst the descriptors well known for face recognition.

... 7 Conclusions In this paper, we have proposed a geometry-based image representation scheme and an algo-rithm/function for retrieving i&D images of relevance (based on spatial similarity) to ... The algorithm is robust-in the sense that it can recognize translation and scale ...

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1199 individuals are included in the FERET database, which is divided into development and sequestered portions. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to (1) assess the state of the art, (2) identify future areas of research, and (3) measure algorithm performance on large databases

Digital cameras are convenient image acquisition devices: they are fast, versatile, mobile, do not touch the object, and are relatively cheap. In OCR applications, however, digital cameras suffer from a number of limitations, like geometrical distortions. In this paper, we deal with the preprocessing step before text recognition, specifically with images from a digital camera. Experiments, performed with the FineReader 7.0 software as the back-end recognition tool, confirm importance of image preprocessing in OCR applications.

In each and every field there is a need of maintaining attendance management system. The system will maintain a log details of each and every in their respective field. From past few years Automatic Face Recognition (AFR) technologies have fair improvements in performance, and such systems are now widely used for security and commercial applications. Automated face recognition system is helpful for recognizing face of humans in a real time background mainly for college to mark the attendance of their students. Automatic Face Recognition System is a real world solution which is useful in everyday activities. The matched face is used to mark attendance of the student. This system maintains the attendance records of students. The traditional approach of manually entering of attendance in logbooks is a difficult task and it also wastes the time. So this system is going to design an efficient module that comprises of face recognition to manage the attendance records of students. The new ...

Wepresenttheapplicationof differentnonlinearimagedeformationmodelsto thetaskof imagerecognition.Thedeformation models are especially suited for local changes as they often occur in the presence of image object variability. We show that, among the discussed models, there is one approach that combines simplicity of implementation, low-computational complexity, and highly competitive performance across various real-world image recognition tasks. We show experimentally that the model performs very well for