In our proposed work localized patch based geometric blur point descriptors are accumulated to generate a global similarity matrix for every pair of focal and query image. A focal image is a randomly selected template image that represents each class and a query image symbolizes all other images that belong to the same class. The similarity matrix is dimensionally reduced using the proposed bidirectional 2-dimensional principal component analysis technique to generate distinctive feature sets. These feature sets are used for training and testing an extreme learning machine classifier. The proposed face recognition structure handles variations in head positions, lighting conditions, facial expressions and cluttered background by exclusively matching template and query images. Extensive experiments are performed using challenging face databases and significant improvements in recognition accuracy were achieved.

Modern fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular 9/7 discrete wavelet transform. Multiresolution analysis tools have been successfully applied for fingerprint image compression for more than a decade; we propose a novel fingerprint image compression technique based on recently proposed wave atoms decomposition. Wave atoms decomposition has specifically been designed for enhanced representation of oscillatory patterns to convey temporal and spatial information. Our proposed compression scheme is based upon linear vector quantization of decomposed wave atoms representation of fingerprint images. Later quantized information is encoded with arithmetic entropy scheme. The proposed image compression standard outperforms the FBI fingerprint image compression standard, the wavelet scalar quantization (WSQ). Data mining, law enforcement, border security, and forensic applications can potentially benefit from our proposed compression scheme.

Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this paper, the image processing techniques for developing low-cost machine vision system for pharmaceutical capsule inspection is explored. By developing image processing techniques, and using PCs, custom USB 2.0 cameras with minimal hardware, a low-cost flexible system is developed. This paper discusses the two-part gelatin capsule inspection system that belongs to USB camera 2.0 and associated hardware, the PCs to acquire the image data of the capsule, image processing techniques using border tracing and approximating the capsule to a circle to perform inspection and a custom system controller to pass the accepted and rejected capsules to the appropriate bin.

This paper presents a motion adaptive deinterlacing method for high quality conversion of interlace video format to progressive video format. A high performance and low complexity algorithm for motion detection is proposed. This algorithm uses five consecutive interlace video fields for motion detection, so it is able to capture a wide range of motions from slow moving objects to fast motions. The proposed motion detection algorithm benefits from a hierarchal structure where the algorithm starts with detecting motion in large partitions of a given field. Depending on the detected motion activity level, the motion detection algorithm might be recursively applied to sub-blocks of the original partition. Two low pass filters are used during the motion detection to increase the algorithm accuracy. The result of motion detection is then used in a motion adaptive interpolator for high quality deinterlacing. Excellent experimental results are obtained for motion detection and deinterlacing performance.

Content-Based Image Retrieval (CBIR) Systems are becoming increasingly important and finding application in diverse areas of discipline. Due to disturbing factors present in images, e.g. non-uniform illumination and lightening intensities, the retrieval results are not always satisfactory. In this paper, we discuss how fuzzy set theory can be used to formulate an efficient image signature. This signature is 1-D Fuzzy Color Histogram (FCH) consisting of a small number of bins. The signature is obtained from image contents by considering the contribution of each pixels color to all the histogram bins through the use of fuzzy-sets membership functions. Experimental results on a database of over 10,000 images demonstrate that the proposed system is less sensitive light intensity changes and more robust than the Conventional Color Histogram (CCH) in retrieval precision.

Reinforcement learning is a framework in which an agent can learn behavior without knowledge on a task or an environment by exploration and exploitation. Striking a balance between exploration and exploitation is one of the key problems of action selection in reinforcement learning. Exploitation causes the agent to reach a locally optimal policy quickly, whereas excessive exploration degrades the performance of the algorithm, though it may improve the learning performance and escape from a locally optimal policy. Recently the human immune systems have aroused researcher's interest due to its useful mechanisms which can be exploited for information processing in a complex cognition system. In this paper, we transplant some immune mechanisms into the basic Q-learning algorithm and convert Q-learning algorithm into a search for the optimum solution in combinatorial optimization. Experiments show that the improved Q-learning converges more quickly than Q-learning or Boltzmann exploration, and easily obtains the global solution set.

The binarization stage is a very critical in document analysis since the quality of a binarized image determines the performance of the entire process. However due to many uncertain factors such as complex signal-dependent noise and variable background intensity, which is caused by non-uniform illumination, shadow, smear, smudge or low contrast, an effective binarization algorithm that will work on a broad set of composite documents has yet to be developed. Here we propose a new algorithm which implements hidden Markov model in the binarization stage. The simulation results prove that this method is efficient in extracting information from various noisy backgrounds.

This paper presents a new algorithm for finding the point of correspondence in stereo images using a Hopfield neural network (HNN). A 2D HNN is used to match primitives extracted form one image to the other. The features used for this operation are edge points extracted using the Sobel operator. A formulation of the stereo correspondence as an optimization problem has been presented where various geometrical properties of multiple view geometry including disparity gradient (DG) have been used as constraints on the objective function. The 2D HNN is then used to minimize this function, thus achieving an optimum correspondence from one to the other image. The results show higher accuracy than template matching and local search methods

Histogram based thresholding methods are mainstream methods in the binarization of document images. However many uncertain factors such as complex signal-dependent noise and variable background intensity, which are caused by non-uniform illumination, shadow, smear, smudge or low contrast make it very difficult to obtain proper threshold through the histograms of images. This is caused by the shapes of the histograms which are arbitrary instead of being bimodal. In this paper we will propose a new edge based thresholding algorithm which converts the histograms of noisy images into bimodal, then it is possible to efficiently extract objects especially texture information from noisy background.

Contrast enhancement of the lateral skull X-ray images is very important in orthodontic practice, cephalometric evaluation and craniofacial landmarking. In this paper we present computational techniques involving contrast enhancement of two-dimensional digitized X-ray images. The algorithm is developed based on the use of local heterogeneity measures of pixel distribution. Image enhancement is accomplished by an adaptive gray scale pixels transformation depending on results of local heterogeneity contribution measures. The proposed approach was tested on different images and the results prove that the proposed method has better performance the existing conventional methods.

This paper presents application of fusion techniques in a handwritten numeral recognition. The authors present two methods of fusing classifiers based on the fuzzy integral for handwritten numeral recognition. We also demonstrate that modification of fuzzy density calculation will result in better recognition rate. Test conducted on a large set of handwritten numeral data yielded high recognition rate with the proposed technique.

A new automatic target recognition algorithm has been developed to extract craniofacial landmarks from lateral skull X-rays (cephalograms). The locations of these landmarks are used by orthodontists in what is referred to as a cephalometric evaluation. The evaluation assists in the diagnosis of anomalies and in the monitoring of treatments. The algorithm is based on gray-scale mathematical morphology. A statistical approach to training was used to overcome subtle differences in skeletal topographies. Decomposition was used to desensitize the algorithm to size differences. A system was trained to locate 20 landmarks. Tests on 40 X-rays showed an 85% recognition rate on average.

Hardware design and implementation of a 2D sample-and-hold semisystolic filter structure are presented. Log and antilog circuits are used with a high-frequency-emphasis IIR filter to provide homomorphic filtering. SPICE simulation using functional blocks as well as particulars on the hardware design with reference to specific device types and characteristics are discussed. A hardware prototype circuit using MSI and LSI circuits on a custom-made PCB is shown along with applications to real-time filtering of broadcasted TV images.>

Passive monocular 3-D position sensing is made possible by a new calibration scheme that relates depth to focus blur through a composite lens and aperture model. The calibration technique enables the recovery of absolute 3-D position coordinates from image coordinates and measured focus blur. A geometric model of the camera's position and orientation in space is used to transform the camera's imaging coordinates into world coordinates. The relationship between the world coordinate system and the screen coordinate system which includes the amount of focus blur, is developed by modeling the camera imaging arrangement. The modeling proceeds first through the perspective view from a pinhole camera located anywhere in space. The camera's lens and aperture system is investigated to find the relationship between depth and focus blur. The aspect ratio of the frame image is considered. Position accuracies comparable to those in stereo based vision systems are possible without the need for solving the difficult point of correspondence problem.>

Advancement in digital-image processing hardware has given the printing industry novel facilities for capturing fonts and has provided new grounds for character scaling, which is an important issue in typesetting and graphical text. An algorithm for digital character scaling by a contour method is developed and implemented. The algorithm is based on scaling the contour of the character through a transformation. Cubic splines are used to interpolate the discrete samples of the contour character. Final results show no jaggies. The algorithm is applied to Arabic fonts and compared to two other algorithms: replication and telescoping template. The superior performance of the contour method is attributed to the cubic spline fitting, which gives better smoothness to the edge of the character.>

In this work an extension of an iterative technique for the thresholding of a given image using a single-level threshold to multi-level thresholding is presented. The technique is applied on images of machine parts and the features are extracted by border-following. The results of the multi-level thresholding and a comparison with results obtained by single-level thresholding are also presented.

A survey of research efforts in the area of speaker recognition indicate that for the same choice of speaker-dependent speech parameters the recognition accuracy is significantly affected by the distance measure used. In this work several distance classifiers are evaluated for use in text-independent speaker identification. The four distance measures investigated are the Mahalanobis distance, maximum a posteriori probability, nearest neighbor criterion and the correlation distance measure. It is found that both the maximum a posteriori probability criterion and the correlation distance measure yield extremely poor results. The Mahalanobis distance and the nearest neighborhood criterion yield relatively poor results (error\sim20-30%) with the former consistently superior to the latter. It is shown that these scores can be improved through a proposed variation of the nearest neighbor method.

This work addresses the development of a reliable, high accuracy text-independent speaker recognition system for a small population, with the reference parameters characterizing each speaker obtained from short segments of speech. Initially the potential for speaker discrimination of several different vocal parameter sets was investigated. These included the LPC, Reflection, Cepstrum and Log Area Ratio coefficients, speech power spectrum parameters and the inverse filter spectral coefficients. It was then decided to use any two parameter sets in a composite decision-making scheme. A "repeat feature" was incorporated into the speaker recognition system, whereby a speaker was asked to read a fresh test speech segment if the decisions made by using the two different parameter sets individually were not coincident. Test results indicate that a significant improvement in accuracy is realizable.

This paper discusses the development of an efficient, general method for the design of 2-D recursive digital filters. It effectively uses the 1-D filter theory. The technique employs the McClellan Transformation (MT). Methods are suggested for the design of McClellan Transformation Coefficients (MTC). A stability error criteria is formed on the basis of Complex Cepstrum (CC) and it guarantees the stability of the filter. Group delay and magnitude characteristics are further refined using nonlinear optimization. The method can also be used for stabilizing unstable filters. Numerical implementation and results are presented with an example.

This paper deals with noise reduction in images using two-dimensional Kalman filtering. To reduce the computational load and the memory storage, updating of state vector is done within a certain distance of the point currently being processed, the support of the filter and dynamical model is restricted to a non-symmetric half plane. Direct histogram specification method is combined with Kalman filtering algorithm. Significant improvement in the quality of the image is obtained. The technique used is particularly attractive for on-line applications. It is also shown that the various gradient computations and template matching schemes work efficiently on estimated image to extract vital features (edges) from the image for the future analysis and studies. The results obtained confirm the feasibility of these algorithms.