Binary Image

An authentication watermarking technique (AWT) inserts some hidden data into an image in order to detect any accidental or malicious image alteration. Recent papers have proposed some cryptography-based AWTs for binary and halftone images, but none of them seems to be well-suited for public-key authentication of binary document images. Many of the previous public-key AWTs for binary images can be assaulted by "parity attacks." This paper proposes a new AWT for binary document images that can detect any modification of the watermarked document, even a single pixel flipping. Moreover, both its secret-and publickey versions are completely immune against parity attacks. Document images watermarked by the proposed technique present high visual quality, without salt-and-pepper noise.

Detection and location of the face as well as ex traction of facial features from images is an important stage for numerous facial image interpretation tasks. Detection of facial feature points, such as corners of eyes, lip corners, nostrils from the images are crucial. In this paper a method for automatic facial feature point detection in image sequences, is introduced. The method uses image normalization, and thresholding tech niques to detect 14 facial feature points. Algorithm proposed by Wolf Kienzle is used for face recognition. The detected face region is then divided into 5 relevant regions of interest, each of which is examined separately, further to detect the location of the facial feature points. In each region image is normalized with respect to brightness. Suitable threshold is set for each region, using which image is converted into binary image. Then in each region extreme ends of binary image wiD locate the facial feature points. In the eye region horizontal and vertical histograms are analyzed to detect eyebaUs. This method when tested on Cobn-Kanade database results in recognition rate of 86%. Moreover, when tested on Informatics and Mathematical Modeling (IMM) face database which consists of tilted faces around y axis, we achieved average recognition rate of 83 %.

A class of shift-variant reduction operations is introduced, that is useful for performing efficient and controllable shape and texture transformations between resolution levels. In their most general form, the operations proceed in three steps: (a) convolve a binary image with a kernel of arbitrary size; (b) threshold the result; (c) subsample to produce the reduced image. Taking a binary structuring element for the kernel, the threshold convolution on a binary image is equivalent to a rank order filter, and the full reduction operation is a threshold reduction. Threshold reductions that use convolution filters and subsample tiles of equal size are optimized by combining the three operations, using only logical raster operations and producing threshold convolution values only at the sampling points. For 2x reduction, the four possible threshold values (1, 2, 3, and 4) refer to the minimum number of ON pixels within each 2x2 tile for which a pixel in the reduced image will be ON. Algorithms for boolean raster operations are given for 2x, 3x, and 4x threshold reduction, and lookup tables that efficiently implement column raster operations are provided. Threshold reduction rates of 2.5x½¼ pixel/second can be achieved with a Sun SparcStation2 Ì Å . A mask-forming image analysis cycle of threshold reduction, augmented by morphology and followed by replicative expansion to full resolution, is described, and some general properties of the cycle are derived. A simple application of threshold reduction to document image analysis, the extraction of halftone regions from scanned images that also contain text and line graphics, is illustrated. A sequence of 2x reductions with first low and then high thresholds is used to create a reduced image consisting of a mask over the halftone regions. In this way, the extraction occurs as a natural consequence of the reductions.

A particularly effective method for analyzing document images, that consist of large numbers of binary pixels, is to generate reduced images whose pixels represent enhancements of textural densities in the full-resolution image. These reduced images are generated using an integrated combination of filtering and subsampling. Previously reported methods used thresholding over a square grid, and cascaded these threshold reduction operations. Here, the approach is generalized to a sequence of arbitrary filtering/subsample operations, with emphasis on several particular filtering operations that respond to salient textural qualities of document images, such as halftones, lines or blocks of text, and horizontal or vertical rules. As with threshold reductions, these generalized "textured reductions" are performed with no regard for connected components. Consequently, the results are typically robust to noise processes that can vitiate analysis based on connected components. Examples of image analysis and segmentation operations using textured reductions are given. Some properties can be determined very quickly; for example, the existence or absence of halftone regions in a full page image can be established in about 10 milliseconds.

An image-based approach to document image analysis is presented. The methods are motivated by a merged view of shape and textural image properties at multiple scales. The principal binary image operations are morphological and multiresolution. The generalized opening is introduced for extraction of both shape and texture from an image. Threshold reduction operations are introduced for performing efficient and controllable shape and texture transformations between resolution levels. Some problems, such as halftone or dark area segmentation, can be in large part solved by a sequence of threshold reductions. Aspects of the approach are illustrated by the problem of identifying italic and bold words in text, using word-level extraction at lowered resolution. The computational costs of the basic operations are given, so that algorithm efficiency can be estimated, and the importance of operating at the lowest feasable resolution is demonstrated. For example, word segmentation and halftone extraction proceed in excess of 1.5x10 image pixels/second on a Sun Sparcstation2 .

This paper presents a method to use View based approach in Bangla Optical Character Recognition (OCR) system providing reduced data set to the ANN classification engine rather than the traditional OCR methods. It describes how Bangla characters are processed, trained and then recognized with the use of a Backpropagation Artificial neural network. This is the first published account of using a segmentation-free optical character recognition system for Bangla using a view based approach. The methodology presented here assumes that the OCR pre-processor has presented the input images to the classification engine described here. The size and the font face used to render the characters are also significant in both training and classification. The images are first converted into greyscale and then to binary images; these images are then scaled to a fit a pre-determined area with a fixed but significant number of pixels. The feature vectors are then formed extracting the characteristics points, which in this case is simply a series of 0s and 1s of fixed length. Finally, an artificial neural network is chosen for the training and classification process.

This paper systematically compares two elastic graph matching methods for off-line signature verification: shape-memory snakes and parallel segment matching, respectively. As in many practical applications (i.e. those related to bank environments), the number of sample signatures to train the system must be very reduced, we selected these two methods which hold that property. Both methods also share some other similarities since they use graph models to perform the verification task and require a registration pre-processing. Experimental results on the same database and using the same evaluation metrics have shown that the shape-memory snakes clearly outperformed to the parallel segment matching approach on the same signature dataset (9% EER compared to 24% EER, respectively).

Automatic detection of Red Blood Cells (RBCs) by using image-processing techniques is important in determining blood disorders and diseases. This study proposes an algorithm for automatic detection of red blood cells in optical microscopy images of blood smears. The proposed method depends on binary conversion at multi-threshold and includes a morphological operation as erosion and image fill according to certain conditions. In this study, we used 50 images from IDB data of blood samples taken with an optical microscope. The suggested method is compared with other modern techniques based on the two accuracy coefficients, namely, detection and false alarm rates. Results show that the proposed method has a high detection accuracy compared to other methods.

It is very difficult to intercept the missiles because of the small radar cross-section and the high maneuverability. In addition, due to the decoy with the similar motion parameters, additional features other than those of the translation motion parameters need to be developed. In this paper, for the successful recognition of missiles, we propose an efficient method to extract micro-motion parameters and scatterers of the missile engaged in the micro motion. The proposed method extracts motion parameters and scatterers by using the matching score between the modeled micro-Doppler function and the time-frequency binary image as a cost function. Simulation results using a target composed of the point scatterer show the parameters and the scatterers were accurately extracted.

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approa...

In watermarking, message is embedded in to another message. A stego message is formed after embedding a new message. In the implementation of the proposed system, the validation tests for CaDEED-EC and CaDEED-N&I are proposed. The selection of optimal local region sizes for CaDEED-N&I is proposed. CaDEED-N&I exploited more by adopting the noise visibility function and proposing the importance factor (IF) for different watermark pixels. The performance of the proposed system can be measured with the help of PSNR, SSIM and CDR measurements, NTPSNR and CB-CDR.

An exact analysis of the hole grating beam sampler with finite width and hole size is presented, based only on the approximations used in the diffraction integral. It is shown that no beam can be exactly reconstructed in the sampling process. The error introduced by the sampling is dependent on the frequency distribution in the sampled beam.

It has been reported that the luminance mechanism mediates shadow perception of binary images. We evaluated the luminance additivity law to determine whether shading, which is produced by gradual intensity changes, is mediated by luminance or brightness using pictures with tones: two still lives and a painting. They were depicted by two colors-a reference color and a test color. The reference color was white with a constant luminance profile, and the test color was a mixture of red and green in various proportions . The observer's task was to adjust the luminance of the test color in order that the impression of depth due to shading just disappeared (shading disappearance setting), where the intensity that produced the shading supposed to be equated between the test and the reference colors. The results show that the luminance additivity law holds for shading disappearance settings. This suggests that shading perception is based on luminance or a luminance-type additive mechanism as well as shadow.

Data hiding (DH) is a technique used to embed a sequence of bits in a cover image with small visual deterioration and the means to extract it afterwards. Authentication watermarking (AW) techniques use DH to insert particular data into an image, in order to detect later any accidental or malicious alterations in the image, as well as to certify that the image came from the right source. In recent years, some AWs for binary images have been proposed in the literature. The authentication of binary images is necessary in practice, because most scanned and computer-generated document images are binary.This publication describes techniques and theories involved in binary image AW: We describe DH techniques for binary images and analyze which of them are adequate to be used in AWs; analyze the most adequate secret- and public-key cryptographic ciphers for the AWs; describe how to spatially localize the alteration in the image (besides detecting it) without compromising the security; prese...

Se analiza el uso de características que representan la información de los Puntos de Alta Presión en la imagen estática de una firma manuscrita, en un sistema orientado a la verificación tipo offline de firmas. A partir de imágenes en niveles de gris, se propone un nuevo método para calcular el umbral de alta presión. Realizando la transformación de dos imágenes a coordenadas polares, una con la información de los Puntos de Alta Presión y otra con la versión binarizada de la imagen original, se calcula la densidad de pixeles entre ellas. El espacio polar se divide en secciones angulares y radiales, lo que permite realizar un análisis localizado de la distribución de los puntos de alta presión. Se calculan los vectores con la distribución de la densidad para las dos regiones radiales definidas (cercada y lejana) a partir del centro geométrico de la imagen original. Para las pruebas se usó una base de datos que contiene muestras de las firmas de 160 personas. Para validar la metodolog...

Ganulometries are defined on function classes over topological vector spaces. The usual Euclidean property for scaling compatibility, set scaling in the binary case and graph scaling in the gray-scale case, is changed so that it is with respect to spatial (domain) scaling for function spaces. As in the binary case, scaling compatible granulometries possess representations as double suprema over scaled generating elements. Without further constraint on the generating elements, the double supremum involves, for each generating element, all scalings exceeding the parameter of the particular granulometric operator. The salient theorem of the present paper concerns necessary and sufficient conditions under which there is a reduction of the double-supremum representation to a single supremum over singularly scaled generating functions. Specifically, and in the context of locally convex topological vector spaces, there is a determination of when a domain-scaled function t*f is f-open for all t :2: 1. Key roles are played by both topology and local convexity.

In a mobile device, two major sources of energy consumption are energy used for computation and energy used for transmission. Computation energy can be reduced by minimizing the time spent on compression. Transmission energy and encryption energy can be reduced by sending a smaller image file that is obtained by compression. Image quality is often sacrificed in the compression process. Therefore, users should have the flexibility to control the image quality to determine whether such a tradeoff is acceptable. This paper proposes an energy efficient image representation system using Binary Space Partitioning (BSP) trees. Our experimental result shows that in most cases, our new tree construction and compression formula use only 40% of the original total energy required for compressing and sending images. BSP tree representation also allows partial encryption, which reduces total energy required for secure transmission by reducing the amount of data that needs to be encrypted. Our experimental results show that BSP tree representation is effective in reducing total energy for secure transmission for computer arts images compared to JPEG.