waashk/dblp huggingface dataset:
an image watermarking scheme using hvs characteristics and spread transform. the paper presents a robust digital image watermarking scheme that uses both the characteristics of the human visual system (hvs) and statistical information measure. spread transform approach is used where data is embedded through transform coefficients of both the cover and the watermark data. the spread transform watermarking technique yields better results in terms of imperceptibility, resiliency, capacity and cost compared to widely used spread spectrum watermarking schemes. hadamard transformation is used not only for simpler implementation but also for its higher data hiding capacity [capacity estimates for data hiding in compressed images]. experimental results show that the visual quality of the extracted watermark is good in spite of several external attacks. the fact is also supported by mutual information values used as objective measure.
parallelizing motion segmentation by perceptual organization of xyt. the front end of many motion analysis algorithms is usually a process that generates bounding boxes around each moving object, roughly segmenting the objects from the background. processing to finely define the moving object boundary can follow, but only within these rough bounding boxes. in this paper, we consider a method that exploits the structure and organization in the spatio-temporal block (xyt) of motion data to create bounding boxes around moving objects. this method has been shown to be robust with respect to illumination changes,noise, and occlusion events. this algorithm, however, begins with a 3d edge detection step across a sequence of images, which is a time consuming process. we have mapped this 3d edge detection to run on any mpi enabled parallel computer, thus achieving significant speedups especially for large image frames. we present results on sequences of various sizes and lengths from the recently formulated human id gait challenge problem dataset. we compare the quality of the automatically created bounding boxes with the semi-autonomously generated boxes that come with the gait challenge dataset.
rotation estimation from spherical images. robotic navigation algorithms increasingly make use of the panoramic field of view provided by omnidirectional images to assist with localization tasks. since the images taken by a particular class of omnidirectional sensors can be mapped to the sphere, the problem of attitude estimation arising from 3d rotations of the camera can be treated as a problem of estimating rotations between spherical images. recently it has been shown that direct signal processing techniques are effective tools in handling rotations of the sphere, but are limited when the signal is altered by larger rotations of omnidirectional cameras. we present an effective solution to the attitude estimation problem under large rotations. our approach utilizes a shift theorem for the spherical fourier transform to produce a solution in the spectral domain.
image flows and one-liner graphical image representation. in this paper we introduce a novel graphical image representation comprising a single curve ¿ the one-liner.the first step involves the detection and linking of image edges. we use a new technique, so-calledf "edge exploration," to simultaneously perform both tasks. this process is based on "image flows." it uses a gradient vector field and a new operator to explore image edges. estimating the derivatives of the image is performed by using local taylor expansions in conjunction with a weighted least-squares estimation method. this process finds all the possible image edges without any pruning, and collects information thatallows us to prioritize the found edges. this enables us to select the most important edges, that form a "skeleton" of the sought representation. the next step connects the selected edges into one continuous curve ¿ the one-liner. it orders the selected edges and finds curves connecting between them. we solve these two problems separately. since the abstract graph setting of the first problem is np-complete, we reduce it to a variant of tsp and compute an approximate solution to it. we solve the second problem by using dijkstra's shortest-path algorithm. we have a full software implementation for the entire one-liner etermination process.
qim watermarking combined to jpeg2000 part i and ii. on the one hand, lossy image compression, especially at very low bit rate, is considered as a strong attack for watermark process, 0n the other hand, with the blossoming demand for efficiently storing and transmitting digital visual information, lossy compression has been increasingly vital. this leads us to combine a watermarking scheme based on qim1 with a jpeg2000 coder. among the coding schemes supported by the standard, we select two coders derived respectively from part i and ii. comparative tests have been made in order to decide which of these coders is more suitable to be combined with our watermark scheme.
adaptation to walking direction changes for gait identification. this paper describes adaptation to gradual changes of walking directions for gait identification. first, we propose a method of body tilt correction due to changes of walking directions when constructing a spatio-temporal gait silhouette volume. next, we propose a view transformation model in the frequency domain to match gait features of different walking directions. finally, experiments of gait identification for a circular path demonstrate the effectiveness of the proposed method.
online learning of color transformation for interactive object recognition under various lighting conditions. this paper describes an online learning method of color transformation for interactive object recognition. in order to recognize objects under various lighting conditions, the system estimates a color transformation from the color of an object model by observing the color of a reference object. the system first initializes a general color transformation. next the system automatically recognizes a target object with the color transformation. when the system fails in recognition, the system recovers the failure with user interaction. then the system improves the color transformation with an observed color pair of the recognized target object. by repeating this process, the system adapts to the new environment. experiments using real-world refrigerator scenes are shown.
object recognition supported by user interaction for service robots. this paper describes an interactive vision system for a robot that finds an object specified by a user and brings it to the user. the system first registers object models automatically. when the user specifies an object, the system tries to recognize the object automatically. when the recognition result is shown to the user, the user may provide additional information via speech such as pointing out mistakes, choosing the correct object from multiple candidates, or giving the relative position of the object. based on the advice, the system tries again to recognize the object. experiments are described using real-world refrigerator scenes.
contour encoding based on extraction of key points using wavelet transform. in many situations it is convenient to represent pictorial data in the form of contours. it may become necessary to compress such contour data for efficient storage and transmission. we present here a technique for achieving very high levels of compression of 2-d contours. the goal here is to represent each contour using a discrete set of representative points known as key points. a novel method of extracting the key points using wavelet transform is presented. the scheme exploits the properties of the high frequency coefficients to identify these points. local peaks in the magnitude plot of high frequency coefficients are designated as key points and are identified using an efficient algorithm. the performance of the scheme is evaluated using multiple actual contours derived from weather radar reflectivity fields.
vector quantization using reflections of triangular subcodevectors. the design of a codebook consisting of codevectors is the goal of vector quantization (vq) schemes. this solution is not unique and many variants of vq have been proposed. vq suffers from computational and memory complexities that increase with the size of codebook and codevector dimensions. the lack of a universal codebook that works across different class of images necessitates the transmission of the codebook along with the codevector indices. this paper proposes a novel method of vector quantization using reflections of triangular subcodevectors. this method jointly reduces the memory and computational resources required for vq. numerical results are presented for the proposed scheme in comparison to a traditional vq method and a method based on reflections.
precise radial un-distortion of images. radial image distortion is a frequently observed defect when using wide angle, low focal length lenses. in this paper a new method for its calibration and removal is presented. an inverse distortion model is derived that is accurate to a sub-pixel level, over a broad range of distortion levels. an iterative technique for estimating the models parameters from a single view is also detailed. results on simulated and real images clearly indicate significantly improved performance compared to existing methods.
motion dependent spatiotemporal smoothing for noise reduction in very dim light image sequences. a new method for noise reduction using spatiotemporal smoothing is presented in this paper. the method is developed especially for reducing the noise that arises when acquiring video sequences with a camera under very dim light conditions. the work is inspired by research on the vision of nocturnal animals and the adaptive spatial and temporal summation that is prevalent in the visual systems of these animals. from analysis using the so-called structure tensor in the three-dimensional spatiotemporal space, motion segmentation and global ego-motion estimation, gaussian shaped smoothing kernels are oriented mainly in the direction of the motion and in spatially homogeneous directions. in static areas, smoothing along the temporal dimension is favoured for maximum preservation of structure. the technique has been applied to various dim light image sequences and results of these experiments are presented here.
three dimensional measurement using color structured patterns and imaging spectrograph. in this paper, we propose a simultaneous measurement system of spectral reflectance and shape of an object with the use of color gray code structured patterns and an imaging spectrograph.color and shape information are very important for us to recognize objects. therefore, we have proposed an earlier system which is able to measure spectral reflectance and shape simultaneously by the imaging spectrograph. this system uses the gray code patterns to measure the shape of target. however, the measurement time for whole target was long, because this system measures only 1 line at a time. therefore, a more effective measuring method is needed.this paper describes a measuring method using color gray code structured patterns. this proposed method uses the spectral characteristics of the projector effectively.
measurement of shape and refractive index of transparent object. when digitalizing characteristics of objects, shapes of objects is very important information. however the measurement methods of transparent objects are few. this paper proposes a new measurement method of a transparent object by changing background patterns. a silhouette of the transparent object is extracted using the change of the background patterns according to refraction, and the shape is measured with the use of the silhouette. furthermore, the refractive index of the transparent object is estimated by changing background patterns and the measured shape.
character segmentation-by-recognition using log-gabor filters. natural scene images coming usually from lowresolution sensors in embedded context suffer from low text recognition results. due to several types of degradations, existing algorithms are not robust enough. in order to improve recognition, we present in this paper a character segmentation using log-gabor filters to take advantage simultaneously of gray-level variation and spatial location. the recognition step is used to determine dynamically some of the parameters needed for the filter. finally, several quantified results are presented to highlight the efficiency of this method against several issues.
a modified fuzzy inference system for pattern classification. the use of fuzzy inferencing systems in pattern classifiers and expert systems is now more popular as the linguistic descriptions of inputs helps to deal with input uncertainty. a problem with these systems, however, is that outputs are monotonic and can only add to an output when extra information is acquired. this paper looks at a possible solution to the problem, which involves the inhibition of some rules' output by other rules making the classification of certain difficult patterns easier. this inhibition is achieved by redefining the consequent not function, such modification enables rules to describe holes in the data. several methods of incorporation are proposed, followed by some areas of suggested usage.
trajectory segmentation using dynamic programming. we consider the segmentation of a trajectory into piece-wise polynomial parts, or possibly other forms. segmentation is typically formulated as an optimization problem which trades off model fitting error versus the cost of introducing new segments. heuristics such as split-and-merge are used to find the best segmentation. we show that for ordered data (eg., single curves or trajectories) the global optimum segmentation can be found by dynamic programming. the approach is easily extended to handle different segment types and top down information about segment boundaries, when available. we show segmentation resultsfor video sequences of a basketball undergoing gravitional and non-gravitaional motion.
factorization-based planar mapping method for generating intermediate views. planar mapping approach for generating intermediate views is presented. whereas most existing mapping procedure employ matching and/or point-patching strategies, the proposed method is based on transformation of moments using a factorization formulation. the extended factorization method makes it possible to obtain shape parameters with planar surfaces. the technique is applied to map the texture of an object with many surfaces as an demonstration of its utility.
on classifier domains of competence. we study the domain of dominant competence of six popular classifiers in a space of data complexity measurements. we observe that the simplest classifiers, nearest neighbor and linear classifier, have extreme behavior of being the best for the easiest and the most difficult problems respectively, while the sophisticated ensemble classifiers tend to be robust for wider types of problems and are largely equivalent in performance. we characterize such behavior in detail using the data complexity metrics, and discuss how such a study can be matured for providing practical guidelines in classifier selection.
hybrid chinese/english text detection in images and video frames. in this paper, we propose a multiscale texture-based method using local energy analysis for hybrid chinese/english text detection in images and video frames. local energy analysis has been shown to work well in text detection, where remarkable local energy variations of pixels correspond to text region or boundary of other objects and lower local energy variations of pixels correspond to background or the interior of non-text objects. local energy variation is calculated in a local region based on the wavelet transform coefficients of images. hybridchinese/english text in images and video frames can be detected whether it is aligned horizontally or vertically. the font size of text to be detected may vary in a wide range of values. the proposed method has been tested on 321 frame images obtained from local tv programs and a tested dataset with low missed rate and false alarm rate.
constructing dense correspondences to analyze 3d facial change. this paper presents an improved method to construct dense correspondences for 3d facial analysis, which are capable of providing a full 3d description of a surface and extending the conventional landmark-based approaches. based on the technique of elastic deformation, the dense correspondences are established by mapping a generic model onto the 3d surface of an individual. the method used here is accurate in driving the conformation of the generic model and efficient in dealing with outliers, which can appear during local deformation. the experiments indicates for open surfaces as face with this method, more than 95 percent of triangles on the deformed generic mesh are within the range of 1mm to the original model and the average landmark errors are only 2mm.
logical entity recognition in multi-style document page images. logical entity recognition in document page images is the essential part of a document image analysis system. a heterogeneous collection of document pages usually has many layout styles. features extracted from same logical entities in different styles may have very different values and vice versa. therefore, logical entity classifiers learned from a training set of multistyle document pages may not be reliable due to possible feature overlap of different logical entities in different styles. in this paper, we propose a novel method in which style information is used in both logical entity classifier training and recognition phases. in the training phase, training data are first classified into distinct styles, and a dedicated support vector machine (svm) is then learned for each style. in the recognition phase, the style of a new document page image is first identified and its logical entities are then recognized using corresponding svm. we show in our experiments that the use of the style information significantly improves the accuracy of logical entity recognition in multi-style document page images.
incorporating conditional independence assumption with support vector machines to enhance handwritten character segmentation performance. learning bayesian belief networks (bbn) from corpora and incorporating the extracted inferring knowledge with a support vector machines (svm) classifier has been applied to character segmentation for unconstrained handwritten text. by taking advantage of the plethora in unlabeled data found in image databases in addition to some available labeled examples, we overcome the expensive task of annotating the whole set of training data and the performance of the character segmentation learner is increased. apart from this approach, which has not previously used for this task, we have experimented with two well-known machine learning methods (learning vector quantization and a simplified version of the transformation-based learning theory). we argue that a classifier generated from bbn and svm is well suited for learning to identify the correct segment boundaries. empirical results will support this claim. performance has been methodically evaluated using both english and modern greek corpora in order to determine the unbiased behavior of the trained models.limited training data are proved to endow with satisfactory results. we have been able to achieve precision exceeding 87.5%.
performance evaluation of object detection algorithms. the continuous development of object detection algorithms is ushering in the need for evaluation tools to quantify algorithm performance. in this paper, a set of seven metrics are proposed for quantifying different aspects of a detection algorithm' s performance. the strengths and weaknesses of these metrics are described. they are implemented in the video performance evaluation resource (viper) system and will be used to evaluate algorithms for detecting text, faces, moving people and vehicles. results for running two previous text-detection algorithms on a common data set are presented.
empirical study of multi-scale filter banks for object categorization. the aim of this work is the evaluation of different multiscale filter banks, mainly based on oriented gaussian derivatives and gabor functions, to be used in the generation of robust features for visual object categorization. in order to combine the responses obtained from several spatial scales, we use the biologically inspired hmax model [13]. we have tested the different sets of features on the challenging caltech-101 database, and we have performed the categorizarion procedure with adaboost, support vector machines and jointboosting classifiers, achieving remarkable results.
methodology for the registration of whole slo sequences. images acquired with the scanning laser ophthalmoscope (slo) device offer a very good tool for the evaluation of retinal diseases and obtaining interesting haemodynamic variables related to retinal blood flow . but because of the unavoidable eye movements, it is necessary a previous registration process to obtain a reliable analysis of the information extracted from these set of frames. this paper explains an algorithm which allows a fully automatic registration of the whole slo sequence by combining two registration techniques: a landmark based registration and a mutual information based one. the accurate registration of the frames will allow the automatic calculation of the so called dye dilution curves, which are computed by means of a robust fitting algorithm based on the optimisation method simulated annealing. with that curves it will be possible to obtain valuable haemodynamic variables, like the arteriovenous passage time described here.
feature selection based on a black hole model of data reorganization. this paper introduces a new model of feature selection based on a pattern recognition model using the concept of black holes. we show that that this method of feature selection is robust and provides an efficient subset of features for classification.
microfossils shape classification using a set of width values. recognition the shape of objects is a particularly relevant problem in pattern recognition. foraminifera are very important microfossil to determine geological age of marine rocks. in this paper we propose an approach to shape recognition that takes as input the measurements of widths of the most common foraminifera shells after a pre-processing step to rotate the object in order to have vertical alignment. a k-nn and a mlp classifiers are compared for classification of chambers arrangement, experimental results show 87.1 and 97.1% of right answers, respectively.
a sofm improves a real time quality assurance machine vision system. we present a high speed machine vision system for the inspection and quality assurance of canned tuna, which is currently working at a rate over 1000 cans per minute. the system inspects the geometry of the can and its contents at a resolution of 4 pixels/mm. it is the evolution of a first prototype through the introduction of a kohonen network, which maps texture features into a two dimensional grid where the user defines quality neighbourhoods. the inspection time, increased from 35 ms to 38 ms per can, allows the introduction of the system in the same production lines without affecting total performance, but with higher accuracy and user satisfaction.