waashk/dblp huggingface dataset:
handwriting authentication by envelopes of sound signatures. frictions between a rigid-nib pen and paper result in audible sounds that are correlated with the dynamics of writing. such writing-sounds were previously used as a biometric identity to achieve writer authentication. this paper presents an alternative and supplement algorithm for sound-based handwriting authentication. envelopes of writing-sounds estimated by the hilbert transforms are found useful in differentiating topologically similar characters written by different individuals. a straightforward supervised neural network in conjunction with a purpose-designed pre-processor can be trained on examples to effectively differentiate patterns of writing-sounds and thus achieve writer authentication, providing a straightforward and potential alternative to existing methods.
handwritten character recognition based on structural characteristics. in this paper a handwritten character recognition algorithm based on structural characteristics, histograms and profiles, is presented. the well- known horizontal and vertical histograms are used, in combination with the newly introduced radial histogram, out-in radial and in-out radial profiles for representing 32 x 32 matrices of characters, as 280- dimension vectors.the k-means algorithm is used for the classification of these vectors. detailed experiments performed in nist and gruhd databases gave promising accuracy results that vary from 72.8% to 98.8% depending on the difficulty of the database and the character category.
object class recognition using images of abstract regions. with the advent of many large image databases, both commercial and personal, content-based image retrieval has become an important research area. while most early efforts retrieved images based on appearance, it is now recognized that most users want to retrieve images based on the objects present in them. this paper addresses the challenging task of recognizing common objects in color photographic images. we represent images as sets of feature vectors of multiple types of abstract regions, which come from various segmentation processes. we model each abstract region as a mixture of gaussian distributions over its feature space. we have developed a new semi-supervised version of the em algorithm for learning the distributions of the object classes. we use supervisory information to tell the procedure the set of objects that exist in each training image, but we do not use any such supervisory information about where (ie. in which regions) the objects are located in the images. instead, we rely on our em-like algorithm to break the symmetry in an initial solution that is estimated with error. experiments are conducted on a set of 860 images to show the efficacy of our approach.
sky-ground representation for local scene description. this paper proposes a new representation, called a sky-ground representation, for describing scenes of environment at a local place. a sky-ground representation is a spherical image with full field of view, combined with a vertical reference which is determined by sensing the direction of gravity. the scene of environment at a local place is observed by a spherical image sensor. the acquired spherical image is divided into two parts, sky part and ground part, along the horizon according to their positions, above or below the horizon. the horizon is determined by sensing the gravity from an acceleration sensor.
a comparison of the gender differentiation capability between facial parts. the main purpose of our research is to evaluate which subject's facial parts are most effective at making the difference between men and women. we prepared the four directional feature fields on multiple facial parts images, such as face, jaw, lip, nose, eyes, and r eye. then, we recognized these images by using the linear discriminate analysis. furthermore, we analyzed the feature space by using cluster discriminate analysis. after experimenting on a large number of subjects, we conclude that face and jaw make good feature spaces.
a clustering based color model and fast algorithm for object tracking. the paper presents a clustering based color model and develops a fast algorithm for object tracking. the color model is built upon k-means clustering, by which the color space of the object can be partitioned adaptively and the histogram bins can be determined accordingly. in addition, in each bin the multi-channel gray level is modelled as gaussian distribution. a metric based on chernov distance is defined to measure similarity between the reference model and the candidate model. the integral images are proposed for computation of mean vector and covariance matrix of color images, through which the similarity metric can be evaluated very fast. comparisons with the wellknown mean shift algorithm demonstrate the validity of the model and performance of the proposed algorithm.
integration of pose recognition for a person wearing short or long sleeves. in this paper; we propose a tv control system for aged and bedridden people. we have been constructing the interface, which changes tv channels and operate volume controls using pose recognition. however; users were required to wear the short-sleeved shirt torecognize the poses by using the skin color regions as arms. to allow the long sleeve for users, we improve our system to introduce the relation of the position of the higher local auto correlation features to identify the pose.first, face and hand regions of the user are detected by using the skin color from the input image. since it assumed that a width of a face region should be larger than arms, the face region is decided from the detected skin color regions by the erosion operation. after detecting the face region, a pose recognition area that is decided on the basis of the face position is divided into 9 (3 x 3) regions. the features are extracted from the 6 areas because the arms appear within the 6 areas. using our algorithm, our system can recognize poses even if the user wears a long sleeve or short-sleeved shirt.
real-time spherical stereo. by defining the disparity of a spherical stereo we reformulate the real-time stereo problem. based upon this definition the realtime spherical stereo becomes a general model which can cope with cameras with any wide field of view including conventional ones. by transforming the rectified spherical images to latitudelongitude representation, the correspondence of feature points can be speeded up by the same processing as planar stereo images. the effectiveness of this approach is shown by realizing a real-time spherical stereo using a pair of fisheye cameras.
real-time detection of anomalous objects in dynamic scene. there are many methods to extract moving objects in a scene using background subtraction. however, most methods assume that there are no moving objects except intruders in the observing space. in this paper, we propose the iterative optimal projection method to estimate a varied background in real time from a dynamic scene with intruders. at first, background images are collected for a while, because we assume that the motion of background is well known. then, the background images are compressed using eigenspace method to form a database. while monitoring the scene, new image is taken by a camera, and the image is projected onto the eigenspace to estimate the background. but however, the estimated image is much affected by the intruders, so the intruder region is calculated by using background subtraction with former estimated background to exclude the region from the projection. thus the image whose intruder region is replaced by the former background is projected to eigenspace and we have updated background. we proved that the cycle converges to a correct background image and we confirmed we can calculate the right region of the object through some experiments.
wavelet-based morphological approach for detection of human face region. in this paper, we present a novel method to detect a human skin region from a given head and shoulder image. the presented method consists of two stages: region segmentation and facial region detection. in the region segmentation, the input image is segmented into an appropriate set of arbitrary regions using the wavelet-based watershed algorithm. then, to merge the regions forming an object, we use a spatial similarity between two regions since the regions forming an object share some common wavelet characteristics. in facial region detection, the facial regions are identified from the segmented results using a skin-color model. the results of region segmentation and facial region detection are integrated to provide facial regions with accurate and closed boundaries. in our experiments, the algorithm detected 87-94% of the faces, including frames from videoconference sequences. the average run time range from 0.23-0.34 sec per frame.
non-contrast based edge descriptor for image segmentation. we present an efficient object segmentation strategy based on edge information to assist object-based video coding, motion estimation and motion compensation for the mpeg system. two parameters are introduced and described based on edge information from the analysis of a local histogram. using these features, a non-contrast based edge function is defined to generate an edge information map, which can be thought as a gradient image. then, an improved marker-based region growing and merging techniques are derived to separate image regions. the proposed algorithm is tested on several standard images and demonstrates high reliability for object segmentation.
a support system for visually impaired persons to understand three-dimensional visual information using acoustic interface. visual information processing technology is very important in the implementation for sensory substitution of visually impaired persons as well as applications to factory automation. this paper outlines the design of a visual support system that provides 3d visual information using 3d virtual sounds. three-dimensional information, such as distance map, object recognition, and object tracking required for the visually impaired user, is obtained by analyzing imagescaptured by stereo cameras. using a 3d virtual acoustic display, which relies on head related transfer functions (hrtfs), the user is informed of the locations and movements of objects. the user's external auditory sense is not impeded as the system uses bone conduction headphones which do not block out environment sounds. the proposed system is expected to be useful in the situations where the infrastructure is incomplete and the situation changes in real-time. we plan experiments using this system to guide users while walking and playing sports.
geographic hypermedia using search space transformation. we present a new approach for linking heterogeneous data of the same objective nature, such as 2d maps, 3d virtual environments and videos with gps data. we have identified three key challenges (georeferencing, content creation for geospatial videos and bidirectional linking) that should be addressed to link among geographic hypermedia. we propose an easily implementable data model that serves well as a foundation for point query in 2d and attribute query in a video. we also present a point query processing algorithm for video browsing by using the modified r-tree. the proposed method supports a geographic hypermedia navigation by providing the bidirectional linking. experimental results indicate that the proposed approach is effective in retrieving geospatial video clips and nonspatial data.
object boundary edge selection using normal direction derivatives of a contour in a complex scene. recently, nguyen proposed a method[tracking nonparameterized object contours in video]for tracking a nonparameterized object (subject) contour in a single video stream. nguyenýs approach combined outputs of two steps: creating a predicted contour and removing background edges. in this paper, we propose a method to increase object tracking accuracy by improving the background edge removal process. nguyen's background edge removal method of leaving many irrelevant edges is subject to inaccurate contour tracking. our accurate tracking is based on reducing affects from irrelevant edges by selecting the boundary edge only. we select high-valued edge pixels of average image intensity gradients in the contour normal direction. our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.
tracking 3d human body using particle filter in moving monocular camera. in this paper, we propose a method for human tracking using 3d human body model in a video sequence with a monocular moving camera. tracking a human with unconstrained movement in moving monocular camera image sequence is extremely challenging. our 3d human body model which is formed with articulation model of hierarchical tree structure can express all human's movement by parameters. we can obtain 3d human body model which has the most similar shape with input image through similarity matching. in order to predict the region and movement of human using 3d human body model in the obtained current frame, we use the particle filter which predicts the posterior distribution by the random probability variable based on monte carlo sampling. as a result, it can be possible to track robustly for human's motion and random movement of camera in the environment with moving camera. we can get the result of converging toward minimized error values using boundary distance between a predicted 3d human body model and an input image. in the result of experiment, the proposed method showed correct tracking result for complex background and various human movements.
background robust object labeling by voting of weight-aggregated local features. in this paper, we present a new voting-based object labeling method that is robust to background clutter. the conventional simple voting method shows very poor performance under clutter. to reduce the effect of clutter, first we aggregate the weights between the features and the support features using similarity and proximity. through the recursive weight aggregation process, features belonging to the same objects get stronger weights, and features belonging to clutter get weaker weights. then, we vote the weightaggregated features to get the object labels. we validate the enhancement of the proposed method by using an open database and a real test set.
a new attributed relational graph matching algorithm using the nested structure of earth mover's distance. in general, object features can be represented as the nodes in attributed relational graph (arg) with the connecting edges implying their relations. therefore, the arg matching plays a significant role in object recognition. actually, the arg matching can be implemented as a 2-step procedure, composed of constructing a distance matrix and establishing the correspondence based on the distance matrix, which seems to be similar to the point matching procedure. in this paper, we present a new arg matching algorithm using the nested structure of earth mover's distance (emd). more specifically, the nested structure of the emd consists of inner emd and outer emd: the inner emd reflects the difference of both nodes and edges between a pair of nodes in two arg's in a perceptual manner, and the outer emd establishes the correspondence between nodes in the two arg's in a natural way. in order to demonstrate the robustness of the proposed algorithm against noise, we have conducted synthetic experiments for fully connected and undirected arg's as in [kronecker product graph matching].
new mrf parameter estimation technique for texture image segmentation using hierarchical gmrf model based on random spatial interaction and mean field theory. this paper presents a new markov random field (mrf) parameter estimation technique using hierarchical mrf model based on the random spatial interaction (rsi) and the mean field theory for the textured image segmentation. by considering spatial interaction of the mrf as random fields, the fluctuation of the spatial interaction that occurs in the conventional mrf model can be efficiently alleviated. also, by assuming randomness of the spatial interaction as the mrf model, it allows us to obtain more robust information for segmentation during the feature extraction. the gaussian mrf model is applied to the proposed hierarchical mrf scheme, and the expectation of the rsi is uniquely obtained by simple linear equation without using a window based on the mean field theory. experimental results on synthetic and real world images show that the proposed algorithm provides good feature extraction and segmentation.
acceleration of similarity-based partial image retrieval using multistage vector quantization. we propose a new method for quick and accurate partial image retrieval from a huge number of images based on a prede ned distance measure. the proposed method utilizes vector quantization (vq) on multiple layers, namely color, block, and feature layers. this can greatly reduce the amount of calculation needed for partial image retrieval. experiments indicate that the proposed method can detect partial images that are similar to queries through 1000 images within 4 seconds. this is approximately 30 times faster than the method to which multistage vq is not applied.
parallel volume segmentation with tetrahedral adaptive grid. we propose a general-purposed parallel algorithm for volume segmentation, which does not require any prior knowledge on volume nor region. the algorithm provides binary tree structured split-and-merge mechanism to search and localize boundaries along discontinuities and adapts the partition of volume to those detected discontinuities. this algorithm is independent from order of processing or seed selection. and, even though overlapping only one voxel wide boundary between process blocks, by adopting the smoothness-based local feature as homogeneity criteria, consistencies are maintained without overhead of communication between adjacent process blocks. our efficient hierarchical step-wised mechanism in merging target evaluation makes merge process so simple and efficient that only two brother blocks are considered at each merge step in binary fashion. experimental results on an artificial and a ct scan volume data are shown.
an extension of the generalized hough transform to realize affine-invariant two-dimensional (2d) shape detection. in this paper, we present a new method for two-dimensional (2d) shape detection applicable under affine transformation. the problem of affine-invariant shape detection is an important and fundamental research subject in computer vision. although various methods have been proposed to solve this problem, most of those approaches are not well suited for the following general cases: (1) a shape to be detected is occluded by other overlapping objects, (2) a shape boundary is partially broken because of noise or other factors. we introduce a new method to deal with such cases, which extends the generalized hough transform [1] to be an affine-invariant shape detector. this method, called the affine-ght, utilizes pairwise parallel tangents and basic properties of an affine transformation to carry the direct computation for six parameters of an affine transformation. experimental result demonstrates that the proposed method performs successfully and efficiently.
bin-picking based on harmonic shape contexts and graph-based matching. in this work we address the general bin-picking problem where 3d data is available. we apply harmonic shape contexts (hsc) features since these are invariant to translation, scale, and 3d rotation. each object is divided into a number of sub-models each represented by a number of hsc features. these are compared with hsc features extracted in the current data using a graph-based scheme. results show that the approach is somewhat sensitive to noise, but works in presence of occlusion.
efficient recognition of planar objects based on hashing of keypoints - an approach towards making the physical world clickable. this paper presents a method of planar object recognition for aiming at accessing information about objects by taking pictures of them. for this purpose efficiency of processing is the central issue because current state-of-the-art technologies with tree structures do not necessarily work well with a large amount of data represented as high dimensional vectors. to solve this problem, we employ hashing of keypoints extracted from images of objects. with the help of hash keys obtained as integers converted from the real valued vectors, keypoints are stored with object ids and retrieved with no search process. voting for object ids is employed to determine a recognized object as the one with the largest vote. experimental results show that the proposed method is at least 400 times faster than a brute-force method while 90% of objects were correctly recognized.
change detection using joint intensity histogram. in the present paper, a method for detecting changes between two images of the same scene taken at different times using their joint intensity histogram is proposed. first, the joint histogram, which is a two-dimensional (2d) histogram of combinatorial intensity levels, (i1(x), i2(x)), is calculated. by checking the characteristics of the ridges of clusters on the joint histogram, clusters that are expected to correspond to background are selected. the combinations of (i1, i2) covered by the clusters are determined as insignificant changes. pixels having a different combinatorial intensity (i1(x), i2(x)) from these combinations, are extracted as candidates for significant changes. based on the gradient correlation between the images for each region consisting of these pixels, only regions with significant changes are distinguished. experiments using real scenes show the practical usefulness of the method.
a deformable model driven method for handling clothes. a model-driven method for handling clothes by two manipulators based on observation with stereo cameras is proposed. the task considered in this paper is to hold up a specific part of clothes (e.g. one shoulder of a pullover) by the second manipulator, when the clothes is held in the air by the first manipulator. first, the method calculates possible 3d shapes of the hanging clothes by simulating the clothes deformation. the 3d shape whose appearance gives the best fit with the observed appearance is selected as estimation of the current state. then, based on the estimated shape, the 3d position and normal direction of the part where the second manipulator should hold are calculated. the experiments using actual two manipulators have shown the good potential of the proposed method.
stealth vision for protecting privacy. we propose an anonymous video capturing system, called "stealth vision", that protects the privacy of objects by fading out their appearance. in order to avoid difficulty in detecting the region of human faces (privacy-protected regions) in an image captured with a mobile camera, the fade-out areas are determined in 3d (three-dimensional) space instead of 2d image space. the 3d position of the captured object is estimated with a simple algorithm, which projects the foreground region in the overhead image onto several horizontal 3d planes and extracts the 3d position while merging the projecting results. by projecting the 3d information onto an image plane of mobile camera, the region to use for the anonymizing process is determined. experimental results show that our system successfully protects the privacy of target objects.