waashk/dblp huggingface dataset:
color decomposition of overlapped watercolors. in this paper we propose a color decomposition method for multicolor printing of watercolors including areas overlapping each print layer. watercolors have an important feature that the observed colors are different by the printing order. this method is based on a particle density model for expressing the effects of watercolors. the approach of this method is to solve an inverse problem, that is, determining a printed area corresponding to a watercolor from an observed image. our purpose in this study is to estimate woodblocks from a woodblock print. we focus on multicolor woodblock prints such as ukiyo-e: japanese traditional woodblock printing. the results of woodblock estimation will be utilized to preserve techniques and prints of ukiyo-e. an ukiyo-e print is created from several woodblocks with several watercolors, and has a mixing color expression similar to the effects of watercolor painting.
mixed anisotropic diffusion. this paper deals with the filtering and the enhancement of strongly oriented patterns. we propose a new filter combining scalar and tensor based diffusivities. the computation of an orientation confidence allows us to choose the best strategy to locally diffuse the gray levels.we will show that this approach overcomes some drawbacks of the classical methods like corner smoothing or pinhole effect. the proposed method was applied on digital images of engravings. this type of images contain strongly oriented patterns and a large amount of details that have to be preserved during the diffusion.
combination of shape descriptors using an adaptation of boosting. many different kinds of shape descriptors have been defined but usually, each of them is only suitable for some particular kinds of shapes. then, a strategy to improve performance in arbitrary shapes is the use of several descriptors. in this paper, we address the problem of how to combine several shape descriptors into a single representation. we present an adaptation of the boosting algorithm that permits to train a different classifier for each descriptor and combine all these classifiers to obtain a global classifier. the contribution of each descriptor to this final classifier is determined according to its performance along the boosting iterations. thus, the most relevant descriptors have the greatest influence in the final classifier.
robust face detection and hand posture recognition in color images for human-machine interaction. a system for the detection of human faces and for the classification of hand postures in color images is presented. we first propose to apply a combination of a skin chrominance-based image segmentation with a color vector gradient-based edge detection [1 ] [2 ] to efficientlydetect faces and hands. a statistical model for face detection based on invariant moments [3] [4 ] is then used to discriminate between faces and hands in the segmented images. a novel approach to hand posture recognition based on phase-only correlation [5 ] is finally applied toclassify a subset of static hand postures of the japanese sign language, each posture representing a given phoneme, and also to discriminate between hand postures and the image scene background. experiments show that the additional use of the color gradient significantly improves the correct rate of face detection, and that the phase-only correlation filter yields a high rate of discrimination between different static hand postures aswell as between hand postures and the scene background.
druide : a real-time system for robust multiple face detection, tracking and hand posture recognition in color video sequences. "druide", which stands for "detection, recognition, unification, interpretation, decision, evolution", is a novel, real-time system primarily designed for the detection and tracking of multiple human faces as well as for the simultaneous recognition of multiple hand postures in color video sequences and in complex environments. the system relies on the three fundamental cues of color, shape and motion, and integrates three mutually complementary sub-systems, in order to achieve high rates of detection, tracking and recognition. preliminary experiments yield an average correct face detection rate of about 90%, and the additional use of tracking increases significantly the robustness of the proposed system, particularly to illumination conditions and to partial occlusions. although we first focus specifically on human faces and hand postures, the ultimate goal of druide extends beyond human-computer interactions, to encompass the adaptive detection, tracking and recognition of various objects under unconstrained scene conditions.
vehicle lateral position estimation method based on matching of top-view images. in this paper, a method to estimate the lateral position of the vehicle from a sequence of moving camera images is proposed. proposed method relies on the plane projective transform (homography) between the ground and the image plane of the present and the next frame. homographies are obtained according to the information of the calibrated camera and the vehicles' speed. the movement of the camera is obtained by the registration of the 2 consecutive input images. the proposed method does not rely on extraction of features such as lines, flow vectors or lane markers, but based on matching of warped top-view images between two consecutive frames. therefore, vehicle movement can be estimated even from images without such explicit image features or without special sensors or gps. in this paper, experiments are done with both synthesized and real images and enough accuracy is shown even compared to conventional method.
occlusion resistant shape classifier based onwarped optimal path matching. in this paper, we present a novel occlusion resistant shape classification scheme for hidden markov modeled shapes. first, hidden markov model (hmm) is built using multiple example shapes for each shape class. a reference path for each class is built from the corresponding hmm, which is nothing but optimal path followed by the most likely example shape. the reference path stores temporal information about the entire shape, while the hmm only retains relationship between temporal information. finally to classify a shape, occluded or not, its optimal path through hmm is calculated and warped to match the reference path using dynamic time warping (dtw). correct class is identified as the one for which the warping cost is minimum. classification results obtained for two shape data sets are presented for varying degrees of occlusion and are compared with the conventional maximum likelihood (ml) hmm classifier.
hierarchical probabilistic models for video object segmentation and tracking. when tracking and segmenting semantic video objects, different forms of representational model can be used to find the object region on a per-frame basis. we propose a novel hierarchical technique using parametric models to describe the appearance and location of an object and then use non-parametric methods to model the sub-object regions for accurate pixel-wise segmentation. our motivation is to use parametric models to locate the object, improving the sensitivity of the non-parametric sub-object region models to background clutter. the results indicate this is a promising approach to extracting video objects.
classification of team behaviors in sports video games. this paper considers the application of pattern recognition techniques in modern computer games. towards the problem of realizing more life-like behavior for artificial game characters, we record the network traffic of online multiplayer games. dealing with a soccer game, we cluster these data and train hmms in order to achieve fast and robust recognition of behaviors and actions in the virtual game world. experimental results indicate that pattern recognition and machine learning provide an auspicious avenue towards more convincing artificial characters.
a low-complexity deformation invariant descriptor. in this paper, we propose a descriptor which is invariant to general deformations (only intensity locations change but not their value) by using hilbert scanning. in our method, an image is converted to a 1-d sequence through hilbert scanning at first. then, we embed this sequence as a 1- d curve in the 2-d space. because hilbert scanning preserves the coherence in a 2-d image, it is easily to understand that the area under the curve is invariant to intensity location changes, naturally. hence, we use some areas for an interest point as a deformation invariant descriptor. this descriptor can be computed in the 2-d space efficiently than other approaches where an image is embedded in the 3-d space or the dimensions of descriptors are very large. the experimental results show that our descriptor is low-complexity and superior to other approaches on interest point matching in deformation images.
an efficient algorithm for point matching using hilbert scanning distance. a fast and accurate similarity named hilbert scanning distance(hsd) [9] has recently been presented for point matching. in this study, we improved an efficient algorithm of search strategy for hsd in the large search space. this search strategy is associated with two ideas: a relaxation greedy search, and an accelerating process using monte carlo sampling. the experimental results implicate that this improved algorithm is robust and efficient for point matching using hsd. it also makes a tradeoff between accuracy and speed under different requirements.
voting weighted modified hausdorff distance through multiscale space for automatic image-map registration. the purpose of image-map registration is to revise the digital map included in geographic information system (gis) with aerial image. the traditional method for this task requires the manual selection of tie points in both image and map. in this study, we propose a distance measure named voting weighted modified hausdorff distance (vwmhd) for this task. in order to overcome the differences in representations between image and map in urban area, after several times of edge extraction through multiscale space, we give weights to each edge point in the initial scale based on its voting times and then compute the vwmhd for registration. the experimental results implicate that our vwmhd can provide sufficient information for automatic image-map registration and is robust to noises.
a shunting inhibitory convolutional neural network for gender classification. demographic features, such as gender, are very important for human recognition and can be used to enhance social and biometric applications. in this paper, we propose to use a class of convolutional neural networks for gender classification. these networks are built upon the concepts of local receptive field processing and weight sharing, which makes them more tolerant to distortions and variations in two dimensional shapes. tested on two separate data sets, the proposed networks achieve better classification accuracy than the conventional feedforward multilayer perceptron networks. on the feret benchmark dataset, the proposed convolutional neural networks achieve a classification rate of 97.1%.
3d texture classification using the belief net of a segmentation tree. this paper presents a statistical approach to 3d texture classification from a single image obtained under unknown viewpoint and illumination. unlike in prior work, in which texture primitives (textons) are defined in a filter-response space, and texture classes modeled by frequency histograms of these textons, we seek to extract and model geometric and photometric properties of image regions defining the texture. to this end, texture images are first segmented by a multiscale segmentation algorithm, and a universal set of texture primitives is specified over all texture classes in the domain of region geometric and photometric properties. then, for each class, a tree-structured belief network (tsbn) is learned, where nodes represent the corresponding image regions, and edges, their statistical dependecies. a given unknown texture is classified with respect to the maximum posterior distribution of the tsbn. experimental results on the benchmark curet database demonstrate that our approach outperforms the state-of-the-artmethods.
detection of artificial structures in natural-scene images using dynamic trees. we seek a framework that addresses localization, detection and recognition of man-made objects in natural-scene images in a unified manner. we propose to model artificial structures by dynamic tree-structured belief networks (dts-bns). dtsbns provide for a distribution over tree structures that we learn using our structured approximation (sva) inference algorithm. furthermore, we propose multi-scale linear-discriminant analysis (mlda) as a feature extraction method, which appears well suited for our goals, as we assume that man-made objects are characterized primarily by geometric regularities and by patches of uniform color. mlda extracts edges over a finite range of locations, orientations and scales, decomposing an image into dyadic squares. both the color of dyadic squares and the geometric properties of extracted edges represent observable input to our dtsbns. experimental results demonstrate that dts-bns, trained on mlda features, offer a viable solution for detection of artificial structures in natural-scene images.
detection over viewpoint via the object class invariant. in this article, we present a new model of object class appearance over viewpoint, based on learning a relationship between scale-invariant image features (e.g. sift) and a geometric structure that we refer to as an oci (object class invariant). the oci is a perspective invariant defined across instances of an object class, and thereby serves as a common reference frame relating features over viewpoint change and object class. a single probabilistic oci model can be learned to capture the rich multimodal nature of object class appearance in the presence of viewpoint change, providing an efficient alternative to the popular approach of training a battery of detectors at separate viewpoints and/or poses. experimentation demonstrates that an oci model of faces can be learned from a small number of natural, cluttered images, and used to detect faces exhibiting a large degree of appearance variation due to viewpoint change and intra-class variability (i.e. (sun)glasses, ethnicity, expression, etc.).
attention navigation by keeping screen layout for switching multiple views. to show a multi-directional scene situation with switching different views, we have to keep a screen layout of a remarkable object for intelligible image media. in this paper, we propose a method to navigate a viewer to one of objects in the scene by keeping its screen layout for switching multiple views. in order to keep the layout, we developed "virtual swiveling" by using fixed-viewpoint pan-tilt-zoom camera, based on area and center of gravity of extracted objects from captured images. we also show several experimental results to confirm an effectiveness of our method.
pen-coordinate information modeling by scpr-based hmm for on-line japanese handwriting recognition. this paper describes stochastic modeling of pencoordinate information in hmms with structured character pattern representation (scpr) for on-line japanese handwriting recognition. scpr allows hmms for kanji character patterns to share common subpatterns. although scpr-based hmms have been successfully applied to kanji character recognition, the pen-coordinate feature has not been modeled since it is unique feature in each character pattern. in this paper, we employ mapping from a common subpattern to each occurrence in kanji patterns and adaptation of state parameters to each character pattern in generating character hmms by composing scprbased hmms. experimental results show that the pencoordinate feature modeled in the scpr-based hmms effects significantly.
discriminatory power of handwritten words for writer recognition. analysis of allographs (characters) and allograph combinations (words) is the key for the identification/verification of a writer's handwriting.while allographs are usually part of words and the segmentation of a word into allographs is a subjective process, analysis of handwritten words is a natural option, complementary to allograph and document-level analysis. we consider four different types of features obtained using both segmentation-based and segmentation-free approaches: (i) gsc(gradient, structural and concavity) features that are extracted from the cells of a grid superimposed on the word image (ii) wmr (word model recognizer) features, extracted from the cells of superimposed grids on the segmented characters (iii) sc (shape curvature) features that describe characters by the distribution of curvature values on their contours and (iv) scon (shape context) features that measure the similarity between character contour shapes. their individual and accumulated performance is evaluated for the writer identification and verification tasks on over 75000 words images, written by more than 1000 writers. experimental results show that handwritten words are very effective in discriminating handwriting and that both segmentation-free and segmentation-based approaches are valid.
estimation of human motion from multiple cameras for gesture recognition. we have been researching human sensing technologies based on computer vision for the realization of the percept-room as an intelligent environment. this room offers several services such as the control of electrical appliances by use of gestures. a more caring technology is strongly expected for providing more human-friendly interfaces.as a first step, the recognition of human motion in the percept-room is an important task. we propose a method for extracting humans and then hand gestures from multi-channel motion images captured in the percept-room. the human position and the rising handgestures are estimated by integrating the silhouettes from multiple cameras by a background image subtraction and a frame subtraction. we describe the proposed methods and the experimental results obtained by use of a pc system implemented in the percept-room.
estimation of surface properties of art paintings using a multi-band camera. this paper describes a method for precisely estimating the surface properties of art paintings, including surface normal, surface spectral reflectance, and reflection model parameters. we use a multi-band camera system with six spectral channels. the surface of a painting is observed under different illumination directions by the camera. we use the torrance- sparrow model as a reflection model. the surface normal is estimated on the different illumination observations by the photometric stereo. the spectral reflectance is estimated on the multi-band data by the wiener method. the model parameters are estimated on the statistical distribution of the specular component. finally, all estimates are combined for rendering various appearances of the painting under different illumination and viewing conditions.
arbitrary viewpoint rendering from multiple omnidirectional images for interactive walkthroughs. recently, the interactive walkthrough which enables us to look around a virtualized real world has been widely investigated. the walkthrough system can be applied to simulation, telepresence, and so on. this paper describes a method of interactive walkthrough which takes a real scene and enables us to look around in the virtualized scene. our method is based on acquiring omnidirectional images at multiple points in the real world by using anomnidirectional camera. the method generates an omni-directional image at an arbitrary location from captured omnidirectional images, and presents a user a converted view-dependent perspective image. in the experiment, we realized the interactive walkthrough using indoor omnidi-rectional images and confirmed the feasibility of the method.
local motion analysis and its application in video based swimming style recognition. in this paper we study the problem of local motion analysis and apply it to swimming style recognition in broadcast sports video. local motion analysis is challenging for two reasons: 1) local motion is usually buried in clutters involving complex motion from multiple objects; and 2) the process is more sensitive to noises compared to the recovery of global motion. however, an effective approach to local motion analysis is significant for understanding human activity from image sequences. in this work, we firstly extract the object-induced local motion by utilizing robust motion estimation and salient color. the object motion is accordingly characterized by compensated motion vectors and confidence measurement. beyond a single image, we attempt to capture the motion periodicity over the local motion sequence. for each period, we locate a so-called salient frame within which we derive a compact representation to distinctly characterize an image sequence with repeated actions. finally, we employ a hierarchical classifier to distinguish local motion based on periodicity and salient frames. promising results have been achieved on swimming style recognition in broadcast sports video.
multiview facial feature tracking with a multi-modal probabilistic model. dynamically tracking facial features with large variation of face pose has been shown as one of the most challenging issues in facial feature tracking. the traditional statistical models employ a principal component analysis (pca) to characterize the statistics of a set of example shapes, however they are restricted to a narrow view due to the global linearity assumption. in this paper, a novel model-based multi-modal probabilistic approach is proposed to capture the complicated relationships among facial features under different face poses by using a mixture of local linear probabilistic pcas. based on the probabilistic evaluation of the component probabilistic pcas, the proposed method provides an effective way to choose the appropriate local pca automatically and accurately when face poses are undergoing large variations. experiment results demonstrate that the proposed method could track the facial features robustly with high accuracy under large variations of pose over time.
an effective and fast soccer ball detection and tracking method. a ball detection and tracking approach in real soccer game is proposed in this paper. in view of difficulties of direct detection, an indirect strategy based on non-ball elimination is applied. we distinguish the ball with a coarse-to-fine process. game field is firstly extracted and the posterior operations are restricted within it. then, at the coarse step, some distinct non-ball regions are removed via evaluation of color and shape. and at the fine step, the remained regions are further examined and the optimal one is determined as ball. afterwards, condensation algorithm is utilized to track ball. region optimization is appended to adapt to the ball's size and color/texture changes in response to movement along sequential frames through maximizing the normalization sum of intensity gradient around its perimeter. moreover, a confidence measure representing the ball region's reliability is presented to guide possible re-detection for continuous tracking. experiments have demonstrated the method is valid and fast in real soccer sequences.