waashk/dblp huggingface dataset:
a ground truth correspondence measure for benchmarking. automatic localisation of correspondences for the construction of statistical shape models from examples has been the focus of intense research during the last decade. several algorithms are available and benchmarking is needed to rank the different algorithms. prior work has focused on evaluating the quality of the models produced by the algorithms by measuring compactness, generality and specificity. in this paper problems with these standard measures are discussed. we propose that a ground truth correspondence measure (gcm) is used for benchmarking and in this paper benchmarking is performed on several state of the art algorithms. minimum description length (mdl) with a curvature cost comes out as the winner of the automatic methods. hand marked models turn out to be best but a semi-automatic method is shown to lie in between the best automatic method and the hand built models in performance.
parameterisation invariant statistical shape models. in this paper novel theory to automate shape modelling is described. the main idea is to develop a theory that is intrinsically defined for curves, as opposed to a finite sample of points along the curves. the major problem here is to define shape variation in a way that is invariant to curve parameterisations. instead of representing continuous curves using landmarks, the problem is treated analytically and numerical approximations are introduced at the latest stage. the problem is solved by calculating the covariance matrix of the shapes using a scalar product that is invariant to global reparameterisations. an algorithm for implementing the ideas is proposed and compared to a state of the art algorithmfor automatic shape modelling. the problems with instability in earlier formulations are solved and the resulting models are of higher quality.
automatic segmentation of zona pellucida in hmc images of human embryos. an important prognostic parameter for assessing the success of an in vitro fertilization treatment is the variation in thickness of the zona pellucida. zona pellucida, the envelope of the human embryo, is usually visualized using hoffman modulation contrast microscopy (hmc). the paper addresses the problem of segmenting the zona pellucida in hmc images of embryos. we propose a variational method based on an image model for the zona which takes advantage of the characteristic appearance of hmc images. the simple topology of the embryo allows us to focus on parametric models. our approach is partly inspired by the works of chan and vese, to which it has some similarities.
how human visual systems recognize objects - a novel computational model. this paper presents a novel computational model of 3d object recognition based on human visual system. conventional schemes have feed forward structure based on the bottom-up process of human vision. however, psychological and physiological evidence suggests that top-down process and feature binding by visual attention are also important. so, we propose a method to integrate these facts under statistical framework, markov chain monte carlo. in this scheme, object recognition is regarded as parameter optimization problem. the bottom-up process is used to initialize parameters and top-down process is used to optimize them. on both processes, feature map binding is performed by spatial attention mechanism. experimental results show that the proposed computational model is feasible for 3d object recognition.
efficient region based indexing and retrieval for images with elastic bucket tries. retrieval and indexing in multimedia databases has been an active topic both in the information retrieval and computer vision communities for a long time. in this paper we propose a novel region based indexing and retrieval scheme for images. first we present our virtual textual description using which, images are converted to text documents containing keywords. then we look at how these documents can be indexed and retrieved using modified elastic bucket tries and show that our approach is one order better than standard spatial indexing approaches. we also show various operations required for dealing with complex features like relevance feedback. finally we analyze the method comparatively and and validate our approach.
a quick video search method based on local and global feature clustering. this paper proposes a quick method of similarity-based video searching to detect and locate a specific video clip given as a query in a stored long video stream.the method employs a two-stage process: local and global feature clustering. the local clustering exploits continuity or local similarities between video features, and the global clustering gathers similar video frames that are not necessarily adjacent to each other.these processes prune irrelevant sections on a video stream.the method guarantees the exactly same search result as the exhaustive search. experiments performed on a pc show that the proposed method can correctly detect and lcoate a 7.5-second clip in a 150-hour video recording in 15 ms on average.
a wavelet-based watershed image segmentation for vop generation. this paper presents a wavelet-based watershed image segmentation method for extracting video objects from a video sequence. the method is based on a multiresolution application of a wavelet and watershed transformation, followed by a wavelet coefficient-based region merging procedure. the procedure toward complete segmentation consists of four steps: pyramid representation, region segmentation, region merging, and region projection. first, the pyramid representation creates multiresolution images using a wavelet transform. second, region segmentation is used to segment the lowest-resolution image of the created multiresolution image. third, the region merging is merged to segmented regions using the third-order central moment values of the wavelet coefficients. finally, the region projection is used to recover a full-resolution image by inverse wavelet transform. experimental results of the presented method can be applied to the segmentation of noise or degraded images and reduction of over-segmentation.
human head tracking in three dimensional voxel space. this paper proposes a new approach to track a human head in 3-d voxel space. information of both color and distance is obtained from multiple stereo cameras and integrated in 3-d voxel space. formulating a likelihood function from voxel location and its color information can achieve stable tracking with particle filtering in 3-d voxel space.
ent-boost: boosting using entropy measure for robust object detection. recently, boosting has come to be used widely in object-detection applications because of its impressive performance in both speed and accuracy. however, learning weak classifiers which is one of the most significant tasks in using boosting is left to users. in discrete adaboost, weak classifiers with binary output are too weak to boost when the training data is complex. meanwhile, determining the appropriate number of bins for weak classifiers learned by real adaboost is a challenging task because small ones might not accurately approximate the real distribution while large ones might cause over-fitting, increase computation time and waste storage space. we have developed ent-boost, a novel boosting scheme for efficiently learning weak classifiers using entropy measures. class entropy information is used to automatically estimate the optimal number of bins through discretization process. then kullback-leibler divergence which is the relative entropy between probability distributions of positive and negative samples is used to select the best weak classifier in the weak classifier set. experiments showed that strong classifiers learned by ent-boost can achieve good performance, and achieve compact storage space. the result of building a robust face detector using ent-boost showed the boosting scheme to be effective.
real-time, 3-d-multi object position estimation and tracking. for autonomously acting robots and driver assistance systems powerful optical stereo sensor systems are required. object positions and environmental conditions have to be acquired in real-time. in this paper a hardware-software co-design is applied, acting within the presented stereophotogrammetric system. for calculation of the depth map an optimized algorithm is implemented as a hierarchical parallel hardware solution. by adapting the image resolution to the distance, real-time processing is possible. the object clustering and the tracking is realized in a processor. the density distribution of the disparity in the depth map (disparity histogram) is used for object detection. a kalman filter stabilizes the parameters of the results.
simultaneous classification and visualword selection using entropy-based minimum description length. in this paper, we present a new entropy-based minimum description length (mdl) criterion for simultaneous classification and visual word selection. conventional mdl criteria focus on how to minimize cluster size and maximize the likelihood of data points. we extend the mdl by replacing the likelihood term with the entropy of class posterior. this new criterion can provide optimal visual words with enough classification accuracy. we validate the entropybased mdl to learn optimal visual words for place classification and categorization of the caltech 101 object database.
camera calibration with a transparent calibration tool using color filters: application to stereo camera calibration for a distant object. in this paper, we propose a transparent calibration tool that can calibrate in a straightforward manner without resetting or removing the tool, which consists of two parallel transparent boards with patterns of dots of a color filter material. the tool is installed in front of a color camera, which acquires images through it. images for calibration and the scene image are acquired simultaneously in different color bands. the proposed method is applied to stereo camera calibration for a distant small object.
estimating intrinsic parameters of cameras using two arbitrary rectangles. in this paper, we propose new camera calibration methods assuming a static camera. two corresponding imaged rectangles whose aspect ratios are unknown are sufficient to calibrate a camera. by warping the images properly, we show that the information from the imaged rectangles can be transformed to the form of camera constraints. based on this results, we propose two methods, one for three or more images and the other for only two images. the proposed methods are verified with synthetic and real images, and the results are comparable with less assumptions on cameras and on scenes.
vizwear-active: distributed monte carlo face tracking for wearable active cameras. in this paper, we discuss a distributed monte carlo (dmc) tracking method which achieves real-time and accurate face tracking for wearable active vision systems. the dmc is an extension of sequential monte carlo approaches to a client-server distributed architecture. the client feeds back the results of tracking for use in the control of the wearable active camera with minimal delay, and the server accurately tracks the faces at the same time. in our method, the client is able to complete the tracking processes even when it is unable to communicate with the server. furthermore, more accurate results are obtained when it is able to communicate with the server. we have implemented and demonstrated this method on the vizwear-active system which includes a wearable active camera.
multi-modal sequential monte carlo for on-line hierarchical graph structure estimation in model-based scene interpretation. we present a computationally efficient, on-line graph structure estimation method for model-based scene interpretation. different scenes have different hierarchical graphical models composed of place, objects, and parts. generally, it is very difficult and time-consuming to estimate dynamic graph structures. the key idea is to represent hypothesized graph structures as multi-modal particles instead of joint particle representation. such monte carlo representation makes the one-line hierarchical graph structure estimation feasible. the proposed method is supported by the neurobiological inference model. large-scale experimental results in an indoor (12 places, 112 3d objects) validate the feasibility of the proposed inference method.
face recognition using lda mixture model. linear discriminant analysis (lda) provides the projection that discriminates data well, and shows a good performance for face recognition. however, since lda provides only one transformation matrix over the whole data, it is not sufficient to discriminate complex data consisting of many classes with high variations, such as human faces. to overcome this weakness, we propose a new face recognition method based on the lda mixture model, where the set of all classes are partitioned into several clusters and we obtain a transformation matrix for each cluster. this accurate and detailed representation will improve classification performance. simulation results of face recognition show that lda mixture model outperforms pca, lda, and pca mixture model in terms of classification performance.
multicue mrf image segmentation: combining texture and color features. herein, we propose a new markov random field (mrf) image segmentation model which aims at combining color and texture features. the model has a multi-layer structure: each feature has its own layer, called feature layer, where an mrf model is defined using only the corresponding feature. a special layer is assigned to the combined mrf model. this layer interacts with each feature layer and provides the segmentation based on the combination ofdifferent features. the uniqueness of our algorithm is that it provides both color only and texture only segmentations as well as a segmentation based on combined color and texturefeatures. the number of classes on feature layers is given by the user but it is estimated on the combined layer.
usefulness of boundary sequences in computing shape features for arbitrary shaped regions. a boundary sequence is a good representation of arbitrary shaped regions, but not directly used in computing shape features such as area, centroid, orientation, and so forth. in this paper, we show that the shape features can be easily computed by using cross-sections derived from a boundary sequence. the cross-sections are vertical line segments in the region and can be determined by tracing the boundary sequence once. furthermore, a boundary sequence extraction method is also proposed, which generates a boundary sequence for each region in a binary image by scanning the image only once. the proposed method workswell even if a region has holes.
direct condensing: an efficient voronoi condensing algorithm for nearest neighbor classifiers. voronoi condensing reduces training patterns of nearest neighbor classifiers without changing the classification boundaries. this method plays important roles not only in the nearest neighbor classifiers but also in the other classifiers such as the support vector machines, because the resulting prototype patterns involve support vectors in many cases. however, previous algorithms for voronoi condensing were computationally inefficient in general pattern recognition tasks. this is because they use proximity graphs for entire training patters, which require computational time exponentially for the dimension of pattern space. for solving this problem, we proposed an efficient algorithm for voronoi condensing named direct condensing that does not require the entire proximity graphs of training patterns. we confirmed that direct condensing efficiently calculates voronoi condensed prototypes in high dimension (from 2 to 20 dimensions).
box-like superquadric recovery in range images by fusing region and boundary information. this work contributes to the robotic bin-picking problem, and more specifically to the problem of localizing piled box-like objects. we employ range imagery, and use box-like superquadrics for modeling the target objects. our approach for superquadric segmentation is an extension of the widespread recover-and-select framework, which employs only region information and therefore suffers from the region over- growing problem. our approach equally considers both region and boundary-based information for performing the recovery task. extensive experimentation with a variety of target object configurations demonstrates that it outperforms the recover-and-select framework in terms of both robustness and computational efficiency. moreover, if implemented in a parallel hardware environment, our approach can operate in real time.
new enhancement algorithm for fingerprint images. in this paper, an improved algorithm for enhancement of fingerprint image is proposed on the basis of the image normalization and gabor filter. firstly, the adaptive normalization based on block processing is suggested for improvement of fingerprint images. an input image is partitioned into sub-blocks with the size of k × l at first and the region of interest (roi) of the fingerprint image is acquired. the parameters for the image normalization are adaptively determined according to the statistics of each block. by utilizing these parameters, the block image is normalized for the next process. secondly, a new technique for selection of two important parameters of gabor filter is devised. these parameters are the ridge direction and the ridge frequency. in this study, the ridge direction of a block image is determined by the probabilistic approach unlike other works. with this ridge direction, the ridge frequency is selected by utilizing the directional projection. the proposed algorithms are tested with nist fingerprint images and show significant improvement in the experiments.
edge detection in range images of piled box-like objects. we present a framework for edge detection in range images acquired by a time of flight laser sensor. our edge detection approach is inspired by [edge detection in range images based on scan line approximation], in the context of which edge detection via scan line approximation with geometric parametric models is performed. the main drawback of this edge detector, namely the scan line over-segmentation problem is addressed by the introduction of a simple merging step. in addition, we incorporate a method for detection of the noisy data points created by the effect of laser beam splitting between surfaces of different ranges. finally, a procedure for fine localization of the edge points is introduced. experimental results on a variety of target object configurations demonstrate that our edge detection framework exhibits increased robustness and accuracy with regard to [edge detection in range images based on scan line approximation]. these characteristics in combination with the computational efficiency of our approach, allows for its usage as a component of a real time system for automatic unloading of piled box-like objects.
a robust license-plate extraction method under complex image conditions. a robust approach for extracting car license plate from images with complex background and relatively poor quality is presented in this paper. the approach focuses on dealing with images taken under weak lighting condition. the proposed method is divided into two steps: 1) searching candidate areas from the input image using gradient information, and 2) determining the plate area among the candidates and adjusting the boundary of the area by introducinga plate template. a set of experiments has been performed to prove the robustness and accuracy of the approach. for many images collected from a large underground parkingplace the result shows that 90% of them are correctly segmented.
robustness of linear discriminant analysis in automatic speech recognitio. this paper focuses on the problem of a robust estimation of different transformation matrices based on the well known linear discriminant analysis (lda) as it is used in automatic speech recognition systems. we investigate the effect of class distributions with artificial features and compare the resulting fisher criterion. this paper shows that it is not very helpful to use only the fisher criterion for an assessment of class separability. furthermore we address the problem of dealing with too many additional dimensions in the estimation. special experiments performed on subsets of the wallstreet journal database (wsj) indicate that a minimum of about 2000 feature vectors per class is needed for robust estimations with monophones. finally we make a prediction to future experiments on the lda matrix estimation with more classes.
stroke verification with gray-level image for hangul video text recognition. traditional ocr uses binarization technique, which makes ocr simple. but it makes strokes ambiguous and that causes recognition errors. main reason of those errors is similar grapheme pair confusing error. it can be reduced by verifying ambiguous area of gray level image. after checking whether there is similar grapheme pair by analyzing traditional ocr result candidates, the base stroke of confused grapheme can be found using the fitness function which reflects the base stroke characteristics. the possibility of confused stroke existence can be measured by analyzing the boundary area of the base stroke. the result is merged with traditional ocr using score-probability converting. we achieved 68.1% error reduction for target grapheme pair errors by the proposed method and it means that 23.1 % total error is reduced.
recognition and segmentation of scene content using region-based classification. we present a novel method for joint segmentation and pixelwise classification of images, classifying each pixel in the image into one of a set of broad categories. we propose a 2-step approach for this problem, first estimating image structure through dense region segmentation, which provides initial spatial grouping (superpixels), then performing recognition by classifying each superpixel according to its features. two types of region features are investigated: perceptual grouping features derived from neighborhood relations in the superpixel graph, and a histogram of pixel textons within the superpixel. region classification is performed by boosting for perceptual features and histogram matching for texton features. we also introduce a novel extension of multi-class boosting: map estimation in the space of classifier ensemble outputs. extensive results on aerial imagery are presented using a label vocabulary of trees, roads, vehicles, grass, shadows, and buildings. we evaluate the two methods across the categories, and compare them to the standard approach of classifying image blocks without prior segmentation. in our experiments perceptual features using multi-class boosting provide the best performance.
joint optimization of image registration and comparametric exposure compensation based on the lucas-kanade algorithm. an iterative registration algorithm, the lucas-kanade algorithm, is combined with an exposure compensation algorithm to jointly optimize the spatial registration and the exposure compensation. the coordinate descent method is employed to minimize a mean squared error between image pairs. based on a simple regression model, a nonparametric estimator, the empirical conditional mean and its polynomial fitting are used as histogram transformation functions for the exposure compensation. the proposed algorithm performs a good registration for real perspective and microscopic images, and can easily adopt other exposure compensation approaches and variations of the lucas- kanade algorithms due to its implicit flexibility.