waashk/dblp huggingface dataset:
toward a speaker-independent real-time affect detection system. the ability to detect the human affective states is rapidly gaining interests among researchers and industrial developers since it has a broad range of applications. this paper reports the advances of human affect detection from acoustic signals in motorola labs. we focus on two parts of affect detection: emotion detection and conversational engagement detection. the emotion detection part is the major component of our system. the system is based only on acoustic information, that is to say, there is no recognizer and no linguistic or semantic information available. given the truth that speech is a short-time stationary signal, we employ the hidden markov model (hmm) to capture the variation and trend of acoustic signal structures caused by affective states. the affect-sensitive segmental features such as pitch, energy, zero crossing rate and energy slope are extracted to capture the finer structures of acoustic signals. each state of the hmm is modeled by a gaussian mixture model (gmm), which captures the range, mean, median and variability of above affect-sensitive measures. besides testing the algorithm in the ldc databases, we implement a real-time conversation monitor, which can recognize and express the eight basic human emotions and can detect the conversational engagement level.
a hybrid face recognition method using markov random fields. we propose a hybrid face recognition method that combines holistic and feature analysis-based approaches using a markov random field (mrf) model. the face images are divided into small patches, and the mrf model is used to represent the relationship between the image patches and the patch id's. the mrf model is first learned from the training image patches, given a test image. the most probable patch id's are then inferred using the belief propagation (bp) algorithm. finally, the id of the test image is determined by a voting scheme from the estimated patch id's. experimental results on several face datasets indicate the significant potential of our method.
moment-based shape priors for geometric active contours. in this paper a new method that incorporates moment-based shape information into geometric active contours is presented. as any shape may theoretically be characterized by its set of moments, the shape prior is represented based on legendre moments. by combining the shape prior with the powerful geometric active contours proposed by chan and vese, the improved model can retain all the advantage of the chan-vese model and have the additional ability of being able to compactly represent global shape of an object. experiments on synthetic and real image segmentation show the efficiency of our method.
a new iris segmentation method for recognition. as the first stage, iris segmentation is very important for an iris recognition system. if the iris regions were not correctly segmented, there would possibly exist four kinds of noises in segmented iris regions: eyelashes, eyelids, reflections and pupil, which will result in poor recognition performance. this paper proposes a new noise-removing approach based on the fusion of edge and region information. the whole procedure includes three steps: 1) rough localization and normalization, 2) edge information extraction based on phase congruency, and 3) the infusion of edge and region information. experimental results on a set of 2,096 images show that the proposed method has encouraging performance for improving the recognition accuracy.
moving object extraction with a localized pyramid. in this paper, we present a tracking initialization method that combines a rough extraction of moving objects and a refined segmentation of their contours. the extraction of moving objects is obtained with a classical global motion compensation. to obtain accurate contours of the objects, a spatial segmentation is performed with an original localized graph pyramid that focuses the segmentation process either on the object areas or on their borders.
a bayesian approach to video object segmentation via merging 3d watershed volumes. in this paper, we propose a bayesian approach to video object segmentation, which consists of two stages. in the first stage, we partition the video data into a set of 3d watershed volumes, where each watershed volume is a series of corresponding 2d image regions. these 2d image regions are obtained by applying to each image frame the marker-controlled watershed segmentation. in the second stage, we use a markov random field to model the spatio-temporal relationship among the 3d watershed volume. then, the desired video objects can be extracted by merging watershed volumes having similar motion characteristics within a bayeysian framework. our experiments have shown that the proposed method has great potential in extracting moving objects from a video sequence.
real-time camera pose and focal length estimation. this paper presents a novel approach to estimate the changing internal and external parameters of the camera in real time using a few 3d-2d point correspondences. we approach the problem by constructing two filters which can individually track motion and zoom respectively in a robust manner. then, the viterbi algorithm is used to select a filter depending on whether the camera is undergoing motion or zoom. we assume that the camera cannot move and zoom simultaneously, though our method can tolerate some hand held movement during zoom. the application of our approach is demonstrated on synthetic and real data.
bayesian feedback in data clustering. in many clustering applications, the user has some vague notion of the number and membership of the desired clusters. however, it is difficult for the user to provide such knowledge explicitly in the clustering process. we propose a solution to circumvent this difficulty by introducing a feedback mechanism. the notion of bayesian inference for relevance feedback in content-based image retrieval is modified for data clustering. given the number of clusters, the proposed algorithm seeks information about the target partition by asking the user a sequence of queries about whether a pair of objects should be put in the same cluster or not. information-theoretic criteria is adopted to select the queries to be presented to the user. the assumption made here is that cluster labels are "smooth", i.e., similar objects should share the same cluster labels. we show that it is possible to obtain reasonable partitions based on the user feedback alone, without the need of specifying a clustering objective function.
biometrics: a grand challenge. reliable person identification is an important problem in diverse businesses. biometrics, identification based on distinctive personal traits, has the potential to become an irreplaceable part of any identification system. while successful in some niche markets, the biometrics technology has not yet delivered its promise of foolproof automatic identification. with the availability of inexpensive biometric sensors and computing power, it is becoming increasingly clear that widespread usage of biometric person identification is being stymied by our lack of understanding of three fundamental problems: (i) how to accurately and efficiently represent and recognize biometric patterns? (ii) how to guarantee that the sensed measurements arenot fraudulent? and (iii) how to make sure that the application is indeed exclusively using pattern recognition for the expressed purpose (function creep [biometrics: personal identification in networked society])? solving these core problems will not only catapult biometrics into mainstream applications but will also stimulate adoption of other pattern recognition applications for providing effective automation of sensitive tasks without jeopardizing our individual freedoms. for these reasons, we view biometrics as a grand challenge - "a fundamental problem in science and engineering with broad economic and scientific impact".
landscape of clustering algorithms. numerous clustering algorithms, their taxonomies and evaluation studies are available in the literature. despite the diversity of different clustering algorithms, solutions delivered by these algorithms exhibit many commonalities. an analysis of the similarity and properties of clustering objective functions is necessary from the operational/user perspective. we revisit conventional categorization of clustering algorithms and attempt to relate them according to the partitions they produce. we empirically study the similarity of clustering solutions obtained by many traditional as well as relatively recent clustering algorithms on a number of real-world data sets. sammon's mapping and a complete-link clustering of the inter-clustering dissimilarity values are performed to detect a meaningful grouping of the objective functions. we find that only a small number of clustering algorithms are sufficient to represent a large spectrum of clustering criteria. for example, interesting groups of clustering algorithms are centered around the graph partitioning, linkage-based and gaussian mixture model based algorithms.
hiding a face in a fingerprint image. with the wide spread utilization of biometric identification systems, establishing the authenticity of biometric data itself has emerged as an important research issue. we present a fingerprint image watermarking method that can embed facial information into host fingerprint images. this scheme has the advantage that in addition to fingerprint matching, the recovered face during the decoding can be used to establish the authenticity of the fingerprint and the user.by computing the roc curves on a fingerprint database of 160 individuals, we show the advantages of the proposed watermarking scheme. further, our scheme does not introduce any significant degradation in the fingerprint matching performance.
preprocessing and recognition of characters in container codes. this paper describes the recognition of container code characters. the system has to deal with outdoor images which usually have damaged characters and obtain an answer in real time. tophat transformation, equalization and adaptive cropping have been applied so as to reduce the noise and correct the inclusion box. the training corpus is artificially grown by applying controlled deformations of the samples which increase the recognition rate. approximated nearest neighbours are used in order to perform a fast search with a large corpus.
an accurate shape reconstruction from photometric stereo using four approximations of surface normal. we present a new shape reconstruction method from photometric stereo. the method gives more accurate shapes than the existing method that uses three images and an algorithm based on the frankot-chellappa's to convert the surface normal to height. the new method, which is applicable to the case of two or more images available, solves the combination of two or more nonlinear equations representing the consistency between the reflectance maps and the observed images in an iterative way for the shape. and this is repeated for each of the four approximations of the surface normal and the results are averaged, to get a better shape estimate. computer experiments are made to show the usefulness of the method and to present some of the characteristics.
locating destination address block in korean mail images. in this paper, we propose a method of locating destination address blocks in both machine-printed and handwritten korean mail images. the proposed method extracts connected components from a binarized mail image, generates rough text lines by merging them, and then groups the text lines into nine clusters. a destination address block is determined by selecting some clusters. considering the geometric characteristics of information on korean mails, we segment a mail image into nine areas with an equal size. the nine clusters are initialized with the center coordinate of each area. a modified manhattan distance is used to calculate the distance between a text line and a cluster. we modified the distance function on which the aspect ratio of mail image could be reflected. the experiment done with live korean mail images has demonstrated the superiority of the proposed method. the success rate for the 1,988 testing images was about 93.56%.
sensor fusion as optimization: maximizing mutual information between sensory signals. sensor fusion is one of the fundamental issues to develop intelligent systems that recognize the scene around them precisely and robustly. previous approaches of sensor fusion combined different kind of sensors after feature extraction and abstraction ("task-level fusion"). this paper proposes a new approach that combines sensory signals from different kind of sensors before abstraction ("signal-level fusion"). by formalizing sensory fusion as an optimization that maximizes mutual information between sensory signals, a target in a changing scene is detected by heuristic search algorithm. as an example, experimental results of a sound source detection with one video camera one microphone are shown.
integrating independent components and support vector machines for gender classification. computer vision and pattern recognition systems play an important role in our lives by means of automated face detection, face and gesture recognition, and estimation of gender and age. we have developed a gender classifier with performance superior to existing gender classifiers. this paper addresses the problem of gender classification using frontal facial images. the testbed consists of 500 images (250 females and 250 males) randomly withdrawn from the feret facial database. independent component analysis (ica) is used to represent each image as a feature vector in a low dimensional subspace. different classifiers are studied in this lower dimensional subspace. our experimental results show the best accuracy of 96% in gender classification by combining ica and support vector machines (svms).
a peer dataset comparison outlier detection model applied to financial surveillance. outlier detection is a key element for intelligent financial surveillance system.the detection procedures generally fall into two categories: comparing every transaction against its account history and further more, comparing against a peer group to determine if the behavior is unusual. the later approach shows particular merits in efficiently extracting suspicious transaction and reducing false positive rate. peer group analysis concept is largely dependent on a cross-datasets outlier detection model. in this paper, we propose a new cross outlier detection model based on distance definition incorporated with the financial transaction data features. an approximation algorithm accompanied with the model is provided to optimize the computation of the deviation from tested data point to the reference dataset. an experiment based on real bank data blended with synthetic outlier cases shows promising results of our model in reducing false positive rate while enhancing the discriminative rate remarkably
fingerprint image enhancement by pixel-parallel processing. this paper proposes a fingerprint image enhancement algorithm that can be mapped onto the compact pixel-parallel architecture, such as a fingerprint identification chip that senses and identifies fingerprints by itself. the algorithm is composed of two parts. first, ridges are enhanced by extracting their center lines and removing white noises using the center line image. then valleys are enhanced by detecting areas where they are thin and disconnected and dilating them at these areas. both enhancements are done with the structures of the fingerprint maintained. the experimental results show fingerprint identification using the proposed image enhancement algorithm becomes much better, that the error ratio becomes a quarter of that of the conventional method, and the execution time is 0.14 msec when the proposed algorithm is performed on the array of processing elements in the fingerprint identification chip.
a hybrid, recursive algorithm for clustering expressed sequence tags in chlamydomonas reinhardtii. we present an efficient, fully automated algorithm to assemble ests into full-length cdna sequences that represent the complete coding regions of a gene. our est clustering algorithm is neither hierarchical nor incremental, but recursive, processing each est once. the algorithm exploits a variety of syntactic and statistical features of the ests. the resulting assembly shows significant improvement in computational efficiency and information extraction over a previous assembly of c. reinhardtii ests. the algorithm was developed using iterative and participatory design on c. reinhardtii; however, it can be used for any organism with a draft genomic sequence.
integrated region-based image retrieval using region??s spatial relationships. among representative content-based image retrieval schemes, region-based retrieval has shown promise in retrieving similar images that exhibit considerable local variations. however,since humans are accustomed to relying on object-level concepts rather than low-level regions, robust and accurate object segmentation is an essential step. while many interestingimage segmentation techniques have been proposed, their performance in practical applications remains limited. thus, integrating related regions into meaningful objects becomes a promising alternative. in this paper, we propose a new multiple-region level image retrieval algorithm based on region-level image segmentation and its spatial relationship. to capturespatial similarity, we apply hausdorff distance (hd) to our region-based image retrieval system-frip (finding region in the pictures). in contrast to other object or multiple region-based retrieval systems, we update classical hd to retrieve similar regions regardless of their spatial translation, insertion, and deletion. furthermore, we incorporate relevance feedback to reflect the user's high-level query and subjectivity to the system and to compensate for performance degradation due to imperfect image segmentation. the efficacy of our method is validated using a set of 3000 images from corel-photo cd.
video mosaicing for curved documents based on structure from motion. various methods for video mosaicing have been already investigated by many researchers. most of these methods, however, assume that the target object is flat or very far from the camera to avoid the disparity problem. this paper describes a novel video mosaicing method for curved documents based on 3-d reconstruction. with the proposed method, the mosaic image of the geometrically restored target document is generated, even if the document has a curved surface. experiments on curved documents have shown the feasibility of the proposed method.
registration of an uncalibrated image pair to a 3d surface model. the following data fusion problem is considered: given a 3d geometric model of an object and two uncalibrated images of the same object, and assuming that the object surface is textured and lambertian, precisely register the images to the model. solving this problem is necessary for building a geometrically accurate, photorealistic model from laser-scanned 3d data and high quality images. we generalise the photo-consistency approach by clarkson et al. [using photo-consistency to register 2d optical images of the human face to a 3d surface model] to the case of uncalibrated cameras, when both intrinsic and extrinsic parameters are unknown. this gives a user the freedom of taking the pictures by a conventional digital camera, from arbitrary positions and with varying zoom. we discuss a number of possible approaches to the problem and propose a method based on manual pre-registration followed by a genetic optimisation algorithm. the issues of speed and robustness are addressed. results for real data are shown.
representing cultural heritage in digital forms for vr systems through computer vision techniques. this paper overviews our research on digital preservation of cultural assets and digital restoration of their original appearance. geometric models are digitally achieved through a pipeline consisting of scanning, registering and merging multiple range images. we have developed a robust simultaneous registration method and an efficient and robust voxel-based integration method. on the geometric models created, we have to align texture images acquired from a color camera. we have developed a texture mapping method to utilize laser reflectance. in an attempt to restore the original appearance of historical heritage objects, we have synthesized several buildings and statues using scanned data and literature survey with advice from experts.
proposal of a parallel architecture for a motion detection algorithm. this paper proposes a parallel architecture for the estimation of motion of an underwater robot. it is well known that image processing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. in a motion estimation algorithm, correspondences between two images has to be solved at the low level. in the underwater imaging, normalised correlation can be a solution in presence of non-uniform illumination. due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed.
digitalwatermarking in contourlet domain. digital watermarking has been proposed as a method of copyright protection of audio, images, video and text. we propose to use the newly introduced transform for two dimensional signals, namely, the contourlet transform for image watermarking application. we have carried out simulations with two kinds of images- maps consisting of lot of curves and texts and those with textures. the watermarked images were subjected to different attacks like mean filtering, quantization and jpeg compression. it is observed that the contourlet based algorithm outperforms wavelet and dct based methods in this application. it is also apparent from the simulation results that contourlet based techniques give a distinct advantage over conventional techniques in images like maps which consist of lot of curves and texts.
gaussian mixture pdf in one-class classification: computing and utilizing confidence values. in this study a confidence measure for probability density functions (pdfs) is presented. the measure can be used in one-class classification to select a pdf threshold for class inclusion. in addition, confidence information can be used to verify correctness of a decision in a multi-class case where for example the bayesian decision rule reveals which class is the most probable. additionally, using confidence valueswhich represent in which quantile of the probability mass a pdf value resides ([0, 1]) - is often straightforward compared to using arbitrarily scaled pdf values. as the main contributions, use of confidence information in classification is described and a method for confidence estimation is presented.
automatic object-of-interest segmentation from natural images. in this paper, we propose a novel ooi (object-of-interest) segmentation algorithm from natural images that is based on human attention and semantic region merging. to do this, we segment an image into regions and merge them as a semantic object. then, we create an attention window based on saliency map and saliency points from an image. within the aw, a support vector machine is used to select the salient regions, which are then clustered into the ooi using the proposed region merging. unlike other algorithms, the proposed method allows multiple oois to be segmented according to the saliency map. experiments with the algorithm on more than 300 natural images have shown results close to human perception.