Relevant bibliographies by topics / Face and Object Recognition

Academic literature on the topic 'Face and Object Recognition'

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Published: 6 September 2023

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Journal articles on the topic "Face and Object Recognition"

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Gülbetekin, Evrim, Seda Bayraktar, Özlenen Özkan, Hilmi Uysal, and Ömer Özkan. "Face Perception in Face Transplant Patients." Facial Plastic Surgery 35, no. 05 (August 20, 2019): 525–33. http://dx.doi.org/10.1055/s-0038-1666786.

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AbstractThe authors tested face discrimination, face recognition, object discrimination, and object recognition in two face transplantation patients (FTPs) who had facial injury since infancy, a patient who had a facial surgery due to a recent wound, and two control subjects. In Experiment 1, the authors showed them original faces and morphed forms of those faces and asked them to rate the similarity between the two. In Experiment 2, they showed old, new, and implicit faces and asked whether they recognized them or not. In Experiment 3, they showed them original objects and morphed forms of those objects and asked them to rate the similarity between the two. In Experiment 4, they showed old, new, and implicit objects and asked whether they recognized them or not. Object discrimination and object recognition performance did not differ between the FTPs and the controls. However, the face discrimination performance of FTP2 and face recognition performance of the FTP1 were poorer than that of the controls were. Therefore, the authors concluded that the structure of the face might affect face processing.
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Biederman, Irving, and Peter Kalocsais. "Neurocomputational bases of object and face recognition." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352, no. 1358 (August 29, 1997): 1203–19. http://dx.doi.org/10.1098/rstb.1997.0103.

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A number of behavioural phenomena distinguish the recognition of faces and objects, even when members of a set of objects are highly similar. Because faces have the same parts in approximately the same relations, individuation of faces typically requires specification of the metric variation in a holistic and integral representation of the facial surface. The direct mapping of a hypercolumn–like pattern of activation onto a representation layer that preserves relative spatial filter values in a two–dimensional (2D) coordinate space, as proposed by C. von der Malsburg and his associates, may account for many of the phenomena associated with face recognition. An additional refinement, in which each column of filters (termed a ‘jet’) is centered on a particular facial feature (or fiducial point), allows selectivity of the input into the holistic representation to avoid incorporation of occluding or nearby surfaces. The initial hypercolumn representation also characterizes the first stage of object perception, but the image variation for objects at a given location in a 2D coordinate space may be too great to yield sufficient predictability directly from the output of spatial kernels. Consequently, objects can be represented by a structural description specifying qualitative (typically, non–accidental) characterizations of an object's parts, the attributes of the parts, and the relations among the parts, largely based on orientation and depth discontinuities (as shown by Hummel and Biederman). A series of experiments on the name priming or physical matching of complementary images (in the Fourier domain) of objects and faces documents that whereas face recognition is strongly dependent on the original spatial filter values, evidence from object recognition indicates strong invariance to these values, even when distinguishing among objects that are as similar as faces.
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Gauthier, Isabel, Marlene Behrmann, and Michael J. Tarr. "Can Face Recognition Really be Dissociated from Object Recognition?" Journal of Cognitive Neuroscience 11, no. 4 (July 1999): 349–70. http://dx.doi.org/10.1162/089892999563472.

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We argue that the current literature on prosopagnosia fails to demonstrate unequivocal evidence for a disproportionate impairment for faces as compared to nonface objects. Two prosopagnosic subjects were tested for the discrimination of objects from several categories (face as well as nonface) at different levels of categorization (basic, subordinate, and exemplar levels). Several dependent measures were obtained including accuracy, signal detection measures, and response times. The results from Experiments 1 to 4 demonstrate that, in simultaneous-matching tasks, response times may reveal impairments with nonface objects in subjects whose error rates only indicate a face deficit. The results from Experiments 5 and 6 show that, given limited stimulus presentation times for face and nonface objects, the same subjects may demonstrate a deªcit for both stimulus categories in sensitivity. In Experiments 7, 8 and 9, a match-to-sample task that places greater demands on memory led to comparable recognition sensitivity with both face and nonface objects. Regardless of object category, the prosopagnosic subjects were more affected by manipulations of the level of categorization than normal controls. This result raises questions regarding neuropsychological evidence for the modularity of face recognition, as well as its theoretical and methodological foundations.
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Campbell, Alison, and James W. Tanaka. "Inversion Impairs Expert Budgerigar Identity Recognition: A Face-Like Effect for a Nonface Object of Expertise." Perception 47, no. 6 (April 24, 2018): 647–59. http://dx.doi.org/10.1177/0301006618771806.

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The face-inversion effect is the finding that picture-plane inversion disproportionately impairs face recognition compared to object recognition and is now attributed to greater orientation-sensitivity of holistic processing for faces but not common objects. Yet, expert dog judges have showed similar recognition deficits for inverted dogs and inverted faces, suggesting that holistic processing is not specific to faces but to the expert recognition of perceptually similar objects. Although processing changes in expert object recognition have since been extensively documented, no other studies have observed the distinct recognition deficits for inverted objects-of-expertise that people as face experts show for faces. However, few studies have examined experts who recognize individual objects similar to how people recognize individual faces. Here we tested experts who recognize individual budgerigar birds. The effect of inversion on viewpoint-invariant budgerigar and face recognition was compared for experts and novices. Consistent with the face-inversion effect, novices showed recognition deficits for inverted faces but not for inverted budgerigars. By contrast, experts showed equal recognition deficits for inverted faces and budgerigars. The results are consistent with the hypothesis that processes underlying the face-inversion effect are specific to the expert individuation of perceptually similar objects.
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Moscovitch, Morris, Gordon Winocur, and Marlene Behrmann. "What Is Special about Face Recognition? Nineteen Experiments on a Person with Visual Object Agnosia and Dyslexia but Normal Face Recognition." Journal of Cognitive Neuroscience 9, no. 5 (October 1997): 555–604. http://dx.doi.org/10.1162/jocn.1997.9.5.555.

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In order to study face recognition in relative isolation from visual processes that may also contribute to object recognition and reading, we investigated CK, a man with normal face recognition but with object agnosia and dyslexia caused by a closed-head injury. We administered recognition tests of up right faces, of family resemblance, of age-transformed faces, of caricatures, of cartoons, of inverted faces, and of face features, of disguised faces, of perceptually degraded faces, of fractured faces, of faces parts, and of faces whose parts were made of objects. We compared CK's performance with that of at least 12 control participants. We found that CK performed as well as controls as long as the face was upright and retained the configurational integrity among the internal facial features, the eyes, nose, and mouth. This held regardless of whether the face was disguised or degraded and whether the face was represented as a photo, a caricature, a cartoon, or a face composed of objects. In the last case, CK perceived the face but, unlike controls, was rarely aware that it was composed of objects. When the face, or just the internal features, were inverted or when the configurational gestalt was broken by fracturing the face or misaligning the top and bottom halves, CK's performance suffered far more than that of controls. We conclude that face recognition normally depends on two systems: (1) a holistic, face-specific system that is dependent on orientationspecific coding of second-order relational features (internal), which is intact in CK and (2) a part-based object-recognition system, which is damaged in CK and which contributes to face recognition when the face stimulus does not satisfy the domain-specific conditions needed to activate the face system.
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McGugin, Rankin W., Ana E. Van Gulick, and Isabel Gauthier. "Cortical Thickness in Fusiform Face Area Predicts Face and Object Recognition Performance." Journal of Cognitive Neuroscience 28, no. 2 (February 2016): 282–94. http://dx.doi.org/10.1162/jocn_a_00891.

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The fusiform face area (FFA) is defined by its selectivity for faces. Several studies have shown that the response of FFA to nonface objects can predict behavioral performance for these objects. However, one possible account is that experts pay more attention to objects in their domain of expertise, driving signals up. Here, we show an effect of expertise with nonface objects in FFA that cannot be explained by differential attention to objects of expertise. We explore the relationship between cortical thickness of FFA and face and object recognition using the Cambridge Face Memory Test and Vanderbilt Expertise Test, respectively. We measured cortical thickness in functionally defined regions in a group of men who evidenced functional expertise effects for cars in FFA. Performance with faces and objects together accounted for approximately 40% of the variance in cortical thickness of several FFA patches. Whereas participants with a thicker FFA cortex performed better with vehicles, those with a thinner FFA cortex performed better with faces and living objects. The results point to a domain-general role of FFA in object perception and reveal an interesting double dissociation that does not contrast faces and objects but rather living and nonliving objects.
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Duchaine, Brad, and Ken Nakayama. "Dissociations of Face and Object Recognition in Developmental Prosopagnosia." Journal of Cognitive Neuroscience 17, no. 2 (February 2005): 249–61. http://dx.doi.org/10.1162/0898929053124857.

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Neuropsychological studies with patients suffering from prosopagnosia have provided the main evidence for the hypothesis that the recognition of faces and objects rely on distinct mechanisms. Yet doubts remain, and it has been argued that no case demonstrating an unequivocal dissociation between face and object recognition exists due in part to the lack of appropriate response time measurements (Gauthier et al., 1999). We tested seven developmental prosopagnosics to measure their accuracy and reaction times with multiple tests of face recognition and compared this with a larger battery of object recognition tests. For our systematic comparison, we used an old/new recognition memory paradigm involving memory tests for cars, tools, guns, horses, natural scenes, and houses in addition to two separate tests for faces. Developmental prosopagnosic subjects performed very poorly with the face memory tests as expected. Four of the seven prosopagnosics showed a very strong dissociation between the face and object tests. Systematic comparison of reaction time measurements for all tests indicates that the dissociations cannot be accounted for by differences in reaction times. Contrary to an account based on speed accuracy tradeoffs, prosopagnosics were systematically faster in nonface tests than in face tests. Thus, our findings demonstrate that face and nonface recognition can dissociate over a wide range of testing conditions. This is further support for the hypothesis that face and nonface recognition relies on separate mechanisms and that developmental prosopagnosia constitutes a disorder separate from developmental agnosia.
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Stevanović, Dušan. "OBJECT DETECTION USING VIOLA-JONES ALGORITHM." Knowledge International Journal 28, no. 4 (December 10, 2018): 1349–54. http://dx.doi.org/10.35120/kij28041349d.

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In this paper it has been described and applied method for detecting face and face parts in images using the Viola-Jones algorithm. The work is based on Computer Vision Systems, artificial intelligence that deals with the recognition of two-dimensional or three-dimensional objects. When Cascade Object Detector script is trained, multimedia content is assigned for recognition. In this work the content will be in the form of an image, where the program will have the task of recognizing the objects in the images, separating the parts of the images in the head area, and on each discovered face, separately mark the area around the eyes, nose and mouth.Algorithm for detection and recognition is based on scanning and analyzing front part of human head. Common usage of face detection and recognition can be find in biometry, photography, on autofocus option which is implemented in professional photo cameras or on smiling detectors (Keller, 2007). Marketing is also popular field where face detection and recognition can be used. For example, web cameras that are implemented in TVs, can detect every face in near area. Calculating different type of algorithms and parameters, based on sex, age, ethnicity, system can play precisely segmented television commercials and campaigns. Example of that kind of systems is OptimEyes. (Strasburger, 2013)In other words, every algorithm that has as its main goal to detect and recognize face from image, should give as a feedback information, is there any face and if answer is positive, where is its location on image. In order to achieve acceptable performances, algorithm should minimize false recognitions. These are the cases when the algorithm ignores and does not recognize the real object from the image, and vice versa, when the wrong object is recognized as real. One of the algorithms that is frequently applied in this area of research is the Viola-Jones algorithm. This algorithm is functional in real time, meaning that besides detection, it is also possible to adjust the ability to monitor faces from video material.In this paper, the problem that will be analyzed is facial image detection. Man can do this task in a very simple way, but to do the same with a computer, it is necessary to have a range of precise and accurate information, formulas, methods and techniques. In order to maximize the precision of recognizing the face of the image using the Viola-Jones algorithm, it is desirable that the objects in the images are completely face-to-face with the image-taking device, which will be shown through experiments.
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Yuille, Alan L. "Deformable Templates for Face Recognition." Journal of Cognitive Neuroscience 3, no. 1 (January 1991): 59–70. http://dx.doi.org/10.1162/jocn.1991.3.1.59.

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We describe an approach for extracting facial features from images and for determining the spatial organization between these features using the concept of a deformable template. This is a parameterized geometric model of the object to be recognized together with a measure of how well it fits the image data. Variations in the parameters correspond to allowable deformations of the object and can be specified by a probabilistic model. After the extraction stage the parameters of the deformable template can be used for object description and recognition.
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Jiang, Hairong, Juan P. Wachs, and Bradley S. Duerstock. "Integrated vision-based system for efficient, semi-automated control of a robotic manipulator." International Journal of Intelligent Computing and Cybernetics 7, no. 3 (August 5, 2014): 253–66. http://dx.doi.org/10.1108/ijicc-09-2013-0042.

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Purpose – The purpose of this paper is to develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient, hands-free WMRM controller. Design/methodology/approach – Two Kinect® cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the operator's face for object positioning, and then send those as commands to control the WMRM. The other sensor was used to automatically recognize different daily living objects selected by the subjects. An object recognition module employing the Speeded Up Robust Features algorithm was implemented and recognition results were sent as a commands for “coarse positioning” of the robotic arm near the selected object. Automatic face detection was provided as a shortcut enabling the positing of the objects close by the subject's face. Findings – The gesture recognition interface incorporated hand detection, tracking and recognition algorithms, and yielded a recognition accuracy of 97.5 percent for an eight-gesture lexicon. Tasks’ completion time were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection, and object recognition) WMRM control modes. The use of automatic face and object detection significantly reduced the completion times for retrieving a variety of daily living objects. Originality/value – Integration of three computer vision modules were used to construct an effective and hand-free interface for individuals with upper-limb mobility impairments to control a WMRM.
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Dissertations / Theses on the topic "Face and Object Recognition"

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Gathers, Ann D. "DEVELOPMENTAL FMRI STUDY: FACE AND OBJECT RECOGNITION." Lexington, Ky. : [University of Kentucky Libraries], 2005. http://lib.uky.edu/ETD/ukyanne2005d00276/etd.pdf.

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Thesis (Ph. D.)--University of Kentucky, 2005.
Title from document title page (viewed on November 4, 2005). Document formatted into pages; contains xi, 152 p. : ill. Includes abstract and vita. Includes bibliographical references (p. 134-148).
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Nilsson, Linus. "Object Tracking and Face Recognition in Video Streams." Thesis, Umeå universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-58076.

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The goal with this project was to improve an existing face recognition system for video streams by using adaptive object tracking to track faces between frames. The knowledge of what faces occur and do not occur in subsequent frames was used to filter false faces and to better identify real ones. The recognition ability was tested by measuring how many faces were found and how many of them were correctly identified in two short video files. The tests also looked at the number of false face detections. The results were compared to a reference implementation that did not use object tracking. Two identification modes were tested: the default and strict modes. In the default mode, whichever person is most similar to a given image patch is accepted as the answer. In strict mode, the similarity also has to be above a certain threshold. The first video file had a fairly high image quality. It had only frontal faces, one at a time. The second video file had a slightly lower image quality. It had up to two faces at a time, in a larger variety of angles. The second video was therefore a more difficult case. The results show that the number of detected faces increased by 6-21% in the two video files, for both identification modes, compared to the reference implementation. In the meantime, the number of false detections remained low. In the first video file, there were fewer than 0.009 false detections per frame. In the second video file, there were fewer than 0.08 false detections per frame. The number of faces that were correctly identified increased by 8-22% in the two video files in default mode. In the first video file, there was also a large improvement in strict mode, as it went from recognising 13% to 85% of all faces. In the second video file, however,neither implementation managed to identify anyone in strict mode. The conclusion is that object tracking is a good tool for improving the accuracy of face recognition in video streams. Anyone implementing face recognition for video streams should consider using object tracking as a central component.
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Banarse, D. S. "A generic neural network architecture for deformation invariant object recognition." Thesis, Bangor University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362146.

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Collin, Charles Alain. "Effects of spatial frequency overlap on face and object recognition." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=36896.

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There has recently been much interest in how limitations in spatial frequency range affect face and object perception. This work has mainly focussed on determining which bands of frequencies are most useful for visual recognition. However, a fundamental question not yet addressed is how spatial frequency overlap (i.e., the range of spatial frequencies shared by two images) affects complex image recognition. Aside from the basic theoretical interest this question holds, it also bears on research about effects of display format (e.g., line-drawings, Mooney faces, etc.) and studies examining the nature of mnemonic representations of faces and objects. Examining the effects of spatial frequency overlap on face and object recognition is the main goal of this thesis.
A second question that is examined concerns the effect of calibration of stimuli on recognition of spatially filtered images. Past studies using non-calibrated presentation methods have inadvertently introduced aberrant frequency content to their stimuli. The effect this has on recognition performance has not been examined, leading to doubts about the comparability of older and newer studies. Examining the impact of calibration on recognition is an ancillary goal of this dissertation.
Seven experiments examining the above questions are reported here. Results suggest that spatial frequency overlap had a strong effect on face recognition and a lesser effect on object recognition. Indeed, contrary to much previous research it was found that the band of frequencies occupied by a face image had little effect on recognition, but that small variations in overlap had significant effects. This suggests that the overlap factor is important in understanding various phenomena in visual recognition. Overlap effects likely contribute to the apparent superiority of certain spatial bands for different recognition tasks, and to the inferiority of line drawings in face recognition. Results concerning the mnemonic representation of faces and objects suggest that these are both encoded in a format that retains spatial frequency information, and do not support certain proposed fundamental differences in how these two stimulus classes are stored. Data on calibration generally shows non-calibration having little impact on visual recognition, suggesting moderate confidence in results of older studies.
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Higgs, David Robert. "Parts-based object detection using multiple views /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1000.

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Mian, Ajmal Saeed. "Representations and matching techniques for 3D free-form object and face recognition." University of Western Australia. School of Computer Science and Software Engineering, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0046.

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[Truncated abstract] The aim of visual recognition is to identify objects in a scene and estimate their pose. Object recognition from 2D images is sensitive to illumination, pose, clutter and occlusions. Object recognition from range data on the other hand does not suffer from these limitations. An important paradigm of recognition is model-based whereby 3D models of objects are constructed offline and saved in a database, using a suitable representation. During online recognition, a similar representation of a scene is matched with the database for recognizing objects present in the scene . . . The tensor representation is extended to automatic and pose invariant 3D face recognition. As the face is a non-rigid object, expressions can significantly change its 3D shape. Therefore, the last part of this thesis investigates representations and matching techniques for automatic 3D face recognition which are robust to facial expressions. A number of novelties are proposed in this area along with their extensive experimental validation using the largest available 3D face database. These novelties include a region-based matching algorithm for 3D face recognition, a 2D and 3D multimodal hybrid face recognition algorithm, fully automatic 3D nose ridge detection, fully automatic normalization of 3D and 2D faces, a low cost rejection classifier based on a novel Spherical Face Representation, and finally, automatic segmentation of the expression insensitive regions of a face.
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Mian, Ajmal Saeed. "Representations and matching techniques for 3D free-form object and face recognition /." Connect to this title, 2006. http://theses.library.uwa.edu.au/adt-WU2007.0046.

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Holub, Alex David Perona Pietro. "Discriminative vs. generative object recognition : objects, faces, and the web /." Diss., Pasadena, Calif. : California Institute of Technology, 2007. http://resolver.caltech.edu/CaltechETD:etd-05312007-204007.

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Vilaplana, Besler Verónica. "Region-based face detection, segmentation and tracking. framework definition and application to other objects." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/33330.

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One of the central problems in computer vision is the automatic recognition of object classes. In particular, the detection of the class of human faces is a problem that generates special interest due to the large number of applications that require face detection as a first step. In this thesis we approach the problem of face detection as a joint detection and segmentation problem, in order to precisely localize faces with pixel accurate masks. Even though this is our primary goal, in finding a solution we have tried to create a general framework as independent as possible of the type of object being searched. For that purpose, the technique relies on a hierarchical region-based image model, the Binary Partition Tree, where objects are obtained by the union of regions in an image partition. In this work, this model is optimized for the face detection and segmentation tasks. Different merging and stopping criteria are proposed and compared through a large set of experiments. In the proposed system the intra-class variability of faces is managed within a learning framework. The face class is characterized using a set of descriptors measured on the tree nodes, and a set of one-class classifiers. The system is formed by two strong classifiers. First, a cascade of binary classifiers simplifies the search space, and afterwards, an ensemble of more complex classifiers performs the final classification of the tree nodes. The system is extensively tested on different face data sets, producing accurate segmentations and proving to be quite robust to variations in scale, position, orientation, lighting conditions and background complexity. We show that the technique proposed for faces can be easily adapted to detect other object classes. Since the construction of the image model does not depend on any object class, different objects can be detected and segmented using the appropriate object model on the same image model. New object models can be easily built by selecting and training a suitable set of descriptors and classifiers. Finally, a tracking mechanism is proposed. It combines the efficiency of the mean-shift algorithm with the use of regions to track and segment faces through a video sequence, where both the face and the camera may move. The method is extended to deal with other deformable objects, using a region-based graph-cut method for the final object segmentation at each frame. Experiments show that both mean-shift based trackers produce accurate segmentations even in difficult scenarios such as those with similar object and background colors and fast camera and object movements. Lloc i
Un dels problemes més importants en l'àrea de visió artificial és el reconeixement automàtic de classes d'objectes. En particular, la detecció de la classe de cares humanes és un problema que genera especial interès degut al gran nombre d'aplicacions que requereixen com a primer pas detectar les cares a l'escena. A aquesta tesis s'analitza el problema de detecció de cares com un problema conjunt de detecció i segmentació, per tal de localitzar de manera precisa les cares a l'escena amb màscares que arribin a precisions d'un píxel. Malgrat l'objectiu principal de la tesi és aquest, en el procés de trobar una solució s'ha intentat crear un marc de treball general i tan independent com fos possible del tipus d'objecte que s'està buscant. Amb aquest propòsit, la tècnica proposada fa ús d'un model jeràrquic d'imatge basat en regions, l'arbre binari de particions (BPT: Binary Partition Tree), en el qual els objectes s'obtenen com a unió de regions que provenen d'una partició de la imatge. En aquest treball, s'ha optimitzat el model per a les tasques de detecció i segmentació de cares. Per això, es proposen diferents criteris de fusió i de parada, els quals es comparen en un conjunt ampli d'experiments. En el sistema proposat, la variabilitat dins de la classe cara s'estudia dins d'un marc de treball d'aprenentatge automàtic. La classe cara es caracteritza fent servir un conjunt de descriptors, que es mesuren en els nodes de l'arbre, així com un conjunt de classificadors d'una única classe. El sistema està format per dos classificadors forts. Primer s'utilitza una cascada de classificadors binaris que realitzen una simplificació de l'espai de cerca i, posteriorment, s'aplica un conjunt de classificadors més complexes que produeixen la classificació final dels nodes de l'arbre. El sistema es testeja de manera exhaustiva sobre diferents bases de dades de cares, sobre les quals s'obtenen segmentacions precises provant així la robustesa del sistema en front a variacions d'escala, posició, orientació, condicions d'il·luminació i complexitat del fons de l'escena. A aquesta tesi es mostra també que la tècnica proposada per cares pot ser fàcilment adaptable a la detecció i segmentació d'altres classes d'objectes. Donat que la construcció del model d'imatge no depèn de la classe d'objecte que es pretén buscar, es pot detectar i segmentar diferents classes d'objectes fent servir, sobre el mateix model d'imatge, el model d'objecte apropiat. Nous models d'objecte poden ser fàcilment construïts mitjançant la selecció i l'entrenament d'un conjunt adient de descriptors i classificadors. Finalment, es proposa un mecanisme de seguiment. Aquest mecanisme combina l'eficiència de l'algorisme mean-shift amb l'ús de regions per fer el seguiment i segmentar les cares al llarg d'una seqüència de vídeo a la qual tant la càmera com la cara es poden moure. Aquest mètode s'estén al cas de seguiment d'altres objectes deformables, utilitzant una versió basada en regions de la tècnica de graph-cut per obtenir la segmentació final de l'objecte a cada imatge. Els experiments realitzats mostren que les dues versions del sistema de seguiment basat en l'algorisme mean-shift produeixen segmentacions acurades, fins i tot en entorns complicats com ara quan l'objecte i el fons de l'escena presenten colors similars o quan es produeix un moviment ràpid, ja sigui de la càmera o de l'objecte.
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Gunn, Steve R. "Dual active contour models for image feature extraction." Thesis, University of Southampton, 1996. https://eprints.soton.ac.uk/250089/.

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Active contours are now a very popular technique for shape extraction, achieved by minimising a suitably formulated energy functional. Conventional active contour formulations suffer difficulty in appropriate choice of an initial contour and values of parameters. Recent approaches have aimed to resolve these problems, but can compromise other performance aspects. To relieve the problem in initialisation, an evolutionary dual active contour has been developed, which is combined with a local shape model to improve the parameterisation. One contour expands from inside the target feature, the other contracts from the outside. The two contours are inter-linked to provide a balanced technique with an ability to reject weak’local energy minima. Additionally a dual active contour configuration using dynamic programming has been developed to locate a global energy minimum and complements recent approaches via simulated annealing and genetic algorithms. These differ from conventional evolutionary approaches, where energy minimisation may not converge to extract the target shape, in contrast with the guaranteed convergence of a global approach. The new techniques are demonstrated to extract successfully target shapes in synthetic and real images, with superior performance to previous approaches. The new technique employing dynamic programming is deployed to extract the inner face boundary, along with a conventional normal-driven contour to extract the outer face boundary. Application to a database of 75 subjects showed that the outer contour was extracted successfully for 96% of the subjects and the inner contour was successful for 82%. This application highlights the advantages new dual active contour approaches for automatic shape extraction can confer.
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Books on the topic "Face and Object Recognition"

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Information routing, correspondence finding, and object recognition in the brain. Berlin: Springer-Verlag, 2010.

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Bennamoun, M., and G. J. Mamic. Object Recognition. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1.

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Grauman, Kristen, and Bastian Leibe. Visual Object Recognition. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3.

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Strat, Thomas M. Natural Object Recognition. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2932-2.

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Dawson, K. M. Object recognition techniques. Dublin: Trinity College, Department of Computer Science, 1991.

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Natural object recognition. New York: Springer-Verlag, 1992.

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Bastian, Leibe, ed. Visual object recognition. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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Wilkes, David. Active object recognition. Toronto: University of Toronto, 1994.

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Strat, Thomas M. Natural Object Recognition. New York, NY: Springer New York, 1992.

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Wechsler, Harry, P. Jonathon Phillips, Vicki Bruce, Françoise Fogelman Soulié, and Thomas S. Huang, eds. Face Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1.

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Book chapters on the topic "Face and Object Recognition"

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Biederman, Irving, and Peter Kalocsai. "Neural and Psychophysical Analysis of Object and Face Recognition." In Face Recognition, 3–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_1.

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Kalocsai, Peter, and Irving Biederman. "Differences of Face and Object Recognition in Utilizing Early Visual Information." In Face Recognition, 492–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_29.

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Griffin, Jason W., and Natalie V. Motta-Mena. "Face and Object Recognition." In Encyclopedia of Evolutionary Psychological Science, 1–8. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-16999-6_2762-1.

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Griffin, Jason W., and Natalie V. Motta-Mena. "Face and Object Recognition." In Encyclopedia of Evolutionary Psychological Science, 2876–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-19650-3_2762.

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Cootes, Timothy F., David Cristinacce, and Vladimir Petrović. "Statistical Models of Shape and Texture for Face Recognition." In Toward Category-Level Object Recognition, 525–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_27.

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Osadchy, Margarita, Yann Le Cun, and Matthew L. Miller. "Synergistic Face Detection and Pose Estimation with Energy-Based Models." In Toward Category-Level Object Recognition, 196–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_10.

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Li, Lei, and Xiaoyi Feng. "Face Anti-spoofing via Deep Local Binary Pattern." In Deep Learning in Object Detection and Recognition, 91–111. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4_4.

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Bashbaghi, Saman, Eric Granger, Robert Sabourin, and Mostafa Parchami. "Deep Learning Architectures for Face Recognition in Video Surveillance." In Deep Learning in Object Detection and Recognition, 133–54. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4_6.

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Kanan, Christopher, Arturo Flores, and Garrison W. Cottrell. "Color Constancy Algorithms for Object and Face Recognition." In Advances in Visual Computing, 199–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17289-2_20.

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Jiang, Xiaoyue, Yaping Hou, Dong Zhang, and Xiaoyi Feng. "Deep Learning in Face Recognition Across Variations in Pose and Illumination." In Deep Learning in Object Detection and Recognition, 59–90. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4_3.

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Conference papers on the topic "Face and Object Recognition"

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Zhang, Yuxuan, Chen Yang, and Qiaodan Zhao. "Face mask recognition based on object detection." In International Conference on Signal Image Processing and Communication (ICSIPC 2021), edited by Siting Chen and Wei Qin. SPIE, 2021. http://dx.doi.org/10.1117/12.2600460.

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Yamasaki, Toshihiko, and Tsuhan Chen. "Face Recognition Challenge: Object Recognition Approaches for Human/Avatar Classification." In 2012 Eleventh International Conference on Machine Learning and Applications (ICMLA). IEEE, 2012. http://dx.doi.org/10.1109/icmla.2012.188.

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Zhang, Lei, and Guo-Fang Tu. "Scalable reduced dimension face object segmentation and tracking." In Third International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Hanqing Lu and Tianxu Zhang. SPIE, 2003. http://dx.doi.org/10.1117/12.539029.

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Wu, Yiming, Xiuwen Liu, and Washington Mio. "Scalable optimal linear representation for face and object recognition." In Sixth International Conference on Machine Learning and Applications (ICMLA 2007). IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.110.

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Burt, Peter J. "Dynamic analysis strategies for real-time object recognition." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mee2.

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Real-time computer vision requires fast search strategies analogous to alerting and focal attention in humans. I describe a pyramid implementation of such techniques and illustrate its application to human face recognition.
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Fachrurrozi, Muhammad, Erwin, Saparudin, and Mardiana. "Multi-object face recognition using Content Based Image Retrieval (CBIR)." In 2017 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2017. http://dx.doi.org/10.1109/icecos.2017.8167132.

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Sanyal, Soubhik, Devraj Mandal, and Soma Biswas. "Aligned discriminative pose robust descriptors for face and object recognition." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296395.

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Meng Meng, Hassen Drira, Mohamed Daoudi, and Jacques Boonaert. "Human-object interaction recognition by learning the distances between the object and the skeleton joints." In 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). IEEE, 2015. http://dx.doi.org/10.1109/fg.2015.7284883.

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Tanaka, H. T., and M. Ikeda. "Curvature-based face surface recognition using spherical correlation-principal directions for curved object recognition." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.547024.

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Alzahrani, T., and W. Al-Nuaimy. "Face segmentation based object localisation with deep learning from unconstrained images." In 10th International Conference on Pattern Recognition Systems (ICPRS-2019). Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/cp.2019.0247.

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Reports on the topic "Face and Object Recognition"

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Wells, III, and William M. Statistical Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada270887.

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Socolinsky, Diego A., and Andrea Selinger. Thermal Face Recognition Over Time. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444423.

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Beymer, David J. Face Recognition Under Varying Pose. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada290205.

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Phillips, P. Jonathon, Patrick Grother, Ross J. Micheals, Duane M. Blackburn, Elham Tabassi, and Mike Bone. Face recognition vendor test 2002 :. Gaithersburg, MD: National Institute of Standards and Technology, 2003. http://dx.doi.org/10.6028/nist.ir.6965.

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Grother, Patrick. Face recognition vendor test 2002 :. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.ir.7083.

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Ngan, M., and P. Grother. Face Recognition Vendor Test (FRVT) :. Gaithersburg, MD: National Institute of Standards and Technology, 2014. http://dx.doi.org/10.6028/nist.ir.7995.

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Grother, Patrick, and Mei Ngan. Face Recognition Vendor Test (FRVT). Gaithersburg, MD: National Institute of Standards and Technology, 2014. http://dx.doi.org/10.6028/nist.ir.8009.

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Weiss, Isaac. Geometric Invariants and Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada255317.

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Mahmood, S. T., and Tanveer F. Syeda-Tanveer. Attentional Selection in Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada271004.

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Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45902.

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The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a dataset that consists of thermal images containing buried objects paired with the corresponding environmental and meteorological conditions, we employ a machine learning approach to determine which environmental conditions are the most impactful on the visibility of the buried objects. We find the key environmental conditions include incoming shortwave solar radiation, soil volumetric water content, and average air temperature. For each image, ROIs are computed using a computer vision approach and these ROIs are coupled with the most important environmental conditions to form the input for the classification algorithm. The environmentally informed classification algorithm produces a decision on whether the ROI contains a buried object by simultaneously learning on the ROIs with a classification neural network and on the environmental data using a tabular neural network. On a given set of ROIs, we have shown that the environmentally informed classification approach improves the detection of buried objects within the ROIs.
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