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Topic: Object recognition

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	Recent Publications on Object Recognition
		Object recognition and Barnes maze performance were significantly impaired in both H1 receptor gene knockout (H1KO) and H2 receptor gene knockout (H2KO) mice when compared to the respective wild-type (WT) mice.
		To investigate the developmental aspects of object recognition and lexical access in children, a large-scale functional MRI (fMRI) study was performed in 283 normal children ages 5-18 using a word-picture matching paradigm in which children would match an aurally presented noun to one of two pictures (line drawings).
		This study shows that it may be possible to account for object recognition impairments after damage to perirhinal cortex within a hierarchical, representational framework, in which complex conjunctive representations in perirhinal cortex play a critical role.
	www.seeingwithsound.com /newpubs/object_recognition/cached.html (4064 words)

	Object Recognition
		The basic idea is to represent the visual appearance of an object as a loosely structured combination of a number of local context regions keyed by distinctive key features, or fragments.
		The basic recognition strategy is to utilize a database (here viewed as an associative memory) of key features embedded in local contexts, which is organized so that access via an unknown key feature evokes associated hypotheses for the identity and configuration of all known objects that could have produced such an embedded feature.
		To find an object of known characteristics in a scene, that is to answer the question of the form "where is the dog in this image?", the same procedure is followed, except that key feature matches are filtered on the basis of whether the came from a view of a dog.
	www.cs.rochester.edu /u/nelson/research/recognition/recognition.html (1646 words)

	Object Recognition
		Object recognition in humans is largely invariant with regard to changes in the size, position, and viewpoint of the object.
		Because the size of an object, such as the sailboat, does not change the structural description of an object (the geons and their spatial organization), RBC predicts that recognition should be size invariant.
		Successful recognition of objects by pigeons, despite changes in size, further suggests that the mechanism of object recognition in the pigeon is similar to the mechanism of object recognition in humans.
	www.pigeon.psy.tufts.edu /avc/kirkpatrick/default.htm (2132 words)

	Object Recognition
		Although objects are generally not flat in the large, if we take a small enough region of the surface, we can treat it as flat.
		The normalized residual of the factorization gives a measure of how rigidly connected the set of patches is. The multiview constraint says that a single set of 3D patch information and a single set of projection matrices should explain all the measured S matrices if the set of patches is indeed rigidly connected.
		Recognition is similar to the beginning stages of modeling.
	www-cvr.ai.uiuc.edu /~rothgang/research/recognition (1687 words)

	Object Recognition
		Recognition of wire-frame objects is more difficult when they are presented in novel orientations (e.g., Edelman and Bülthoff, 1992), whereas recognition of common objects is good regardless of orientation (e.g., Biederman and Gerhardstein, 1993).
		Others have proposed that common objects are recognized easily regardless of viewpoint because we have had a great deal of exposure to them and have become familiar with their appearance in different orientations.
		This result resembles the Experiment II result and suggests that the difference in recognition in that experiment at both 0 degrees and 45 degrees was at least in part a consequence of the features that the subjects used to recognize the objects.
	aris.ss.uci.edu /wgrad/personnel/liter/thesis.html (1638 words)

	[No title]
		There exists a process that, given an image in which the target object o occurs, generates efficiently ``explanations'' in M that are present in the image and such that, with high probability, at least one of them is in the representation of o.
		Table 1 shows the recognition rates of nearest neighbor classifiers in several experiments in which 36 poses of each object are used for templates and the remaining 36 poses are used for tests.
		clear that object recognition in isolation is not the ultimate goal, this study shows the potential of this computational approach as a basis for studying and supporting more realistic visual inferences.
	vision.ai.uiuc.edu /mhyang/object-recognition.html (2877 words)

	Models of object recognition - Nature Neuroscience (Site not responding. Last check: 2007-10-09)
		There is no need then to collect examples of one object or object class at all positions in the image to be able to generalize across positions from a single view.
		For instance, objects sharing a similar 3D structure, such as faces, would be expected to be represented in terms of a sparse population code, as activity in a small group of cells tuned to prototypes of the class.
		Objects that do not belong to such a class (paperclips) should need to be represented for unique identification in terms of a more punctate representation, similar to a look-up table and requiring, in the extreme limit, the activity of a single 'grandmother' cell.
	www.nature.com /cgi-taf/DynaPage.taf?file=/neuro/journal/v3/n11s/full/nn1100_1199.html (4724 words)

	Object Recognition
		Three dimensional object recognition is the identification of a model structure with a set of image data, such that geometrically consistent model-to-data correspondences are established and the object's three dimensional scene position is known.
		Hence, the output of recognition is a set of fully instantiated or explained object hypotheses positioned in three dimensions, which are suitable for reconstructing the object's appearance.
		These objects were viewed in semi-cluttered laboratory scenes that contained both obscured and unobscured views (example in the next section).
	homepages.inf.ed.ac.uk /rbf/BOOKS/FSTO/node3.html (1282 words)

	Object recognition
		The problem in object recognition is to determine which, if any, of a given set of objects appear in a given image or image sequence.
		Thus object recognition is a problem of matching models from a database with representations of those models extracted from the image luminance data.
		Thus object recognition is a process of hypothesizing an object-to-model correspondence and then verifying that the hypothesis is correct.
	homepages.inf.ed.ac.uk /rbf/CVonline/LOCAL_COPIES/OWENS/LECT13/node5.html (932 words)

	Object Recognition and Reconstruction (Site not responding. Last check: 2007-10-09)
		Object recognition is the subfield of computer vision whose goal is to recognize objects from image data and, often, to estimate the positions and orientations of the recognized objects in the 3D world.
		Object reconstruction refers to the construction of 3D object models from image or range data.
		We are developing symbolic 3D object models that can be used in several different applications, including modeling the organs of the human body and modeling man-made objects for use in augmented reality environments.
	www.cs.washington.edu /homes/shapiro/objectrec.html (232 words)

	Object recognition system and method (US6069696)
		The weight of the object can be measured with a scale, and the density of the object calculated, with the weight and density being used by the pattern recognizer to further classify the object.
		The object recognition system may be integrated in a single unit along with an optical code reader, and may share all or part of the same exit aperture therewith.
		The object recognition system may include thermal detection or a particle source and secondary emission detection device, either alone or in conjunction with other object recognition means.
	www.delphion.com /details?pn10=US06069696 (597 words)

	Object Recognition System (Site not responding. Last check: 2007-10-09)
		Since the video camera is aligned with the line of sight, by gazing at interesting objects, the user directs the input to the recognition system which tries to recognize previously recorded objects.
		A major result of their work is that a statistical representation based on local object descriptors provides a reliable means for the representation and recognition of object appearances.
		Objects are represented by multidimensional histograms of vector responses from local neighborhood operators.
	www1.cs.columbia.edu /~jebara/htmlpapers/DyPERSTR/node5.html (627 words)

	Probabilistic Object Recognition:
		In the context of probabilistic object recognition we are interested in the calculation of the probability of the object O
		A visible object portion of approximately 62% is sufficient for the recognition of all 1327 test images (the same result is provided by histogram matching).
		Using 13.5% of the object the recognition rate is still above 90%.
	web.media.mit.edu /~testarne/TR465/node9.html (365 words)

	Object Recognition in Image Processing
		The hope is that after reading and reviewing this website that the reader will have a good foundation in the basic techniques of object recognition and that the reader could, with some additional directed research, implement simple object recognition software.
		With this basic understanding of object detection we found some basic techniques that could be used for detecting objects in a scene.
		While these objects do not have as strong a match as the tanks themselves (as can be seen by the relative whiteness of the pixels at those locations), they could be mistakenly identified as tanks.
	www.public.iastate.edu /~knutzonj/ee424projectMain.htm (2240 words)

	Objectives
		The ability to recognize living creatures and inanimate objects in photographs or video clips is a critical enabling technology for a wide range of applications including defense, health care, human-computer interaction, image retrieval and data mining, industrial and personal robotics, manufacturing, scientific image analysis, space exploration, surveillance and security, and transportation.
		The tenet of this workshop is that fundamental new advances in automated object recognition can be achieved by integrating the sophisticated geometric and physical image formation models developed in the computer vision community with the effective models of data distribution and classification procedures developed in the statistical learning theory and theoretical computer science communities.
		Its goals are (1) to promote the creation of an international object recognition community, with common datasets and evaluation procedures, (2) to map the state of the art and identify the main open problems and opportunities for synergistic research, and (3) to articulate the industrial and societal needs and opportunities for object recognition research worldwide.
	vasc.ri.cmu.edu /~hebert/04workshop/workshop.html (532 words)

	RVL: Object Recognition Project (Site not responding. Last check: 2007-10-09)
		We have investigated the 3D object recognition using both structured light sensors for 3D map generation and conventional TV camera sensors for stereo vision.
		The laser-illuminated images are used to compute a depth map of the scene (as in the Tube Recognition System.) In addition, at each laser-striped pixel location, the corresponding pixel in the white-light-illuminated image is sampled to determine the RGB color triplet at that point in the scene.
		For such objects in bins, various factors exacerbate the complexity of object recognition and pose calculation, the principal factor being the distortion of object appearance caused by occlusions and exposed background features.
	cobweb.ecn.purdue.edu /RVL/OLD_1/object-recognition/object-recognition.new.html (1353 words)

	Journal of Vision - Object recognition by scene alignment, by Torralba, Oliva, & Freeman
		Traditional approaches in object detection and recognition in computer vision consider an image as a collection of patches or regions that have to be classified.
		We show that scene features (obtained by pooling low-level features across the whole image) can be use to prime the presence/absence of objects in the scene and to predict their location, scale and appearance before exploring the image.
		Torralba, A., Oliva, A., and Freeman, W. Object recognition by scene alignment [Abstract].
	journalofvision.org /3/9/196 (280 words)

	Object Class Recognition by Unsupervised Scale-Invariant Learning - Fergus, Perona, Zisserman (ResearchIndex) (Site not responding. Last check: 2007-10-09)
		Objects' are modeled as' flexible constellations of parts.
		A probabilistic representation is' used for all aspects of the object: shape, appearance, occlusion and relative scale.
		In learning the parameters' of the scale-invariant object model are estimated....
	citeseer.ist.psu.edu /fergus03object.html (567 words)

	Object and Concept Recognition for Content-Based Image Retrieval (Site not responding. Last check: 2007-10-09)
		Automatic object recognition is needed, but most successful computer vision object recognition systems can only handle particular objects, such as industrial parts, that can be represented by precise geometric models.
		The goal of this research is to develop the necessary methodology for automated recognition of generic object and concept classes in digital images.
		The results of this work will be a new generic object recognition paradigm that can immediately be applied to automated or semi-automated indexing of large image databases and will be a step forward in object recognition.
	www.cs.washington.edu /research/imagedatabase/index.html (387 words)

	Computer vision - Wikipedia, the free encyclopedia
		Finally, pattern recognition is a field which uses various methods to extract information from signals in general, mainly based on statistical approaches.
		The existing methods for dealing with this problem can at best solve it only for specific objects, such as simple geometric objects (e.g., polyhedrons), human faces, printed or hand-written characters, or vehicles, and in specific situations, typically described in terms of well-defined illumination, background, and pose of the object relative to the camera.
		Recognition: one or several pre-specified or learned objects or object classes can be recognized, usually together with their 2D positions in the image or 3D poses in the scene.
	en.wikipedia.org /wiki/Object_recognition (2927 words)

	DLR Institut für Robotik und Mechatronik - Object Recognition and Scene Analysis (Site not responding. Last check: 2007-10-09)
		A scene may be constrained to feature only specific objects from an a priori known set, arbitrary objects from certain abstract classes, or may even contain completely unknown objects.
		Model-based object recognition or, more generally, scene interpretation may be conceptualized as a two-part process: one that generates a sequence of hypotheses on object identities and poses, the other that evaluates them based on the object models.
		Suppose we constrain objects from a known set to stand in certain `upright' poses on a supporting plane, for instance, chairs and tables on the floor, or glasses and bottles on a table.
	www.dlr.de /rm/en/Desktopdefault.aspx/tabid-440 (366 words)

	Journal of Vision - Modeling contextual influences on object recognition, by Torralba, Sinha, & Oliva
		The drawback of this conceptualization is that it renders the complexity of context analysis to be at par with the problem of individual object recognition.
		This approach is algorithmically attractive since it dispenses with the need for a prior step of individual object recognition.
		Given a novel image, the scheme is able to indicate where in the image a particular object is likely to be found and at what scale.
	www.journalofvision.org /1/3/299 (336 words)

	Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary (Site not responding. Last check: 2007-10-09)
		Pinar Duygulu, Kobus Barnard, Nando de Freitas, and David Forsyth, "Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary", Seventh European Conference on Computer Vision, pp IV:97-112, 2002 (Awarded ECVision best paper in cognitive computer vision).
		We describe a model of object recognition as machine translation.
		In this model, recognition is a process of annotating image regions with words.
	kobus.ca /research/publications/ECCV-02-1/index.html (298 words)

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