View 1 excerpt, cites background. View 2 excerpts, references background. A Bayesian similarity measure for direct image matching. Proceedings of 13th International Conference on Pattern Recognition. View 2 excerpts, references methods and background. Learning and recognition of 3D objects from appearance. Highly Influential. View 7 excerpts, references background and methods.
Human Face Detection in Visual Scenes. Surface Learning with Applications to Lipreading. View-based and modular eigenspaces for face recognition.
View 9 excerpts, references methods and background. Automatic location of visual features by a system of multilayered perceptrons. View 1 excerpt. In the past few years, modeling objects directly in 3D has become increasingly popular T HE merits of part-based and hierarchical approaches to object modeling have often been put forward in the vision community [1], [2], [3], [4], [5], [6], [7], [8], [11], [12], [13], [14], [15], [16].
The main advantage of these methods lies in their natural ability to handle projective transformations and self-occlusions. Part-based models typically separate structure The main contribution of this paper is a framework from appearance, which allows them to deal with vari- that encodes the 3D geometry and visual appearance of ability separately in each modality.
A hierarchy of parts an object into a part-based model, and mechanisms for takes this idea further, by introducing scale-dependent autonomous learning and probabilistic inference of the variability: small part configurations can be tightly con- model. Our representation combines local appearance strained, while wider associations can allow for more and 3D spatial relationships through a hierarchy of variability.
Furthermore, part-based models do not only increasingly expressive features. Features at the bottom allow for the detection and localization of an object, of the hierarchy are bound to local 3D visual perceptions but also parsing of its constituent parts. They lend called observations. Features at other levels represent themselves to part sharing and reuse, which should help combinations of more elementary features, encoding in overcoming the problem of storage size and detection probabilistic relative spatial relationships between their cost in large object databases.
Finally, these models not children. The top level of the hierarchy contains a single only allow for bottom-up inference of object parameters feature which represents the whole object. To detect A large body of the object modeling literature focuses instances of a model in a scene, observational evidence on modeling the 2D projections of a 3D object.
A major is propagated throughout the hierarchy by probabilistic issue with this approach is that all variations intro- inference mechanisms, leading to one or more consistent duced by projective geometry geometrical transforma- scene interpretations. Detry Renaud. Detry ULg.
The model is bound to Manuscript received 16 Aug. In this paper, we present ; published online 17 Mar. Ji, A. Torralba, T. Huang, E. Sudderth, and J. This is an author postprint. Learning and detection algorithms reason directly on For information on obtaining reprints of this article, please send e-mail to: sets of local 3D visual perceptions which we will re- tpami computer.
These observations should Digital Object Identifier no. These generally aim at classifying objects into generic categories, and put a strong accent on learning in order to capture intra-class variability. Intra-class variability is Early vision Learning very important for these methods also because it allows them to capture the affine transformations that the object undergoes when projected to 2D images.
Our learn- ing procedure is similar in spirit, although it requires much less sophistication. Since we work directly in 3D, Early vision Representation incl. Pose estimation system and projective deformations do not need to be learned. Compared to these 2D methods, the most distinguishing aspects of our approach are its explicit 3D support and points characterized by a 3D location, a local appearance, a unified probabilistic formalization through a Markov and possibly an orientation.
Prior research on model network. Articulated objects are often formalized From stereo imagery, this system extracts pixel patches through a graphical model, and probabilistic inference along image contours, and computes 3D patch positions algorithms such as belief propagation [29] or its variants and orientations by stereopsis across image pairs. The are widely used for detection [23], [24], [27], [28]. The resulting reconstruction consists of a set of short edge model topology, i.
Model parameters compatibility and observation ECV observations. Each ECV observation corresponds to potentials are also often defined by hand [10], [13], [24], a patch of about 25 square millimeters of object surface. An ECV reconstruction of a scene typically Despite the similar formalism, the issue addressed contains — observations.
The The task on which we evaluate our model is object work cited in the previous paragraph seeks unique pose estimation. Observations are provided by the ECV system. The learning algorithm builds a hierarchy from a set of In our work, a part is an abstract concept that may observations from a segmented object; the hierarchy is have any number of instances; a parent-child potential then used to recover the pose of the object in a cluttered thus encodes a one-to-many relationship, as a set of scene.
Preliminary results appeared in conference and relationships between the parent part and many child workshop proceedings [20], [21], [22]. We note that even part instances. Coughlan et al. On the right: an example of a hierarchy for the Other methods organize local appearance descriptors on traffic sign shown in the bottom-left corner.
X1 through a 3D shape model, obtained by CAD [16] or 3D homog- X3 are primitive features; each of these is linked to an raphy [14]. To detect an object in an image, these meth- observed variable Yi. X4 through X6 are meta-features. Our object model consists of a set of generic features Rothganger et al. Features that form the bottom results, although it seems obvious that a 3D pose is level of the hierarchy, referred to as primitive features, are implicitly computed during detection.
The rest of the features are meta-features which embody relative spatial config- Instead of using a precise 3D shape model, objects urations of more elementary features, either meta or have also successfully been represented by a set of primitive. In this context, a part is a of an object, i. At the bottom of the hierarchy, primitive Compared to our approach, the preceding 3D methods features correspond to local parts that each may have represent an object as a whole, and do not intrinsically al- many instances in the object.
Climbing up the hierarchy, low for parsing an object into parts. Another distinguish- meta-features correspond to increasingly complex parts ing aspect of our work is its probabilistic formalization defined in terms of constellations of lower parts. Even- through a graphical model. Finally, the 3D methods cited tually, parts become complex enough to satisfactorily above work with image data, and encompass the entire represent the whole object. In this paper, a primitive reasoning process from image pixels to object pose.
In feature represents a class of ECV observations of similar our case, a large part of the work is done by the upstream appearance, e. Given the large number of observations system, or range scanning. This allows us to concentrate produced by the ECV system, a primitive feature will on the encoding of 3D geometry and appearance, and usually have hundreds of instances in a scene.
Ignoring the nodes labeled Yi for now, the figure The performance of our system is strongly influ- shows the traffic sign as the combination of two features: enced by the characteristics of the 3D observations we a triangular frame feature 5 and a bridge pattern use. While the 3D methods cited above rely on affine- feature 4.
The fact that the bridge pattern has to be invariant descriptors, for which textured objects are in the center of the triangle to form the traffic sign is ideal, the ECV system used in this paper extracts surface encoded in the links between features The trian- edges, and thus strongly prefers objects with clear edges gular frame is further encoded using a single generic and little texture.
The For prior work on top-down parsing of scenes and link between feature 3 and feature 5 encodes the fact that objects, we refer the reader to the work of Lee and many short red-white edge segments several hundreds Mumford [5], and of Tu et al.
We note that our of instances of feature 3, i. Con- the activation of a single feature e. The input observations that hold an appearance descriptor lower-level the feature, the larger generally the number resembling the codebook of Yi.
An observation potential of instances. The next section explains how instances are represented Model instantiation is the process of detecting instances as one spatial probability density per feature, therefore of an object model in a scene.
It provides pose densi- avoiding specific model-to-scene correspondences. Features correspond to hidden nodes of and the algorithms that make use of the model. Funda- the network. When a model is associated to a scene mentally, instantiation involves two operations: during learning or instantiation , the pose of feature i in 1 Define priors observation potentials from input that scene will be represented by the probability density observations; function of a random variable Xi , effectively linking 2 Propagate this information through the graph us- feature i to its instances.
Random variables are thus ing an applicable inference algorithm. As noted above, a meta- of observations that hold an appearance descriptor that feature encodes the relationship between its children, is close enough, in the appearance space, to the codebook which is done by recording the relative relationships vector associated to Yi.
The The inference algorithm we use to propagate informa- relationship between a meta-feature i and one of its tion is currently the belief propagation BP algorithm children j is parametrized by a compatibility potential [29], [34], [35], discussed in Section 5.
Each configuration of feature i and feature j, the likelihood of message carries the belief that the sending node has finding these two features in that relative configuration. Let us consider, The potential between i and j will be denoted equiv- for example, nodes X3 and X5 in the network of Fig. We only consider Through the message that X3 sends to X5 , X3 probabilis- rigid-body, relative spatial configurations.
Through this exchange pose of the parent feature; a potential can be represented of messages, each feature probabilistically votes for all by a probability density defined on SE 3. During inference, a consensus emerges among variable Yi. Observed variables are tagged with an ap- the available evidence, leading to one or more consis- pearance descriptor, called a codebook vector, that defines tent scene interpretations.
The system never commits to a class of observation appearance. In the case of ECV specific feature correspondences, and is thus robust to observations, a codebook vector will be composed of two substantial clutter and occlusions. After inference, the colors. The set of all codebook vectors forms a codebook pose likelihood of the whole object can be read out of that binds the object model to feature observations. The the top feature; if the object is present twice in a scene, statistical dependency between a hidden variable Xi and the top feature density should present two major modes.
We generally cannot observe meta-features; their observation potentials are thus uniform. Further- of density functions, for both random variables and more, the observations we are dealing with reflect di- potentials. Formally, a density is represented by a set of weighted Hidden Node Feature samples called particles. The probabilistic density in a Observed Node region of space is given by the local density of the par- Codebook Vector X6 ticles in that region.
The continuous density function is Classification accessed by assigning a kernel function to each particle, a technique generally known as kernel density estimation [36]. Evaluation is performed by summing the evaluation Learned of all kernels.
Compared to traditional parametric methods, the non- parametric approach eliminates problems such as fitting of mixtures or the choice of a number of components. Active learning aims to reduce labeling costs by selecting only the most informative samples on a dataset. Few existing works have addressed active learning for object detection.
Most of these methods are based on multiple models or are straightforward extensions of classification methods, hence estimate an image's informativeness using only the classification head. In humans this process is not based purely on bottom-up processing and is Documents: Advanced Search Include Citations. Authors: Advanced Search Include Citations.
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