Part-level object recognition using superquadrics

This paper proposes a technique for object recognition using superquadric built models. Superquadrics, which are three-dimensional models suitable for part-level representation of objects, are reconstructed from range images using the recover-and-select paradigm. Using interpretation trees, the presence of an object from the model database can be hypothesized. These hypotheses are verified by projecting and re-fitting the object model to the range image of the scene which at the same time enables a better localization of the object in the scene

Računalniški vid nekdaj in danes

Podan bo pregled razvoja računalniškega vida od 70-tih let prejšnjega stoletja do danes. Računalniški vid je precej heterogena znanstvena disciplina, za katero ne obstaja niti standardna definicija, niti standardna formulacija, kako naj se rešuje probleme na tem področju. Obstaja cela vrsta specifičnih metod za reševanje ozko definiranih nalog, ki se jih težko generalizira na širši krog aplikacij. Mnogo nogo metod in aplikacij je še na stopnji bazičnih raziskav, toda mnogo metod se na drugi strani že uporablja v komercialnih produktih. Razloženo bo, kako je računalniški vid povezan z drugimi disciplinami kot so umetna inteligenca, fizika, biologija ozir oziroma psihologija, procesiranjem slik in računalniško grafiko. Računalniški vid je bil zaradi visokih zahtev po procesorski in spominski kapaciteti nekdaj rezerviran le za področja, ki so zmogla visoke vložke (vojaške, medicinske, industrijske aplikacije). Danes pa je možno zaradi višjih in cenejših procesorskih kapacitet ter cenenih vizualnih senzorjev metode računalniškega vida implementirati že na namiznih računalnikih. Zato je spekter problemov, ki se ga danes lotevamo z metodami računalniškega vida veliko širši in bogatejši. Zaradi večje zmogljivosti računalnikov se razvijajo metode za interpretacijo video sekvenc in metod, ki delajo v realnem času. Tipične naloge računalniškega vida kot so razpoznavanje in sledenje objektov ter interpretacija scen se danes ne uporabljajo le na tradicionalnih področjih uporabe, na primer v industriji in medicini, temveč segajo danes tudi na tako različna področja kot sta arheologija na eni in zagotavljanje varnosti in nadzor na drugi strani

The role of surface-based representations of shape in visual object recognition

This study contrasted the role of surfaces and volumetric shape primitives in three-dimensional object recognition. Observers (N�=�50) matched subsets of closed contour fragments, surfaces, or volumetric parts to whole novel objects during a whole�part matching task. Three factors were further manipulated: part viewpoint (either same or different between component parts and whole objects), surface occlusion (comparison parts contained either visible surfaces only, or a surface that was fully or partially occluded in the whole object), and target�distractor similarity. Similarity was varied in terms of systematic variation in nonaccidental (NAP) or metric (MP) properties of individual parts. Analysis of sensitivity (d�) showed a whole�part matching advantage for surface-based parts and volumes over closed contour fragments�but no benefit for volumetric parts over surfaces. We also found a performance cost in matching volumetric parts to wholes when the volumes showed surfaces that were occluded in the whole object. The same pattern was found for both same and different viewpoints, and regardless of target�distractor similarity. These findings challenge models in which recognition is mediated by volumetric part-based shape representations. Instead, we argue that the results are consistent with a surface-based model of high-level shape representation for recognition

On-Orbit Manoeuvring Using Superquadric Potential Fields

On-orbit manoeuvring represents an essential process in many space missions such as orbital assembly, servicing and reconfiguration. A new methodology, based on the potential field method along with superquadric repulsive potentials, is discussed in this thesis. The methodology allows motion in a cluttered environment by combining translation and rotation in order to avoid collisions. This combination reduces the manoeuvring cost and duration, while allowing collision avoidance through combinations of rotation and translation. Different attractive potential fields are discussed: parabolic, conic, and a new hyperbolic potential. The superquadric model is used to represent the repulsive potential with several enhancements. These enhancements are: accuracy of separation distance estimation, modifying the model to be suitable for moving obstacles, and adding the effect of obstacle rotation through quaternions. Adding dynamic parameters such as object translational velocity and angular velocity to the potential field can lead to unbounded actuator control force. This problem is overcome in this thesis through combining parabolic and conic functions to form an attractive potential or through using a hyperbolic function. The global stability and convergence of the solution is guaranteed through the appropriate choice of the control laws based on Lyapunov's theorem. Several on-orbit manoeuvring problems are then conducted such as on-orbit assembly using impulsive and continuous strategies, structure disassembly and reconfiguration and free-flyer manoeuvring near a space station. Such examples demonstrate the accuracy and robustness of the method for on-orbit motion planning

Combining shape and color. A bottom-up approach to evaluate object similarities

The objective of the present work is to develop a bottom-up approach to estimate the similarity between two unknown objects. Given a set of digital images, we want to identify the main objects and to determine whether they are similar or not. In the last decades many object recognition and classification strategies, driven by higher-level activities, have been successfully developed. The peculiarity of this work, instead, is the attempt to work without any training phase nor a priori knowledge about the objects or their context. Indeed, if we suppose to be in an unstructured and completely unknown environment, usually we have to deal with novel objects never seen before; under these hypothesis, it would be very useful to define some kind of similarity among the instances under analysis (even if we do not know which category they belong to). To obtain this result, we start observing that human beings use a lot of information and analyze very different aspects to achieve object recognition: shape, position, color and so on. Hence we try to reproduce part of this process, combining different methodologies (each working on a specific characteristic) to obtain a more meaningful idea of similarity. Mainly inspired by the human conception of representation, we identify two main characteristics and we called them the implicit and explicit models. The term "explicit" is used to account for the main traits of what, in the human representation, connotes a principal source of information regarding a category, a sort of a visual synecdoche (corresponding to the shape); the term "implicit", on the other hand, accounts for the object rendered by shadows and lights, colors and volumetric impression, a sort of a visual metonymy (corresponding to the chromatic characteristics). During the work, we had to face several problems and we tried to define specific solutions. In particular, our contributions are about: - defining a bottom-up approach for image segmentation (which does not rely on any a priori knowledge); - combining different features to evaluate objects similarity (particularly focusiing on shape and color); - defining a generic distance (similarity) measure between objects (without any attempt to identify the possible category they belong to); - analyzing the consequences of using the number of modes as an estimation of the number of mixture’s components (in the Expectation-Maximization algorithm)

A highly adaptable model based – method for colour image interpretation

This Thesis presents a model-based interpretation of images that can vary greatly in appearance. Rather than seek characteristic landmarks to model objects we sample points at regular intervals on the boundary to model objects with a smooth boundary. A statistical model of form in the exponent domain of an extended superellipse is created using sampled points and appearance by sampling inside objects. A colour Maximum Likelihood Ratio criterion (MLR) was used to detect cues to the location of potential pedestrians. The adaptability and specificity of this cue detector was evaluated using over 700 images. A True Positive Rate (TPR) of 0.95 and a False Positive Rate (FPR) of 0.20 were obtained. To detect objects with axes at various orientations a variant method using an interpolated colour MLR has been developed. This had a TPR of 0.94 and an FPR of 0.21 when tested over 700 images of pedestrians. Interpretation was evaluated using over 220 video sequences (640 x 480 pixels per frame) and 1000 images of people alone and people associated with other objects. The objective was not so much to evaluate pedestrian detection but the precision and reliability of object delineation. More than 94% of pedestrians were correctly interpreted