We discuss a variety of object recognition experiments in which human subjects were presented with realistically rendered images of computer-generated three-dimensional objects, with tight control over stimulus shape, surface properties, illumination, and viewpoint, as well as subjects' prior exposure to the stimulus objects. In all experiments recognition performance was: (1) consistently viewpoint dependent; (2) only partially aided by binocular stereo and other depth information, (3) specific to viewpoints that were familiar; (4) systematically disrupted by rotation in depth more than by deforming the two-dimensional images of the stimuli. These results are consistent with recently advanced computational theories of recognition based on view interpolation.

Cette thèse porte sur la reconnaissance visuelle d'objets, un domaine qui reste un défi majeur en vision par ordinateur. En effet, malgré plus de vingt années de recherche, de nombreuses facettes du problème restent a ce jour irrésolues. La conception d'un système de reconnaissance d'objets repose essentiellement sur trois aspects: la représentation, la détection et l'apprentissage automatique. La principale contribution de cette thèse est de proposer un système générique pour la représentation statistique des caractéristiques visuelles et leur détection dans les images. Le modèle proposé combine différents concepts récemment proposés en vision par ordinateur, machine learning et neurosciences: a savoir les relations spatiales entre des caractéristiques visuelles, les modèles graphiques ainsi que les hiérarchies de cellules complexes. Le résultat de cette association prend la forme d'une hiérarchie de classes de caractéristiques visuelles. Son principal intérêt est de fournir un modèle représentant, à la fois, les aspects visuels locaux et globaux, en utilisant la structure géométrique et l'apparence des objets. L'exploitation des modèles graphiques offre un cadre probabiliste pour la représentation des hiérarchies et leur utilisation pour l'inférence. Un algorithme d'échange de messages récemment proposé (NBP) est utilisé pour inférer la position des caractéristiques dans les images. Lors de l'apprentissage, les hiérarchies sont construites de manière incrémentale en partant des caractéristiques de bas-niveaux. L'algorithme est basé sur l'analyse des co-occurrences. Il permet d'estimer la structure et les paramètres des hiérarchies. Les performances offertes par ce nouveau système sont évaluées sur différentes bases de données d'objets de difficulté croissante. Par ailleurs, un survol de l'état de l'art concernant les méthodes de reconnaissances d'objets et les détecteurs de caractéristiques offre une vue globale du domaine.

A great number of methods based on local features or global appearances have been proposed in the literature for geospatial object detection and recognition from satellite images. However, since these approaches do not have enough discriminative capabilities between object and non-object classes, they produce results with innumerable false positives during their detection process. Moreover, due to the sliding window mechanisms, these algorithms cannot yield exact location information for the detected objects. Therefore, a geospatial object recognition algorithm based on the object shape mask is proposed to minimize the aforementioned imperfections. In order to develop such a robust recognition system, foreground extraction performance of some of popular fully and semi-automatic image segmentation algorithms, such as normalized cut, k-means clustering, mean-shift for fully automatic, and interactive Graph-cut, GrowCut, GrabCut for semi-automatic, are evaluated in terms of their subjective and objective qualities. After this evaluation, the retrieval performance of some shape description techniques, such as ART, Hu moments and Fourier descriptors, are investigated quantitatively. In the proposed system, first of all, some hypothesis points are generated for a given test image. Then, the foreground extraction operation is achieved via GrabCut algorithm after utilizing these hypothesis points as if these are user inputs. Next, the extracted binary object masks are described by means of the integrated versions of shape description techniques. Afterwards, SVM classifier is used to identify the target objects. Finally, elimination of the multiple detections coming from the generation of hypothesis points is performed by some simple post-processing on the resultant masks. Experimental results reveal that the proposed algorithm has promising results in terms of accuracy in recognizing many geospatial objects, such as airplane and ship, from high resolution satellite imagery.

In this thesis, contributions are presented in the areas of shape parsing for view-based object recognition and spatio-temporal feature description for action recognition. A probabilistic model for parsing shapes into several distinguishable parts for accurate shape recognition is presented. This approach is based on robust geometric features that permit high recognition accuracy. As the second contribution in this thesis, a binary spatio-temporal feature descriptor is presented. Recent work shows that binary spatial feature descriptors are effective for increasing the efficiency of object recognition, while retaining comparable performance to state of the art descriptors. An extension of these approaches to action recognition is presented, facilitating huge gains in efficiency due to the computational advantage of computing a bag-of-words representation with the Hamming distance. A scene's motion and appearance is encoded with a short binary string. Exploiting the binary makeup of this descriptor greatly increases the efficiency while retaining competitive recognition performance.

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Over the past five decades, per capita caloric intake has increased by approximately 28% in the United States. A hallmark of the current standard American diet is an excess of energy sources from saturated fat and refined carbohydrates. High energy diets such as the “Western” diet cause numerous pathologies, including non-alcoholic fatty liver disease (NAFLD), high blood pressure, dyslipidemia, and peripheral insulin resistance. High energy diets also negatively impact the hippocampus, a brain area important for learning and memory. It is not surprising, then, that high energy diets impair hippocampal-dependent memory. The experiments in this dissertation investigate possible diet-induced consequences that may contribute to the impairing effects of high energy diets on hippocampal-dependent memory. Our initial experiments found that diet-induced NAFLD impairs hippocampal-dependent memory, but these cognitive deficits were not due to decreases in insulin-like growth factor-1 (IGF-1) or hippocampal insulin signaling. Next, we found that a high energy diet increased the ability of epinephrine to increase blood glucose concentrations, indicating a novel way in which high energy diets impair liver function. The final set of experiments found that high energy diets do not necessarily impair memory but instead may prevent the memory-enhancing effects of acute stress. Taken together, these results indicate that high energy diets interact with acute stress to negatively impact hippocampal-dependent memory, and that hippocampal insulin resistance and IGF-1are not likely involved.

This thesis treats topics within the area of object recognition. A real-time view matching method has been developed to compute the transformation between two different images of the same scene. This method uses a color based region detector called MSCR and affine transformations of these regions to create affine-invariant patches that are used as input to the SIFT algorithm. A parallel method to compute the SIFT descriptor has been created with relaxed constraints so that the descriptor size and the number of histogram bins can be adjusted. Additionally, a matching step to deduce correspondences and a parallel RANSAC method have been created to estimate the undergone transformation between these descriptors. To achieve real-time performance, the implementation has been targeted to use the parallel nature of the GPU with CUDA as the programming language. Focus has been put on the architecture of the GPU to find the best way to parallelize the different processing steps. CUDA has also been combined with OpenGL to be able to use the hardware accelerated anisotropic sampling method for affine transformations of regions. Parts of the implementation can also be used individually from either Matlab or by using the provided C++ library directly. The method was also evaluated in terms of accuracy and speed. It was shown that our algorithm has similar or better accuracy at finding correspondences than SIFT when the 3D geometry changes are large but we get a slightly worse result on images with flat surfaces.

Object recognition is of much interest in recent industrial automation. Although a variety of approaches have been proposed to tackle the recognition problem, some cases such as overlapping objects, articulated objects, and low-resolution images, are still not easy for the existing schemes. Coping with these more complex images has remained a challenging task in the field. This dissertation, aiming to recognize objects from such images, proposes a new integrated method. For images with overlapping or articulated objects, graph matching methods are often used, seeing them as solving a combinatorial optimization problem. Both Hopfield network and the genetic algorithm are decent tools for the combinatorial optimization problems. Unfortunately, they both have intolerable drawbacks. The Hopfield network is sensitive to its initial state and stops at a local minimum if it is not properly given. The GA, on the other hand, only finds a near-global solution, and it is time-consuming for large-scale tasks. This dissertation proposes to combine these two methods, while eliminating their bad and keeping their good, to solve some complex recognition problems. Before the integration, some arrangements are required. For instance, specialized 2-D GA operators are used to accelerate the convergence. Also, the ¡§seeds¡¨ of the solution of the GA is extracted as the initial state of the Hopfield network. By doing so the efficiency of the system is greatly improved. Additionally, several fine-tuning post matching algorithms are also needed. In order to solve the homomorphic graph matching problem, i.e., multiple occurrences in a single scene image, the Hopfield network has to repeat itself until the stopping criteria are met. The method can not only be used to obtain the homomorphic mapping between the model and the scene graphs, but it can also be applied to articulated object recognition. Here we do not need to know in advance if the model is really an articulated object. The proposed method has been applied to measure some kinematic properties, such as the positions of the joints, relative linear and angular displacements, of some simple machines. The subject about articulated object recognition has rarely been mentioned in the literature, particularly under affine transformations. Another unique application of the proposed method is also included in the dissertation. It is about using low-resolution images, where the contour of an object is easily affected by noise. To increase the performance, we use the hexagonal grid in dealing with such low-resolution images. A hexagonal FFT simulation is first presented to pre-process the hexagonal images for recognition. A feature vector matching scheme and a similarity matching scheme are also devised to recognize simpler images with only isolated objects. For complex low-resolution images with occluded objects, the integrated method has to be tailored to go with the hexagonal grid. The low-resolution, hexagonal version of the integrated scheme has also been shown to be suitable and robust.

The main aim of 3D Object recognition is to recognize objects under translation and rotation. Geometric Hashing is one of the methods which represents a rotation and translation invariant approach and provides indexing of structural features of the objects in an efficient way. In this thesis, Geometric Hashing is used to store the geometric relationship between discriminative surface properties which are based on surface curvature. In this thesis surface is represented by shape index and splash where shape index defines particular shaped surfaces and splash introduces topological information. The method is tested on 3D object databases and compared with other methods in the literature.

3D object recognition is performed by using geometric hashing where transformation and scale invariant 3D surface features are utilized. 3D features are extracted from object surfaces after a scale space search where size of each feature is also estimated. Scale space is constructed based on orientation invariant surface curvature values which classify each surface point&#039 / s shape. Extracted features are grouped into triplets and orientation invariant descriptors are defined for each triplet. Each pose of each object is indexed in a hash table using these triplets. For scale invariance matching, cosine similarity is applied for scale variant triple variables. Tests were performed on Stuttgart database where 66 poses of 42 objects are stored in the hash table during training and 258 poses of 42 objects are used during testing. %90.97 recognition rate is achieved.