Part-based Probabilistic Point Matching using Equivalence Constraints

Correspondence algorithms typically struggle with shapes that display part-based variation. We present a probabilistic approach that matches shapes using independent part transformations, where the parts themselves are learnt during matching. Ideas from semi-supervised learning are used to bias the algorithm towards finding ‘perceptually valid’ part structures. Shapes are represented by unlabeled point sets of arbitrary size and a background component is used to handle occlusion, local dissimilarity and clutter. Thus, unlike many shape matching techniques, our approach can be applied to shapes extracted from real images. Model parameters are estimated using an EM algorithm that alternates between finding a soft correspondence and computing the optimal part transformations using Procrustes analysis

3D-reconstruction of human jaw from a single image : integration between statistical shape from shading and shape from shading.

Object modeling is a fundamental problem in engineering, involving talents from computer-aided design, computational geometry, computer vision and advanced manufacturing. The process of object modeling takes three stages: sensing, representation, and analysis. Various sensors may be used to capture information about objects; optical cam- eras and laser scanners are common with rigid objects, while X-ray, CT and MRI are common with biological organs. These sensors may provide a direct or indirect inference about the object, requiring a geometric representation in the computer that is suitable for subsequent usage. Geometric representations that are compact, i.e., capture the main features of the objects with minimal number of data points or vertices, fall into the domain of computational geometry. Once a compact object representation is in the computer, various analysis steps can be conducted, including recognition, coding, transmission, etc. The subject matter of this thesis is object reconstruction from a sequence of optical images. An approach to estimate the depth of the visible portion of the human teeth from intraoral cameras has been developed, extending the classical shape from shading (SFS) solution to non-Lambertian surfaces with known object illumination characteristics. To augment the visible portion, and in order to have the entire jaw reconstructed without the use of CT or MRI or even X-rays, additional information will be added to database of human jaws. This database has been constructed from an adult population with variations in teeth size, degradation and alignments. The database contains both shape and albedo information for the population. Using this database, a novel statistical shape from shading (SSFS) approach has been created. To obtain accurate result from shape from shading and statistical shape from shading, final step will be integrated two approaches (SFS,SSFS) by using Iterative Closest Point algorithm (ICP). Keywords: computer vision, shading, 3D shape reconstruction, shape from shading, statistical, shape from shading, Iterative Closest Point

Online sketch-based image retrieval using keyshape mining of geometrical objects

Online image retrieval has become an active information-sharing due to the massive use of the Internet. The key challenging problems are the semantic gap between the low-level visual features and high-semantic perception and interpretation, due to understating complexity of images and the hand-drawn query input representation which is not a regular input in addition to the huge amount of web images. Besides, the state-of-art research is highly desired to combine multiple types of different feature representations to close the semantic gap. This study developed a new schema to retrieve images directly from the web repository. It comprises three major phases. Firstly a new online input representation based on pixel mining to detect sketch shape features and correlate them with the semantic sketch objects meaning was designed. Secondly, training process was developed to obtain common templates using Singular Value Decomposition (SVD) technique to detect common sketch template. The outcome of this step is a sketch of variety templates dictionary. Lastly, the retrieval phase matched and compared the sketch with image repository using metadata annotation to retrieve the most relevant images. The sequence of processes in this schema converts the drawn input sketch to a string form which contains the sketch object elements. Then, the string is matched with the templates dictionary to specify the sketch metadata name. This selected name will be sent to a web repository to match and retrieve the relevant images. A series of experiments was conducted to evaluate the performance of the schema against the state of the art found in literature using the same datasets comprising one million images from FlickerIm and 0.2 million images from ImageNet. There was a significant retrieval in all cases of 100% precision for the first five retrieved images whereas the state of the art only achieved 88.8%. The schema has addressed many low features obstacles to retrieve more accurate images such as imperfect sketches, rotation, transpose and scaling. The schema has solved all these problems by using a high level semantic to retrieve accurate images from large databases and the web

Part-Based Probabilistic Point Matching

We present a probabilistic technique for matching partbased shapes. Shapes are represented by unlabeled point sets, so discontinuous boundaries and non-boundary points do not pose a problem. Occlusions and significant dissimilarities between shapes are explained by a ‘background model ’ and hence, their impact on the overall match is limited. Using a part-based model, we can successfully match shapes which differ as a result of independent part transformations – a form of variation common amongst real objects of the same class. A greedy algorithm that learns the parts sequentially can be used to estimate the number of parts and the initial parameters for the main algorithm.

Part-based Probabilistic Point Matching using Equivalence Constraints

Correspondence algorithms typically struggle with shapes that display part-based variation. We present a probabilistic approach that matches shapes using independent part transformations, where the parts themselves are learnt during matching. Ideas from semi-supervised learning are used to bias the algorithm towards finding ‘perceptually valid ’ part structures. Shapes are represented by unlabeled point sets of arbitrary size and a background component is used to handle occlusion, local dissimilarity and clutter. Thus, unlike many shape matching techniques, our approach can be applied to shapes extracted from real images. Model parameters are estimated using an EM algorithm that alternates between finding a soft correspondence and computing the optimal part transformations using Procrustes analysis.